Precoding And Scheduling Schemes For Downlink Multiuser MIMO Systems

A downlink wireless system features a centralized basestation communicating to a number of users physically scattered around the basestation. The purpose of resource allocation at the basestation is to intelligently allocate the limited resources, e.g. total transmit power and available frequency bandwidth, among users to meet users' service requirements. Channel-aware adaptive resource allocation has been shown to achieve higher system performance than static resource allocation, and is becoming more critical in current and future wireless communication systems as the user data rate requirements increase. Adaptive resource allocation in a multiple antennas downlink system is more challenging because of the additional degree of freedom for resources, but offers the potential to provide higher user data rates. This dissertation aims to study the system performance, e.g. total throughput, in multi-user MIMO (multiple-input multiple-output) systems with adaptive resource allocation, as well as low complexity algorithms that are suitable for cost-effective real-time implementations in practical systems.The first contribution concerns precoding using block diagonalization (BD) and Geometric Mean Decomposition (GMD) for downlink multiuser MIMO systems with the perfect CSI (channel status information) at transmitter. It is shown that GMD-BD scheme, as a practically realizable precoding technique, can achieve a significant part of the sum capacity achieved by optimal dirty paper coding (DPC), and is asymptotically optimal for (moderately) high SNR in terms of SER (symbol error rate) performance without either additional training phrase or global CSI at the receivers.The second contribution concerns a low-complexity GMD-BD precoding algorithm for systems with a large number of users. Due to the zero inter-user interference requirement imposed by GMD-BD, the maximum number of simultaneously supportable users is limited. The brute-force search for the optimal user set, however, is computationally prohibitive when the number of users is large. The dissertation proposes a suboptimal user selection algorithm for GMD-BD that has linear complexity in the number of users, yet achieves total throughput close to the optimal. The third contribution of this dissertation is a low complexity joint beamforming and scheduling algorithm for downlink multi-user MISO (multiple-input single-output) systems with limited feedback. In this algorithm, a multi-beam selection is also proposed to obtain performance improvement when the number of users is not very large. In order to improve the performance further, an enhancement version of above scheme with a larger constructed codebook is proposed either.The fourth contribution of this dissertation is to propose an enhancement of Orthonormal Random Beamforming (ORBF) strategy based on an adaptive beam selection procedure. Instead of transmitting all the generated beams, the scheduler picks the optimum subset of beams that maximizes the system throughout according to the feedback information. This dissertation proposes and compares several beam selection algorithms according to different complexity requirements. In particular, it has been shown that the proposed approaches give substantial gains with respect to conventional opportunistic schemes.A common characteristic of the resource allocations for multiuser MIMO systems is that the limited resources shall be allocated among multiple users. As MIMO have been widely adopted in various standards, the research in this dissertation contributes to a better understanding of the system performance, and bridges the theory to practical implementations with the proposed low complexity algorithms.