Interference mitigation methods for vehicular ISAC systems in dynamic environments

In the rapidly advancing domain of Advanced Driver Assistance Systems, Integrated Sensing and Communication (ISAC) technology stands out for its high-integration and cost-efficiency. Nonetheless, traditional ISAC interference avoidance methods require coordination of central nodes and a lot of information exchange, leading to reduced real-time decision-making and increased system complexity and maintenance costs. To address these challenges, we propose a no-regret learning algorithm featuring selectable utility functions. By integrating interference measurements into the utility function, each vehicle dynamically selects frequency bands in real time based on the measured interference level. The algorithm also balances frequency band allocation between communication and detection tasks by employing task-specific reward mechanisms. The proposed algorithm enables single-node frequency band selection, offering greater generalizability and lower complexity than conventional interference-avoidance methods. Moreover, we implement frequency-hopping signals to enhance interference mitigation and a time-domain wideband synthesis algorithm to improve detection accuracy and stability. Theoretical analysis and simulation indicate that, in high-density vehicular ISAC environments, our method enables vehicles to achieve both superior sensing and communication performance. When the SNR exceeds -39 dB, the bit error rate drops below ${10}^{-6}$. We further analyze the allocation process and interference mitigation capability of different task vehicles to demonstrate the convergence and effectiveness of the algorithm. Finally, by varying the number of segments in the linear frequency-modulated signals, we show that appropriate segmentation not only enhances communication throughput but also improves radar detection accuracy and interference-mitigation capability.