{"Affiliation":[{"label":"Affiliation","value":"Applied Science, Faculty of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Electrical and Computer Engineering, Department of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."}],"AggregatedSourceRepository":[{"label":"Aggregated Source Repository","value":"DSpace","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","classmap":"ore:Aggregation","property":"edm:dataProvider"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","explain":"A Europeana Data Model Property; The name or identifier of the organization who contributes data indirectly to an aggregation service (e.g. Europeana)"}],"Campus":[{"label":"Campus","value":"UBCV","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","classmap":"oc:ThesisDescription","property":"oc:degreeCampus"},"iri":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","explain":"UBC Open Collections Metadata Components; Local Field; Identifies the name of the campus from which the graduate completed their degree."}],"Creator":[{"label":"Creator","value":"Zhu, Cindy Yue","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."}],"DateAvailable":[{"label":"Date Available","value":"2009-10-01T17:52:46Z","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"edm:WebResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"DateIssued":[{"label":"Date Issued","value":"2009","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"oc:SourceResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"Degree":[{"label":"Degree (Theses)","value":"Master of Applied Science - MASc","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","classmap":"vivo:ThesisDegree","property":"vivo:relatedDegree"},"iri":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","explain":"VIVO-ISF Ontology V1.6 Property; The thesis degree; Extended Property specified by UBC, as per https:\/\/wiki.duraspace.org\/display\/VIVO\/Ontology+Editor%27s+Guide"}],"DegreeGrantor":[{"label":"Degree Grantor","value":"University of British Columbia","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","classmap":"oc:ThesisDescription","property":"oc:degreeGrantor"},"iri":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","explain":"UBC Open Collections Metadata Components; Local Field; Indicates the institution where thesis was granted."}],"Description":[{"label":"Description","value":"In this thesis, we revisit differential and non-coherent transmission techniques over fading channels. In particular, we consider receiver design for differential space-time modulation (DSTM) over correlated multiple-input multiple-output (MIMO) fading channels and the performance analysis of differential phase shift keying (DPSK) and non-coherent frequency shift keying (FSK) in generalized K-fading. \n\nFor DSTM over spatially correlated MIMO channels, we derive a multiple-symbol differential detection (MSDD) and a novel MSDD-based decision-feedback differential detection (MS-DFDD) receiver. We show that MS-DFDD outperforms previously proposed decision-feedback differential detection (DFDD) schemes that are based on scalar and vector prediction. In addition, we prove that at high signal-to-noise ratio (SNR) vector prediction decision-feedback differential detection (VP-DFDD) is equivalent to scalar prediction decision-feedback differential detection (SP-DFDD) and thus fails to properly exploit the spatial fading correlations. \n\nFurthermore, we derive closed-form expressions for the bit error probability (BEP) of two non-coherent transmission schemes over L diversity branches being subject to generalized K-fading. Specifically, focus is on binary DPSK (DBPSK) and binary non-coherent FSK modulation with equal-gain combining (EGC) at the receiver. We also discuss the extension of our results to M-ary modulation schemes. Considering both independent and correlated fading across the L branches, we derive expressions for the asymptotic diversity order, which reveal an interesting interplay between the two parameters, k and m, of the generalized K-distribution. Moreover, we show that the diversity order of the considered non-coherent transmission schemes is the same as in the case of coherent transmission. Finally, numerical performance results are presented, and our analytical results are corroborated by means of Monte-Carlo simulation.","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/description","classmap":"dpla:SourceResource","property":"dcterms:description"},"iri":"http:\/\/purl.org\/dc\/terms\/description","explain":"A Dublin Core Terms Property; An account of the resource.; Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource."}],"DigitalResourceOriginalRecord":[{"label":"Digital Resource Original Record","value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/13469?expand=metadata","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","classmap":"ore:Aggregation","property":"edm:aggregatedCHO"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","explain":"A Europeana Data Model Property; The identifier of the source object, e.g. the Mona Lisa itself. This could be a full linked open date URI or an internal identifier"}],"Extent":[{"label":"Extent","value":"1355767 bytes","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/extent","classmap":"dpla:SourceResource","property":"dcterms:extent"},"iri":"http:\/\/purl.org\/dc\/terms\/extent","explain":"A Dublin Core Terms Property; The size or duration of the resource."}],"FileFormat":[{"label":"File Format","value":"application\/pdf","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/elements\/1.1\/format","classmap":"edm:WebResource","property":"dc:format"},"iri":"http:\/\/purl.org\/dc\/elements\/1.1\/format","explain":"A Dublin Core Elements Property; The file format, physical medium, or dimensions of the resource.; Examples of dimensions include size and duration. Recommended best practice is to use a controlled vocabulary such as the list of Internet Media Types [MIME]."}],"FullText":[{"label":"Full Text","value":"NEW RESULTS ON DIFFERENTIAL AND NON\u2013COHERENT TRANSMISSION: MSDD FOR CORRELATED MIMO FADING CHANNELS AND PERFORMANCE ANALYSIS FOR GENERALIZED K\u2013FADING by CINDY YUE ZHU B.ASc., The University of British Columbia, 2007 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE STUDIES (Electrical and Computer Engineering) The University of British Columbia (Vancouver) September 2009 c\u00a9 Cindy Yue Zhu, 2009 Abstract In this thesis, we revisit differential and non\u2013coherent transmission techniques over fading channels. In particular, we consider receiver design for differen- tial space\u2013time modulation (DSTM) over correlated multiple\u2013input multiple\u2013 output (MIMO) fading channels and the performance analysis of differential phase shift keying (DPSK) and non\u2013coherent frequency shift keying (FSK) in generalized K\u2013fading. For DSTM over spatially correlated MIMO channels, we derive a multiple\u2013 symbol differential detection (MSDD) and a novel MSDD\u2013based decision\u2013 feedback differential detection (MS\u2013DFDD) receiver. We show that MS\u2013 DFDD outperforms previously proposed decision\u2013feedback differential detec- tion (DFDD) schemes that are based on scalar and vector prediction. In addition, we prove that at high signal\u2013to\u2013noise ratio (SNR) vector prediction decision\u2013feedback differential detection (VP\u2013DFDD) is equivalent to scalar prediction decision\u2013feedback differential detection (SP\u2013DFDD) and thus fails to properly exploit the spatial fading correlations. Furthermore, we derive closed\u2013form expressions for the bit error probabil- ity (BEP) of two non\u2013coherent transmission schemes over L diversity branches being subject to generalized K\u2013fading. Specifically, focus is on binary DPSK (DBPSK) and binary non\u2013coherent FSK modulation with equal\u2013gain com- bining (EGC) at the receiver. We also discuss the extension of our results to M\u2013ary modulation schemes. Considering both independent and correlated fading across the L branches, we derive expressions for the asymptotic diver- sity order, which reveal an interesting interplay between the two parameters, ii k and m, of the generalized K\u2013distribution. Moreover, we show that the di- versity order of the considered non\u2013coherent transmission schemes is the same as in the case of coherent transmission. Finally, numerical performance re- sults are presented, and our analytical results are corroborated by means of Monte\u2013Carlo simulation. iii Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Abbreviations and Symbols . . . . . . . . . . . . . . . . . . . . . x Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . 1 1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Non\u2013Coherent Detectors for DPSK and DSTM . . . . . . . . . . . . 7 2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Noncoherent Detectors . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 MSDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 MS\u2013DFDD . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.3 The Relationship between MS\u2013DFDD and SP\u2013\/VP\u2013DFDD 10 2.3 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Simulation and Numerical Results . . . . . . . . . . . . . . . . . 12 3 The Generalized K\u2013Fading Model . . . . . . . . . . . . . . . . . . . . 19 3.1 PDF of the Instantaneous Received SNR . . . . . . . . . . . . . 19 3.2 MGF of Sum SNR for the Case of I.N.D. Fading . . . . . . . . . 21 iv 3.3 MGF of Sum SNR for Correlated Composite Shadowing and Multipath Fading . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4 Generalized K\u2013Fading: Binary Modulation Scenario . . . . . . . . . . 25 4.1 BEP for I.N.D. Fading . . . . . . . . . . . . . . . . . . . . . . . 25 4.2 BEP for Correlated Composite Shadowing and Multipath Fading 27 4.3 Asymptotic Analysis and Diversity Order . . . . . . . . . . . . . 29 4.3.1 The Case of Independent Fading . . . . . . . . . . . . . 29 4.3.2 Correlated Composite Shadowing and Multipath Fading 32 5 Generalized K\u2013Fading: Extensions to M\u2013ary Modulation . . . . . . . 35 5.1 BEP for I.N.D. Fading . . . . . . . . . . . . . . . . . . . . . . . 35 5.2 BEP for Correlated Composite Shadowing and Multipath Fading 37 5.3 Simulation and Numerical Results . . . . . . . . . . . . . . . . . 38 5.3.1 The Case of Independent Fading . . . . . . . . . . . . . 38 5.3.2 Correlated Composite Shadowing and Multipath Fading 43 5.3.3 Performance of M\u2013ary Modulation Schemes . . . . . . . 47 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 v List of Figures 2.1 BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. Eb\/N0 for diagonal DSTM with R = 2 bit\/(channel use), NT = 2, NR = 1, \u03c1 = 0.9, and BfT = 0.05. Numerical results: Solid lines. Simulation results: Markers. . . . 14 2.2 BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. Eb\/N0 for DQPSK, NT = 1, NR = 2, \u03c1 = 0.9, and BfT = 0.05. Numerical results: Solid lines. Simu- lation results: Markers. . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. transmit antenna correlation \u03c1 for di- agonal DSTM with R = 2 bit\/(channel use), NT = 2, NR = 1, Eb\/N0 \u2192\u221e, and BfT = 0.05. Simulation results. . . . . . . . . 16 2.4 BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. receive antenna correlation \u03c1 for DQPSK, NT = 1, NR = 2, Eb\/N0 \u2192 \u221e, and BfT = 0.05. Simulation results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.5 BEP vs. BfT for diagonal DSTM with R = 2 bit\/(channel use), NT = 2, and NR = 1 under various channel conditions. a) MS\u2013DFDD (proposed) and VP\u2013DFDD designed for B\u0302fT = 0.05, \u03c1\u0302 = 0.6, and \u0302Eb\/N0 = 35 dB; b) MS\u2013DFDD (proposed) designed for matched and mismatched (B\u0302fT = 0.05, \u03c1\u0302 = 0.6, and \u0302Eb\/N0 = 35 dB) channel parameters. . . . . . . . . . . . . . 18 vi 5.1 Average BEP P\u0304b (\u03b8) versus average SNR L\u03b8 in dB for the case of i.i.d. double Rayleigh fading (k = 1,m = 1). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.8) \/(4.9) using the values k = 1.01 and m = 0.99. Dashed lines represent corresponding analytical results for the case of i.i.d. Rayleigh fading evaluated based on (4.10)\/(4.11). Corresponding simulation results for Rayleigh fading and double Rayleigh fading (k = 1,m = 1) are indicated by markers \u2018\u25e6\u2019. . . . . . . . . . . . . . . . . . . . . . . 39 5.2 Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case of i.i.d. double Rayleigh fading (k = 1,m = 1). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.8) \/(4.9) using the values k = 1.01 and m = 0.99. Dashed lines represent corresponding analytical results for coherent BPSK modulation with MRC at the receiver evaluated based on (3.11), (4.26) using numerical integration. Corresponding simulation results for k = 1 and m = 1 are indicated by markers \u2018\u25e6\u2019 (both for DPSK and PSK modulation). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 vii 5.3 Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for different cases of cascade fading (k = 1 and m \u2208 {1, 3, 5}). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.8) \/(4.9) us- ing the values k = 1.01 and m \u2208 {0.99, 2.99, 4.99}, respec- tively. Dashed lines represent corresponding analytical results for coherent BPSK modulation with MRC at the receiver eval- uated based on (3.11), (4.26) using numerical integration. Cor- responding simulation results for k = 1 and m \u2208 {1, 3, 5} are indicated by markers \u2018\u25e6\u2019 (both for DPSK and PSK modulation). The dotted lines represent asymptotic BEP curves for the case m = 3, L = 3 evaluated based on (4.18)\/(4.19) for DPSK mod- ulation and based on (4.27) for PSK modulation. . . . . . . . . 42 5.4 Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case k=3 and m=1 (mild shadowing). Solid lines represent analytical results for DBPSK modulation with EGC at the re- ceiver evaluated based on (4.15) using the values k=3.01 and m=0.99. Dashed lines represent corresponding analytical re- sults for coherent BPSK modulation with MRC at the receiver evaluated based on (3.16) , (4.26) using numerical integration. Corresponding simulation results for k=3 and m=1 are indi- cated by markers \u2018o\u2019 (both for DPSK and PSK modulation). . . 44 5.5 Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case k=3 and m=1 (mild shadowing). Solid lines represent analytical results for DBPSK modulation with EGC at the re- ceiver, evaluated based on (4.15) using the values k=3.01 and m=0.99. Dashed lines represent corresponding asymptotic re- sults evaluated based on (4.30) . . . . . . . . . . . . . . . . . . . 45 viii 5.6 Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case k=1 and m=3 (severe shadowing). Solid lines repre- sent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.15) using the values k=1.01 and m=2.99. Dashed lines represent corresponding analytical re- sults for coherent BPSK modulation with MRC at the receiver evaluated based on (3.16) , (4.26) using numerical integration. Corresponding simulation results for k=1 and m=3 are indi- cated by markers \u2018o\u2019 (both for DPSK and PSK modulation). The dotted lines represent asymptotic BEP curves for the case L=4 evaluated based on (4.30) for DPSK modulation and based on (4.33) for PSK modulation. . . . . . . . . . . . . . . . . . . . 47 5.7 Average BEP P\u0304b (\u03b8) versus overall average SNR per bit L\u03b8\/2 in dB for the case of cascade fading with k = 1 and m = 3. Solid lines represent analytical results for DQPSK modulation with EGC at the receiver evaluated based on (3.11) and (5.1) using the values k = 1.01 and m = 2.99. Dashed lines represent corresponding analytical results for coherent QPSK modulation with MRC at the receiver evaluated based on (3.11) and (4.26) using numerical integration. Corresponding simulation results for k = 1 and m = 3 are indicated by markers \u2018o\u2019 (both for DPSK and PSK modulation). . . . . . . . . . . . . . . . . . . . 48 ix List of Abbreviations and Symbols Acronyms AWGN Additive white Gaussian noise BEP Bit error probability CD Coherent detection CDD Conventional differential detection CSI Channel state information DBPSK Binary differential phase shift keying DFDD Decision\u2013feedback differential detection DPSK Differential phase shift keying DQPSK Quadrature differential phase shift keying DSTM Differential space\u2013time modulation EGC Equal\u2013gain combining FSK Frequency shift keying I.I.D Independent and identically distributed I.N.D Independent but not necessarily identically distributed MGF Moment generating function MIMO Multiple\u2013input multiple\u2013output MRC Maximum\u2013ratio combining MS\u2013DFDD MSDD\u2013based decision\u2013feedback differential detection MSDD Multiple\u2013symbol differential detection PDF Probability density function PEP Pairwise error probability x PSK Phase shift keying QAM Quadrature amplitude modulation QPSK Quadrature phase shift keying SEP Symbol error probability SNR Signal\u2013to\u2013noise ratio SP Scalar predictor SP\u2013DFDD Scalar prediction decision\u2013feedback differential detection VP Vector predictor VP\u2013DFDD Vector prediction decision\u2013feedback differential detection Operators and Notation |\u00b7| Absolute value of a complex number E {\u00b7} Expectation [\u00b7]\u2217 Complex conjugate [\u00b7]T Matrix or vector transposition [\u00b7]H Matrix or vector Hermitian transposition Im Identity matrix with dimension m\u00d7m . = Asymptotic equality \u2297 Kronecker product diag{x} Diagonal matrix with the elements of vector x in its main diagonal [X]i,j Element in row i and column j of matrix X xi Acknowledgments I would like to express my sincere appreciation toward my supervisor, Dr. Robert Schober, for his outstanding supervision, support and encouragement throughout my research projects. Professor Schober provided much of the initial motivation for pursuing this investigation and also provided priceless feedback that has improved this work in nearly every aspect. In addition, I would like to thank Dr. Simon Yiu and Dr. Jan Mietzner for their help in my research. Finally, I would like to thank my family for their support and encourage- ment in my quest for higher education. I would also like to extend my thanks to the colleagues at the Department of Electrical and Computer Engineering, UBC, for creating a stimulating and a friendly environment at work. Cindy Yue Zhu The University of British Columbia Vancouver, Canada July 2009 xii Chapter 1 1 Introduction The following section provides an overview of the background information and motivation for this work in detail. We also review the related work that has been reported by other researchers in this field. The contributions of this work are briefly summarized in the second section of this chapter, and the concluding section outlines the organization of this thesis. 1.1 Background and Motivation In recent years, the application of multiple antennas in different wireless com- munication environments has received considerable interest from academia and industry. In particular, in practical cases where accurate channel knowledge at the receiver is not available, differential and non\u2013coherent transmission schemes eliminate the need for channel estimation at the receiver and are thus attractive for high\u2013mobility and low SNR scenarios as well as for low\u2013 cost receiver implementations. DPSK and DSTM [1] are popular modulation schemes if channel state information (CSI) is not available at the receiver side. Since conventional differential detection (CDD) causes significant per- formance degradations compared to coherent detection (CD) in time\u2013variant fading channels, in the last few years various DFDD and MSDD schemes have been proposed for performance improvement, cf. e.g. [2, 3]. However, while 1 in practice the fading gains may be spatially correlated due to insufficient an- tenna spacing, [2, 3] only considered the spatially uncorrelated case. The per- formance of DSTM and beamforming for DSTM in spatially correlated fading were considered in [4] and [5, 6], respectively. However, the detection scheme applied in [4, 5, 6] was simple CDD which does not take into account spatial correlations. A sequence\u2013detection based non\u2013coherent detection scheme tak- ing into account spatial fading correlations was proposed in [7]. Interestingly, if the number of trellis states of the sequence\u2013detection based scheme in [7] is reduced to zero, it can be interpreted as DFDD for correlated fading, where the DFDD coefficients are the coefficients of a scalar predictor (SP) or a vec- tor predictor (VP) for the fading\u2013plus\u2013noise process. Thus, we refer to the corresponding detectors as SP\u2013DFDD and VP\u2013DFDD, respectively. Since the transmission environment is essential to a wireless communica- tion system, we also investigate the generalized K\u2013fading model. The perfor- mance of wireless communication systems is largely governed by shadowing and multipath fading effects [8, Ch. 2]. While major obstacles between trans- mitter and receiver cause macroscopic fading effects, i.e., fluctuations in the average received SNR, scatterers in the vicinity of transmitter and receiver en- tail microscopic fading effects, i.e., fluctuations in the instantaneous received SNR. The generalized K\u2013fading model is characterized by two parameters, k > 0 and m > 0, which accurately capture the effects of composite shadowing and multipath fading. In particular, it comprises a large variety of channel conditions, ranging from severe shadowing (small values of k) to mild shadow- ing (large values of k) and from severe multipath fading (small values of m) to mild multipath fading (large values ofm). Moreover, the generalized K\u2013fading model can also be employed to model cascade multipath fading, which occurs, e.g., in keyhole and in mobile\u2013to\u2013mobile communication scenarios [9], [10]. For the special case where k = m = 1, the generalized K\u2013fading model re- duces to the double Rayleigh\u2013fading model. By varying the fading parameters accordingly, more or less severe cascade multipath fading can be modeled. 2 A favorable property of the generalized K\u2013fading model is that it allows for a closed\u2013form expression for the probability density function (PDF) of the instantaneous received SNR, which is in contrast to, e.g., competing composite shadowing\/multipath fading models that are based on the lognormal PDF. As a result, several analytical performance results for generalized K\u2013fading and ordinary K\u2013fading (when m = 1) have been reported in the literature. Moreover, analytical performance results for the special case of double Rayleigh fading were presented in [10, 11, 12]. Most of the papers mentioned above have focused on coherent transmission schemes, which rely on the availability of accurate channel knowledge at the receiver side. However, since non\u2013coherent transmission schemes eliminate the need for channel estimation at the receiver, we derive closed\u2013form expressions for the BEP of two non\u2013coherent transmission schemes over L generalized K\u2013fading branches with (post\u2013detection) EGC at the receiver. Specifically, focus is on DBPSK modulation with CDD at the receiver (i.e., based on two subsequent received symbols) and orthogonal binary frequency\u2013shift keying (FSK) modulation with non\u2013coherent detection at the receiver [13, Ch. 9.4]. We also discuss the extension of our results to M\u2013ary modulation schemes. Concerning the K\u2013fading model we consider two scenarios. First, we focus on the case of independent fading across the L branches. This scenario appears to be relevant for cascade multipath fading, if the underlying assumption is a rich\u2013 scattering radio environment (see [10] for examples). Here, the assumption of uncorrelated diversity branches \u2013 created, e.g., by multiple receive antennas with sufficiently large antenna spacing \u2013 appears to be reasonable. Afterwards, we turn to the case of composite shadowing and multipath fading. Here, we consider the scenario where the shadowing part is fully correlated across links, whereas the multipath fading is independent and identically distributed (i.i.d.) across the L branches. Since shadowing represents a large\u2013scale fading effect, it can be expected to affect all diversity branches simultaneously, while in a rich\u2013scattering environment the multipath fading part can again be considered 3 independent across links. For both scenarios, we present a high\u2013SNR analysis and provide expressions for the resulting asymptotic diversity orders, which reveal an interesting interplay between the two fading parameters k and m. It is worth noting that the existing papers on non\u2013coherent transmission schemes over generalized K\u2013fading or double Rayleigh\u2013fading links [10]-[14, 15] are all restricted to a single branch (L = 1). In particular, to the best of our knowledge closed\u2013form expressions for the BEP and the asymptotic diversity order of the considered non\u2013coherent transmission schemes in generalized K\u2013 fading have not yet been presented in the literature. 1.2 Contributions The main contributions of the present research work are as follows: \u2022 We derive an MSDD and a low-complexity MS\u2013DFDD receiver for DSTM transmitted over spatially correlated MIMO fading channels. The pro- posed DFDD scheme is obtained by introducing decision-feedback sym- bols into the MSDD metric. We show that MS-DFDD outperforms both SP\u2013DFDD and VP\u2013DFDD. Furthermore, we prove that at high SNR SP\u2013 DFDD and VP\u2013DFDD are equivalent. Thus, at high SNR VP-DFDD does not result in performance gains compared to the simpler SP-DFDD. \u2022 We also derive closed\u2013form expressions for the BEP of two non\u2013coherent transmission schemes over L generalized K\u2013fading branches with EGC at the receiver. Specifically, focus is on DBPSK and binary non\u2013coherent FSK modulation. Note that the existing papers on non-coherent trans- mission schemes over (generalized) K\u2013fading or double Rayleigh\u2013fading links are all restricted to the case of a single link (L = 1). \u2013 First, we focus on the case of i.i.d. fading across the L branches, which appears to be relevant for the case of cascade multipath fad- 4 ing. \u2013 We then turn to the case of composite shadowing and multipath fading. Here, we consider the scenario where the shadowing part is fully correlated across links, whereas the multipath fading is i.i.d. across the L branches. For both cases, we present a high\u2013SNR analysis and state expressions for the resulting asymptotic diversity order, which reveal an interesting interplay between the two fading parameters k and m. The results of our work are summarized in the following papers: \u2022 C. Zhu, S. Yiu, and R. Schober. On Noncoherent Receivers for DSTM in Spatially Correlated Fading. Accepted for publication in the IEEE Transactions on Communications, December 2008. \u2022 C. Zhu, J. Mietzner, and R. Schober. On the Performance of Non\u2013 Coherent Transmission Schemes over Multiple Generalized K\u2013Fading Links. Submitted to the IEEE Transaction of Wireless Communications, February 2009. \u2022 C. Zhu, J. Mietzner, and R. Schober. On the Performance of Non\u2013 Coherent Transmission Schemes with Equal\u2013Gain Combining in Cor- related Generalized K\u2013Fading. Accepted for presentation at the IEEE Vehicular Technology Conference, Anchorage, Alaska, USA, September 2009. 1.3 Thesis Organization To explain the above findings in detail, this thesis is organized as follows. In Chapter 2, we will describe DSTM in detail. The proposed differential detec- tors for DPSK and DSTM are also discussed in this chapter. The simulations results for the improved differential detectors are also provided. In Chapter 5 3, the generalized K\u2013fading model is introduced. The performance analysis of both binary and M\u2013ary modulation over generalized K\u2013fading channels is discussed in Chapters 4 and 5, respectively. The simulation results for the generalized K\u2013fading channels are provided also included in Chapter 5, and some conclusions are given in Chapter 6. 6 Chapter 2 1 Non\u2013Coherent Detectors for DPSK and DSTM In this section, we first give a brief introduction to DSTM followed by the derivation of the optimum MSDD decision rule and the related MS\u2013DFDD scheme for spatially correlated fading channels. We also compare MS\u2013DFDD with SP\u2013DFDD and VP\u2013DFDD. 2.1 System Model In this section, we assume a MIMO channel with NT transmit and NR receive antennas. In DSTM, the data\u2013carrying NT \u00d7 NT matrix symbols V [k] are taken from a suitable alphabet V of unitary matrices [1]. Here, k denotes the matrix symbol index and the NT \u00d7NT transmit symbol S[k] is obtained from V [k] via differential encoding S[k] = V [k]S[k \u2212 1]. For the special case when NT = 1, DSTM simplifies to DPSK. The signals received at the NR receive antennas in NT consecutive symbol intervals are collected in a column vector r[k] = B[k]h[k] + n[k], (2.1) where r[k] , [r11[k] . . . rNT 1[k] r12[k] . . . rNTNR [k]] T , h[k] , [h11[k] . . . hNT 1[k] h12[k] . . . hNTNR [k]] T , n[k] , [n11[k] . . . nNT 1[k] n12[k] . . . nNTNR [k]] T , and 7 B[k] , INR \u2297 S[k]. rntnr [k], hntnr [k], and nntnr [k] denote the received sig- nal, the fading gain, and the noise, respectively, corresponding to transmit antenna nt, 1 \u2264 nt \u2264 NT , and receive antenna nr, 1 \u2264 nr \u2264 NR. The Rayleigh fading component h[k] is modeled as a zero\u2013mean Gaussian random vector with correlation matrix E{h[k]hH [k]} = Rrx \u2297 Rtx, where Rrx and Rtx de- note the NR \u00d7 NR receive and the NT \u00d7 NT transmit correlation matrices respectively and E {\u00b7} denotes statistical expectation. The temporal fading correlation is modeled as Rt[\u03bb] , E{hntnr [k + \u03bb]h\u2217ntnr [k]} = J0(2\u03c0BfTNT\u03bb) [16], where J0(\u00b7), Bf , and T denote the zeroth order Bessel function of the first kind, the Doppler bandwidth, and the symbol interval, respectively. nntnr [k] is spatially and temporally i.i.d. additive white Gaussian noise (AWGN) with variance \u03c32n , E{|nntnr [k]|2}. 2.2 Noncoherent Detectors 2.2.1 MSDD To derive the MSDD decision rule we first collect N received vectors r[k] in a new vector r , [rT [k] rT [k\u2212 1] . . . rT [k\u2212N +1]]T which can be modeled as r = Bh+ n, (2.2) with B , diag{B[k] . . . B[k \u2212N + 1]}, h , [hT [k] . . . hT [k \u2212N + 1]]T , and n , [nT [k] . . . nT [k \u2212N + 1]]T . From (2.2) we observe that the PDF p(r|B) of r conditioned on B is a zero\u2013mean Gaussian PDF with covariance matrix BRBH , where R , E{hhH}+ E{nnH} = Rt\u2297Rrx\u2297Rtx + \u03c32nINNRNT with [Rt]i,j = Rt[i \u2212 j], 0 \u2264 i, j \u2264 N \u2212 1. Thus, performing maximum\u2013likelihood detection leads to the MSDD decision rule 8 V\u0302 = argmax V \u2208VN\u22121, [k\u2212N+1]\u2208S {p (r|B)} (2.3) = argmin V \u2208VN\u22121, [k\u2212N+1]\u2208S {rHBR\u22121BHr}, where V , [V [k] . . . V [k \u2212 N + 2]], V\u0302 is the estimate of V , and S is the alphabet of the transmit symbols S[k]. We note that for DSTM constellations that form a group S = V holds [1]. For further development of (2.3) , the Cholesky factorization of R\u22121 = UHU is introduced. We partition the upper triangular matrix U into NTNR \u00d7NTNR sub\u2013matrices U i,j, 0 \u2264 i, j \u2264 N \u2212 1 [17]. Thus, (2.3) can be rewritten as V\u0302 = argmin V \u2208VN\u22121,[k\u2212N+1]\u2208S \uf8f1\uf8f2\uf8f3 N\u22121\u2211 i=0 \u2225\u2225\u2225\u2225\u2225 N\u22121\u2211 j=i U i, jB H [k \u2212 j]r[k \u2212 j] \u2225\u2225\u2225\u2225\u2225 2 \uf8fc\uf8fd\uf8fe . (2.4) For spatially uncorrelated fading we can show that U i,j , u t i,jINTNR with uti,j , [U t]i,j where U t denotes an upper triangular matrix obtained by Cholesky factorization of Rt + \u03c3 2 nIN . Therefore, in this special case, (2.4) is the well\u2013known MSDD decision rule for uncorrelated fading, cf. e.g. [16, 18]. Since the complexity of MSDD grows exponentially with the observation win- dow sizeN , in the next section, we will introduce MS\u2013DFDD whose complexity grows only linearly with N . 2.2.2 MS\u2013DFDD The fundamental idea of MS\u2013DFDD is to replace V [k \u2212 j], 1 \u2264 j \u2264 N \u2212 2, and S[k \u2212 N + 1] in (2.4) by the corresponding previously decided symbols V\u0302 [k\u2212 j] and S\u0302[k\u2212N + 1]. After all irrelevant terms are neglected, this leads to the MS\u2013DFDD decision rule given by V\u0302 [k] = argmin V [k]\u2208V \uf8f1\uf8f2\uf8f3 \u2225\u2225\u2225\u2225\u2225E ( B\u0303 H [k]r[k] + N\u22121\u2211 j=1 P jB\u0302 H [k \u2212 j]r[k \u2212 j] )\u2225\u2225\u2225\u2225\u2225 2 \uf8fc\uf8fd\uf8fe , (2.5) 9 where E , U 0,0, P j , U \u22121 0,0U 0,j, 1 \u2264 j \u2264 N \u2212 1, B\u0302[k] , INR \u2297 S\u0302[k], S\u0302[k] = V\u0302 [k]S\u0302[k \u2212 1], and B\u0303[k] , INR \u2297 (V [k]S\u0302[k \u2212 1]). Since the U i,j are obtained through Cholesky factorization of R\u22121, it can be shown that the P j are the coefficients of the optimum linear (N \u2212 1)th order VP for the fading\u2013 plus\u2013noise process f [k] , h[k]+n[k] [17], i.e., the P j minimize the prediction error variance \u03c32e , E{||e[k]||2} with e[k] , f [k]\u2212 N\u22121\u2211 j=1 P jf [k \u2212 j]. (2.6) Furthermore, (EHE)\u22121 = (UH0,0U 0,0) \u22121 can be shown to be the covariance matrix of the prediction error, i.e., Ree , E{e[k]eH [k]} = (UH0,0U 0,0)\u22121. Con- sequently, (2.5) can be interpreted as temporal linear vector prediction with subsequent spatial prediction error whitening. We note that for the special case of spatially uncorrelated fading E = ut0,0INTNR and P j = (u t 0,j\/u t 0,0)INTNR , where (ut0,0) \u22122 and ut0,j\/u t 0,0, 1 \u2264 j \u2264 N \u2212 1, denote the error variance and the coefficients of the optimum (N\u22121)th order SP for the scalar fading\u2013plus\u2013noise process fntnr [k] , hntnr [k] + nntnr [k]. Thus, in this case, (2.5) is equivalent to the DFDD scheme proposed in [16] for spatially independent fading. 2.2.3 The Relationship between MS\u2013DFDD and SP\u2013 \/VP\u2013DFDD It is of interest to compare the MS\u2013DFDD decision rule in (2.5) with SP\u2013 DFDD and VP\u2013DFDD, which can be obtained as special cases of the sequence detection scheme in [7] if the number of trellis states is reduced to zero. In particular, by letting E = INTNR and P j = (u t 0,j\/u t 0,0)INTNR , 1 \u2264 j \u2264 N \u2212 1, (2.5) simplifies to the SP\u2013DFDD metric in [7, Eq. (5)]. Obviously, SP\u2013 DFDD completely ignores the fading correlations. The VP\u2013DFDDmetric in [7, Eq. (10)] is obtained by lettingE = INTNR and P j , U \u22121 0,0U 0,j, 1 \u2264 j \u2264 N\u22121, in (2.5). Thus, similar to MS\u2013DFDD, VP\u2013DFDD contains an optimum linear VP. However, in contrast to MS\u2013DFDD, spatial whitening of the prediction 10 error is not performed in VP\u2013DFDD. We will show in Section 2.4 that this might have some unexpected consequences. It is also worth noting that, similar to the schemes in [7, 19], the metric in (2.5) can also be used as branch metric in a Viterbi algorithm. This leads to an improved performance as compared to DFDD since error propagation is mitigated but increases receiver complexity. For this purpose, we compare all three DFDD schemes for the case \u03c32n \u2192 0. Define R\u0303t as the (N\u22121)\u00d7(N\u22121) lower sub\u2013block matrix of N\u00d7N matrixRt, rt , [Rt[\u22121] . . . Rt[\u2212(N \u2212 1)]]T , and P , [P T1 . . . P TN\u22121]T . The Yule\u2013Walker equation for the optimum VP is given by (R\u0303t \u2297Rrx \u2297Rtx + \u03c32nI(N\u22121)NTNR)P = rt \u2297Rrx \u2297Rtx. (2.7) We observe from (2.7) that for \u03c32n \u2192 0 the optimum VP coefficients are given by P = (R\u0303 \u22121 t rt) \u2297 INTNR . Since vector R\u0303 \u22121 t rt contains the coefficients of the optimum scalar predictor of length N \u2212 1, we conclude that surprisingly for \u03c32n \u2192 0 VP\u2013DFDD simplifies to SP\u2013DFDD. Similarly, it can be shown that for \u03c32n \u2192 0 the prediction error covariance matrix becomes Ree = (1 \u2212 rHt R\u0303 \u22121 t rt)(Rrx \u2297Rtx), i.e., E = (Rrx \u2297Rtx)\u22121\/2\/ \u221a 1\u2212 rHt R\u0303 \u22121 t rt. Therefore, unlike VP\u2013DFDD, MS\u2013DFDD exploits spatial fading correlations also for high SNR. As mentioned before, the complexity of all considered DFDD schemes is linear in N . However, for metric calculation SP\u2013DFDD requires the smallest number of multiplications, whereas MS\u2013DFDD requires the most. In partic- ular, for SP\u2013DFDD all predictor coefficients are scalars and E can be omit- ted in (2.5), while for MS\u2013DFDD, in general, all predictor coefficients are NTNR \u00d7 NTNR matrices and E cannot be omitted. The complexity of VP\u2013 DFDD is between that of SP\u2013DFDD and MS\u2013DFDD since the predictor coef- ficients are matrices but E can be omitted in (2.5). 11 2.3 Performance Analysis The performance analysis of MSDD and DFDD in correlated fading closely follows the corresponding analyses for the uncorrelated case in [16, 18]. There- fore, we only give a brief sketch of the analysis here. To simplify our exposition, we assume group codes in which case the elements V l, 0 \u2264 l \u2264 L\u2212 1, of V are diagonal matrices [1]. The BEP of DFDD can be approximated as Pb \u2248 xeRNT \u2211L\u22121 l=1 Pe(V l \u2192 V 0) [16, Eqs. (45)\u2013(47)], where R and Pe(V l \u2192 V 0) are the rate of the considered DSTM scheme and the pairwise error probability (PEP) assuming S\u0302[k \u2212 j] = S[k \u2212 j], 1 \u2264 j \u2264 N \u2212 1 (genie\u2013aided DFDD), respectively. If decision feedback is not required, xe = 1, otherwise xe = 2. Based on (2.5) it can be shown that since the V l, 0 \u2264 l \u2264 L\u2212 1, are diagonal matrices, feedback is not needed for N = 2 for SP\u2013DFDD. In contrast, for VP\u2013 DFDD and MS\u2013DFDD feedback in form of S\u0302[k\u22121] is required even for N = 2 except for the special case of DPSK (NT = 1). The PEP Pe(V l \u2192 V 0) itself can be obtained using standard tools, cf. e.g. [16, 18] and references therein, since the corresponding metric difference is a quadratic form of Gaussian ran- dom variables. Similarly, for MSDD the metric difference is also a Gaussian quadratic form and the approach outlined in [18, Section IV] can be used to compute an approximation for the BEP based on the PEPs. 2.4 Simulation and Numerical Results In this section, we compare optimumMSDD, MS\u2013DFDD, SP\u2013DFDD, and VP\u2013 DFDD based on simulations and numerical results. For all results a normalized Doppler bandwidth of BfT = 0.05 is assumed. In Fig. 2.1, we show the BEP of the considered detection strategies for diag- onal DSTM [1] vs. Eb\/N0 (Eb: received energy per bit, N0: single\u2013sided power spectral density of the underlying continuous\u2013time noise process). NT = 2, 12 NR = 1, R = 2 bit\/(channel use), and the correlation between both transmit antennas is \u03c1 = 0.9.1 For N = 2 we observe from Fig. 2.1 that while MSDD leads to substantial performance gains compared to SP- and VP\u2013DFDD, this is not true for the proposed MS\u2013DFDD. For N = 2 MS\u2013DFDD is negatively af- fected by error propagation negating any potential performance gains over SP- and VP\u2013DFDD which both do not suffer from error propagation in this case. In contrast, for N = 3 all DFDD schemes are affected by error propagation and MS\u2013DFDD yields substantial gains compared to SP- and VP\u2013DFDD, espe- cially in the error floor region. For low\u2013to\u2013medium SNRs (e.g. Eb\/N0 = 15 dB) all considered DFDD schemes have a comparable performance, while MSDD still yields considerable gains. For N = 10, where simulation of MSDD is too time consuming because of its high complexity, MS\u2013DFDD achieves a gain of approximately 0.6 dB at BEP = 10\u22126 compared to VP\u2013DFDD. However, even for N = 10 the performance gap between MS\u2013DFDD and CD is quite large because of the relatively large normalized Doppler bandwidth. For N = 2 and N = 3 SP\u2013DFDD and VP\u2013DFDD achieve a similar performance in the considered range of SNRs. In contrast, for N = 10 VP\u2013DFDD outperforms SP\u2013DFDD, especially for Eb\/N0 \u2265 20 dB. We note that the theoretical results (solid lines) obtained with the methods outlined in Section 2 agree well with the simulation results (markers) at high SNR. In Fig. 2.2, we consider the same detection schemes as in Fig. 2.1. How- ever, now quaternary DPSK (DQPSK) transmission with NT = 1 and NR = 2 is assumed, and the two receive antennas have correlation \u03c1 = 0.9. We ob- serve that both MS\u2013DFDD and MSDD yield substantial performance gains compared to SP- and VP\u2013DFDD, especially in the error floor region. Since unlike for DSTM with NT \u2265 2, for single\u2013antenna transmission feedback is not required for MS\u2013DFDD with N = 2, MS\u2013DFDD also outperforms SP- and VP\u2013DFDD in this case. 1We note that experiments have shown that antenna correlations of \u03c1 = 0.9 and more can occur for example in small handsets accommodating multiple antennas [20]. 13 Figure 2.1: BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. Eb\/N0 for diagonal DSTM with R = 2 bit\/(channel use), NT = 2, NR = 1, \u03c1 = 0.9, and BfT = 0.05. Numerical results: Solid lines. Simulation results: Markers. In Figs. 2.3 and 2.4, we show the simulated error floor (Eb\/N0 \u2192 \u221e) of diagonal DSTM (NT = 2, NR = 1, R = 2 bit\/(channel use)) and DQPSK (NT = 1, NR = 2) caused by the various considered receivers as a func- tion of the transmit\/receive antenna correlation \u03c1, respectively. As expected, for \u03c1 = 0 SP-, VP-, and MS\u2013DFDD yield the same performance as these schemes are identical in this case. However, as \u03c1 increases, both MS\u2013DFDD and MSDD outperform SP\u2013DFDD and VP\u2013DFDD, which yield identical per- formance, cf. Section 2.2.3. Interestingly, Fig. 2.3 shows that the performance of MS\u2013DFDD and MSDD may even improve with increasing \u03c1, whereas that of SP\u2013DFDD and VP\u2013DFDD always deteriorates. In order to explain this observation, two different effects have to be taken into account. On the one hand, the spatial diversity, and thus the performance, is negatively affected 14 Figure 2.2: BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. Eb\/N0 for DQPSK, NT = 1, NR = 2, \u03c1 = 0.9, and BfT = 0.05. Numerical results: Solid lines. Simulation results: Markers. by increasing \u03c1. On the other hand, spatial fading correlations lead to spatial correlations in the prediction error vector e[k], which constitutes the effective noise vector for detection purpose, cf. Section 2.2.3, (2.5), (2.6). These corre- lations in the prediction error are beneficial if they are properly exploited by the detector. Since both MS\u2013DFDD and MSDD exploit the spatial correla- tion in the prediction error vector, their performance improves with increasing \u03c1 if this positive effect of spatial correlation outweighs the negative effect of the decreased diversity. In contrast, the performance of SP\u2013DFDD and VP\u2013 DFDD always deteriorates with increasing spatial fading correlation since these schemes do not exploit the resulting spatial correlation of the prediction error vector. In Figs. 2.1 to 2.4, we have assumed that the spatial and temporal correla- tions as well as the operating SNR are perfectly known for DFDD and MSDD 15 Figure 2.3: BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. transmit antenna correlation \u03c1 for diagonal DSTM with R = 2 bit\/(channel use), NT = 2, NR = 1, Eb\/N0 \u2192 \u221e, and BfT = 0.05. Simulation results. design. In a practical scenario, these parameters are not known a priori and have to be estimated by the receiver which may lead to a mismatch between the estimated channel parameters and the true channel parameters. The effects of such a mismatch are investigated in Fig. 2.5 for diagonal DSTM (NT = 2, NR = 1, R = 2 bit\/(channel use)). In Fig. 2.5, we show the BEP as a function of BfT for different \u03c1 and Eb\/N0 adopting mismatched estimated channel pa- rameters of B\u0302fT = 0.05, \u03c1\u0302 = 0.6, and E\u0302b\/N0 = 35 dB for receiver design. In Fig. 2.5a), the performance of VP\u2013DFDD (solid lines) and MS\u2013DFDD (dashed lines) are compared under these conditions for N = 3. As can be observed, even for mismatched channel parameters MS\u2013DFDD outperforms VP\u2013DFDD in all cases for the considered parameter range. In Fig. 2.5b), the performances of MS\u2013DFDD with mismatched (solid lines) and matched (dashed lines) chan- 16 Figure 2.4: BEP of SP\u2013DFDD, VP\u2013DFDD, MS\u2013DFDD (proposed), and MSDD (proposed) vs. receive antenna correlation \u03c1 for DQPSK, NT = 1, NR = 2, Eb\/N0 \u2192\u221e, and BfT = 0.05. Simulation results. nel parameters are compared forN = 5. In the mismatched case, the estimated channel parameters (B\u0302fT = 0.05, \u03c1\u0302 = 0.6, and E\u0302b\/N0 = 35 dB) were used for MS\u2013DFDD design, whereas in the matched case the true channel parameters were employed. As expected, Fig. 2.5b) shows that mismatch causes a certain performance degradation. We note that in practice the predictor coefficients P j, 1 \u2264 j \u2264 N \u2212 1, and the prediction error covariance matrix Ree may be directly computed using an adaptive algorithm, which would avoid the need for explicit estimation of the channel parameters. For this purpose, the approach presented in [21] for scalar DFDD may be extended to the vector case. 17 Figure 2.5: BEP vs. BfT for diagonal DSTM with R = 2 bit\/(channel use), NT = 2, and NR = 1 under various channel conditions. a) MS\u2013DFDD (pro- posed) and VP\u2013DFDD designed for B\u0302fT = 0.05, \u03c1\u0302 = 0.6, and E\u0302b\/N0 = 35 dB; b) MS\u2013DFDD (proposed) designed for matched and mismatched (B\u0302fT = 0.05, \u03c1\u0302 = 0.6, and E\u0302b\/N0 = 35 dB) channel parameters. 18 Chapter 3 1 The Generalized K\u2013Fading Model In this section, a brief review of the PDF of the instantaneous received SNR for generalized K\u2013fading is introduced. Subsequently, certain moment gener- ating functions (MGFs) for the case of cascade multipath fading and the case of composite shadowing\/multipath fading are also derived. For the remainder of this thesis, we assume quasi\u2013static channel conditions, i.e., the instanta- neous SNRs of all fading branches remain constant over an entire block of data symbols and change randomly from one block to the next. 3.1 PDF of the Instantaneous Received SNR In order to derive the generalized K\u2013distribution, we first consider the case of composite shadowing and multipath fading [22]. In this case, the generalized K\u2013fading model describes a composite Gamma\u2013shadowing\/Nakagami\u2013m fad- ing process. The PDF of the instantaneous SNR \u03b3, conditioned on the average SNR \u03b3\u0304, is given by p\u03b3|\u03b3\u0304(\u03b3|\u03b3\u0304) = m m\u03b3m\u22121 \u0393(m)\u03b3\u0304m exp ( \u2212m\u03b3 \u03b3\u0304 ) , m > 0, \u03b3 \u2265 0, (3.1) where \u0393(x) denotes the Gamma function. The average SNR \u03b3\u0304 itself is a random variable with PDF given by 19 p\u03b3\u0304(\u03b3\u0304) = \u03b3\u0304k\u22121 \u0393 (k) \u03b3\u0304k exp ( \u2212 \u03b3\u0304 \u03b3\u0304 ) , k > 0, \u03b3\u0304 \u2265 0, (3.2) where \u03b3\u0304 , E {\u03b3\u0304}. By combining the above two equations we obtain the PDF of the instantaneous SNR \u03b3 given by p\u03b3(\u03b3) = a\u03b2+1 \u0393(k)\u0393(m)2\u03b2 \u03b3 \u03b2\u22121 2 K\u03b1(a \u221a \u03b3), (3.3) where a , 2 \u221a m \u03b3\u0304 , \u03b1 , k \u2212m, \u03b2 , k +m\u2212 1, and Kv(x) denotes the modified Bessel function of the second kind and order v. This instantaneous SNR may also be used to model cascade multipath fading. As an example, the case of double Rayleigh\u2013fading [9, 10] is obtained when k = m = 1, where p\u03b3(\u03b3) = 2 \u03b3\u0304 K0 ( 2 \u221a m \u03b3\u0304 ) . (3.4) More or less severe cascade multipath fading can be modeled by varying the parameters k and m accordingly. In particular, for the special case where k = 1, we obtain a cascade multipath fading model composed of a Rayleigh fading process and a Nakagami\u2013m fading process. Finally, it is worth noting that in the case of cascade multipath fading, \u03b3\u0304 is a deterministic quantity and \u03b3\u0304 = \u03b3\u0304. In the following, we consider transmission over L generalized K\u2013fading branches and derive expressions for the MGF of the instantaneous sum SNR \u03b3t , L\u2211 l=1 \u03b3l, (3.5) where \u03b3l is the instantaneous SNR associated with the lth branch (l \u2208 {1, . . . , L}). These expressions will later be utilized in Section 4.3 to determine the diversity order of coherent transmission with maximum\u2013ratio combining (MRC) at the receiver, and in Section 5 to extend our performance analysis for DBPSK\/non\u2013coherent FSK modulation with EGC at the receiver (Section 4) to the case of DQPSK and M\u2013ary non\u2013coherent FSK modula- tion. We note that the derived MGF expressions could also be useful for other performance analyses (e.g., outage analysis) and are thus of general interest. 20 We start with the case of independent but not necessarily identically dis- tributed (i.n.d.) fading across branches, which is relevant for the case of cas- cade multipath fading. Subsequently, we address the case of composite shad- owing and multipath fading. 3.2 MGF of Sum SNR for the Case of I.N.D. Fading Let \u03b3\u0304l , E{\u03b3l} denote the average SNR associated with the lth branch (l \u2208 {1, . . . , L}). Moreover, we define \u03b8 ,minl\u2208{1,...,L}{\u03b3\u0304l}, i.e., the average SNR \u03b3\u0304l can be written as \u03b3\u0304l , \u03b4l \u00b7 \u03b8 with constant \u03b4l \u2265 1 for all indices l \u2208 {1, . . . , L}. In the following, the individual branches are assumed to be characterized by independent generalized K\u2013fading, where for the lth branch the parameters of the PDF (3.3) are given by al , 2 \u221a ml \u03b3\u0304l , \u03b1l , kl \u2212ml, and \u03b2l , kl +ml \u2212 1. The MGF of the instantaneous branch SNR \u03b3l, M\u03b3l (x) , E{ex\u03b3l}, can be derived based on (3.3) by employing [\u00a76.643, no. 3] from [23]. Using the relation [24, Ch. 13] W\u00b5,\u03bd (x) = e \u2212x\/2x\u03bd+1\/2U (1\/2 + \u03bd \u2212 \u00b5, 1 + 2\u03bd;x) (3.6) between the Whittaker function W\u00b5,\u03bd (x) and the confluent hypergeometric function of the second kind U (a, b;x), one obtains the following closed-form expression: M\u03b3l (x) = (\u2212ml x\u03b4l\u03b8 )kl U ( kl, 1 + \u03b1l; \u2212ml x\u03b4l\u03b8 ) . (3.7) Note that U (a, b;x) is also known as Kummer\u2019s function of the second kind or Tricomi\u2019s confluent hypergeometric function. In addition, for numerical evaluation, the representation [24, Ch. 13] 21 U (a, b;x) = \u0393 (1\u2212 b) \u0393 (a\u2212 b+ 1) 1F1 (a, b;x) (3.8) + x1\u2212b \u0393 (b\u2212 1) \u0393 (a) 1F1 (a\u2212 b+ 1, 2\u2212 b;x) of U (a, b;x) in terms of the Kummer confluent hypergeometric function 1F1 (a, b;x) is sometimes preferable, which leads to the following expression for M\u03b3l (x): M\u03b3l (x) = (\u2212ml x\u03b4l\u03b8 )kl \u0393 (\u2212\u03b1l) \u0393 (ml) 1F1 ( kl, 1 + \u03b1l; \u2212m x\u03b4l\u03b8 ) (3.9) + (\u2212ml x\u03b4l\u03b8 )ml \u0393 (\u03b1l) \u0393 (kl) 1F1 ( ml, 1\u2212 \u03b1l; \u2212m x\u03b4l\u03b8 ) . Due to the assumption of independent fading, the MGF of the instantaneous sum SNR \u03b3t according to (3.5), M\u03b3t (x) , E{ex\u03b3t}, is given by M\u03b3t (x) = L\u220f l=1 (\u2212ml x\u03b4l\u03b8 )kl U ( kl, 1 + \u03b1l; \u2212ml x\u03b4l\u03b8 ) . (3.10) In the case of i.i.d. fading, the above expression reduces to M\u03b3t (x) = (\u2212ml x\u03b8 )kL [ U ( k, 1 + \u03b1; \u2212m x\u03b8 )]L (3.11)( k1 = . . . = kL , k,m1 = . . . = mL , m, \u03b41 = . . . = \u03b4L , 1 ) . 3.3 MGF of Sum SNR for Correlated Com- posite Shadowing and Multipath Fading In the case of composite shadowing and multipath fading, it is assumed that the shadowing part is fully correlated across links, whereas the multipath fad- ing is i.i.d. across the L branches ( k1 = . . . = kL , k,m1 = . . . = mL , m ) . Correspondingly, all branches are characterized by the same average SNR, \u03b3\u0304, which itself is a random variable with PDF given by (3.2). Moreover, we 22 have \u03b3\u03041 = . . . = \u03b3\u0304L , \u03b8. The joint PDF of the instantaneous branch SNRs \u03b3l (l \u2208 {1, . . . , L}), conditioned on the average SNR \u03b3\u0304, is given by p\u03b31,...,\u03b3L|\u03b3\u0304 (\u03b31, . . . , \u03b3L|\u03b3\u0304) = L\u220f l=1 p\u03b3l|\u03b3\u0304 (\u03b3l|\u03b3\u0304) , (3.12) due to the assumption of independent multipath fading across the L branches. Correspondingly, the conditional MGF of the instantaneous sum SNR \u03b3t, M\u03b3t|\u03b3\u0304 (x) , \u222b\u221e 0 ex\u03b3tp\u03b3t|\u03b3\u0304 (\u03b3t|\u03b3\u0304) d\u03b3t, is given by M\u03b3t|\u03b3\u0304 (x) = L\u220f l=1 M\u03b3l|\u03b3\u0304 (x) . (3.13) Based on (3.1) and [\u00a73.381, no. 4] from [23], the conditional MGF of the instan- taneous branch SNR \u03b3l, M\u03b3l|\u03b3\u0304 (x) , \u222b\u221e 0 ex\u03b3lp\u03b3l|\u03b3\u0304 (\u03b3l|\u03b3\u0304) d\u03b3l, can be calculated as M\u03b3l|\u03b3\u0304 (x) = ( m m\u2212 x\u03b3\u0304 )m , \u211c{x} < 0, (3.14) which is the well-known MGF for Nakagami-m fading [13, Ch. 2.2]. Based on (3.2), (3.13) and (3.14), the (unconditional) MGF of \u03b3t can be written as M\u03b3t (x) = 1 \u0393 (k) \u03b8k \u222b \u221e 0 \u03b3\u0304k\u22121( 1\u2212 x m \u03b3\u0304 )mL \u00b7 e\u2212\u03b3\u0304\/\u03b8d\u03b3\u0304. (3.15) As will be seen in Section 5.3, error probabilities for values m \u2208 N, where N denotes the set of all integers greater than zero, can typically be evaluated with a high accuracy by replacing m with a slightly different value m\u00b1 \u01eb \/\u2208 N, where \u01eb > 0 is a small perturbation value. Therefore, assuming that m is a finite non-integer value and employing [\u00a73.383, no. 5] from [23], we find the following closed-form expression for the MGF of \u03b3t: 23 M\u03b3t(x) = (k)\u2212mL \u00b7 (mL)1\u2212k (3.16) \u00d7 [(\u2212m x\u03b8 )mL \u0393(mL) \u0393(1\u2212mL) \u0393(1\u2212k) \u00b7 L \u2212\u2206k,m \u2212mL (\u2212m x\u03b8 ) \u2212 (\u2212m x\u03b8 )k \u0393(k) \u0393(1\u2212k) \u0393(1\u2212mL) \u00b7 L \u2206k,m \u2212k (\u2212m x\u03b8 )] , \u03b8<\u221e, \u211c{x}<0. where (x)\u03bd , \u0393 (x+ \u03bd) \/\u0393 (x) denotes the Pochhammer symbol and L b a (x) de- notes the generalized Laguerre function. Moreover, the identity \u0393 (x) \u0393 (1\u2212 x) = \u03c0\/ sin (\u03c0x) is used for the Gamma function and the short-hand notation \u2206k,m , k\u2212mL is introduced. Finally, similar to Section 3.2, the MGF (3.16) can be ex- pressed in terms of the Kummer confluent hypergeometric function 1F1 (a, b;x): M\u03b3t (x) = \u03b3 (\u2206k,m) \u00b7 [(\u2212m x\u03b8 )mL 1 \u0393 (k) 1F1 ( mL, 1\u2212\u2206k,m; \u2212m x\u03b8 ) (3.17) \u2212 (\u2212m x\u03b8 )k 1 \u0393 (mL) \u0393 (1\u2212\u2206k,m) \u0393 (1 + \u2206k,m) 1F1 ( k, 1 + \u2206k,m; \u2212m x\u03b8 )] , where we have used the relation [24, Ch. 13] Lba (x) = (b+ 1)a \u03b3 (a+ 1) 1F1 (\u2212a, b+ 1;x) . (3.18) for non-integer values of b. 24 Chapter 4 1 Generalized K\u2013Fading: Binary Modulation Scenario In this section, closed\u2013form BEP expressions for DBPSK\/non\u2013coherent FSK modulation over L generalized K\u2013fading branches with EGC at the receiver are derived. We start with the case of i.n.d. fading across branches. Further- more, we will derive the expressions for the case of composite shadowing and multipath fading. 4.1 BEP for I.N.D. Fading In this section, we consider the case of DPSK\/non\u2013coherent FSK modulation over L branches with EGC at the receiver, the instantaneous EGC output SNR is given by \u03b3t , \u2211L l=1 \u03b3l [13, Ch. 9.4], where \u03b3l denotes the instantaneous SNR associated with the lth branch. For a fixed value of \u03b3t, the BEP of DBPSK\/non\u2013coherent FSK modulation over L branches with EGC at the receiver is given by [25, Ch. 14.4] Pb(\u03b3t) = 1 22L\u22121 exp(\u2212g\u03b3t) L\u22121\u2211 l=0 cl(g\u03b3t) l, (4.1) cl , 1 l! L\u22121\u2212l\u2211 \u03ba=0 ( 2L\u2212 1 \u03ba ) , (4.2) 25 where g , 1 for DBPSK and g , 1\/2 for binary non\u2013coherent FSK mod- ulation. In order to derive a closed\u2013form expression for the average BEP P\u0304b(\u03b8) , E\u03b3t {Pb(\u03b3t)}, we first note that the joint PDF of the instantaneous branch SNRs \u03b3l (l \u2208 {1, . . . , L}) is given by p\u03b31,...,\u03b3L(\u03b31, . . . , \u03b3L) = L\u220f l=1 p\u03b3l(\u03b3l), (4.3) due to the assumption of independent fading across the L branches. Further- more, we define the index vector \u03ba , [\u03ba1, . . . , \u03baL] \u2208 NL0 and the index set Kl , { \u03ba \u2208 NL0 | \u03ba1 + . . .+ \u03baL = l } , (4.4) where N0 denotes the set of all integers greater than or equal to zero. We also note that \u03b3lt can be expressed as [24, Ch. 24] \u03b3lt = (\u03b31 + . . .+ \u03b3L) l = \u2211 \u03ba\u2208Kl ( l \u03ba ) \u03b3\u03ba11 . . . \u03b3 \u03baL L , (4.5) where ( l \u03ba ) , l!\/(\u03ba1! . . . \u03baL!). Based on the above findings, the average BEP P\u0304b(\u03b8) can be expressed as P\u0304b(\u03b8) = 1 22L\u22121 L\u22121\u2211 l=0 clg l \u2211 \u03ba\u2208Kl ( l \u03ba )( L\u220f \u03bb=1 \u222b \u221e 0 exp(\u2212g\u03b3\u03bb)\u03b3\u03ba\u03bb\u03bb p\u03b3\u03bb (\u03b3\u03bb) d\u03b3\u03bb ) . (4.6) Plugging in (3.3) for the PDFs p\u03b3l (\u03b3l), l \u2208 {1, . . . , L}, and employing [\u00a76.643, no. 3] from [23] in conjunction with (3.6), we obtain for the average BEP the following closed\u2013form expression: P\u0304b(\u03b8) = 1 22L\u22121 L\u22121\u2211 l=0 cl \u2211 \u03ba\u2208Kl ( l \u03ba ) (4.7) \u00d7 ( L\u220f \u03bb=1 (k\u03bb)\u03ba\u03bb(m\u03bb)\u03ba\u03bb gk\u03bb ( m\u03bb \u03b4\u03bb\u03b8 )k\u03bb U ( k\u03bb + \u03ba\u03bb, 1 + \u03b1\u03bb; m\u03bb g\u03b4\u03bb\u03b8 )) . For the special case of i.i.d. fading, where k1 = . . . = kL , k, m1 = . . . = mL , m, \u03b1 , k \u2212m, and \u03b41 = . . . = \u03b4L , 1, (4.7) simplifies to P\u0304b (\u03b8) = 1 22L\u22121 ( m g\u03b8 )kL L\u22121\u2211 l=0 cl \u2211 \u03ba\u2208Kl ( l \u03ba ) (4.8) \u00d7 ( L\u220f \u03bb=1 (k)\u03ba\u03bb (m)\u03ba\u03bb U ( k + \u03ba\u03bb, 1 + \u03b1; m g\u03b8 )) . 26 Finally, for the special case L = 1, (4.1) reduces to Pb (\u03b3t) = Pb (\u03b31) = 1 2 e\u2212g\u03b31 , and P\u0304b (\u03b8) can be evaluated as P\u0304b (\u03b8) = 1 2 ( m g\u03b8 )k U ( k, 1 + \u03b1; m g\u03b8 ) . (4.9) For comparison, in the case of i.i.d. Rayleigh fading the average BEP P\u0304b (\u03b8) is given by [25, Ch. 14.4] P\u0304b (\u03b8) = 1 22L\u22121 (L\u2212 1)! (1 + g\u03b8)L L\u22121\u2211 l=0 cl (L\u2212 1 + l)! ( g\u03b8 1 + g\u03b8 )l , (4.10) and we have P\u0304b (\u03b8) = 1 2 (1 + g\u03b8) , (4.11) for the special case L = 1. 4.2 BEP for Correlated Composite Shad- owing and Multipath Fading In the case of composite shadowing and multipath fading, we again assume that the shadowing part is fully correlated across links, whereas the multipath fading is i.i.d. across the L branches. In or to arrive at a closed\u2013form expression for the average BEP P\u0304b (\u03b8), we first average (4.1) over the instantaneous branch SNRs \u03b3l, while conditioning on \u03b3\u0304. In the final step, the resulting conditional BEP, denoted as P\u0304b (\u03b3\u0304), is then averaged over \u03b3\u0304. Similar to (4.6), the conditional BEP P\u0304b (\u03b3\u0304) can be written as P\u0304b (\u03b3\u0304) = 1 22L\u22121 L\u22121\u2211 l=0 clg l \u2211 \u03ba\u2208Kl ( l \u03ba )( L\u220f \u03bb=1 \u222b \u221e 0 e\u2212g\u03b3\u03bb\u03b3\u03ba\u03bb\u03bb p\u03b3\u03bb|\u03b3\u0304 (\u03b3\u03bb|\u03b3\u0304) d\u03b3\u03bb ) , (4.12) where we have used the joint PDF p\u03b31,...,\u03b3L|\u03b3\u0304 (\u03b31, . . . , \u03b3L|\u03b3\u0304), conditioned on the average SNR \u03b3\u0304, can be written as the product of the conditional PDFs 27 p\u03b3l|\u03b3\u0304 (\u03b3l|\u03b3\u0304) of the instantaneous branch SNRs \u03b3l (l \u2208 {1, . . . , L}), cf. (3.12). Plugging in (3.1) for the conditional PDFs p\u03b3l|\u03b3\u0304 (\u03b3l|\u03b3\u0304) and employing [\u00a73.381, no.4] from [23], we find the following expression for P\u0304b (\u03b3\u0304): P\u0304b (\u03b3\u0304) = 1 22L\u22121 ( mm \u0393 (m) )L L\u22121\u2211 l=0 clg l \u2211 \u03ba\u2208Kl ( l \u03ba ) (4.13) \u00d7 ( L\u220f \u03bb=1 \u0393 (m+ \u03ba\u03bb) \u00af\u03b3\u03ba\u03bb (g\u03b3\u0304 +m)m+\u03ba\u03bb ) , ( m1 = . . . = mL , m ) . Based on the PDF (3.2) of the average SNR \u03b3\u0304, the average BEP P\u0304b ( \u03b8\u0304 ) , E\u03b3\u0304{P\u0304b (\u03b3\u0304)} can be written as P\u0304b ( \u03b8\u0304 ) = 1 22L\u22121 1 \u0393 (k) (\u0393 (m))L \u03b8k L\u22121\u2211 l=0 clg l (4.14) \u00d7 \u2211 \u03ba\u2208Kl ( l \u03ba )\u220fL \u03bb=1 \u0393 (m+ \u03ba\u03bb) ml \u222b \u221e 0 \u03b3\u0304k+l\u22121 \u00b7 e\u2212\u03b3\u0304\/\u03b8( g m \u03b3\u0304 + 1 )mL+l d\u03b3\u0304. Employing [\u00a73.383, no. 5] from [23] and assuming thatm is a finite non-integer value and k 6= mL, we find the following closed\u2013form expression for the average BEP P\u0304b (\u03b8): P\u0304b (\u03b8) = 1 22L\u22121 1 \u0393 (k) \u03c0 sin (\u03c0\u2206k,m) (4.15) \u00d7 L\u22121\u2211 l=0 cl [\u2211 \u03ba\u2208Kl ( l \u03ba )( L\u220f \u03bb=1 (m)\u03ba\u03bb )] \u00d7 [( m g\u03b8 )mL \u0393 (1\u2212 \u03d5m,l) \u0393 (1\u2212 \u03c8k,l) L \u2212\u2206k,m \u2212\u03d5m,l ( m g\u03b8 ) \u2212 ( m g\u03b8 )k sin (\u03c0\u03d5m,l) sin (\u03c0\u03c8k,l) L \u2206k,m \u2212\u03c8k,l ( m g\u03b8 )] , where we have introduced the short\u2013hand notations \u03c8k,l , k + l and \u03d5m,l , mL+ l. 28 4.3 Asymptotic Analysis and Diversity Or- der Since the closed\u2013form BEP expressions (4.8) and (4.12) involve non\u2013standard functions and the primary behavior of the resulting BEP curves is not obvious, in this section, we will investigate the behavior of (4.8) and (4.12) for high SNR values (\u03b8 \u2192\u221e). In particular, we derive expressions for the resulting (asymptotic) diversity order1. d , lim \u03b8\u2192\u221e d (\u03b8) , d (\u03b8) , \u2212\u2202 log ( P\u0304b (\u03b8) ) \u2202 log (\u03b8) . (4.16) In particular, we show that the diversity order of DBPSK\/non-coherent FSK modulation is, in fact, the same as that in the case of coherent transmission. 4.3.1 The Case of Independent Fading For the ease of exposition, we focus on the case of i.i.d. fading here, i.e., \u03b3\u03041 = . . . = \u03b3\u0304L = \u03b8, m1 = . . . = mL , m, k1 = . . . = kL , k. An extension to the case of i.n.d fading is, however, straightforward. In the following, we derive approximate expressions for the average BEP (4.9), by employing corre- sponding approximations of the confluent hypergeometric function U (a, b;x). Consider first the case where the two fading parameters k and m are dif- ferent, i.e., \u03b1 = k \u2212m 6= 0. For the ease of exposition, we assume that \u03b1 is a non\u2013integer value. For x \u2192 \u221e and non\u2013integer values of b, the confluent hypergeometric function U (a, b;x) can be approximated as [24, Ch. 13] U (a, b;x) =\u0307 \u0393 (1\u2212 b) \u0393 (1\u2212 b+ a) + \u0393 (b\u2212 1) \u0393 (a) x1\u2212b, (4.17) 1The (asymptotic) diversity order is the negative slope of the BEP curve for high SNR values on a log\u2013log scale. It has been shown to be a useful measure for characterizing the principal behavior of digital transmission schemes over various fading channels [25, Ch. 14.4]. 29 where =\u0307 denotes asymptotic equality. For \u03b8 \u2192 \u221e and non\u2013integer values of \u03b1, the average BEP (4.8) can thus be approximated as P\u0304b (\u03b8) =\u0307 1 22L\u22121 ( m g\u03b8 )\u03be1L L\u22121\u2211 l=0 cl \u2211 \u03ba\u2208Kl ( l \u03ba )( L\u220f \u03bb=1 (k)\u03ba\u03bb (m)\u03ba\u03bb \u0393 (|\u03b1|) \u0393 (k + \u03ba\u03bb) \u0393 (\u03be2 + \u03ba\u03bb) ) . (4.18) where \u03be1 ,min{k,m} and \u03be2 ,max{k,m}. In the case k < m, (4.18) simplifies to P\u0304b (\u03b8) =\u0307 1 22L\u22121 ( m g\u03b8 )\u03be1L( \u0393 (|\u03b1|) \u0393 (k) \u0393 (m) )L L\u22121\u2211 l=0 cl \u2211 \u03ba\u2208Kl ( l \u03ba ) . (4.19) From (4.18) and (4.19) we find d = \u03be1L = min{k,m} \u00b7 L. (4.20) Interestingly, the smaller of the two fading parameters, k and m, limits the asymptotic diversity order. For example, in the case of cascade Rayleigh\/ Nakagami\u2013m fading with k = 1 and m \u2265 1 the asymptotic diversity order is always given by d = L, just as in the case of pure Rayleigh fading, where [25, Ch. 14.4] P\u0304b (\u03b8) =\u0307 ( 1 2g\u03b8 )L( 2L\u2212 1 L ) . (4.21) Next, consider the case \u03b1 = 0, i.e., k = m. For x \u2192 0 and b = 1, the confluent hypergeometric function U (a, b;x) can be approximated as [24, Ch. 13] U (a, b;x) =\u0307\u2212 1 \u0393 (a) (ln (x) + \u03a8 (a) + 2\u03b3\u2032) , (4.22) where \u03a8 (x) , ( \u2202 \u2202x \u0393 (x) ) \/\u0393 (x) denotes the Digamma function and \u03b3\u2032 the Euler\u2013Mascheroni constant. For \u03b8 \u2192 \u221e and \u03b1 = 0, the average BEP (4.9) can thus be approximated as 30 P\u0304b (\u03b8) =\u0307 1 22L\u22121 [ \u2212 ( m g\u03b8 )k ln ( m g\u03b8 )]L (4.23) \u00d7 L\u22121\u2211 l=0 cl \u2211 \u03ba\u2208Kl ( l \u03ba )( L\u220f \u03bb=1 (k)\u03ba\u03bb (m)\u03ba\u03bb \u0393 (k + \u03ba\u03bb) ) . Correspondingly, we find d (\u03b8) = ( k \u2212 1 ln (\u03b8) ) L = ( m\u2212 1 ln (\u03b8) ) L, (4.24) i.e., the asymptotic diversity order is given by d = kL = mL. (4.25) This result is in accordance with [10], where the diversity order of various coherent modulation schemes was determined for the special case of a sin- gle branch (L = 1) being subject to double Rayleigh fading (k = m = 1). Moreover, note that (4.25) is also in accordance with (4.20). Finally, we compare the above results for DBPSK\/non\u2013coherent FSK mod- ulation to the asymptotic diversity order obtained in the case of a coherent transmission scheme. As an example, we consider a binary phase shift keying (BPSK) scheme over L i.i.d. generalized K\u2013fading links with MRC at the receiver. The corresponding average BEP can be determined via the following finite\u2013range integral [26]: P\u0304b (\u03b8) = 1 \u03c0 \u222b pi\/2 0 M\u03b3t ( \u2212 1 sin2 (\u03c6) ) d\u03c6, (4.26) where the MGF M\u03b3t(x) of the instantaneous MRC output SNR \u03b3t = \u2211L l=1 \u03b3l is given by (3.11). As earlier, we assume for simplicity that \u03b1 is a non\u2013integer value. Based on (4.17) and employing [\u00a73.621, no. 1] from [23], the average BEP (4.26) for high SNR values \u03b8 \u2192\u221e can be approximated as P\u0304b (\u03b8) =\u0307 1 2\u03c0 ( \u0393 (|\u03b1|) \u0393 (\u03be2) )L( 4m \u03b8 )\u03be1L B (\u03be1L+ 1\/2, \u03be1L+ 1\/2) , (4.27) 31 where B(x, y) denotes the Beta function. Correspondingly, the diversity order of BPSK modulation over L i.i.d. generalized K\u2013fading links with MRC at the receiver is given by d = \u03be1L = min{k,m} \u00b7 L, (4.28) just as in the case of the considered non\u2013coherent transmission schemes, cf. (4.20). 4.3.2 Correlated Composite Shadowing and Multipath Fading In this section, we derive an approximate expression for the average BEP (4.15) for high SNR values \u03b8 \u2192 \u221e, by employing a corresponding approximation of the generalized Laguerre function Lba (x). For x\u2192\u221e, the generalized Laguerre function Lba (x) can be approximated as [27, Ch. 13.2] Lba(x) . = (b+1)a \u0393(a+1) . (4.29) For \u03b8 \u2192\u221e, the average BEP (4.15) can thus be approximated as P\u0304b (\u03b8) . = 1 22L\u22121 sign (\u2206k,m) \u0393 (k) \u03c0 sin (\u03c0\u2206k,m) ( m g\u03b8 )\u03b61 L\u22121\u2211 l=0 cl\u039el (4.30) \u00d7 [\u2211 \u03ba\u2208Kl ( l \u03ba )( L\u220f \u03bb=1 (m)\u03ba\u03bb )] (1\u2212 |\u2206k,m|)\u2212\u03b61\u2212l \u0393(1\u2212 \u03c8k,l) , where \u03b61 , min{k,mL}, sign(x) denotes the sign function (i.e., sign(x)=+1 for all x \u2265 0 and sign(x)= -1 otherwise), and2 2As earlier, we assume that k 6= mL, since otherwise (4.15) is not valid. However, it turns out that (4.30) yields nearly identical results for k = mL + \u01eb and k = mL \u2212 \u01eb, if \u01eb is chosen sufficiently small. 32 \u039el , \uf8f1\uf8f2\uf8f3 sin(\u03c0\u03d5m,l)\/ sin(\u03c0\u03c8k,l) for k<mL1 for k>mL . (4.31) Correspondingly, the asymptotic diversity order in the case of correlated com- posite shadowing and multipath fading is obtained as d = \u03b61 = min{k,mL}. (4.32) This result reveals an interesting interplay between macroscopic diversity due to shadowing effects and microscopic diversity due to multipath fading: the asymptotic diversity order is always limited by either the shadowing effect (k\u2264mL) or the multipath fading (mL<k), depending on which one of the two fading effects is more severe. In order to arrive at (4.30) , we have utilized that for \u03b8\u2192\u221e only one of the two Lba(x)\u2013terms in (4.15) dominates, namely the one which is associated with the term ( m g \u03b8 )\u03b61 . Correspondingly, if k\u2248mL the convergence of the asymptotic solution (4.30) to the exact expression (4.15) can be expected to be rather slow, since the dominant term will only emerge for very large values of \u03b8. However, if k and mL are sufficiently different, the convergence of (4.30) is typically quite fast, as will be seen from the numerical performance results presented in Section 5.3. In order to compare the asymptotic diversity order (4.32) for DBPSK\/non\u2013 coherent FSK modulation to that in the case of BPSK modulation, we first note that (4.26) is valid for arbitrary fading correlations (if an expression for the MGF M\u03b3t(x) of the instantaneous MRC output SNR \u03b3t is available). In the case of correlated composite shadowing and multipath fading, the MGF M\u03b3t(x) is given by (3.16). Based on (4.29) and employing [\u00a73.621, no. 1] from [23], the average BEP (4.26) for \u03b8 \u2192\u221e can be approximated as 33 P\u0304b (\u03b8) . = sign (\u2206k,m) (k)\u2212mL (mL)1\u2212k 2\u03c0 (4.33) \u00d7 \u0393 (\u03b61) (1\u2212 |\u2206k,m|)\u2212\u03b61 \u0393 (1\u2212 \u03b62) ( 4m \u03b8 )\u03b61 B (\u03b61 + 1\/2, \u03b61 + 1\/2) , where \u03b62,max{k,mL}. Correspondingly, the diversity order of BPSK mod- ulation over L correlated composite shadowing\/multipath fading links with MRC at the receiver is given by d = \u03b61 = min{k,mL}, (4.34) just as in the case of the non\u2013coherent transmission schemes, cf. (4.32). 34 Chapter 5 1 Generalized K\u2013Fading: Extensions to M\u2013ary Modulation The closed-form expressions (3.10) and (3.16) for the MGF of the instan- taneous sum SNR \u03b3t in the case of i.n.d. fading and correlated composite shadowing\/multipath fading, respectively, can be utilized to extend our per- formance analysis in Section 4 to the case of non\u2013binary transmission. As an example, we will focus on the average BEP of DQPSK modulation with Gray mapping, the average BEP of M\u2013ary orthogonal FSK modulation, and the average symbol error probability (SEP) of coherent M\u2013ary phase shift keying (PSK) modulation. 5.1 BEP for I.N.D. Fading In the case of i.n.d. fading, the average BEP of DQPSK modulation with Gray mapping over L branches with EGC at the receiver is given by [13, Ch. 9.4] P\u0304b (\u03b8) = 1 \u03c022L \u222b pi \u2212pi f (L, \u03c1;\u03c6) 1 + 2\u03c1 sin (\u03c6) + \u03c12 L\u220f l=1 M\u03b3l ( \u22122\u2212 \u221a 2 sin (\u03c6) ) d\u03c6, (5.1) where 35 f (L, \u03c1;\u03c6) , L\u2211 l=1 c\u2032l \u00b7 [a1 (\u03c1) cos ((l \u2212 1) (\u03c6+ \u03c0\/2)) (5.2) \u2212 a2 (\u03c1) cos (l (\u03c6+ \u03c0\/2))] , c\u2032l , ( 2L\u2212 1 L\u2212 l ) , a1 (\u03c1) , \u03c1 \u2212l+1 \u2212 \u03c1l+1, a2 (\u03c1) , \u03c1\u2212l+2 \u2212 \u03c1l, (5.3) \u03c1 , \u221a 2\u2212\u221a2 2 + \u221a 2 , (5.4) and M\u03b3l(x) is given by (3.7). Thus, the average BEP (5.1) for the case of i.n.d. generalized K\u2013fading can be evaluated numerically via a single finite\u2013 range integral over known functions. Similarly, the average BEP for M\u2013ary orthogonal FSK over L branches with non\u2013coherent detection and EGC at the receiver can be evaluated numerically based on the single finite\u2013range integral expression (9.130) in [13, Ch. 9.4], which again depends on the product of the MGFs M\u03b3l(x), l \u2208 {1, . . . , L}. Finally, the average SEP for coherent M\u2013ary PSK modulation over L branches with MRC at the receiver can be calculated via the finite\u2013range integral1 [26] P\u0304S (\u03b8) = 1 \u03c0 \u222b (M\u22121)pi\/M 0 L\u220f l=1 M\u03b3l ( \u2212sin 2 (\u03c0\/M) sin2 (\u03c6) ) d\u03c6. (5.5) For the special case L = 1, there is also a finite\u2013range integral expression for the average SEP of M\u2013ary DPSK modulation [13, Ch. 8.2.5]: P\u0304S (\u03b8) = 1 \u03c0 \u222b (M\u22121)pi\/M 0 M\u03b3t ( \u2212 sin 2 (\u03c0\/M) 1 + \u221a 1\u2212 sin2 (\u03c0\/M) cos (\u03c6) ) d\u03c6. (5.6) Next, we consider the case of correlated composite shadowing and multipath fading. 1Similar expressions can also be stated for M\u2013ary amplitude\u2013shift\u2013keying (ASK) modu- lation and M\u2013ary quadrature\u2013amplitude modulation (QAM) [26]. 36 5.2 BEP for Correlated Composite Shad- owing and Multipath Fading Since (5.1) is also valid for the case of fully correlated shadowing\/i.i.d. multi- path fading (proof is given at the end of the section), we have P\u0304b (\u03b8) = 1 \u03c022L \u222b pi \u2212pi f (L, \u03c1;\u03c6) 1 + 2\u03c1 sin (\u03c6) + \u03c12 M\u03b3t ( \u22122\u2212 \u221a 2 sin (\u03c6) ) d\u03c6, (5.7) where M\u03b3t (x) is given by (3.16) . 2 Similarly, (5.5) again holds for arbitrary fading correlations, i.e., we have P\u0304S (\u03b8) = 1 \u03c0 \u222b (M\u22121)pi\/M 0 M\u03b3t ( \u2212sin 2 (\u03c0\/M) sin2 (\u03c6) ) d\u03c6. (5.8) Based on (3.16) , the average SEP (5.8) for correlated composite shadowing and multipath fading can thus be evaluated numerically via a single finite\u2013 range integral over known functions. To prove the validity of (5.7) for the case of fully correlated shadowing\/i.i.d. multipath fading, we extend the derivation of (5.1) presented in [13, Ch. 9.4] accordingly. Given a fixed value of the instantaneous EGC output SNR \u03b3t, the BEP of DQPSK modulation with Gray mapping over L branches with EGC at the receiver can be written as P\u0304b (\u03b3t) = 1 \u03c022L \u222b pi \u2212pi f (L, \u03c1;\u03c6) 1 + 2\u03c1 sin (\u03c6) + \u03c12 (5.9) \u00d7 L\u220f l=1 exp ( \u2212b 2\u03b3l 2 ( 1 + 2\u03c1 sin (\u03c6) + \u03c12 )) d\u03c6, where f (L, \u03c1;\u03c6) and \u03c1 are given by (5.2) and (5.4), respectively, and b ,\u221a 2 + \u221a 2 [13, Ch. 9.4]. The average BEP P\u0304b (\u03b8) can thus be written as P\u0304b (\u03b8) = \u222b \u221e 0 \u00b7 \u00b7 \u00b7 \u222b \u221e 0 P\u0304b (\u03b3t) \u222b \u221e 0 L\u220f l=1 p\u03b3l|\u03b3\u0304 (\u03b3l|\u03b3\u0304) p\u03b3\u0304 (\u03b3\u0304) d\u03b3\u0304d\u03b31 \u00b7 \u00b7 \u00b7 \u03b3L, (5.10) 2Note that (5.7) is not valid for arbitrary fading correlations. 37 where we have used that the joint PDF p\u03b31\u00b7\u00b7\u00b7\u03b3L|\u03b3\u0304 (\u03b31 \u00b7 \u00b7 \u00b7 \u03b3L|\u03b3\u0304), conditioned on the average SNR \u03b3\u0304, can be written as the product of the conditional PDFs p\u03b3l|\u03b3\u0304 (\u03b3l|\u03b3\u0304) of the instantaneous branch SNRs \u03b3l (l \u2208 {1, . . . , L}), cf. (3.12). Using (5.8) one obtains P\u0304b (\u03b8) = 1 \u03c022L \u222b pi \u2212pi f (L, \u03c1;\u03c6) 1 + 2\u03c1 sin (\u03c6) + \u03c12 \u00d7 \u222b \u221e 0 [ L\u220f l=1 \u222b \u221e 0 exp ( \u2212b 2\u03b3l 2 ( 1 + 2\u03c1 sin (\u03c6) + \u03c12 )) \u00d7 p\u03b3l|\u03b3\u0304 (\u03b3l|\u03b3\u0304) d\u03b3l ] p\u03b3\u0304 (\u03b3\u0304) d\u03b3\u0304 d\u03c6 (5.11) = 1 \u03c022L \u222b pi \u2212pi f (L, \u03c1;\u03c6) 1 + 2\u03c1 sin (\u03c6) + \u03c12 \u00d7 \u222b \u221e 0 [ M\u03b3l|\u03b3\u0304 ( \u2212b 2 2 ( 1 + 2\u03c1 sin (\u03c6) + \u03c12 ))]L p\u03b3\u0304 (\u03b3\u0304) d\u03b3\u0304d\u03c6 = 1 \u03c022L \u222b pi \u2212pi f (L, \u03c1;\u03c6) 1 + 2\u03c1 sin (\u03c6) + \u03c12 M\u03b3t ( \u2212b 2 2 ( 1 + 2\u03c1 sin (\u03c6) + \u03c12 )) d\u03c6 where we have used that the multipath fading is i.i.d., i.e., the conditional MGFs M\u03b3l|\u03b3\u0304 (x) are identical for all branches l \u2208 {1, . . . , L} and the (uncondi- tional) MGF M\u03b3t (x) is given by M\u03b3t (x) = \u222b\u221e 0 [ M\u03b3l|\u03b3\u0304 (x) ]L p\u03b3\u0304 (\u03b3\u0304) d\u03b3\u0304. Combin- ing (5.11) with the values for \u03c1 and b, we finally arrives at (5.7). 5.3 Simulation and Numerical Results In the following, numerical performance results are presented which illustrate our findings in Section 4 and Section 5. In particular, we will present Monte\u2013 Carlo simulation results, so as to corroborate our analytical performance re- sults. 5.3.1 The Case of Independent Fading In this section, we investigate the BEP performance of DBPSK modulation over L independent generalized K\u2013fading branches with EGC at the receiver 38 (cf. Section 4.1 and 4.3.1). As an example, we focus on the case of i.i.d. cascade Rayleigh\/Nakagami\u2013m fading with k = 1 and m \u2265 1. Figure 5.1: Average BEP P\u0304b (\u03b8) versus average SNR L\u03b8 in dB for the case of i.i.d. double Rayleigh fading (k = 1,m = 1). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.8) \/(4.9) using the values k = 1.01 and m = 0.99. Dashed lines represent corresponding analytical results for the case of i.i.d. Rayleigh fading evaluated based on (4.10)\/(4.11). Corresponding simulation results for Rayleigh fading and double Rayleigh fading (k = 1,m = 1) are indicated by markers \u2018\u25e6\u2019. Fig. 5.1 shows the average BEP P\u0304b (\u03b8) for DBPSK versus the overall average received SNR L\u03b8 in dB for the case of i.i.d. double Rayleigh fading (k = 1,m = 1). The solid lines represent analytical results for L \u2208 {1, . . . , 4} evaluated based on (4.8) and (4.9) using the values k = 1.01 and m = 0.99. Corresponding simulation results (for k = 1 and m = 1), obtained by Monte\u2013 39 Carlo simulations over a large number of independent channel realizations, are indicated by markers \u2018\u25e6\u2019. As a reference, we have also included corresponding performance results for i.i.d. Rayleigh fading (L \u2208 {1, . . . , 4}). As can be seen, the relative performance gains obtained for L > 1 diversity branches are quite similar for double Rayleigh fading and conventional Rayleigh fading. However, in comparison the BEP performance for double Rayleigh fading is significantly worse than that for Rayleigh fading (for all values of L).3 For example, in the case of L = 4 diversity branches, the performance difference between double Rayleigh fading and conventional Rayleigh fading at a BEP of 10\u22124 is about 5.3 dB. Vice versa, in order to achieve a BEP of less than 3 \u00d7 10\u22124 at an overall SNR of 20 dB, one requires L = 4 diversity branches in the case of double Rayleigh fading, whereas in the case of conventional Rayleigh fading L = 2 diversity branches are sufficient. Finally, we note that the analytical results and the simulation results are in good agreement, which corroborates our analysis in Section 4.1. In Fig. 5.2, the average P\u0304b (\u03b8) of DBPSK modulation with EGC at the receiver is compared to that of coherent BPSK modulation with MRC at the receiver. As an example, we consider again the case of i.i.d. double Rayleigh fading (k = 1,m = 1). The analytical curves for BPSK modulation were obtained based on (3.11) and (4.26) using numerical integration. As can be seen, the general behavior of the curves for growing values of L is quite similar in the case of DBPSK and BPSK modulation. In particular, the asymptotic slopes of the curves are identical in both cases, as predicted by our asymptotic analysis in Section 4.3.1. Interestingly, the performance difference between DBPSK and BPSK modulation at high SNR values is about 3.8 dB (for all values of L), which is slightly larger than the well\u2013known 3 dB difference in the case of conventional Rayleigh fading. Finally, Fig. 5.3 compares the average BEP P\u0304b (\u03b8) of DBPSK with EGC 3For the special case L = 1 and coherent PSK modulation, this observation was already made in [10]. 40 Figure 5.2: Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case of i.i.d. double Rayleigh fading (k = 1,m = 1). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.8) \/(4.9) using the values k = 1.01 and m = 0.99. Dashed lines represent corresponding analytical results for coherent BPSK modulation with MRC at the receiver evaluated based on (3.11), (4.26) using numerical inte- gration. Corresponding simulation results for k = 1 and m = 1 are indicated by markers \u2018\u25e6\u2019 (both for DPSK and PSK modulation). at the receiver for various examples of i.i.d. cascade Rayleigh\/Nakagami\u2013m fading (k = 1,m \u2208 {1, 3, 5}, L \u2208 {1, 3}). For the example m = 3, we have also included the average BEP of coherent BPSK modulation with MRC at the receiver. Moreover, for the example m = 3, L = 3 we have included the asymptotic BEP curves as a reference (dotted lines), which were evaluated based on (4.18)\/(4.19) and (4.27) for DBPSK and BPSK modulation, respec- 41 Figure 5.3: Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for dif- ferent cases of cascade fading (k = 1 and m \u2208 {1, 3, 5}). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.8) \/(4.9) using the values k = 1.01 and m \u2208 {0.99, 2.99, 4.99}, re- spectively. Dashed lines represent corresponding analytical results for coherent BPSK modulation with MRC at the receiver evaluated based on (3.11), (4.26) using numerical integration. Corresponding simulation results for k = 1 and m \u2208 {1, 3, 5} are indicated by markers \u2018\u25e6\u2019 (both for DPSK and PSK modula- tion). The dotted lines represent asymptotic BEP curves for the case m = 3, L = 3 evaluated based on (4.18)\/(4.19) for DPSK modulation and based on (4.27) for PSK modulation. tively. As can be seen, the performance of DBPSK improves significantly, if the fading parameter m is increased from m = 1 to m = 3. As opposed to this, increasing m further to m = 5 yields comparatively small additional perfor- 42 mance gains, which indicates that the BEP performance is somewhat limited by the small value of the fading parameter k. Another interesting observation is that the performance difference between DBPSK and BPSK modulation at high SNR values is slightly reduced if the fading parameter m is increased. For example, in the case m = 3 the performance difference is about 3.3 dB, as opposed to 3.8 dB in the case of double Rayleigh fading, cf. Fig. 5.2. Finally, we again note that the analytical results (evaluated based on (4.8) and (4.9) using the values k = 1.01 and m \u2208 {0.99, 2.99, 4.99}) and the simulation re- sults for k = 1 andm \u2208 {1, 3, 5} are in good agreement for all considered cases. Moreover, the asymptotic BEP curves accurately represent the behavior of the BEP curves at high SNR values, which corroborates our asymptotic analysis in Section 4.3.1. 5.3.2 Correlated Composite Shadowing and Multipath Fading Next, we consider the BEP performance of DBPSKmodulation over L diversity branches that are subject to correlated composite shadowing and multipath fading (cf. Section 4.2 and 4.3.2). Fig. 5.4 presents numerical results for the average BEP P\u0304b (\u03b8) as a function of the overall average received SNR L\u03b8 in dB for the case k = 3 and m = 1 (mild shadowing) and L \u2208 {1, . . . , 4}. Solid lines represent analytical results evaluated based on (4.15) , using the values k = 3.01 and m = 0.99. Dashed lines represent analytical results for coherent BPSK modulation with MRC at the receiver (for the cases L \u2208 {1, 3, 4}), evaluated based on (3.16) and (4.26) using the same values k = 3.01 and m = 0.99. Corresponding simulation results for k = 3 and m = 1, obtained by Monte\u2013Carlo simulations over a large number of independent channel realizations, are indicated by markers \u2018\u25e6\u2019 (both for DPSK and PSK modulation). As can be seen, the analytical results and the simulation results are in good agreement, which corroborates our analysis in Section 4.2. Note 43 Figure 5.4: Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case k=3 and m=1 (mild shadowing). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.15) using the values k=3.01 and m=0.99. Dashed lines represent corresponding analytical results for coherent BPSK modulation with MRC at the receiver evaluated based on (3.16) , (4.26) using numerical integration. Corresponding simulation results for k=3 and m=1 are indicated by markers \u2018o\u2019 (both for DPSK and PSK modulation). that significant diversity gains are accomplished in the case L > 1, both in the case of DPSK and PSK modulation. As can be seen, the general behavior of the BEP curves is the same for coherent and non\u2013coherent transmission (similar to the case of i.i.d. cascade Rayleigh\/Nakagami\u2013m fading). The asymptotic advantage of BPSK over DBPSK modulation is about 3 dB, similar to the case of pure Rayleigh fading. 44 Figure 5.5: Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case k=3 and m=1 (mild shadowing). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver, evaluated based on (4.15) using the values k=3.01 and m=0.99. Dashed lines represent corresponding asymptotic results evaluated based on (4.30) . In Fig. 5.5, we compare the exact analytical BEPs for DPSK modulation according to (4.15) with the asymptotic BEPs according to (4.30) .4 As earlier, the values k = 3.01 andm = 0.99 were employed for evaluating the expressions (4.15) and (4.30) . It can be seen that convergence is comparatively fast for the cases L = 2 and L = 4. In particular, the BEP curves exhibit the predicted diversity orders of d = 2m = 2 and d = k = 3, respectively. However, as 4For BPSK modulation with MRC at the receiver we have obtained very similar results (not depicted) by evaluating asymptotic BEPs according to (4.33) and comparing them with the corresponding analytical BEPs according to (3.16) and (4.26). 45 discussed in Section 4.3.2, in the case L = 3 convergence is very slow, since k \u2248 mL. In this example, SNR values on the order of 100 dB are required, until the exact analytical BEP (4.15) approaches the asymptotic BEP (4.30) and assumes the predicted asymptotic diversity order of d = 3m \u2248 k \u2248 3. Note that since the maximum diversity order is accomplished for L = 3, the relative performance advantage of L > 3 branches is comparatively small in this example. Finally, in Fig. 5.6 numerical performance results for the case k = 1 and m = 3 (severe shadowing) and L \u2208 {1, 4} are presented. Again it can be seen that the analytical results (solid lines for DBPSK and dashed lines for BPSK modulation) and the simulation results (markers \u2018\u25e6\u2019) are in good agreement. The analytical results for DBPSK and BPSK modulation were again evaluated based on (4.15) and (3.16) , (4.26), respectively, using the values k = 1.01 and m = 2.99. Interestingly, in contrast to the case of mild shadowing, L > 1 branches offer no diversity benefit at all. As can be seen, in the case of DBPSK modulation the BEP curve for L = 4 is even slightly worse than the BEP curve for L = 1 (due to the SNR normalization). The BEP curves for L = 2 and L = 3 (not depicted) lie in between the curves for L = 1 and L = 4. As predicted by the asymptotic BEP (4.30) , included here for the case L = 4 (dotted line), the BEP curves of DBPSK for L \u2265 1 branches are all characterized by the same asymptotic diversity order of d = k = 1. Also note that the convergence of the asymptotic BEP (4.30) to the exact BEP (4.15) is comparatively fast in this example. Finally, we note that while in the case of BPSK modulation the asymptotic diversity order is the same as for DBPSK modulation, the order of the curves is swapped here, i.e., L = 4 offers a slight performance advantage over L = 1 (the BEP curves for L = 2 and L = 3 were again found in between the curves for L = 1 and = 4). 46 Figure 5.6: Average BEP P\u0304b (\u03b8) versus overall average SNR L\u03b8 in dB for the case k=1 andm=3 (severe shadowing). Solid lines represent analytical results for DBPSK modulation with EGC at the receiver evaluated based on (4.15) using the values k=1.01 and m=2.99. Dashed lines represent corresponding analytical results for coherent BPSK modulation with MRC at the receiver evaluated based on (3.16) , (4.26) using numerical integration. Corresponding simulation results for k=1 and m=3 are indicated by markers \u2018o\u2019 (both for DPSK and PSK modulation). The dotted lines represent asymptotic BEP curves for the case L=4 evaluated based on (4.30) for DPSK modulation and based on (4.33) for PSK modulation. 5.3.3 Performance of M\u2013ary Modulation Schemes Finally, we present some numerical performance results forM\u2013ary modulation. As an example, we focus on the case of DQPSK and coherent quaternary 47 Figure 5.7: Average BEP P\u0304b (\u03b8) versus overall average SNR per bit L\u03b8\/2 in dB for the case of cascade fading with k = 1 and m = 3. Solid lines represent analytical results for DQPSK modulation with EGC at the receiver evaluated based on (3.11) and (5.1) using the values k = 1.01 and m = 2.99. Dashed lines represent corresponding analytical results for coherent QPSK modulation with MRC at the receiver evaluated based on (3.11) and (4.26) using numer- ical integration. Corresponding simulation results for k = 1 and m = 3 are indicated by markers \u2018o\u2019 (both for DPSK and PSK modulation). PSK (QPSK) modulation over L i.i.d. cascade Rayleigh\/ Nakagami\u2013m fading branches with k = 1 and m = 3. Fig. 5.7 displays the corresponding average BEPs P\u0304b (\u03b8) versus the overall average received SNR per bit L\u03b8\/2 in dB for L \u2208 {1, . . . , 4} diversity branches. The analytical results for DQPSK modulation with EGC at the receiver (solid lines) were evaluated based on (3.11) and (5.1) via numerical integration using the values k = 1.01 and m = 2.99. The 48 analytical results for coherent QPSK modulation with MRC at the receiver (dashed lines) were evaluated based on (3.11) and (4.26), exploiting the fact that the average BEP of QPSK with Gray mapping is identical to that of BPSK modulation. As can be seen, the basic behavior of the BEP curves is very similar to the case of DBPSK\/BPSK modulation.5 In particular, the asymptotic slope of the BEP curves as well as the performance difference between DQPSK and QPSK modulation is the same as in the case of binary transmission (cf. Fig. 5.3). Again, we note that the analytical results and the simulation results are in good agreement, which corroborates our analysis in Section 5. 5We have made the same observation for the case of correlated composite shadowing and multipath fading (not depicted). 49 Chapter 6 1 Conclusion In this thesis, we have focused on the receiver design for DSTM over correlated MIMO channels. In addition, the performance analysis of DPSK and non\u2013 coherent FSK over generalized fading channels is considered. For DSTM receiver design, we have derived novel MSDD and low\u2013complexity MS\u2013DFDD receivers for DSTM in spatially correlated fading. We have com- pared MS\u2013DFDD with previously proposed SP- and VP\u2013DFDD, and shown that SP- and VP\u2013DFDD are equivalent at high SNR. In contrast to VP\u2013DFDD, MS\u2013DFDD performs spatial whitening of the prediction error and thus exploits spatial fading correlations in an optimum manner. The generalized K\u2013fading model, which is characterized by two fading parameters, k > 0 and m > 0, is versatile enough to cover both scenarios with cascade multipath fading and scenarios with composite shadowing and multipath fading. In this thesis, we have derived closed\u2013form expressions for the BEP of DBPSK modulation and binary non\u2013coherent FSK modulation over L generalized K\u2013fading links. In particular, we have considered the case of independent fading across links, which is relevant for cascade multipath fading scenarios, and the case of correlated composite shadowing and multipath fading. Moreover, we have conducted an asymptotic performance analysis for high SNR values and have studied the resulting diversity orders for various cases. We have also discussed the extension of our results toM\u2013ary modulation 50 schemes. Our results have shown that there is an interesting interplay between the two fading parameters k and m. In the case of independent fading, the smaller of the two fading parameters limits the asymptotic diversity order. Similarly, in the case of correlated composite shadowing and multipath fading, the asymptotic diversity order is always limited by either the shadowing effect or the multipath fading, depending on which one of the two fading effects is more severe. Moreover, for both scenarios we have shown that the diversity order of the considered non\u2013coherent transmission schemes is, in fact, the same as in the case of coherent transmission. Finally, numerical performance results were presented, in order to illustrate the above findings, and our analytical performance results were corroborated by means of Monte\u2013Carlo simulations. Some recommendations for future work may include the study of amplify\u2013 and\u2013forward and\/or decode\u2013and\u2013forward relaying under general K\u2013fading (on each link). One could again study the interplay of shadowing and multipath fading. Since the individual nodes are spatially distributed on a larger scale, the assumption of independent fading on each link would apply. 51 Bibliography [1] B. M. Hochwald and W. Sweldens. Differential unitary space\u2013time mod- ulation. IEEE Trans. Commun., 48:2041\u20132052, December 2000. [2] D. Divsalar and M. K. Simon. Multiple symbol differential detection of MPSK. IEEE Trans. Commun., 38(3):300\u2013308, March 1990. [3] P. Ho and D. Fung. Error performance of multiple\u2013symbol differential de- tection of PSK signals transmitted over correlated Rayleigh fading chan- nels. IEEE Trans. Commun., 40(10):1566\u20131569, October 1992. [4] B. Allen, Y. Kuroda, F. Said, and A. Aghvami. Comparison of coherent and differential space-time block codes over spatially correlated channels. IEEE Trans. Consumer Electronics, 50:1232\u20131236, November 2004. [5] X. Cai and G. Giannakis. Differential space\u2013time modulation with eigen\u2013 beamforming for correlated MIMO fading channels. IEEE Trans. Signal Processing, 54:1279\u20131288, April 2006. [6] V. Nguyen. A differential space\u2013time modulation scheme for correlated Rayleigh fading channels: Performance analysis and design. IEEE Trans. Signal Processing, 55:299\u2013312, January 2007. [7] P. Tarasak, H. Minn, and V. K. Bhargava. Linear prediction receiver for differential space\u2013time block codes with spatial correlation. IEEE Commun. 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Further results on Tarokh\u2019s space\u2013 time differential technique. IEEE Trans. Commun., 51(7):1093\u20131101, July 2003. [20] W. Kottermann, G. Pedersen, K. Olesen, and P. Eggers. Correlation properties for radio channels from multiple base stations to two antennas on a small handheld terminal. In Proc. IEEE Veh. Techn. Conf. (VTC), pages 462\u2013466, September 2002. [21] R. Schober and W. Gerstacker. Decision\u2013feedback differential detection based on linear prediction for MDPSK signals transmitted over Ricean fading channels. IEEE J. Select. Areas Commun., 18(3):391\u2013402, March 2000. [22] P. Shankar. Error rates in generalized shadowed fading channels. Wireless Personal Commun., 28(4):233\u2013238, February 2004. [23] I. S. Gradsheteyn and I. M. Ryzhik. Table of Integrals, Series, and Prod- ucts. Academic Press, New York, NY, 7th edition, 1994. [24] M. Abramowitz and I. A. Stegun (Eds.). Handbook of Mathematical Func- tions. 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