Opportunistic multiple antenna systems with partial channel knowledge and limited feedback

In recent years, beamforming and diversity techniques have evolved into an excellent technology to improve wireless communications over fading channels. For instance, the channel fluctuations of the users in a network are exploited as multiuser diversity by selecting the user with the best SNR. When fading is slow, beamforming at a multiple antenna transmitter is used to induce artificial channel fluctuations to ensure multiuser diversity in the network. Such a beamforming scheme is called opportunistic beamforming since the transmitter uses random beamforming to artificially induce opportunism in the network [1].; Opportunistic beamforming through multiuser diversity has offered some advantages over true beamforming methods that rely on full channel feedback or/and robust channel estimation methods. Opportunistic beamforming achieves good throughput with only signal-to-noise ratio (SNR) feedback from the users. The quality of the SNR feedback such as the degree of SNR quantization is essential for opportunistic beamforming because the base station selects the best receiving user based on the SNR measurements sent by the users. Furthermore, opportunism requires a large number of users in the system in order to reach the performance of the true beamforming that uses perfect channel state information (CSI).; In this dissertation, we develop and analyze opportunistic multiple antenna techniques for practical systems. We first derive the optimum quantization scheme that maximizes the average throughput of the opportunistic beamforming. Second, we investigate the benefit of having partial channel state information at the transmitter. Third, based on the feedback from the users, we propose a novel channel estimation method to obtain partial channel states. Some of the theoretical in this dissertation analysis are validated via real radio frequency (RF) experiments using our multiple antenna testbed developed here at The University of Texas at Dallas.