Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917154825175040 |
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| author | Abolhassani, Bahman Erpek, Tugba Davaslioglu, Kemal Sagduyu, Yalin E. Kompella, Sastry |
| author_facet | Abolhassani, Bahman Erpek, Tugba Davaslioglu, Kemal Sagduyu, Yalin E. Kompella, Sastry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning Abolhassani, Bahman Erpek, Tugba Davaslioglu, Kemal Sagduyu, Yalin E. Kompella, Sastry Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning Signal Processing 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. |
| title | Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning |
| topic | Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2512.16813 |