{"ID":2827668,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16813","arxiv_id":"2512.16813","title":"Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning","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.","short_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 adversar...","url_abs":"https://arxiv.org/abs/2512.16813","url_pdf":"https://arxiv.org/pdf/2512.16813v1","authors":"[\"Bahman Abolhassani\",\"Tugba Erpek\",\"Kemal Davaslioglu\",\"Yalin E. Sagduyu\",\"Sastry Kompella\"]","published":"2025-12-18T17:54:20Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\",\"cs.DC\",\"cs.LG\",\"eess.SP\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}