Real-Time Location/Mobility-Aware Resource Allocation in D2D Communications—A Deep Reinforcement Learning Approach

Reinforcement learning (RL) is a promising AI algorithm supporting latency-sensitive applications in 6G to enable ultra-reliable low-latency communication (URLLC). This work focuses on RL-assisted real-time resource allocation in mobile communications. Location and mobility of cellular user equipments (CUEs) are critical information for rapid convergence of resource allocation, in which deep Q-network (DQN) algorithm is useful to facilitate resource allocation to maximize sum rate of a cellular network, whose up-link channels are shared by device-to-device (D2D) UEs (DUEs). To ensure signal-to-interference-plus-noise ratios (SINRs) of both CUEs and DUEs, CUE’s guaranteed and prohibited areas are defined to align CUE locations with DUEs’ transmit powers for precise power allocation. The simulation results show that the proposed RL approach offers a fast tracking convergence with a balanced quality of services (QoSs) for both CUEs and DUEs. The application of the DQN-based algorithm can be extended beyond D2D communications to support low-latency location/mobility-aware communications.