Joint Link-Channel Selection and Rate Allocation heuristic for cognitive radio Mesh Networks

Exponential growth of number of users and their diversified applications in Wireless Mesh Networks (WMNs) require extended bandwidth to carry huge traffic loads. Cognitive radio (CR)-enabled mesh routers facilitate opportunistic spectrum access from licensed bands and might diminish bandwidth scarcity problem in WMNs. In this work, we propose a joint link-channel selection and rate allocation algorithm (LCR) concerning to mitigate network interference and congestion to raise overall network throughput. The LCR greedily finds suitable channel allocations over forwarding links through perception based spectrum-learning method and allocate desired minimum rate (and power) over forwarding links to minimize traffic backlog. Thus, it reduces interference, allows more concurrent transmissions and boosts up overall network throughput. The simulation results, carried out in ns-3, demonstrate that the proposed LCR significantly outperforms state-of-the-art works in terms of throughput and reliability performances.