Wireless Beam Selection Using Sensing Signals
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Solution Overview
Problem
Current techniques for determining wireless beams in 5G communication networks are resource-intensive, leading to significant computational burdens and delays, as many calculations are performed for beams that are not ultimately used, thereby hindering other processes.
Innovation Solution
A system that employs a base station with a sensing signal configurator, processor, machine learning model, and beam selector to predict and select optimal beams using sensing signals from user equipment, leveraging machine learning to rank and select beams based on signal strength and quality, thereby optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional beam determination techniques are used, then beam selection can be performed, but significant computational resources are consumed and delays occur
Solution Approach 1:
The system performs preliminary actions by having the user equipment transmit sensing signals before the actual beam determination process. The base station uses these pre-collected sensing signals to predict optimal beams, avoiding the need to perform exhaustive calculations for all possible beams during the actual communication setup.
Solution Approach 2:
The invention extracts only the essential information needed for beam selection by using sensing signals transmitted by the user equipment. Instead of processing all possible beam calculations, the system extracts beam quality information from the sensing signals and uses this extracted data to determine the optimal beam, eliminating unnecessary computational steps.
2Measurement precision
If exhaustive beam calculations are performed, then all beam options are evaluated, but compute resources become unavailable for other processes
Solution Approach 1:
The system extracts only the necessary beam quality information from sensing signals transmitted by user equipment, rather than performing exhaustive calculations on all possible beams. This extraction approach maintains sufficient measurement precision for beam selection while dramatically reducing the time and computational resources required.
Solution Approach 2:
Instead of performing complete exhaustive calculations on all beams, the system performs partial action by evaluating only the most promising beam candidates identified through sensing signal analysis. This partial evaluation approach is sufficient for achieving reliable beam selection without consuming all available compute resources.
3Reliability
If many beam calculations are performed, then beam determination can be completed, but resources are wasted on beams that are not used
Solution Approach 1:
The invention extracts beam quality metrics from sensing signals transmitted by user equipment, obtaining only the essential information needed for accurate beam determination. This extraction method eliminates the need to perform calculations on beams that will not be selected, thereby reducing computational resource waste while maintaining determination accuracy.
Solution Approach 2:
The user equipment itself performs the initial sensing signal transmission that provides the base station with the information needed for beam determination. This self-service approach allows the system to obtain accurate beam quality data without the base station needing to perform exhaustive calculations on all possible beams.
Data Source
AI summary
Apparatuses, systems, and techniques to select one or more beams to transmit signals. In at least one embodiment, a system includes one or more circuits to select one or more wireless signal beams based, at least in part, on measuring one or more received reference signals.