Multi-Modal Beam Management for Low-Overhead mmWave Alignment
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Solution Overview
Problem
Existing beam management techniques in mmWave communication suffer from beam misalignment and high pilot overhead, leading to degraded beamforming gain and increased latency due to the finite number of beam codewords and terminal movement, which are not effectively addressed in 5G NR systems.
Innovation Solution
A multi-modal sensing-aided beam management (MMBM) technique that utilizes sensors like LiDAR and RGB cameras to generate beam focusing vectors by extracting terminal location information through cloud point and image data, employing AI-based object detection to accurately form beams with low pilot overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If codebook-based beam management with finite beam codewords is used, then device complexity is reduced, but beam misalignment occurs due to terminal movement leading to degraded beamforming gain
Solution Approach 1:
The system performs preliminary beam training to establish initial beam correspondence between base station and terminal. This preliminary action creates a foundation for subsequent precise beam management, allowing the system to maintain reliable beam alignment without requiring complex real-time adjustments for every terminal movement.
Solution Approach 2:
The patent implements feedback mechanisms where terminals report beam measurement results and movement information back to the base station. This feedback enables the base station to adjust beam directions dynamically, maintaining beam alignment accuracy despite terminal movement while avoiding the need for exhaustive codebook searches.
2Measurement precision
If beam sweeping and refinement processes are performed frequently to track terminal movement, then beam alignment accuracy is improved, but pilot overhead and latency increase
Solution Approach 1:
Instead of performing complete beam sweeping and refinement processes continuously, the system applies partial actions by updating only those beam parameters that have changed due to terminal movement. This approach maintains beam alignment precision while significantly reducing the time and overhead associated with full beam training procedures.
Solution Approach 2:
The patent introduces dynamic beam management where beam directions and codebook selections are adapted in real-time based on terminal movement detection. This dynamic approach allows the system to respond to terminal position changes without requiring periodic full beam sweeps, thereby reducing latency and pilot overhead while maintaining alignment precision.
3Measurement precision
If AI-based object detection with multi-modal sensing is implemented, then terminal detection accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent merges multiple sensing modalities (camera, LiDAR, radar) into a unified terminal detection system. By combining these sensors and their AI-based object detection capabilities, the system achieves high terminal detection accuracy through multi-modal data fusion, while the integrated architecture manages complexity through shared processing resources and coordinated sensor operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
MMBM enhances terminal detection accuracy and reduces beam training overhead by precisely aligning beams with terminal locations, improving beamforming gain and reducing computational complexity.
Implementation Method 1
obtaining cloud point information through a LiDAR sensor
Implementation Method 2
obtaining image information through a camera
Data Source
AI summary
The disclosure relates to a 5G or 6G communication system for supporting higher data rates compared to a 4G communication system such as LTE. A method of a BS in a wireless communication system includes obtaining cloud point information through a LiDAR sensor, obtaining image information through a camera, extracting a region of interest based on the cloud point information; projecting the region of interest onto the image information, identifying an image of a terminal within the region of interest projected onto the image information, calculating three-dimensional location information for the terminal, and performing beamforming based on the three-dimensional location information.