Base Station Sensing for Beam Management
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Solution Overview
Problem
Current wireless communication systems face challenges in optimizing beam management, particularly in 5G NR systems, due to beam quantization errors and heavy pilot overhead in THz bands, which affect beamforming gain and communication quality.
Innovation Solution
A method using sensing information to estimate the location of user equipment (UE) through cameras installed at base stations, employing deep learning models to determine beamforming vectors, thereby reducing beam training overhead and power consumption, and improving location accuracy even in low SNR areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional beam management methods are used in THz bands, then beamforming gain can be achieved, but beam quantization errors increase and pilot overhead becomes heavy
Solution Approach 1:
The patent replaces traditional pilot-based beam training methods with sensing-based location estimation. Sensors (cameras, LIDAR, mmWave/THz sensors) detect physical characteristics of UEs to estimate locations, which then determine beamforming vectors. This substitution eliminates beam quantization errors associated with traditional pilot sweeping methods while reducing pilot overhead.
Solution Approach 2:
The patent changes the fundamental parameter for beam determination from pilot signal measurements to sensing data (images, point clouds, detected objects). By using sensing information about UE locations, shapes, and movements rather than pilot-based channel state information, the system achieves more accurate beamforming vectors with reduced quantization errors.
2Measurement precision
If traditional pilot-based beam training is performed, then beam direction can be determined, but pilot overhead increases significantly
Solution Approach 1:
The system performs preliminary sensing to obtain UE location information before beamforming transmission. Sensors continuously or periodically detect UE positions, shapes, and movements in advance, so that when data transmission is needed, the beamforming vector is already determined from the sensing data without requiring time-consuming pilot sweeping procedures.
Solution Approach 2:
The patent substitutes pilot-based beam training with sensing-based location estimation. Instead of transmitting multiple pilot signals in different directions to determine beam direction, the system uses sensors to directly detect UE locations and compute beamforming vectors, dramatically reducing pilot overhead and training time.
3Measurement precision
If sensing information is used to estimate UE location, then location accuracy improves, but device complexity increases
Solution Approach 1:
The patent makes base stations multi-functional by equipping them with both traditional communication functions and sensing functions. The same base station infrastructure performs both data transmission and UE location estimation using integrated sensors. This universal approach increases individual base station complexity but eliminates the need for separate sensing devices, distributing the complexity across existing network infrastructure.
Solution Approach 2:
The base station uses its own sensing capabilities to determine beamforming vectors for its own transmissions. The sensing system at each base station independently estimates UE locations and computes appropriate beamforming vectors without requiring complex coordination or additional processing infrastructure, enabling self-service operation.
4Reliability
If beamforming vectors are determined using sensing data, then beam focusing gain increases, but processing complexity increases
Solution Approach 1:
The patent extracts essential location information from complex sensing data to determine beamforming vectors. Instead of processing complete images or point clouds, the system extracts key parameters such as UE position coordinates, orientation, and movement direction from sensing data. This extraction approach maintains high beam focusing gain while reducing processing complexity by working with condensed location parameters rather than raw sensing data.
Data Source
AI summary
The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). A method and apparatus in the wireless communication system includes detecting at least one first object including at least one user equipment (UE), transmitting, to a second base station (BS), a first message including sensing information of the at least one first object, receiving, as response to the first message, from the second BS, a second message including sensing information of at least one second object, identifying the at least one UE based on the at least one first object and the at least one second object, estimating a location of the identified at least one UE, determining a beamforming vector based on the estimated location, and transmitting data, to the at least one UE, based on the determined beamforming vector.