Genetic and Greedy User Scheduling for Multiuser MIMO Systems with Successive Zero-Forcing

Abstract

In this paper we consider efficient and low complex- ity scheduling algorithms for multiuser multiple-input multiple- output (MIMO) systems. The optimal user scheduling involves an exhaustive search, which becomes very complex for realistic num- bers of users and transmit antennas. Among various suboptimal but low complexity algorithms, 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 (in this case 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 successive zero-forcing precoding. We demonstrate that at lower K, the genetic algorithm performs better than the greedy algorithm, where K denotes the total number of users requesting service. For large K, however, the greedy algorithm outperforms the genetic algorithm. The greedy algorithm also achieves similar sum-rate growth (with K) as the exhaustive search. 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 K 2 ,w hereK0 denotes the maximum number