A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA

Multiuser scheduling is an important aspect in the performance optimization of a wireless network since it allows multiple users to access a shared channel efficiently by exploiting multiuser diversity. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. In this paper, a more realistic imperfect CSI feedback, in the form of a finite set of Channel Quality Indicator (CQI) values, is assumed as specified in the HSDPA standard. A mathematical model of the problem is developed for use in the optimization process. A hybrid heuristic approach based on particle swarm optimization and simulated annealing is used to solve the problem. Simulation results indicate that the hybrid approach outperforms individual implementations of both simulated annealing and particle swarm optimization.

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► We provide a mathematical programming model for multi-user resource scheduling in High Speed Downlink Package Access wireless communication system.
► We solve this problem with a hybrid swarm intelligence algorithm, which combines particle swarm optimization and simulated annealing algorithms.
► We highlight how to combine these two algorithms to solve the problem.
► We evaluate the performances of three algorithms; the hybrid swarm intelligence algorithm, and the standalone versions of both approaches which form the hybrid algorithm.