Leveraging the trade-off between spatial reuse and channel contention in wireless mesh networks

Performance optimization strategies in wireless mesh networks have witnessed many diverged directions, out of which the recent studies have explored the use of spatial multiplexing through data rate and transmit power adaptation to increase spatial re-usability while minimizing network interference. However, joint data rate and transmit power adaptation shows a clear trade-off, where higher transmit power helps in sustaining high achievable data rates with the cost of increased interference to the neighboring receiver nodes. Such a tradeoff results in channel access unfairness among contending flows, where a node with high transmit power gains more performance benefit compared to its neighbors. Unfairness among nodes in a mesh network results in performance drops for the end-to-end flows. In this paper, we formulate a multivariate optimization problem to maximize network utilization and fairness, while minimizing average transmit power for all transmitter nodes. The Pareto optimality nature of the vector optimization has been explored to design a distributed localized heuristic where every node individually decides their scheduling slots and transmit power while keeping fairness as a constraint. The performance of the proposed scheme has been evaluated through simulation, and the comparisons with recent studies reveal that it improves fairness that results approximately 10% ??? 40% improvement in end-to-end throughput for different network and traffic scenarios.