We consider distributed optimization over orthogonal collision channels inspatial random access networks. Users are spatially distributed and each useris in the interference range of a few other users. Each user is allowed totransmit over a subset of the shared channels with a certain attemptprobability. We study both the non-cooperative and cooperative settings. In theformer, the goal of each user is to maximize its own rate irrespective of theutilities of other users. In the latter, the goal is to achieve proportionallyfair rates among users. Simple distributed learning algorithms are developed tosolve these problems. The efficiencies of the proposed algorithms aredemonstrated via both theoretical analysis and simulation results.
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