ML Beam Alignment Reduces Signaling Overhead
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Solution Overview
Problem
Current beam alignment technologies for communication between user devices and base stations are inefficient, as they require testing all possible beam combinations, leading to high overhead and prolonged alignment times, and often fail to identify the strongest communication channel effectively.
Innovation Solution
A machine-learning model is trained using beam alignment datasets to select the optimal beam pair combinations based on received signal strength, reducing the number of necessary beam combinations to test and potentially identifying channels outside the initial dataset, using techniques such as neural networks or support vector machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible beam combinations are tested to identify the strongest communication channel, then measurement precision is improved, but time consumption and overhead increase significantly
Solution Approach 1:
The system performs preliminary beam alignment by testing only a selected subset of beam pairs before actual communication begins. This preliminary action identifies candidate beam pairs with above-threshold signal qualities, avoiding the need to test all possible beam combinations while still achieving accurate channel identification.
Solution Approach 2:
Instead of performing complete beam alignment by testing all beam pairs, the system applies partial action by testing only a sufficient subset of beam pairs. The selection criteria and threshold mechanisms ensure that the partial testing achieves the necessary measurement precision without the excessive time cost of exhaustive testing.
2Loss of time
If a subset of beam pairs is tested to reduce alignment time, then time consumption is reduced, but the ability to identify the strongest communication channel deteriorates
Solution Approach 1:
The system performs preliminary beam alignment by testing only a selected subset of beam pairs before actual communication begins. This preliminary action identifies candidate beam pairs with above-threshold signal qualities, avoiding the need to test all possible beam combinations while still achieving accurate channel identification.
Solution Approach 2:
The system uses feedback mechanisms including signal quality thresholds and selection criteria to guide the beam pair selection process. Measurement results from tested beam pairs feed back into the selection algorithm, which adjusts which beam pairs to test next, ensuring that the strongest communication channels are identified even with limited testing.
3Productivity
If traditional beam alignment methods are used without machine learning, then device complexity is reduced, but productivity and alignment efficiency deteriorate
Solution Approach 1:
The machine learning model is trained in advance on historical beam alignment data to learn patterns and correlations between beam pairs and signal qualities. During actual operation, the pre-trained model rapidly predicts optimal beam pairs, achieving high productivity without adding significant real-time computational complexity.
Solution Approach 2:
The system uses a machine learning model that captures the complex relationships between beam pairs and communication channel qualities. The model serves as a computational copy of the underlying physical patterns, replacing complex real-time measurements and analysis with faster predictions based on learned patterns from training data.
Data Source
AI summary
A method, apparatus and computer program is described, comprising: obtaining a first beam alignment dataset, wherein the first beam alignment dataset comprises measurement data for a first plurality of beam pair transmissions, wherein each beam pair transmission of the first plurality is between one of a plurality of communication beams of a first user device and one of a plurality of communication beams of a base station and wherein the first plurality of beam pair transmissions is a subset of all available beam pair transmissions between the first user device and the base station; and selecting a first beam pair combination for communications between the first user device and the base station, wherein the first beam pair combination comprises one of the plurality of beams of the first user device and one of the plurality of beams of the base station, wherein the means for selecting the first beam pair combination comprises a machine-learning model trained with a second beam alignment dataset.