FedSwap: A Federated Learning based 5G Decentralized Dynamic Spectrum Access System

Abstract

The era of 5G extends the available spectrum from the microwave band to the millimeter-wave band. The thriving Internet of Things (IoT) also enriches the user equipment (UEs) we used in our daily life, such as smart glasses, smart watches, and drones. With such a larger spectrum and massive UEs, existing dynamic spectrum access (DSA) suffers both low spectrum utilization efficiency and unfair spectrum allocation. Thus, a more sophisticated dynamic spectrum access (DSA) system is required in the 5G context. In this paper, we propose a federated learning based system, FedSwap, the first decentralized DSA system that improves both efficiency and fairness simultaneously. In FedSwap, we deploy an improved multi-agent reinforcement learning (iMARL) algorithm on each UE, enabling UEs to share the spectrum coordinately with fewer collisions. Furthermore, we also propose a novel swapping mechanism for aggregating UEs' models periodically so that UEs can fairly share the spectrum resources. Meanwhile, the sensory data of UEs are not transmitted and hence privacy is protected. We evaluate FedSwap's performance in 5G simulations with various settings. Compared to the state-of-the-art decentralized DSA methods, FedSwap can significantly improve the efficiency and fairness of spectrum utilization.