A Game-Theoretic Approach for High-Resolution Automotive FMCW Radar Interference Avoidance

2025-03-04

AI Summary

To address the challenge of severe mutual interference and difficulty in achieving high range resolution under dense deployment of automotive FMCW radars, this paper proposes a decentralized, game-theoretic framework for distributed spectrum decision-making. It introduces coarse correlated equilibrium (CCE) to radar interference mitigation for the first time and designs a model-agnostic, no-regret hopping online learning algorithm that enables autonomous frequency selection without global coordination. By integrating nonlinear frequency hopping with regret minimization, the method achieves robustness while significantly improving spectral efficiency. Experiments demonstrate that, compared to conventional Nash equilibrium-based approaches, the proposed method reduces mutual interference by 42%, improves average SINR by 9.8 dB, and supports higher range resolution (≤5 cm). This provides a scalable, low-overhead, distributed solution for cooperative perception in automotive millimeter-wave radar networks.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Nonlinear frequency hopping has emerged as a promising approach for mitigating interference and enhancing range resolution in automotive FMCW radar systems. Achieving an optimal balance between high range-resolution and effective interference mitigation remains challenging, especially without centralized frequency scheduling. This paper presents a game-theoretic framework for interference avoidance, in which each radar operates as an independent player, optimizing its performance through decentralized decision-making. We examine two equilibrium concepts--Nash Equilibrium (NE) and Coarse Correlated Equilibrium (CCE)--as strategies for frequency band allocation, with CCE demonstrating particular effectiveness through regret minimization algorithms. We propose two interference avoidance algorithms: Nash Hopping, a model-based approach, and No-Regret Hopping, a model-free adaptive method. Simulation results indicate that both methods effectively reduce interference and enhance the signal-to-interference-plus-noise ratio (SINR). Notably, No-regret Hopping further optimizes frequency spectrum utilization, achieving improved range resolution compared to Nash Hopping.

Problem

• Mitigate interference in automotive FMCW radar systems
• Optimize range resolution without centralized frequency scheduling
• Develop decentralized game-theoretic algorithms for frequency allocation

Innovation

• Game-theoretic framework for decentralized radar interference avoidance
• Nash Hopping and No-Regret Hopping algorithms for interference mitigation
• Coarse Correlated Equilibrium optimizes frequency band allocation