Statistical Beam Tracking for Low-Overhead mmWave Search
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Solution Overview
Problem
The challenge in mmWave frequencies is the need for a large number of beams to cover wide angular regions and the overhead associated with finding the best beam between base stations and user equipment, which is inefficient and computationally complex.
Innovation Solution
A system and method for beam tracking using statistical learning that utilizes historical state information to determine best next narrow beam candidates, reducing the need for extensive searches and optimizing beam management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a significant number of beams are used to cover wide angular regions in mmWave frequencies, then beam coverage and data transmission capability are improved, but beam management overhead and system complexity increase
Solution Approach 1:
The patent segments the beam search process into two stages: first identifying a small set of candidate beams using statistical learning from historical data, then performing exhaustive search only on these candidates. This segmentation reduces the overall search space from hundreds of beams to a manageable subset, thereby reducing beam management overhead while maintaining comprehensive coverage capability
Solution Approach 2:
The patent performs preliminary action by using statistical learning to predict and identify candidate beams before the actual beam search. Historical beam state information is analyzed in advance to generate a shortlist of promising candidates, which are then used as the search space for the exhaustive beam search, reducing the complexity of real-time beam management
2Measurement precision
If many reference signals are used to find the best beam between base station and user equipment, then beam selection accuracy is improved, but computational intensity and processing requirements increase
Solution Approach 1:
The patent extracts only the most promising candidate beams from the full beam codebook using statistical learning methods. Instead of evaluating all reference signals corresponding to hundreds of beams, the system extracts a small subset of candidate beams based on historical patterns, thereby maintaining high beam selection accuracy while dramatically reducing computational intensity
Solution Approach 2:
The patent changes the parameter of search space size dynamically. By using statistical learning to adaptively determine the number and identity of candidate beams based on historical data, the system adjusts the effective search space parameter to optimize the trade-off between measurement precision and computational power requirements
3Measurement precision
If exhaustive beam search is performed across all beams, then beam tracking accuracy is improved, but time consumption and processing delay increase
Solution Approach 1:
The patent segments the beam search into a two-stage process: first using statistical learning to quickly identify candidate beams, then performing exhaustive search only on these candidates. This segmentation maintains beam tracking accuracy by ensuring the best beam is found among candidates, while reducing time consumption by limiting the exhaustive search to a small subset rather than all beams
Solution Approach 2:
The patent performs preliminary identification of candidate beams using statistical learning before the exhaustive search. This preliminary action filters out unlikely candidates in advance, so that the time-consuming exhaustive search is applied only to a small set of promising beams, thereby maintaining accuracy while reducing overall search time
Data Source
AI summary
A method includes obtaining information representing a current state of communication with a user equipment (UE) performed using one or more beams. The method also includes comparing the information to statistical historical state information to determine one or more best next narrow beam candidates. The method further includes performing a beam search using the one or more best next narrow beam candidates in order to select a next narrow beam. The method also includes communicating with the UE using the selected next narrow beam.