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Due to the scarcity in the wireless spectrum and limited energy resourcesespecially in mobile applications, efficient resource allocation strategies arecritical in wireless networks. Motivated by the recent advances in deepreinforcement learning (DRL), we address multi-agent DRL-based joint dynamicchannel access and power control in a wireless interference network. We firstpropose a multi-agent DRL algorithm with centralized training (DRL-CT) totackle the joint resource allocation problem. In this case, the training isperformed at the central unit (CU) and after training, the users makeautonomous decisions on their transmission strategies with only localinformation. We demonstrate that with limited information exchange and fasterconvergence, DRL-CT algorithm can achieve 90% of the performance achieved bythe combination of weighted minimum mean square error (WMMSE) algorithm forpower control and exhaustive search for dynamic channel access. In the secondpart of this paper, we consider distributed multi-agent DRL scenario in whicheach user conducts its own training and makes its decisions individually,acting as a DRL agent. Finally, as a compromise between centralized and fullydistributed scenarios, we consider federated DRL (FDRL) to approach theperformance of DRL-CT with the use of a central unit in training while limitingthe information exchange and preserving privacy of the users in the wirelesssystem. Via simulation results, we show that proposed learning frameworks leadto efficient adaptive channel access and power control policies in dynamicenvironments.

Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning