Ultra-reliable intelligent link scheduling based on DRL for manned/unmanned aerial vehicle cooperative scenarios

Abstract

When manned/unmanned aerial vehicle (MAV–UAV) perform cooperative missions in complex and variable situations, the communication link between manned aerial vehicle (MAV) and unmanned aerial vehicle (UAV) must be extremely dependable to guarantee the reliable transfer of command and control information. To address the above issues, this study provides an intelligent link scheduling algorithm based on MAVlink protocol that leverages deep reinforcement learning (DRL) for highly reliable communication in MAV–UAV multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) communication systems. This algorithm combines a deep neural network (DNN) with a Q-learning (QL) algorithm to get a deep Q-network (DQN). The downlink communication link between MAV and UAV uses it to intelligently schedule the modulation and coding scheme (MCS) and the number of spatial multiplexing layers. Results from system simulations indicate that the proposed algorithm outperforms other representative scheduling algorithms in the communication between MAV and UAV in complex scenarios with high-speed movement and large noise interference, improving the reliability of communication in MAV–UAV cooperative missions.