Optimal downlink space-time scheduling design with convex utility functions - multiple antenna systems with orthogonal transmit beamforming

We consider the optimal downlink MISO scheduling design framework for a general class of convex utility functions. The access point or base station is equipped with n_T transmit antennas. There are K mobiles in the system with single receive antenna. For practical reasons, we constraint the physical/link layer of the base station and mobile devices to linear processing complexity in signal transmission/reception respectively. We shall apply the design framework to two common utility functions, namely the maximum throughput and the proportional fair. The scheduling problem is a mixed nonlinear integer programming problem and therefore, the complexity of the optimal solution is enormous. Greedy algorithms is widely used in today´s wireless data systems (3GIX, HDR, UMTS) and is optimal when n_T = 1. However, we found that there is a large performance penalty of greedy algorithms (relative to optimal performance) when n_T > 1 and this motivates the search for more efficient heuristics. In this paper, we address genetic-based heuristics and discuss their complexity-performance tradeoff.