Multiple-input, multiple-output (MIMO) techniques have been proposed to meet the needs for higher data rates and lower delays in future wireless communication systems. The downlink capacity of multiuser MIMO systems is achieved when the system transmits to several users simultaneously. Frequently, many more users request service than the transmitter can simultaneously support. Thus, the transmitter requires a scheduling algorithm for the users, which must balance the goals of increasing throughput, reducing multiuser interference, lowering delays, ensuring fairness and quality of service (QoS), etc. In this thesis, we investigate the application of genetic algorithms (GAs) to perform scheduling in multiuser MIMO systems. GAs are a fast, suboptimal, low-complexity method of solving optimization problems, such as the maximization of a scheduling metric, and can handle arbitrary functions and QoS constraints. We first examine a system that transmits using capacity-achieving dirty paper coding (DPC). Our proposed GA structure both selects users and determines their encoding order for DPC, which affects the rates they receive. Our GA can also schedule users independently on different carriers of a multi-carrier system. We demonstrate that the GA performance is close to that of an optimal exhaustive search, but at a greatly reduced complexity. We further show that the GA convergence time can be significantly reduced by tuning the values of its parameters. While DPC is capacity-achieving, it is also very complex. Thus, we also investigate GA scheduling with two linear precoding schemes, block diagonalization and successive zero-forcing. We compare the complexity and performance of the GA with "greedy" scheduling algorithms, and find the GA is more complex, but performs better at higher signal-to-noise ratios (SNRs) and smaller user pool sizes. Both algorithms are near-optimal, yet much less complex than an exhaustive search. We also propose hybrid greedy-genetic algorithms to gain benefits from both types of algorithms. Lastly, we propose an improved method of optimizing the transmit covariance matrices for successive zero-forcing. Our algorithm significantly improves upon the performance of the existing method at medium to high SNRs, and, unlike the existing method, can maximize a weighted sum rate, which is important for fairness and QoS considerations. / Communications

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