Distributed Deep Reinforcement Learning for Adaptive Medium Access and Modulation in Shared Spectrum
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Spectrum scarcity has led to growth in the use of unlicensed spectrum for cellular systems. This motivates intelligent adaptive approaches to spectrum access for both WiFi and 5G that improve upon traditional carrier sensing and listen-before-talk methods. We study decentralized contention-based medium access for base stations (BSs) of a single Radio Access Technology (RAT) operating on unlicensed shared spectrum. We devise a learning-based algorithm for both contention and adaptive modulation that attempts to maximize a network-wide downlink throughput objective. We formulate and develop novel distributed implementations of two deep reinforcement learning approaches - Deep Q Networks and Proximal Policy Optimization - modelled on a two stage Markov decision process. Empirically, we find the (proportional fairness) reward accumulated by the policy gradient approach to be significantly higher than even a genie-aided adaptive energy detection threshold. Our approaches are further validated by improved sum and peak throughput. The scalability of our approach to large networks is demonstrated via an improved cumulative reward earned on both indoor and outdoor layouts with a large number of BSs.
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