Distributed learning algorithms for spectrum sharing in spatial random access networks

We consider distributed optimization over orthogonal collision channels in spatial multi-channel ALOHA networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. We develop simple distributed learning algorithms to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.