Interference mitigation methods for vehicular ISAC systems in dynamic environments

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Zhenpeng Sun, Chen Miao, Yue Ma, Ruoyu Zhang, Wen Wu
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引用次数: 0

Abstract

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 106. 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.
动态环境下车载ISAC系统的干扰抑制方法
在快速发展的高级驾驶辅助系统领域,集成传感和通信(ISAC)技术以其高集成度和成本效益脱颖而出。然而,传统的ISAC抗干扰方法需要中心节点的协调和大量的信息交换,导致决策实时性降低,增加了系统的复杂性和维护成本。为了解决这些挑战,我们提出了一种具有可选择效用函数的无悔学习算法。通过将干扰测量值集成到效用函数中,每辆车根据测量到的干扰水平实时动态选择频段。该算法还通过采用特定任务的奖励机制来平衡通信和检测任务之间的频带分配。该算法支持单节点频带选择,与传统的干扰避免方法相比,具有更高的通用性和更低的复杂性。此外,我们还实现了跳频信号以增强干扰抑制,并实现了时域宽带合成算法以提高检测精度和稳定性。理论分析和仿真结果表明,在高密度车载ISAC环境下,该方法能使车辆同时获得优越的感知和通信性能。当信噪比超过- 39db时,误码率降至10−6以下。进一步分析了不同任务车辆的分配过程和干扰抑制能力,验证了算法的收敛性和有效性。最后,通过改变线性调频信号中的段数,我们表明适当的分割不仅可以提高通信吞吐量,还可以提高雷达探测精度和抗干扰能力。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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