Adaptive Throughput Maximization for Multi-input Multi-output Systems with Adaptive Modulation and Coding

Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either
complete channel state information (CSI) or knowledge of the optimal beamforming vector; both are hard to realize. Thus, the feedback of CSI from the receiver to the transmitter is needed. Nevertheless, the feedback bandwidth is limited, and the quantization of feedback CSI is necessary. The quantization methods are studied in the thesis.
With beamforming, the matrix channel can be transformed into several parallel subchannels. Because of the quantization, the CSI at transmitter is imperfect, and there exists some interference between different parallel
channels. A power allocation that takes the interference into consideration besides the noise is used to increase the mean signal-to-interference-plusnoise-ratio (SINR) of all subchannels.
Adaptive modulation and coding (AMC) is applied to each parallel subchannel. The modulation and coding scheme (MCS) in each parallel channel is decided by its SINR, and the thresholds of different MCSs are adaptively adjust by hybrid automatic repeat-request (HARQ) to maximize the
throughput. Thus, by better quantization, more practical power allocation, and AMC with threshold adjustment, the total throughput of the MIMO channel is maximized.