Trajectory-Based Beamforming Prediction for Dynamic mmWave Links
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing beamforming technologies fail to accurately reflect millimeter-wave transmission states in dynamic environments due to user movement and environmental obstacles, leading to suboptimal communication quality.
Innovation Solution
A beamforming prediction device that utilizes a trajectory-based fingerprint database and sparse coding to predict beamforming, incorporating a storage unit, trajectory prediction unit, fingerprint estimation unit, and beamforming calculation unit to calculate optimal beamforming based on user trajectory and environmental influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional fingerprint based on stationary position is used, then beamforming can be performed, but it cannot correctly reflect the millimeter-wave transmission state in dynamic environments where users move
Solution Approach 1:
The patent transitions from static position-based fingerprints to dynamic trajectory-based fingerprints. The system collects beamforming data along the entire movement trajectory of the user, not just at stationary positions. This dynamic approach allows the fingerprint to accurately reflect millimeter-wave transmission states that change with user movement and environmental obstacles, resolving the contradiction between adaptability to dynamic environments and measurement precision.
2Productivity
If deep neural network is applied to collected beamforming data, then beamforming can be performed, but computation time increases
Solution Approach 1:
The patent extracts only the essential features from the collected beamforming data along the trajectory, creating a compressed fingerprint representation. Instead of processing the entire raw dataset through computationally intensive deep neural networks, the system extracts key propagation characteristics (line-of-sight components, reflected components, diffraction components) and stores them in a pre-processed fingerprint database. This extraction approach significantly reduces computation time while maintaining beamforming accuracy.
Solution Approach 2:
The system performs preliminary processing of beamforming data by collecting and organizing trajectory-based fingerprints in advance before actual beamforming operations. The fingerprint database is pre-populated with propagation characteristics measured along various trajectories, so that during real-time operation, the system only needs to query and apply the pre-computed fingerprint rather than performing complex computations from scratch, thereby achieving high-speed beamforming.
3Measurement precision
If trajectory-based fingerprint database is used, then accurate millimeter-wave transmission state can be reflected, but device complexity increases
Solution Approach 1:
The patent creates simplified copies of the complex millimeter-wave propagation environment in the form of trajectory-based fingerprints. Instead of directly modeling and processing the full complexity of dynamic environments, obstacles, and user movements, the system captures these effects in pre-measured fingerprint data that represents the propagation characteristics along typical trajectories. This copying approach maintains measurement precision while reducing the complexity of real-time processing.
Data Source
AI summary
The present disclosure is to perform beamforming corresponding to the influence of a dynamic environment in which a user moves. The present disclosure relates to a beamforming prediction device that includes: a storage unit that stores a dictionary D obtained by learning fingerprints based on trajectories, and a fingerprint database based on trajectories; a trajectory prediction unit that calculates a trajectory of a mobile terminal, using location information about the mobile terminal; a fingerprint estimation unit that applies the trajectory of the mobile terminal to an input of the dictionary D, and calculates the sparse coefficient X corresponding to the trajectory of the mobile terminal; and a beamforming calculation unit that calculates beamforming of the mobile terminal, using the sparse coefficient X calculated by the fingerprint estimation unit and the fingerprint database.