Environment Semantic Communication: Enabling Distributed Sensing Aided Networks

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.

Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration

Accurately aligning millimeter-wave (mmWave) and terahertz (THz) narrow beams is essential to satisfy reliability and high data rates of 5G and beyond wireless communication systems. However, achieving this objective is difficult, especially in vehicle-to-vehicle (V2V) communication scenarios, where both transmitter and receiver are constantly mobile. Recently, additional sensing modalities, such as visual sensors, have attracted significant interest due to their capability to provide accurate information about the wireless environment. To that end, in this paper, we develop a deep learning solution for V2V scenarios to predict future beams using images from a 360 camera attached to the vehicle. The developed solution is evaluated on a real-world multi-modal mmWave V2V communication dataset comprising co-existing 360 camera and mmWave beam training data. The proposed vision-aided solution achieves $\approx 85\%$ top-5 beam prediction accuracy while significantly reducing the beam training overhead. This highlights the potential of utilizing vision for enabling highly-mobile V2V communications.

Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios

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.

Environment Semantic Aided Communication: A Real World Demonstration for Beam Prediction

Millimeter-wave (mmWave) and terahertz (THz) communication systems adopt large antenna arrays to ensure adequate receive signal power. However, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. Recently proposed vision-aided beam prediction solutions, which utilize \textit{raw RGB images} captured at the basestation to predict the optimal beams, have shown initial promising results. However, they still have a considerable computational complexity, limiting their adoption in the real world. To address these challenges, this paper focuses on developing and comparing various approaches that extract lightweight semantic information from the visual data. The results show that the proposed solutions can significantly decrease the computational requirements while achieving similar beam prediction accuracy compared to the previously proposed vision-aided solutions.

Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond

This article presents a vision where \textit{real-time} digital twins of the physical wireless environments are continuously updated using multi-modal sensing data from the distributed infrastructure and user devices, and are used to make communication and sensing decisions. This vision is mainly enabled by the advances in precise 3D maps, multi-modal sensing, ray-tracing computations, and machine/deep learning. This article details this vision, explains the different approaches for constructing and utilizing these real-time digital twins, discusses the applications and open problems, and presents a research platform that can be used to investigate various digital twin research directions.

DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset

This article presents the DeepSense 6G dataset, which is a large-scale dataset based on real-world measurements of co-existing multi-modal sensing and communication data. The DeepSense 6G dataset is built to advance deep learning research in a wide range of applications in the intersection of multi-modal sensing, communication, and positioning. This article provides a detailed overview of the DeepSense dataset structure, adopted testbeds, data collection and processing methodology, deployment scenarios, and example applications, with the objective of facilitating the adoption and reproducibility of multi-modal sensing and communication datasets.

Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction

Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management. However, these drones need to deploy large antenna arrays and use narrow directive beams to maintain a sufficient link budget. The large beam training overhead associated with these arrays makes adjusting these narrow beams challenging for highly-mobile drones. To address these challenges, this paper proposes a vision-aided machine learning-based approach that leverages visual data collected from cameras installed on the drones to enable fast and accurate beam prediction. Further, to facilitate the evaluation of the proposed solution, we build a synthetic drone communication dataset consisting of co-existing wireless and visual data. The proposed vision-aided solution achieves a top-$1$ beam prediction accuracy of $\approx 91\%$ and close to $100\%$ top-$3$ accuracy. These results highlight the efficacy of the proposed solution towards enabling highly mobile mmWave/THz drone communication.

User Identification: The Key Enabler for Multi-User Vision-Aided Wireless Communications

Vision-aided wireless communication is attracting increasing interest and finding new use cases in various wireless communication applications. These vision-aided communication frameworks leverage the visual data (captured, for example, by cameras installed at the infrastructure or mobile devices) to construct some perception about the communication environment (geometry, users, scatterers, etc.). This is typically achieved through the use of deep learning and advances in computer vision and visual scene understanding. Prior work has investigated various problems such as vision-aided beam, blockage, and handoff prediction in millimeter wave (mmWave) systems and vision aided covariance prediction in massive MIMO systems. This prior work, however, has focused on scenarios with a single object (user) moving in front of the camera. To enable vision-aided wireless communication in practice, however, it is important for these systems to be able to operate in crowded scenarios with multiple objects in the visual scene. In this paper, we define the user identification task as the key enabler for realistic vision-aided wireless communication systems that can operate in crowded scenarios and support multi-user applications. The objective of the user identification task is to identify the target communication user from the other candidate objects (distractors) in the visual scene. We develop machine learning models that process either one frame or a sequence of frames of visual and wireless data to efficiently identify the target user in the visual/communication environment. Using the large-scale multi-modal sense and communication dataset, DeepSense 6G, which is based on real-world measurements, we show that the developed approaches can successfully identify the target users with more than 97% accuracy ...

Multi-Modal Beam Prediction Challenge 2022: Towards Generalization

Beam management is a challenging task for millimeter wave (mmWave) and sub-terahertz communication systems, especially in scenarios with highly-mobile users. Leveraging external sensing modalities such as vision, LiDAR, radar, position, or a combination of them, to address this beam management challenge has recently attracted increasing interest from both academia and industry. This is mainly motivated by the dependency of the beam direction decision on the user location and the geometry of the surrounding environment -- information that can be acquired from the sensory data. To realize the promised beam management gains, such as the significant reduction in beam alignment overhead, in practice, however, these solutions need to account for important aspects. For example, these multi-modal sensing aided beam selection approaches should be able to generalize their learning to unseen scenarios and should be able to operate in realistic dense deployments. The "Multi-Modal Beam Prediction Challenge 2022: Towards Generalization" competition is offered to provide a platform for investigating these critical questions. In order to facilitate the generalizability study, the competition offers a large-scale multi-modal dataset with co-existing communication and sensing data collected across multiple real-world locations and different times of the day. In this paper, along with the detailed descriptions of the problem statement and the development dataset, we provide a baseline solution that utilizes the user position data to predict the optimal beam indices. The objective of this challenge is to go beyond a simple feasibility study and enable necessary research in this direction, paving the way towards generalizable multi-modal sensing-aided beam management for real-world future communication systems.

Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction

Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power. The beam training overhead associated with these arrays, however, make it hard for these systems to support highly-mobile applications such as drone communication. To overcome this challenge, this paper proposes a machine learning-based approach that leverages additional sensory data, such as visual and positional data, for fast and accurate mmWave/THz beam prediction. The developed framework is evaluated on a real-world multi-modal mmWave drone communication dataset comprising of co-existing camera, practical GPS, and mmWave beam training data. The proposed sensing-aided solution achieves a top-1 beam prediction accuracy of 86.32% and close to 100% top-3 and top-5 accuracies, while considerably reducing the beam training overhead. This highlights a promising solution for enabling highly mobile 6G drone communications.