Leveraging sensory information to aid the millimeter-wave (mmWave) and sub-terahertz (sub-THz) beam selection process is attracting increasing interest. This sensory data, captured for example by cameras at the basestations, has the potential of significantly reducing the beam sweeping overhead and enabling highly-mobile applications. The solutions developed so far, however, have mainly considered single-candidate scenarios, i.e., scenarios with a single candidate user in the visual scene, and were evaluated using synthetic datasets. To address these limitations, this paper extensively investigates the sensing-aided beam prediction problem in a real-world multi-object vehicle-to-infrastructure (V2I) scenario and presents a comprehensive machine learning-based framework. In particular, this paper proposes to utilize visual and positional data to predict the optimal beam indices as an alternative to the conventional beam sweeping approaches. For this, a novel user (transmitter) identification solution has been developed, a key step in realizing sensing-aided multi-candidate and multi-user beam prediction solutions. The proposed solutions are evaluated on the large-scale real-world DeepSense $6$G dataset. Experimental results in realistic V2I communication scenarios indicate that the proposed solutions achieve close to $100\%$ top-5 beam prediction accuracy for the scenarios with single-user and close to $95\%$ top-5 beam prediction accuracy for multi-candidate scenarios. Furthermore, the proposed approach can identify the probable transmitting candidate with more than $93\%$ accuracy across the different scenarios. This highlights a promising approach for nearly eliminating the beam training overhead in mmWave/THz communication systems.

翻译：利用传感器信息辅助毫米波（mmWave）和亚太赫兹（sub-THz）波束选择过程正日益受到关注。这种传感器数据（例如由基站处的摄像头捕获）具有显著减少波束扫描开销并支持高移动性应用的潜力。然而，迄今为止开发的解决方案主要考虑了单候选场景（即视觉场景中只有一个候选用户的场景），并且使用合成数据集进行评估。为了解决这些局限性，本文深入研究了真实世界多目标车联网（V2I）场景中的感知辅助波束预测问题，并提出了一个基于机器学习的综合框架。具体而言，本文提出利用视觉和位置数据来预测最优波束索引，作为传统波束扫描方法的替代方案。为此，开发了一种新颖的用户（发射器）识别解决方案，这是实现感知辅助多候选和多用户波束预测解决方案的关键步骤。所提出的解决方案在真实世界的大规模DeepSense $6$G数据集上进行了评估。在真实V2I通信场景中的实验结果表明，所提方案在单用户场景下实现了接近$100\%$的前五波束预测准确率，在多候选场景下实现了接近$95\%$的前五波束预测准确率。此外，所提出的方法在不同场景下能够以超过$93\%$的准确率识别可能的发射候选。这凸显了一种近乎消除毫米波/太赫兹通信系统中波束训练开销的有前景的方法。