AI Beam Pair Prediction for Low-Overhead Information Exchange
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Solution Overview
Problem
High-frequency communication systems face significant overheads in beam training due to the need to traverse and compare candidate beams, leading to inefficient beam management and increased air interface usage.
Innovation Solution
Implement an AI-driven method for beam prediction by transmitting reference signals and using AI models to determine optimal beam pairs, reducing the number of sweeping times and feedback required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional beam training methods are used to traverse and compare candidate beams, then accurate beam pair selection can be achieved, but beam training overheads and air interface usage are significantly increased
Solution Approach 1:
The patent applies preliminary action by using AI models to predict optimal beam pairs before actual beam training occurs. The network device obtains channel state information and uses pre-trained AI models to predict the most likely optimal beam pair, then performs limited verification only on predicted beams rather than exhaustive sweeping of all candidate beams. This preliminary prediction step significantly reduces the number of beams that need to be actually trained and measured.
Solution Approach 2:
The patent uses copying by creating virtual beam training outcomes through AI prediction. Instead of physically traversing and measuring all candidate beams, the system uses AI models to generate predicted beam pair performance data that copies the essential characteristics of actual beam training results. This virtual copying allows the system to identify optimal beams without performing the complete physical measurement process for all candidates.
2Reliability
If exhaustive beam sweeping is performed to ensure optimal beam selection, then communication reliability is improved, but time consumption and system efficiency are reduced
Solution Approach 1:
The system performs preliminary AI-based prediction of optimal beam pairs before actual communication begins. By using channel state information and AI models to predict which beam pairs will be optimal, the system avoids time-consuming exhaustive beam sweeping while still achieving reliable beam selection. The prediction step quickly identifies candidate beams that need verification, dramatically reducing training time.
Solution Approach 2:
The patent changes the parameter of beam training from exhaustive physical measurement to AI-based prediction with limited verification. Instead of measuring all candidate beams equally, the system changes the approach to using predicted beam quality metrics to prioritize which beams to actually train on. This parameter change from uniform sweeping to priority-based verification reduces time while maintaining reliability.
3Strength
If large-scale antenna arrays are used for beamforming to compensate for limited propagation distance, then beam gain is improved, but the complexity of channel information acquisition is significantly increased
Solution Approach 1:
The patent uses copying by employing AI models to generate predicted channel state information that copies the essential characteristics of actual channel measurements. Instead of acquiring and processing complete channel information for all beams in large-scale antenna arrays, the system uses AI predictions to estimate channel states and identify optimal beam pairs, significantly reducing the complexity of channel information acquisition while maintaining beamforming effectiveness.
Solution Approach 2:
The system extracts only the essential channel information needed for beam prediction rather than acquiring complete channel state information for all beams. By using AI models to predict optimal beam pairs from limited channel inputs, the system extracts only the critical information necessary for effective beamforming, avoiding the complexity of processing complete channel data for large-scale antenna arrays.
Data Source
AI summary
This application relates to the field of mobile communication, and in particular, to an information exchange method, which includes: determining a first receiving beam, transmitting T1 reference signals by using T1 transmitting beams corresponding to the first receiving beam, receiving a first CSI report, where the first CSI report includes T1 beam measurement results, and transmitting first information, where the first information is used to determine a to-be-swept second receiving beam set, the second receiving beam set is a subset of receiving beams, the first information is determined based on a first prediction result, and the first prediction result is a processing result of inputting the T1 beam measurement results into an AI model to perform a transmitting and receiving beam pair prediction. A quantity of sweeping times can be reduced, an amount of feedback or a quantity of beam measurement times can be reduced, and overheads can be reduced.