Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a multi-agent reinforcement learning framework using QMIX to enhance the resilience of swarm networks against reactive jamming, outperforming traditional countermeasures in simulated environments.

Contribution

It develops a novel MARL approach with QMIX for coordinated anti-jamming in swarm networks, demonstrating significant improvements over baseline methods.

Findings

QMIX achieves near-optimal cooperative policies in simulations.

The approach increases throughput and reduces jamming incidents.

It outperforms UCB and reactive policies in dynamic environments.

Abstract

Reactive jammers pose a severe security threat to robotic-swarm networks by selectively disrupting inter-agent communications and undermining formation integrity and mission success. Conventional countermeasures such as fixed power control or static channel hopping are largely ineffective against such adaptive adversaries. This paper presents a multi-agent reinforcement learning (MARL) framework based on the QMIX algorithm to improve the resilience of swarm communications under reactive jamming. We consider a network of multiple transmitter-receiver pairs sharing channels while a reactive jammer with Markovian threshold dynamics senses aggregate power and reacts accordingly. Each agent jointly selects transmit frequency (channel) and power, and QMIX learns a centralized but factorizable action-value function that enables coordinated yet decentralized execution. We benchmark QMIX against a genie-aided optimal policy in a no-channel-reuse setting, and against local Upper Confidence Bound (UCB) and a stateless reactive policy in a more general fading regime with channel reuse enabled. Simulation results show that QMIX rapidly converges to cooperative policies that nearly match the genie-aided bound, while achieving higher throughput and lower jamming incidence than the baselines, thereby demonstrating MARL's effectiveness for securing autonomous swarms in contested environments.