| Adaptive beamforming is a. powerful approach to receive or transmit signals of interest in a spatially selective way in the presence of interference and noise. Recently, there has been renewed interest in adaptive beamforming driven by applications in wireless communications, where multiple-input multiple-output (MEMO) techniques have emerged as one of the key technologies to accommodate the high number of users as well as the increasing demand for new high data rate services.;Beamforming techniques promise to increase the spectral efficiency of next generation wireless systems and are currently being incorporated in future industry standards. Although a significant amount of research has focused on theoretical capacity analysis, little is known about the performance of such systems in practice. In thesis, I experimentally and analytically evaluate the performance of adaptive beamforming techniques on the downlink channel of a wireless LAN.;To this end. I present the design and implementation of the first multi-user beam-forming system and experimental framework for wireless LANs. Next, I evaluate the benefits of such system in two applications.;First, I investigate the potential of beamforming to increase the unicast throughput through spatial multiplexing. Using extensive measurements in an indoor environment, I evaluate the impact of user separation distance, user selection, and user population size on the multiplexing gains of multi-user beamforming. I also evaluate the impact of outdated channel information due to mobility and environmental variation on the multiplexing gains of multi-user beamforming. Further, I investigate the potential of beamforming to eliminate interference at unwanted locations and thus increase spatial reuse.;Second, I investigate the potential of adaptive beamforming for efficient wireless multicasting. I address the joint problem of adaptive beamformer design at the PHY layer and client scheduling at the MAC layer by proposing efficient algorithms that are amenable to practical implementation. Next, I present the implementation of the beamforming based multicast system on the WARP platform and compare its performance against that of omni-directional and switched beamforming based multicast. Finally, I evaluate the performance of multicast beamforming under client mobility and infrequent channel feedback, and propose solutions that increase its robustness to channel dynamics. |
