Skip to Main Content
It is well known that link level throughput could be significantly increased by using multiple antennae at the transmitter and receiver without increasing the bandwidth and power budget. However, optimizing the link level performance of multiple-antenna systems does not always imply achieving system level optimization. Therefore, cross-layer optimization across the link layer and the scheduling layer is very important to fully exploit the temporal and spatial dimensions of the communication channel. In this paper, we consider the optimal downlink space-time scheduling design for a general class of convex utility functions. The access point or base station is equipped with transmit antennas. There are mobiles in the system with a single receive antenna. For practical reasons, we assume zero-forcing processing at the physical layer of the base station and mobile. We will apply the design framework to two common utility functions, namely the maximum throughput and the proportional fair. The cross-layer scheduling design is a mixed convex and combinatorial optimization problem and the search space of the optimalsolution is enormous. Greedy algorithm, which has been widely used in today's wireless data systems (3G1X, high data rate system (HDR), Universal Mobile Terrestrial Service), is optimal when . However, we found that there is a large performance penalty of greedy algorithms (relative to optimal performance) when and this motivates the search for more efficient heuristics. In this paper, we will address genetic-based heuristics and discuss their complexity-performance tradeoff.