DRQN-based Task Offloading in UAV-assisted Mobile Edge Computing Environments with Hidden Channel Conditions

Mobile edge computing (MEC) with unmanned aerial vehicle (UAV) assistance offers promising solutions for reducing latency in 5G networks. However, uncertainty in task processing requirements poses challenges for efficient task of-floading. This paper introduces a deep recurrent Q-network (DRQN) based algorithm for task offloading in UAV-assisted MEC environments with hidden channel conditions. We for-mulate the problem as a partially observable Markov decision process (POMDP), where the UAV decides whether to process tasks locally or offload them to a cloud server without complete information about the channel state. Our approach leverages temporal dependencies in the environment to make informed decisions under uncertainty. Simulations demonstrate that our DRQN-based algorithm outperforms previous methods, including DQN-based, offload-only, and local-only strategies. This work contributes to the development of robust task offloading strategies for dynamic edge computing environments in 5G and beyond networks.