Adaptive Reinforcement Learning Framework for NOMA-UAV Networks

Abstract

We propose an adaptive reinforcement learning (A-RL) framework to maximize the sum-rate for non-orthogonal multiple access-unmanned aerial vehicle (NOMA-UAV) network. In this framework, Mamdani fuzzy inference system (MFIS) supervises a reinforcement learning (RL) policy based on multi-armed bandits (MAB). UAV as learning agent serves an internet of things (IoT) region. It manages an interference affected, channel block for NOMA uplink. Sum-rate, rate outage probability and average bit error rate (BER) for far-user are compared. Simulations reveal superior performance of A-RL, compared to non-adaptive RL counterpart. Joint maximum likelihood detection (JMLD) and successive interference cancellation (SIC) are also compared for BER performance and implementation complexity.