Geolocation-Based Beam Pair Selection for Faster mmWave Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional beam pair selection techniques in wireless networks, particularly at mmWave frequencies, are time-consuming and inefficient, leading to suboptimal beamforming due to fixed periodic searches that do not adapt quickly to changing network conditions, thereby reducing network performance and resource utilization.
Innovation Solution
Implementing a machine learning-based approach to predict optimal beam pairs using geolocation data, dynamically determining beam selection periods based on user equipment position and velocity, reducing the need for exhaustive searches and optimizing beamforming operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional periodic exhaustive beam pair search is performed, then beam selection accuracy is maintained, but beam search time and network latency increase significantly
Solution Approach 1:
The system performs preliminary beam pair measurements and stores measurement results in advance. When beam refinement is needed, previously measured beam pairs are retrieved from storage rather than performing exhaustive searches, significantly reducing beam search time while maintaining selection accuracy through pre-collected data.
Solution Approach 2:
The beam search period is made dynamic rather than fixed. The system adjusts the beam search period based on channel conditions, mobility state, and measured signal quality. When conditions are stable, longer periods are used; when conditions change rapidly, shorter periods trigger more frequent searches, optimizing the balance between accuracy and time consumption.
2Adaptability or versatility
If frequent beam pair searches are performed to adapt to changing network conditions, then beamforming performance is improved, but network overhead and resource consumption increase
Solution Approach 1:
The system dynamically adjusts beam search frequency based on mobility detection and channel condition monitoring. For stationary or low-mobility users, beam searches are performed less frequently, conserving energy. For high-mobility users or when signal quality degrades, the system increases search frequency to maintain performance, optimizing energy consumption adaptively.
Solution Approach 2:
The system implements feedback mechanisms where beam measurement results and channel quality indicators are continuously monitored. Based on this feedback, the network decides whether to trigger beam refinement procedures or maintain current beam configurations, avoiding unnecessary searches and reducing energy consumption while maintaining adaptability.
3Device complexity
If fixed periodic beam selection is used, then system complexity is reduced, but network performance deteriorates due to inability to adapt quickly
Solution Approach 1:
The beam search period is transitioned from fixed to dynamic. The system monitors channel conditions, mobility state, and signal quality metrics to adaptively determine optimal beam search intervals. This dynamic approach maintains relatively simple system architecture while significantly improving network performance through adaptive timing.
Solution Approach 2:
The system uses its own measured channel conditions and mobility detection capabilities to automatically adjust beam search timing without requiring complex external control. The network self-regulates beam refinement frequency based on observed performance metrics, maintaining simplicity while achieving adaptability.
Data Source
AI summary
Optimizing or improving the operations of selecting a beam pair for communication and updating the beam pair are disclosed. A model is trained such that a beam pair can be selected based on a geolocation of user equipment. Once a beam pair is selected, the node and/or user equipment are configured such that subsequent transmissions use the selected beam pair. The update time is determined using the user equipment's location and velocity. The update period is based on a time required for the user equipment to reach an area associated with a different beam pair as determined using a decision function of the beam pair selection model.