A computationally efficient genetic algorithm for MIMO broadcast scheduling
Multiple-input multiple-output (MIMO) is suitable technique to ensure high speed data transmission as well as low delay communication networks. In MIMO, dirty paper coding (DPC) is an efficient scheme to support multiple users with optimum sum rate capacity of the system. However, DPC is a complex scheme where the user encoding sequence is important to transmit data to multiple users. An optimal exhaustive search as used in DPC is prohibited due to the extremely large size of the search space in this optimization problem. Evolutionary algorithm (genetic algorithm) can be used as an alternative for this optimization problem to reduce the complexity of the search (scheduling problem). The performance of the genetic algorithm with elitism and adaptive mutation is demonstrated to be near optimal as obtained with an exhaustive search. It has been demonstrated in this paper that GA achieves about 9899% of system sum rate as obtained with DPC with significant reduction in time and computational complexity. The proposed BGA is able to provide the optimum solution well within the packet duration of modern wireless packet data communications.
dirty paper coding (dpc) multiple-input multiple-output (mimo) scheduling genetic algorithms (gas) multi user mimo (mu mimo)