The problem of distributed rate maximization in multi-channel ALOHA networksis considered. First, we study the problem of constrained distributed ratemaximization, where user rates are subject to total transmission probabilityconstraints. We propose a best-response algorithm, where each user updates itsstrategy to increase its rate according to the channel state information andthe current channel utilization. We prove the convergence of the algorithm to aNash equilibrium in both homogeneous and heterogeneous networks using thetheory of potential games. The performance of the best-response dynamic isanalyzed and compared to a simple transmission scheme, where users transmitover the channel with the highest collision-free utility. Then, we consider thecase where users are not restricted by transmission probability constraints.Distributed rate maximization under uncertainty is considered to achieve bothefficiency and fairness among users. We propose a distributed scheme whereusers adjust their transmission probability to maximize their rates accordingto the current network state, while maintaining the desired load on thechannels. We show that our approach plays an important role in achieving theNash bargaining solution among users. Sequential and parallel algorithms areproposed to achieve the target solution in a distributed manner. Theefficiencies of the algorithms are demonstrated through both theoretical andsimulation results.
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