A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networks

TL;DR

This paper proposes a collaborative multi-agent reinforcement learning algorithm for anti-jamming in wireless networks, effectively addressing external malicious jamming and mutual interference among users, outperforming existing methods.

Contribution

Introduction of a novel collaborative multi-agent reinforcement learning algorithm (CMAA) for anti-jamming in multi-user wireless networks, considering coordination and interference.

Findings

CMAA outperforms sensing-based and independent Q-learning methods.

CMAA achieves the highest normalized data rate.

Effective against external jamming and mutual interference.

Abstract

In this letter, we investigate the anti-jamming defense problem in multi-user scenarios, where the coordination among users is taken into consideration. The Markov game framework is employed to model and analyze the anti-jamming defense problem, and a collaborative multi-agent anti-jamming algorithm (CMAA) is proposed to obtain the optimal anti-jamming strategy. In sweep jamming scenarios, on the one hand, the proposed CMAA can tackle the external malicious jamming. On the other hand, it can effectively cope with the mutual interference among users. Simulation results show that the proposed CMAA is superior to both sensing based method and independent Q-learning method, and has the highest normalized rate.