Title: Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access,
Abstract: 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.