Correlation-based beamforming for multi-user MIMO channels.

摘要

Transmit precoding strategies in multiple-input multiple-output (MIMO) systems provide a mechanism for increasing the performance of point-to-point links and enable spatial division multiple access in multi-user networks. However, communication node mobility in such systems can lead to rapid channel variation which limits the quality of attainable channel state information (CSI). This work explores the performance loss of point-to-point and multi-user precoding and detection strategies based on CSI which goes out of date and channel distribution (correlation) information which provides a more average channel representation and ultimately stability in achievable performance.;This correlation-based method is a linear beamforming precoding strategy based on channel distribution information in the form of a full spatial correlation matrix for each user. This algorithm is shown to provide highly stable communication, with a throughput that is higher than that for optimal precoders operating on outdated CSI, in a time-variant environment, indicating that this approach can operate with significantly reduced feedback frequency. Furthermore, the dissertation demonstrates the use of the well-known Kronecker and Weichselberger models to parameterize the full correlation matrix to enable further reduction in the amount of feedback data required for implementation of the new beamforming technique. Channel measurements based on an experimentally obtained MIMO channels in indoor and outdoor environments are used in defining the statistical nature of the wireless channel.;Finally, the examined beamforming algorithms are extended to work for all possible scheduled, half-duplex link topologies using explicit channel state information or distribution information. Each type of derived beamformer, whether utilizing CSI or CDI, can provide additional scheduling tools for the medium access control and other network layers in optimizing the overall network throughput, in a cross-layer fashion, depending on the state of the network and available information at the nodes.