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Accurate localization is crucial for various applications, including autonomous vehicles and next-generation wireless networks. However, the reliability and precision of Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are compromised by multi-path errors and non-line-of-sight scenarios. This paper presents a novel approach to enhance GPS accuracy by combining visual data from RGB cameras with wireless signals captured at millimeter-wave (mmWave) and sub-terahertz (sub-THz) basestations. We propose a sensing-aided framework for (i) site-specific GPS data characterization and (ii) GPS position de-noising that utilizes multi-modal visual and wireless information. Our approach is validated in a realistic Vehicle-to-Infrastructure (V2I) scenario using a comprehensive real-world dataset, demonstrating a substantial reduction in localization error to sub-meter levels. This method represents a significant advancement in achieving precise localization, particularly beneficial for high-mobility applications in 5G and beyond networks., Comment: Datasets and code files are available on the DeepSense website: https://deepsense6g.net/. To appear in IEEE GLOBECOM 2024

High data rate and low-latency vehicle-to-vehicle (V2V) communication are essential for future intelligent transport systems to enable coordination, enhance safety, and support distributed computing and intelligence requirements. Developing effective communication strategies, however, demands realistic test scenarios and datasets. This is important at the high-frequency bands where more spectrum is available, yet harvesting this bandwidth is challenged by the need for direction transmission and the sensitivity of signal propagation to blockages. This work presents the first large-scale multi-modal dataset for studying mmWave vehicle-to-vehicle communications. It presents a two-vehicle testbed that comprises data from a 360-degree camera, four radars, four 60 GHz phased arrays, a 3D lidar, and two precise GPSs. The dataset contains vehicles driving during the day and night for 120 km in intercity and rural settings, with speeds up to 100 km per hour. More than one million objects were detected across all images, from trucks to bicycles. This work further includes detailed dataset statistics that prove the coverage of various situations and highlights how this dataset can enable novel machine-learning applications., Comment: 14 pages, 15 figures, 2 tables. The dataset is available on the DeepSense6G website: https://deepsense6g.net/

Millimeter-wave (mmWave) and terahertz (THz) communication systems require large antenna arrays and use narrow directive beams to ensure sufficient receive signal power. However, selecting the optimal beams for these large antenna arrays incurs a significant beam training overhead, making it challenging to support applications involving high mobility. In recent years, machine learning (ML) solutions have shown promising results in reducing the beam training overhead by utilizing various sensing modalities such as GPS position and RGB images. However, the existing approaches are mainly limited to scenarios with only a single object of interest present in the wireless environment and focus only on co-located sensing, where all the sensors are installed at the communication terminal. This brings key challenges such as the limited sensing coverage compared to the coverage of the communication system and the difficulty in handling non-line-of-sight scenarios. To overcome these limitations, our paper proposes the deployment of multiple distributed sensing nodes, each equipped with an RGB camera. These nodes focus on extracting environmental semantics from the captured RGB images. The semantic data, rather than the raw images, are then transmitted to the basestation. This strategy significantly alleviates the overhead associated with the data storage and transmission of the raw images. Furthermore, semantic communication enhances the system's adaptability and responsiveness to dynamic environments, allowing for prioritization and transmission of contextually relevant information. Experimental results on the DeepSense 6G dataset demonstrate the effectiveness of the proposed solution in reducing the sensing data transmission overhead while accurately predicting the optimal beams in realistic communication environments., Comment: The code and dataset are available on the DeepSense website https://www.deepsense6g.net/