Temporal variation and frequency selectivity of wireless channels constitutea major drawback to the attainment of high gains in capacityand reliability offered by multiple antennas at the transmitter and receiverof a mobile communication system. Limited feedback and adaptive transmissionschemes such as adaptive modulation and coding, antenna selection,power allocation and scheduling have the potential to provide the platformof attaining the high transmission rate, capacity and QoS requirements incurrent and future wireless communication systems. Theses schemes requireboth the transmitter and receiver to have accurate knowledge of ChannelState Information (CSI). In Time Division Duplex (TDD) systems, CSI atthe transmitter can be obtained using channel reciprocity. In Frequency DivisionDuplex (FDD) systems, however, CSI is typically estimated at thereceiver and fed back to the transmitter via a low-rate feedback link. Due tothe inherent time delays in estimation, processing and feedback, the CSI obtainedfrom the receiver may become outdated before its actual usage at thetransmitter. This results in significant performance loss, especially in highmobility environments. There is therefore a need to extrapolate the varyingchannel into the future, far enough to account for the delay and mitigate theperformance degradation.The research in this thesis investigates parametric modeling and predictionof mobile MIMO channels for both narrowband and wideband systems.The focus is on schemes that utilize the additional spatial information offeredby multiple sampling of the wave-field in multi-antenna systems toaid channel prediction. The research has led to the development of severalalgorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficientmethods for the extrapolation of narrowband MIMO channels are proposed.Various extensions were also developed. These include methods for widebandchannels, transmission using polarized antenna arrays, and mobile-to-mobilesystems.Performance bounds on the estimation and prediction error are vital whenevaluating channel estimation and prediction schemes. For this purpose, analyticalexpressions for bound on the estimation and prediction of polarizedand non-polarized MIMO channels are derived. Using the vector formulationof the Cramer Rao bound for function of parameters, readily interpretableclosed-form expressions for the prediction error bounds were found for caseswith Uniform Linear Array (ULA) and Uniform Planar Array (UPA). Thederived performance bounds are very simple and so provide insight into systemdesign.The performance of the proposed algorithms was evaluated using standardizedchannel models. The effects of the temporal variation of multipathparameters on prediction is studied and methods for jointly tracking thechannel parameters are developed. The algorithms presented can be utilizedto enhance the performance of limited feedback and adaptive MIMOtransmission schemes.
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