Beam Prediction Model for Wireless Network Alignment
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Solution Overview
Problem
Current beam alignment methods in wireless communication networks are inefficient due to the need for exhaustive searches across all available beams, which is costly and time-consuming, especially with large numbers of antennas, and lack flexibility, requiring full channel knowledge.
Innovation Solution
A method where a first radio node adjusts a set of beams for communication with a second radio node by using a beam prediction model to identify the best beam and neighboring beams, sending training symbols, receiving feedback, adapting the model, and deciding on the number of beams to adjust, thereby reducing the need for exhaustive searches and improving alignment efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search across all available beams is performed, then beam alignment quality is improved, but complexity and time consumption increase
Solution Approach 1:
The system performs preliminary actions by using a beam prediction model to predict the best beam before actual communication. This prediction step reduces the search space from all available beams to only neighboring beams around the predicted best beam, thereby maintaining alignment quality while reducing complexity
Solution Approach 2:
The beam search space is segmented into two parts: the predicted best beam and its neighboring beams. Instead of searching all beams uniformly, the system divides the search into a focused region around the prediction, reducing the number of beams to sweep while preserving alignment quality
2Measurement precision
If exhaustive search across all available beams is performed, then beam alignment quality is improved, but time consumption increases
Solution Approach 1:
The beam prediction model performs a preliminary estimation of the best beam before actual beam sweeping. This allows the system to skip time-consuming searches in directions unlikely to yield good results, reducing overall alignment time while maintaining quality through focused searching of neighboring beams
Solution Approach 2:
Instead of performing complete exhaustive search, the system performs partial action by searching only the necessary subset of beams (neighboring beams around the prediction). This partial search is sufficient to achieve good alignment quality without the excessive time cost of searching all beams
3Device complexity
If fixed number of beams is used for communication, then device complexity is reduced, but adaptability to different channel conditions decreases
Solution Approach 1:
The system dynamically adjusts the number of beams to sweep based on channel conditions and prediction confidence. When channel conditions are stable and prediction is accurate, fewer beams are swept. When conditions change rapidly, more beams are searched. This dynamic approach maintains adaptability while managing complexity
Solution Approach 2:
The system changes the parameter of beam search space size based on prediction quality and channel conditions. Instead of using a fixed number of beams, the search space is adjusted as a variable parameter, allowing the system to adapt to different scenarios while keeping the base mechanism simple
Data Source
AI summary
A method performed by a first radio node for adjusting a set of beams for communication with a second radio node. The first radio node obtains an indication of a first set of beams based on a beam prediction model. The first radio node sends a training symbol on each beam in the first set of beams. The first radio node receives from the second radio node, feedback relating to the sent training symbols. A second beam is identified based on the feedback and is used for transmission. The received feedback and the second beam are used to adapt the beam prediction model. Further, the first radio node decides whether to adjust the number of beams in the first set of beams based on a relationship between the first and second beam. The adjusted first set of beams is to be used for sending training symbols before an upcoming communication.