Adaptive energy-efficient resource allocation for cognitive wireless local area network over fiber

Cognitive wireless local area network over fiber (CWLANoF) has been considered as a promising architecture of the traditional infrastructure-based IEEE 802.11 WLANs. However, most of previous works focused on channel allocation, throughput improvement and collision avoidance, and the energy efficiency is largely ignored. In this paper, we study the energy efficient aspect of spectrum sharing and channel allocation in CWLANoF. Taking different parts of energy consumption during a frame into consideration, we formulate the radio resource management problem in CWLANoF as energy efficiency maximum problems and propose an adaptive resource allocation mechanism. This nonlinear 0-1 programming problem is very hard to solve. To overcome this difficulty, we choose artificial fish-swarm algorithm (AFSA) for solving it. Simulation results are presented to demonstrate energy efficiency can be improved significantly in the proposed scheme.