UAV-assisted fair communications for multi-pair users: A multi-agent deep reinforcement learning method

Abstract

Unmanned aerial vehicle (UAV) plays an important role in scenarios like search and rescue, remote communication relay, battlefield mobile networks, etc. In this paper, we investigate multiple UAV relays providing real-time transmission for multi-pair ground users (GUs) in the absence of ground-based stations (GBSs). Due to the limited load capacities and energy resources on-board, fairness between multi-pair users, throughput maximization, as well as the connectivity maintenance between UAVs are jointly considered. We formulate the energy-efficient fair throughput objective function, which turns to be non-convex with hybrid variables. To solve this intractable problem, we utilize the power of neural networks in function approximation and propose a multi-agent deep reinforcement learning (MADRL)-based algorithm. Different from traditional MADRL algorithms, we utilize the method, named independent proximal policy optimization (IPPO), which allows agents to update according to their own observations memory and encourages more explorations to some extend, as the base of our solution. Simulation results demonstrate that our algorithm outperforms some baselines in terms of fairness, throughput as well as energy consumption.