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UBC Theses and Dissertations

A hybrid precoding and signal detection framework for future wireless systems Dsouza, Kevin Bradley


With energy efficiency and spectrum management being major concerns in future wireless systems, this thesis primarily focuses on the precoding and signal detection capabilities of next-generation wireless transceivers. In the first part of the thesis, we present a parallel framework to make hybrid precoding competitive in fast-fading environments. To enumerate, (i) a low-complexity algorithm which exploits the block diagonal phase-only nature of the analog precoder in a partially connected structure is proposed to arrive at a hybrid precoding solution for a multi-carrier single-user system using orthogonal frequency division multiplexing (OFDM), (ii) the original problem is broken down into independent subproblems of finding the magnitude and the phase components which are solvable in parallel, (iii) a per-RF chain power constraint is introduced instead of the sum power constraint over all antennas, which is much more practical in real systems, (iv) an alternating version of this scheme is proposed for increased spectral-efficiency gains, (v) wideband PCS architecture is critiqued for its applicability in future wireless systems and possible alternatives are discussed. In the second part of the thesis, we present a signal detection and time-frequency localization framework for smart transceivers. Although deep learning techniques for image analysis have been advancing at a breakneck pace for the past few years, their application to RF data has been relatively less explored. To enumerate our contributions, (i) we present a modification of an existing state-of-the-art object classification technique called Faster-RCNN (FRCNN) \cite{C108} for detection and time-frequency localization of the signal in a spectrogram of a wideband RF capture, (ii) insights into the design choices pertaining to the variables such as short-time Fourier transform (STFT) parameters, spectrogram and anchor sizes and network thresholds are discussed, (iii) synthetic data as per the recently proposed WiFi High Throughput (WiFi-HT) protocol \cite{wifi_ht} is generated and a mean average precision (mAP) of up to 0.9 is achieved when the model is trained and tested on positive signal to noise ratio (SNR) values, (iv) certain drawbacks of the model with respect to low SNR levels and disparate signal sizes are brought to light and possible solutions are discussed.

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