• A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA

      Aydin, Mehmet Emin; Kwan, Raymond; Leung, Cyril; Maple, Carsten; Zhang, Jie; University of Bedfordshire (Elsevier, 2012-05-17)
      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 optimisation.
    • Multiuser scheduling in high speed downlink packet access

      Kwan, Raymond; Aydin, Mehmet Emin; Leung, Cyril; Zhang, J. (IET, 2009-08)
      Multiuser scheduling is an important aspect in the performance optimisation of a wireless network as it allows multiple users to efficiently access a shared channel by exploiting multiuser diversity. For example, the 3GPP cellular standard supports multiuser scheduling in the high speed downlink packet access (HSDPA) feature. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. Multiuser scheduling is studied assuming the availability of perfect CSI, which would require a high bandwidth overhead. A more realistic imperfect CSI feedback in the form of a finite set of channel quality indicator values is assumed, as specified in the HSDPA standard. A global optimal approach and a simulated annealing (CSA) approach are used to solve the optimisation problem. Simulation results suggest that the performances of the two approaches are very close even though the complexity of the simulated annealing (SA) approach is much lower. The performance of a simple greedy approach is found to be significantly worse.