Particle swarm optimization for adaptive resource allocation in communication networks

A generalized model of particle swarm optimization (PSO) technique is proposed as a low complexity method for adaptive centralized and distributed resource allocation in communication networks. The proposed model is applied to adaptive multicarrier cooperative communications (MCCC) technique which utilizes the subcarriers in deep fade using a relay node in order to improve the bandwidth efficiency. Centralized PSO, based on virtual particles (VPs), is introduced for single layer and cross-layer subcarrier allocation to improve the bit error rate performance in multipath frequency selective fading channels. In the single layer strategy, the subcarriers are allocated based on the channel gains. In the cross-layer strategy, the subcarriers are allocated based on a joint measure of channel gains and distance provided by the physical layer and network layer to mitigate the effect of path loss. The concept of training particles in distributed PSO is proposed and then is applied for relay node selection. The computational complexity and traffic of the proposed techniques are investigated, and it is shown that using PSO for subcarrier allocation has a lower complexity than the techniques in the literature. Significant reduction in the traffic overhead of PSO is demonstrated when using trained particles in distributed optimizations.