  We consider distributed optimization over orthogonal collision channels in
spatial random access 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. Simple distributed learning algorithms are developed to
solve these problems. The efficiencies of the proposed algorithms are
demonstrated via both theoretical analysis and simulation results.
