Intelligent Beam Prediction Using Environmental Obstacle Detection
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Solution Overview
Problem
The high cost and resource-intensive process of globally scanning and measuring all beams in wireless massive MIMO systems, especially in 6G communication scenarios, make it impractical to achieve accurate and cost-effective beam prediction for increasing the capacity of future communication systems.
Innovation Solution
An intelligent beam prediction method that uses environment images to determine obstacle information, target edge points, emission angles, incidence angles, and propagation distances to predict the direction of target beams, converting Non-Line-of-Sight (NLOS) to Line-of-Sight (LOS) scenarios, thereby reducing the need for extensive sampling and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global beam scanning and measurement is performed to achieve accurate beam prediction, then beam direction accuracy is improved, but resource consumption and system cost increase significantly
Solution Approach 1:
The system performs preliminary actions by obtaining environment images and predicting beam directions based on environmental information before actual beam transmission. This allows the system to pre-determine optimal beam directions using environmental data (buildings, obstacles, terrain) without requiring exhaustive beam scanning, thereby reducing resource consumption while maintaining prediction accuracy
Solution Approach 2:
The patent introduces environment images as an intermediary between the base station and terminal for beam prediction. Instead of directly scanning all possible beam directions, the system uses environmental images to infer obstacle information and calculate reflected beam paths, serving as a mediator that reduces the need for extensive beam measurements while providing accurate beam direction predictions
2Productivity
If the number of beams is increased to improve communication capacity, then system capacity is improved, but the cost of scanning and measuring all beams becomes unacceptable
Solution Approach 1:
The system extracts only the essential environmental information needed for beam prediction from environment images, such as obstacle locations and reflected path characteristics. By extracting this key information, the system can predict beam directions without performing exhaustive scanning of all increased beam directions, thus managing the complexity that arises from having more beams while maintaining high communication capacity
Solution Approach 2:
The system performs preliminary beam direction prediction based on environmental images before actual beam transmission. This preliminary action allows the system to pre-calculate optimal beam directions using environmental data, reducing the need for extensive real-time scanning and measurement of all beam directions, thereby managing scanning complexity even when the number of beams is increased
Data Source
AI summary
An intelligent beam prediction method includes obtaining an environment image, the environment image including environmental location information of a base station and a terminal, based on the environmental location information in the environment image, determining obstacle information on a direct path from the base station to the terminal, in response to the obstacle information indicating that an obstacle exists on the direct path, determining a target edge point of the obstacle, based on the target edge point, determining an emission angle, an incidence angle, and a propagation distance of a target beam between the base station and the terminal, and based on the emission angle, the incidence angle, and the propagation distance of the target beam, determining a target beam direction.