DRL-Based Resource Allocation for NOMA-Enabled D2D Communications Underlay Cellular Networks
Since the emergence of device-to-device (D2D) communications, an efficient rubbermaid 8 gallon trash can resource allocation (RA) scheme with low-complexity suited for high variability of network environments has been continuously demanded.As a solution, we propose a RA scheme based on deep reinforcement learning (DRL) for D2D communications exploi