This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.

中文翻译：

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用于车载网络中集成传感和通信的基于学习的预测波束成形

本文研究了车辆到基础设施 (V2I) 网络中的集成传感和通信 (ISAC)。为了实现 ISAC，有效的波束成形设计是必不可少的，然而，这在很大程度上取决于需要大量训练开销和计算复杂性的准确信道跟踪的可用性。受此启发，我们采用深度学习 (DL) 方法来隐式学习历史通道的特征，并直接预测下一个时隙将采用的波束成形矩阵，以最大限度地提高 ISAC 系统的平均可实现总速率。所提出的方法可以绕过显式信道跟踪过程的需要并显着减少信令开销。为此，考虑到多址干扰，首先为所考虑的 ISAC 系统制定了基于 Cramer-Rao 下界感知约束的一般和速率最大化问题。然后，通过利用惩罚方法，开发了一种通用的基于 DL 的无监督预测波束成形设计框架来解决公式化设计问题。作为所开发框架的实现，设计了一个基于历史通道的卷积长短期记忆 (LSTM) 网络 (HCL-Net)，用于基于 ISAC 的 V2I 网络中的预测波束成形。具体而言，在所提出的 HCL-Net 中相继采用卷积和 LSTM 模块，以利用通信通道的空间和时间依赖性，进一步提高学习性能。最后，