MPRdeep: Multi-Objective Joint Optimal Node Positioning and Resource Allocation for FANETs with Deep Reinforcement learning

Qi Wang, Jianmin Liu, Cunzhuang Liu, Jianhui Huang, Chentao He, Yongjun Xu

Abstract: This paper addresses the problem of UAV positioning and resource allocation under dynamic network conditions and under instantaneous communication demands in FANETs. We propose MPRdeep, an adaptive, deep reinforcement learning (DRL) approach considering several QoS requirements concurrently. MPRdeep learns to optimize relay UAVs’ positions and forwarding probabilities to minimize reliability-achieving delay and reliability-achieving energy consumption. The key advantage is that MPRdeep is able to learn and dynamically adjust the node positioning and resource allocation according to ongoing network conditions. The results show that MPRdeep converges fast and has generalization ability that adapts well under dynamic network conditions and dynamic locations of users. Compared with baseline methods, MPRdeep shows superior performance in terms of lower reliability-achieving delay and lower reliability-achieving energy consumption via simulations and experiments.