Greedy and Genetic User Scheduling Algorithms for Multiuser MIMO Systems with Block Diagonalization

In this paper, we consider efficient and low complexity scheduling algorithms for multiuser multiple-input multiple-output (MIMO) systems. Due to the dimensionality constraint imposed by linear precoding techniques like block diagonalization (BD), user scheduling is required. Optimal user scheduling involves an exhaustive search, which becomes very complex for realistic numbers of users and transmit antennas. Hence, various suboptimal but low complexity algorithms have been considered in the literature. Among them, greedy algorithms with heuristic scheduling metrics have been shown to achieve performance close to an exhaustive search. Meanwhile, genetic algorithms (GAs) are a rapid, though suboptimal, option of performing a utility (i.e. scheduling) metric optimization. In this paper, we propose and analyze the performance and complexity of greedy and genetic scheduling algorithms for multiuser MIMO systems with BD precoding. We demonstrate that except at low SNR with a smaller number of transmit antennas, the genetic algorithm outperforms the greedy algorithm. A detailed complexity analysis shows that the order of complexity of the genetic algorithm is higher than that of the greedy algorithm by a factor equal to Ko, where Ko denotes the maximum number of simultaneously supported multiple-antenna users.