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Channel estimation and feedback for multiple antenna communication


This dissertation studies several aspects of feedback- based communication with multiple antennas, such as the estimation of the Channel State Information (CSI), the quantization of the CSI with a finite number of bits to enable its feedback to the transmitter, as well as the effect of errors in the feedback channel on the performance of the communication system. Channel estimation is doubly important in feedback-based communication because inaccurate CSI affects not only the receiver performance, but also results in sub-optimal transmission. In this context, Multiple Input Multiple Output (MIMO) flat-fading channel estimation when the transmitter employs Maximum Ratio Transmission (MRT) is studied. Two competing schemes for estimating the transmit and receive beamforming vectors of the channel matrix are analyzed: a training based conventional least squares estimation (CLSE) scheme and a closed-form semi-blind (CFSB) scheme that employs training followed by information-bearing spectrally white data symbols. Employing matrix perturbation theory, expressions for the mean squared error (MSE) in the beamforming vector, the average received SNR and the symbol error rate (SER) performance of both the semi-blind and the conventional schemes are derived. Another important issue in beamforming-based communication with multiple antennas is the feedback of CSI. Hence, the design and analysis of quantizers for Equal Gain Transmission (EGT) systems with finite rate feedback-based communication in flat-fading Multiple Input Single Output (MISO) systems is considered. Two popular approaches for quantizing the phase angles are contrasted: vector quantization (VQ) and scalar quantization (SQ). Closed-form expressions are derived for the performance of quantized feedback in terms of capacity loss and outage probability in the case of i.i.d. Rayleigh flat-fading channels. In the work described above, the feedback channel is assumed to be free of delay and noise. With the view to understand the effect of errors on quantization, this dissertation considers the more general problem of characterizing the high-rate performance of source coding for noisy discrete symmetric channels with random index assignment. Theoretical expressions for the performance of source coding for noisy channels are derived for a large class of distortion measures. The theoretical expressions are used to derive new results for two specific applications. The first is the quantization of the CSI for MISO systems with beamforming at the transmitter. The second application is in the wideband speech compression problem, i.e., that of quantizing the linear predictive coding parameters in speech coding systems with the log spectral distortion as performance metric

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