Downlink Scheduling for Multiple Antenna Systems with Dirty Paper Coding Via Genetic Algorithms

MIMO systems are of interest to meet the expected demands for higher data rates and lower delays in future wireless systems. The introduction of multiple transmit antennas adds additional complexity to any scheduling algorithm for the multi-user system. It is optimal to transmit to multiple users simultaneously in contrast to a single user in a single-input single-output (SISO) system, resulting in a combinatorial optimization problem. In this paper, we analyze the performance of scheduling through utility functions implemented via a genetic algorithm. Namely, we investigate the maximum throughput and the proportionally fair utility functions. The analysis is in the context of a MIMO broadcast channel using dirty paper coding (DPC). This paper builds upon earlier work using zero-forcing beamforming instead of DPC. Under DPC, the order of user encoding affects the user data rates and hence the performance of the scheduling algorithm. We demonstrate that the genetic algorithm is able to achieve a near-optimal performance relative to an exhaustive search at a significant reduction in computational complexity.