Fairness for All, rate allocation for mobile wireless networks

Fair rate allocation deals with the fundamental problem of sharing the channel efficiently and fairly. In wireless networks, several notable works have proposed optimal solutions to this problem. These approaches work well for static networks, but rely on an assumption that renders them sub-optimal when nodes are mobile: at each computation step, nodes must collect the state of all their neighbors (1-hop knowledge assumption). In large-scale mobile networks, nodes need to continuously adapt to changing network conditions. Under these circumstances, it is hard to gather complete 1-hop information accurately and promptly. The key to any efficient solution in mobile networks is fast convergence with limited information. In this paper, we propose a simple decentralized algorithm for fair rate allocation that works well even with partial 1-hop information. The algorithm converges linearly and can be tuned to approach a wide range of trade-offs (from proportional to harmonic fairness). Our evaluation, using real-world mobility traces from 400 taxi cabs, shows that even in the challenging case of highly dynamic and dense networks, the algorithm assigns rate efficiently (mean error of 2.5% from the optimum), while using on average 37% of the 1-hop information.