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Title	Analysis and design of mimo heterogeneous cellular networks
Creator	Khoshkholgh Dashtaki, Mohammad Ghadir
Publisher	University of British Columbia
Date Issued	2018
Description	The structure of cellular networks is under disruptive innovations as a response to the growth of data traffic demands and the emergence of new applications. On the one hand, cellular networks are evolving into complex infrastructures comprising of several tiers of base stations (BS), known as heterogenous cellular networks (HetNets). On the other hand, multiple-input multiple-output (MIMO) multi-stream (multiplexing) communications are deployed to improve the communication reliability and increase the transmission rate. A comprehensive network-level analysis of MIMO multiplexing HetNets in terms of influential system parameters is therefore required. The dissertation focuses on this matter and studies the networl performance of several prominent MIMO multiplexing HetNets by adopting the powerful tool of stochastic geometry. Unfortunately, the current literature lacks an accurate definition of the coverage probability in multiplexing systems, often considers simplistic cell association (CA) scenarios, and commonly provides the analytical results in numerically expensive forms. In general, these drawbacks render complexities in performance evaluation and hinder scalable system design. With these regards, this thesis aims at 1) analyzing the coverage performance from a link-level perspective; 2) considering the maximum signal-to-interference-plus-noise ration (max-SINR) CA rule; 3) deriving the network performance through easy-to-compute formulas. Our analytical results are insightful and permits us to further explore various practical design issues. Specifically, thanks to compact formats and manageable computational costs of our analytical results, we are able to 1) comprehensively study the correlation across data streams of a given communication link, and prove that this correlation undermines the coverage performance; 2) prove that MIMO multiplexing HetNets growing the multiplexing gains reduces the coverage probability, thus diversity systems stands as the best option to maximize coverage probability; 3) investigate the relationship between spectral efficiency, multiplexing gains, and densification from a network-level perpective; 4) optimize the network in order to maximize aggregated multiplexing gains under prescribed coverage loss against the best possible coverage performance; and 5) explore the spectral efficiency optimization of the network subject to prescribed constraints.
Genre	Thesis/Dissertation
Type	Text
Language	eng
Date Available	2018-08-14
Provider	Vancouver : University of British Columbia Library
Rights	Attribution 4.0 International
DOI	10.14288/1.0371002
URI	http://hdl.handle.net/2429/66767
Degree	Doctor of Philosophy - PhD
Program	Electrical and Computer Engineering
Affiliation	Applied Science, Faculty of Electrical and Computer Engineering, Department of
Degree Grantor	University of British Columbia
GraduationDate	2018-09
Campus	UBCV
Scholarly Level	Graduate
Rights URI	http://creativecommons.org/licenses/by/4.0/
AggregatedSourceRepository	DSpace

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0 0.2 0.4 0.6 0.8 100.050.10.150.20.250.30.35P(X>1)100 10100.20.40.60.811.21.42Coverage Probabilitysim.upper−boundlower−bound1=10−4, Nr=81=10−4, Nr=41=10−3, Nr=8100 1010.20.40.60.811.21.41.61Coverage Probabilitysim.upper−boundlower−bound1=10−3, Nr=81=10−4, Nr=81=10−4, Nr=42 4 6 8 1000.10.20.30.40.50.60.70.8NrCoverage ProbabilitySingle Stream, =5Single Stream, =3.8Single Stream, =2.5Full MUX, =5Full MUX, =3.8Full MUX, =2.510−4 10−3 10−20.250.30.350.40.450.50.550.60.652Coverage Probabilityupper−bound, S1=4sim.  S1=4lower−bound, S1=4upper−bound, S1=2sim.  S1=2lower−bound, S1=210−4 10−3 10−20.20.30.40.50.60.71Coverage Probabilityupper−bound, S1=4sim.  S1=4lower−bound, S1=4upper−bound., S1=2sim. S1=2lower−bound, S1=210−4 10−3 10−20.10.20.30.40.50.60.70.82Coverage Probabilitymax−SINR, S1=4grid layout,  S1=4closest−BS, S1=4max−SINR S1=2grid layout,  S1=2closest−BS, S1=210−4 10−3 10−20.10.20.30.40.50.60.70.81Coverage Probabilitymax−SINR, S1=4grid layout,  S1=4closest−BS, S1=4max−SINR S1=2grid layout,  S1=2closest−BS, S1=210−10 10−500.10.20.30.40.50.60.72Coverage Probabilitysim.upper−boundlower−boundNo Noise No Noise (closed−from) 1=10−31=10−410−6 10−4 10−202468102S 1(a)1=10−61=10−41=10−31=5  10−210−6 10−4 10−202468102S 2(b)1=10−61=10−41=10−31=5  10−22 4 6 8 10 12 14 1610−810−610−410−2100102S11(a)S2=1S2=3S2=5S2=152 4 6 8 10 12 14 1610−810−610−410−2100102S12(b)S2=1S2=3S2=5S2=152 4 6 8 10 12 14 16123456S11(a)S2=1S2=3S2=5S2=152 4 6 8 10 12 14 16024681012S12(b)S2=1S2=3S2=5S2=1551015 51015200.020.0250.030.0350.04B2B1ZF (bps/Hz/m2 )1=10−4, 2=10−21=10−2, 2=10−25 1015 2051015200.0150.020.0250.030.0350.04B1B2ZF (bps/Hz/m2 )1=10−4, 2=10−21=10−2, 2=10−200.0050.0100.0050.0100.20.40.60.82S1=S2=6, =4, 1=0.11correlation coefficient00.0050.0100.0050.0100.20.40.60.82S1=S2=6, =4, 1=0.11correlation coefficient246824680.0550.060.0650.070.075S21=10−4, 2=10−3, =4, 1=0.1S1correlation coefficient00.51234560.040.060.080.10.120.141S1=S2=6, 1=10−4, 2=10−3correlation coefficient2 4 6 8 10 12 14 16246810121416S1S 21=5, 2=2, 1= 2=0.11=5, 2=10, 1= 2=0.251=5, 2=10, 1= 2=0.12 4 6 8 10 12 14 16246810121416S1S 21=5, 2=2, 1= 2=0.11=5, 2=10, 1= 2=0.251=5, 2=10, 1= 2=0.11 2 3 4 5 610−210−1100cMRC/cZFS(a): =5, =4Nr=6Nr=10Nr=20Nr=402 4 6 8 10 12 1410−210−1100cMRC /cZF(b): S=4, Nr=10=2.5=5=6.52 4 6 8 10 12 14 1611.522.5Nr* (1,2)(a)2=02=.052=0.12 4 6 8 10 12 14 1624681012141618(b)NrS 1+S22=02=.052=0.110−6 10−4 10−202468102S 1(a)1=10−61=10−41=10−31=5  10−210−6 10−4 10−202468102S 2(b)1=10−61=10−41=10−31=5  10−210−6 10−5 10−4 10−3 10−2 10−10123456789102S 1(a)1=10−61=10−41=10−31=5  10−210−6 10−5 10−4 10−3 10−2 10−10123456789102S 2(b)1=10−61=10−41=10−31=5  10−20 0.2 0.4 0.6 0.800.10.20.30.40.50.60.70.8Coverage Probability sim. =2.5upper−bound =2.5FC =2.5NC =2.5sim. =4upper−bound =4FC =4NC =4sim. =6upper−bound =6FC =6NC =6ZFBF100 10101234562Coverage Probabilityupper−bound, S1=3,S2=2Sim. S1=3,S2=2FC S1=3,S2=2NC S1=3,S2=2upper−bound, S1=6,S2=2Sim. S1=6,S2=2FC S1=6,S2=2NC S1=6,S2=210−6 10−5 10−4 10−3 10−200.10.20.30.40.50.60.70.80.911sim. S1=4,S2=1upper−bound, S1=4,S2=1FC S1=4,S2=1NC S1=4,S2=1sim. S1=4,S2=2upper−bound, S1=4,S2=2FC S1=4,S2=2NC S1=4,S2=2single−streamZFBF (S1=4,S2=2)ZFBF (S1=4,S2=1)10−6 10−5 10−4 10−3 10−200.10.20.30.40.50.60.70.80.911Coverage Probabilitysim. S1=4,S2=1upper−bound, S1=4,S2=1FC S1=4,S2=1NC S1=4,S2=1sim. S1=4,S2=2upper−bound, S1=4,S2=2FC S1=4,S2=2NC S1=4,S2=2single−stremZFBF(S1=4, S2=2)ZFBF(S1=4, S2=1)10−6 10−5 10−4 10−3 10−200.10.20.30.40.50.60.70.80.911sim. S1=1,S2=2upper−bound, S1=1,S2=2FC S1=1,S2=2NC S1=1,S2=2sim. S1=8,S2=2upper−bound, S1=8,S2=2FC S1=8,S2=2NC S1=8,S2=2single−streamZFBF(S1=8,S2=2)ZFBF(S1=1,S2=2)10−6 10−5 10−4 10−3 10−200.10.20.30.40.50.60.70.80.911sim. S1=1,S2=2upper−bound, S1=1,S2=2FC S1=1,S2=2NC S1=1,S2=2sim. S1=8,S2=2upper−bound, S1=8,S2=2FC S1=8,S2=2NC S1=8,S2=2single−strem ZFBF(S1=8,S2=2)ZFBF(S1=1,S2=2)510152023456−0.200.20.40.6Nrcorrelation coefficient100 1010.10.20.30.40.50.60.70.80.911Coverage probabilitysim.analysisgrid modelNr=4Nr=2100 1010.10.20.30.40.50.60.70.80.912Coverage probabilitysim.analysisgrid modelNr=2Nr=410−5 10−4 10−3 10−20.20.250.30.350.40.450.51Coverage probability(a)sim. Nr=4analysis Nr=4sim. Nr=2analysis Nr=210−5 10−4 10−3 10−20.20.250.30.350.40.450.52Coverage probability(b)sim. Nr=4analysis Nr=4sim. Nr=2analysis Nr=21 2 3 4 5 6 7 800.20.40.60.811.21.4NrCoverage ProbabilitySVD sim. Nt2=8, P2=1SVD analysis Nt2=8, P2=1SVD sim. Nt2=16, P2=1SVD analysis Nt2=16, P2=1SVD sim. Nt2=8, P2=NrSVD analysis Nt2=8,P2=NrMRC (all scenarios)ZFBF (all scenarios)10 20 30 40 50 6000.20.40.60.811.21.4Nt1Coverage ProbabilityAnalysis (solid line); Simulation (dashed line)SVDscenario 4MRC scenarios1,2,3,4SVDscenario 3SVDscenario 1SVDscenario 2 ZFBF scenarios1,2,3,42 4 6 8 10 12 14 1600.10.20.30.40.50.60.70.80.91Nt2Coverage ProbabilityAnalysis (solid line); Simulation (dashed line)SVD scenario4SVD scenario1SVD scenario3SVD scenario2MRC scenarios1,2,3,4ZFBF scenarios1,2,3,4

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