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

• URL: http://arxiv.org/abs/2512.16813v1
• Date: Thu, 18 Dec 2025 17:54:20 GMT
• Title: Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning
• Authors: Bahman Abolhassani, Tugba Erpek, Kemal Davaslioglu, Yalin E. Sagduyu, Sastry Kompella,
• Abstract summary: Reactive jammers pose a severe security threat to robotic-swarm networks by selectively disrupting inter-agent communications.<n>Conventional countermeasures such as fixed power control or static channel hopping are largely ineffective against such adaptive adversaries.<n>This paper presents a multi-agent reinforcement learning framework based on the QMIX algorithm to improve the resilience of swarm communications under reactive jamming.
• Score: 8.533838668681737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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