Beam Training Optimization for 5G Electronic Devices
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Solution Overview
Problem
In 5G communication systems, electronic devices face challenges in minimizing the time and power consumption required for searching and reconfiguring TX and RX beams when multiple neighboring base stations are involved, especially in mobile scenarios where environmental changes or obstacles affect signal quality.
Innovation Solution
An electronic device equipped with a processor and communication circuits performs beam training by identifying the direction to a base station using synchronization signals, selecting optimal beams, and adjusting them as needed to maintain communication quality, thereby reducing the number of beams to search and minimizing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beam training is performed for all neighboring base stations to ensure comprehensive communication coverage, then communication reliability is improved, but time consumption and power consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing beam training only for base stations that are predicted to become neighboring cells based on current position and movement trajectory. Instead of training all possible base stations, the system pre-identifies which ones are likely to be needed, thereby reducing beam training time while maintaining communication reliability.
Solution Approach 2:
The patent segments the beam training process by dividing neighboring base stations into different groups based on their likelihood of being served. The system performs comprehensive beam training only for high-priority base stations while using simplified procedures for others, thus reducing overall time consumption while maintaining reliability.
2Reliability
If beam training is performed for all neighboring base stations to ensure comprehensive communication coverage, then communication reliability is improved, but power consumption increases significantly
Solution Approach 1:
The system uses preliminary action by predicting which base stations will become neighboring cells based on current position and movement patterns. Beam training is then performed only for these predicted base stations, significantly reducing power consumption while maintaining communication reliability through selective training of only necessary base stations.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the beam training parameters and scope based on movement speed, direction, and environmental factors. When movement is slow or environment is stable, the system reduces beam training intensity and scope, thereby lowering power consumption while maintaining adequate communication reliability.
3Measurement precision
If the number of beams to search is increased to improve beam selection accuracy, then beam training precision is improved, but time consumption and power consumption increase
Solution Approach 1:
The patent applies local quality by concentrating beam search efforts on specific directional sectors where neighboring base stations are most likely to be located, based on movement trajectory and environmental analysis. Instead of uniformly searching all directions with equal beam density, the system increases beam search precision only in relevant local sectors, thereby maintaining accuracy while reducing overall time consumption.
4Measurement precision
If the number of beams to search is increased to improve beam selection accuracy, then beam selection accuracy is improved, but power consumption increases
Solution Approach 1:
The system applies local quality by concentrating computational and energy resources on beam search operations only in directional sectors where neighboring base stations are predicted to be located. This selective approach maintains high beam selection accuracy in relevant directions while significantly reducing overall power consumption by avoiding exhaustive searches in all directions.
Data Source
AI summary
An electronic device according to various embodiments of the present disclosure includes: a first communication circuit; a second communication circuit; a processor operatively coupled with the first communication circuit and the second communication circuit; and a memory operatively coupled with the processor. The memory may store instructions, when executed, causing the processor to receive from a base station (BS) a synchronization signal including identification information of the BS via the first communication circuit, identify a direction from the electronic device to the BS by transmitting and receiving at least one signal with respect to the BS via the second communication circuit based on the identification information of the BS, and perform beam training by using some beams corresponding to the identified direction among a plurality of beams supported by the electronic device. Other embodiments are also possible.