Fair Gain Based Dynamic Channel Allocation for Cognitive Radios in Wireless Mesh Networks

Abstract

Wireless mesh networks have the potential to deliver Internet broadband access, wireless local area network coverage and network connectivity at low costs. The capacity of a wireless mesh network is improved by equipping mesh nodes with multi-radios tuned to non-overlapping channels. By letting these nodes utilize the available channels opportunistically, we increase the utilization of the available bandwidths in the channel space. The essential problem is how to allocate the channels to these multi-radio nodes, especially when they are heterogeneous with diverse transmission types and bandwidths. Most of current work has been based on the objective to achieve maximal total bandwidths. In this paper, we propose a new bipartite-graph based model and design channel allocation algorithms that maximize the minimal channel gain to achieve relative fairness. Our model maps heterogeneous network environment to a weighted graph. We then use augmenting path to update channel allocation status and use canonical form to compare the new status with previous status to achieve better fairness. Evaluations demonstrate that our algorithms improve fairness compared with related algorithms.