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

Yunian Pan, Jun Li, Lifan Xu, Shunqiao Sun, Quanyan Zhu

TL;DR

The paper addresses radar-to-radar interference in high-resolution FMCW automotive radars by formulating an anti-coordination $K$-stage game where radars select subbands to maximize $U_i=10\log_{10}(\\mathrm{SINR})$. It presents two strategies: Nash Hopping, a model-based approach targeting an NE, and No-Regret Hopping, a model-free method that learns a Coarse Correlated Equilibrium (CCE) via regret minimization. Theoretical connections between no-regret learning and CCE are established, with practical convergence guarantees showing that empirical strategies approach a CCE as the horizon grows. Numerical results demonstrate effective interference mitigation for both methods, with No-Regret Hopping offering better spectrum utilization and enhanced range resolution without inter-radar communication, highlighting its suitability for decentralized automotive radar networks.

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.

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

TL;DR

The paper addresses radar-to-radar interference in high-resolution FMCW automotive radars by formulating an anti-coordination -stage game where radars select subbands to maximize . It presents two strategies: Nash Hopping, a model-based approach targeting an NE, and No-Regret Hopping, a model-free method that learns a Coarse Correlated Equilibrium (CCE) via regret minimization. Theoretical connections between no-regret learning and CCE are established, with practical convergence guarantees showing that empirical strategies approach a CCE as the horizon grows. Numerical results demonstrate effective interference mitigation for both methods, with No-Regret Hopping offering better spectrum utilization and enhanced range resolution without inter-radar communication, highlighting its suitability for decentralized automotive radar networks.

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.

Paper Structure

This paper contains 11 sections, 15 equations, 2 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. The Anti-Coordination Game
  4. Signal Model
  5. Frequency Hopping with Mixed Strategy
  6. Solution Concepts and Algorithms
  7. Nash Equilibrium and Coarse Correlated Equilibrium
  8. Regret Minimization and Implicit Regularization
  9. Connection to Coarse Correlated Equilibrium
  10. Numerical Results
  11. Conclusion

Figures (2)

  • Figure 1: The evolution of mixed strategies for Nash Hopping (left), and No-Regret Hopping (right).
  • Figure 2: The range profiles at velocity $\dot{r}=-15m/s$ after applying the three strategies: Nash Hopping, No-Regret Hopping, and Uniformly Random Hopping.

Theorems & Definitions (2)

  • Definition 1: Nash Equilibrium (NE)
  • Definition 2