Distributed Sensing for Multi-User Beam Prediction and Blockage Avoidance
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Solution Overview
Problem
Existing mmWave and sub-terahertz communication systems face challenges in supporting highly mobile wireless applications due to beam training overhead, sensitivity to blockages, and unpredictable channel conditions, particularly in complex urban environments, leading to latency and connectivity issues.
Innovation Solution
A distributed sensing-aided wireless communication system using multiple nodes equipped with sensors like RGB cameras and large antenna arrays to extract environment semantics, leveraging machine learning for beam prediction and blockage avoidance, integrating visual and radio data to identify communication users among multiple candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large antenna arrays and narrow directive beams are used to guarantee sufficient receive power in mmWave/sub-THz systems, then communication reliability is improved, but beam training overhead increases
Solution Approach 1:
The system performs preliminary actions by using distributed sensing nodes to extract environment semantics (object positions, movements, blockages) before communication occurs. This environmental awareness enables the system to predict optimal beam directions in advance, eliminating the need for extensive beam training overhead while maintaining reliable receive power through targeted narrow beams.
2Loss of time
If machine learning approaches leverage side information for beam prediction, then beam training overhead is reduced, but the ability to scale to complex/crowded scenarios is limited
Solution Approach 1:
The system segments the complex environment into manageable components by deploying multiple distributed sensing nodes, each capturing local environment semantics. These segmented environmental observations are then integrated to form a comprehensive understanding of complex/crowded scenarios, enabling the ML model to scale effectively while maintaining reduced beam training overhead.
Solution Approach 2:
Environment semantics extracted from distributed sensing data serve as an intermediary that bridges the gap between simple ML approaches and complex real-world scenarios. This intermediary representation captures essential environmental features (object positions, movements, potential blockages) that enable ML models to generalize to complex/crowded scenarios without requiring extensive beam training.
3Speed
If vision, radar, and LiDAR sensory data are used for fast beam prediction, then beam training is accelerated, but single-candidate scenarios are not adequately addressed for multi-user settings
Solution Approach 1:
The distributed sensing system provides universal environmental awareness that serves multiple functions simultaneously: it enables fast beam prediction for individual users while also supporting multi-user identification and selection. The environment semantics extracted from vision, radar, and LiDAR data create a unified representation that works across single-candidate and multi-user scenarios, making the system versatile without sacrificing prediction speed.
4Reliability
If mmWave/sub-THz signals rely on direct line-of-sight paths, then sufficient receive power is achieved, but obstacles blocking LOS links cause communication interruption or degradation
Solution Approach 1:
The distributed sensing nodes perform preliminary detection of potential blockages by continuously monitoring environment semantics (object positions and movements) before they actually block the LOS link. This early warning enables the system to proactively switch to alternative beams or paths, maintaining sufficient receive power and preventing communication degradation caused by obstacles.
Solution Approach 2:
The system implements feedback through continuous environmental monitoring by distributed sensing nodes, which provide real-time information about potential blockages. This feedback loop enables the communication system to adapt beam selection dynamically, maintaining reliable receive power by avoiding blocked paths while preserving the benefits of narrow directive beams.
Data Source
AI summary
A system and method for identifying a communication user in a crowded scenario and support multi-user applications, and for identifying the target communication user from the other candidate objects (distractors) in the visual scene. Machine learning models process either one frame or a sequence of frames of sensor data from distributed nodes to identify the target communication user in the semantic environment. Large antenna arrays and narrow directive beams are used to ensure a receive signal power. Optimal beams for millimeter-wave (mmWave) and terahertz (THz) large antenna arrays are selected. Distributed nodes equipped with sensors to receive sensor data extract environment semantics from the captured sensor data. The semantic data are transmitted to the base station. A communication user identification and tracking process is executed at the base station.