AI-Based Compressed Sensing for MIMO Beam Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional beam searching methods in mmWave communications are time-inefficient due to excessive beam training overhead, necessitating improvements in compressed sensing measurement and prediction for enhanced network efficiency.
Innovation Solution
The implementation of AI-based compressed sensing for beam prediction, where user equipment (UE) performs beam sweeping to generate measurement matrices, compresses these matrices to obtain sensing matrices, and conducts weighted beam measurements to predict optimal beams efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive beam searching method is used to measure beam quality information for all candidate beams, then measurement precision is improved, but loss of time increases due to excessive beam training overhead
Solution Approach 1:
The patent extracts only the essential beam quality information needed for optimal beam selection rather than measuring all candidate beams exhaustively. Compressed sensing techniques extract key channel state information from reduced measurements, enabling accurate beam prediction without exhaustive searching through all beams in the codebook.
Solution Approach 2:
The patent performs preliminary beam training to acquire channel state information and train AI models in advance. By pre-training neural networks with channel characteristics during initial beam training phases, the system enables fast beam prediction without requiring exhaustive measurement of all beams during data transmission.
2Loss of time
If hierarchical beam searching method is used to search among wide beams first then narrow beams, then loss of time is reduced, but measurement precision deteriorates as beam selection is based on approximate metrics
Solution Approach 1:
The patent implements feedback mechanisms where AI models continuously learn from beam measurement results and channel state information. The system uses measured beam qualities to update neural network predictions, refining beam selection accuracy over time while maintaining reduced training overhead through intelligent prediction rather than exhaustive searching.
Solution Approach 2:
The patent changes the approach from fixed hierarchical searching to adaptive beam selection using AI models. By transforming beam quality metrics into predicted optimal beam selections through neural networks, the system achieves accurate beam prediction without being constrained by hierarchical search limitations or approximate metrics.
3Productivity
If AI models are used for matrix training and reconstruction in compressed sensing, then productivity is improved through faster beam prediction, but device complexity increases
Solution Approach 1:
The patent introduces AI models as intermediaries between raw channel measurements and beam selection decisions. The neural networks serve as mediators that process compressed sensing measurements and channel state information to predict optimal beams, simplifying the overall system architecture while enabling fast prediction without requiring exhaustive beam searching or complex real-time optimization.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Apparatus and methods are provided for AI-based compressed sensing for beam prediction. In one novel aspect, the UE obtains one or more sensing matrices with matrices training and performs weighted beam measurements. In one embodiment, the UE performs beam sweeping to generate one or more measurement matrices, obtains one or more sensing matrices by compressing the one or more measurement matrices with matrices training, and performs weighted beam measurements to generate one or more weighted beam measurement matrices. In one embodiment, the matrices training is performed by the UE or by the wireless network using an AI model. In one embodiment, the UE performs measurement matrix reconstruction based on the one or more weighted beam measurement matrices and the one or more sensing matrices. In one embodiment, the UE predicts an optimal beam based on the one or more predicted weighted beam measurement matrices.