Smart Spectrum Access: Multi-Armed Bandit Algorithms in an Adaptive Interference Mitigation Strategy for Automotive Radar

Abstract

Since frequency-modulated continuous-wave (FMCW) radars are widely used in automotive applications, mutual interference has become a critical challenge in dense traffic scenarios. Increasing radar density degrades target detection performance and limits the effectiveness of existing mitigation strategies. This article proposes BanditFMCW, a learning-based smart spectrum access strategy that detects interference in real time and adaptively reallocates the radar operating band using multi-armed bandit (MAB) algorithms. In particular, a sliding-window upper confidence bound (SW-UCB) algorithm with side observations is developed to cope with nonstationary interference conditions. BanditFMCW is evaluated through Monte Carlo simulations and realistic highway traffic simulations. It is compared with the random orthogonalization (RO) bandit algorithm and five state-of-the-art mitigation strategies using four figures of merit (FOMs). The results show that SW-UCB with side observations reaches orthogonal transmit signals faster than RO while reducing both the likelihood of operating in interfered bands and increasing the expected signal-to-interference ratio (SIR) in local and global deployment settings.