A computationally efficient genetic algorithm for MIMO broadcast scheduling

Graphical abstractDisplay Omitted HighlightsMultiple-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 98-99% 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. In conventional single-input single-output (SISO) systems, the capacity is limited as base station can provide service to only one user at any instant. However, multiuser (MU) multiple-input multiple-output (MIMO) systems deliver optimum system capacity by providing service to multiple users (as many as transmit antennas) simultaneously according to dirty paper coding (DPC) scheme. However, DPC is an exhaustive search algorithm (ESA) where the user encoding sequence is important to transmit data to multiple users. Exhaustive search becomes imperative as the search space grows with number of users and number of transmit antennas in the MU MIMO system. This can be treated as an optimization problem of maximizing the achievable system sum-rate. In this paper, it has been demonstrated that combined user and antenna scheduling (CUAS) with binary genetic algorithm (BGA) adopting elitism and adaptive mutation (AM) achieves about 97-99% of system sum-rate obtained by ESA (DPC) with significantly reduced computational and time complexity. It has been shown that BGA is able to find the globally optimum solution for MU MIMO systems well within the time interval of modern wireless packet data communications. However, it is interesting to observe that BGA is able to find a solution to CUAS close to the optimum value quite rapidly. In this paper, it is also shown that BGA with elitism and AM achieves higher throughput than limited feedback scheduling schemes as well.

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