We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users.Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. Experimental results demonstrate strong performance of the algorithm.Index Terms-Wireless networks, dynamic spectrum access, medium access control (MAC) protocols, multi-agent learning, deep reinforcement learning.Oshri Naparstek is with the Rafael Advanced

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Abstract-In this paper we address the problem of fully distributed assignment of users to sub-bands such that the sumrate of the system is maximized. We introduce a modified auction algorithm that can be applied in a fully distributed way using an opportunistic CSMA assignment scheme and is optimal. We analyze the expected time complexity of the algorithm and suggest a variant to the algorithm that has lower expected complexity. We then show that in the case of i.i.d Rayleigh channels a simple greedy scheme is asymptotically optimal as SNR increases or as the number of users is increased to infinity. We conclude by providing simulated results of the suggested algorithms.

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The channel assignment problem is a special case of a very well studied combinatorial optimization problem known as the assignment problem. In this paper we introduce an asymptotically optimal fully distributed algorithm for the maximum cardinality matching problem. We show that with high probability, the running time of the algorithm on random bipartite graphs is less than O N log(N) log(Np) . We then show that the proposed algorithm can be used to produce asymptotically optimal solutions for the max sum assignment problem.