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

2025-12-18

AI Summary

Reactive jammers selectively disrupt communication in robot swarms by targeting specific channels or time slots, degrading coordination and mission performance. Method: This paper proposes a QMIX-based multi-agent reinforcement learning (MARL) framework for joint dynamic optimization of channel selection and transmission power allocation. It is the first to apply QMIX to anti-jamming swarm communications, enabling centralized training with decentralized execution and learning near-optimal cooperative policies under a Markovian dynamic jamming model. Contribution/Results: Compared to fixed-power transmission, static frequency hopping, and local adaptive baselines, the proposed method achieves a 32% increase in throughput and a 47% reduction in jamming hit rate in simulations. Its performance approaches the priori optimal bound, significantly improving formation integrity and task robustness under adversarial interference.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT 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.

Problem

• Enhancing swarm network resilience against reactive jamming attacks
• Developing coordinated anti-jamming strategies via multi-agent reinforcement learning
• Optimizing joint channel and power selection for secure swarm communications

Innovation

• Multi-agent reinforcement learning for swarm anti-jamming
• QMIX algorithm enables coordinated decentralized frequency-power selection
• Centralized factorizable action-value function improves communication resilience