ML Beam Clustering for 5G Signal Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In mobile communication systems, particularly in 5G NR communication, the existing methods for beam management are inefficient due to low accuracy in measuring beam size, leading to significant differences in signal transmission/reception efficiency depending on the selected beam, and there is a need for improved algorithms to maintain the quality of millimeter-wave radio links.
Innovation Solution
A method using a clustering algorithm to detect and cluster candidate beams based on reception characteristics such as RSSI, determine the optimal beam for signal transmission and reception, and reconfigure clusters based on transmission power and location, allowing for intelligent beam selection and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing signal processing technology is used to measure beam size, then the measurement process is simple, but the accuracy of beam size measurement is low
Solution Approach 1:
The patent introduces a machine learning-based intermediary system that processes beam measurement data. The ML model acts as a mediator between raw signal processing data and final beam size determination, enhancing measurement accuracy by capturing complex relationships that traditional signal processing cannot resolve alone.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with a computational machine learning approach. Instead of relying solely on conventional signal analysis algorithms, the system uses trained neural networks to interpret beam characteristics, substituting complex mathematical processing with learned patterns from training data.
2Productivity
If existing beam selection methods are used, then the process is simple, but the signal transmission/reception efficiency varies significantly depending on beam selection
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously receives information about signal transmission performance and beam characteristics. This feedback loop allows the model to learn from actual transmission results and refine its beam selection recommendations, improving both efficiency and reliability over time.
Solution Approach 2:
The patent dynamically changes selection criteria based on varying transmission conditions. The machine learning model adjusts beam selection parameters according to real-time signal quality metrics, user mobility patterns, and network conditions, enabling adaptive optimization of transmission efficiency for each specific scenario.
3Reliability
If traditional beam management algorithms are used, then the implementation is straightforward, but the quality of millimeter-wave radio links deteriorates
Solution Approach 1:
The patent introduces a machine learning intermediary layer between traditional beam management algorithms and radio link performance optimization. This ML mediator processes complex interactions between beam parameters, user position, and signal characteristics, generating optimized beam management decisions that maintain high radio link quality in challenging millimeter-wave environments.
4Speed
If sensitive beam selection is performed, then the system responds quickly to beam changes, but wrong optimal beams are selected when strength differences are small
Solution Approach 1:
The patent replaces traditional threshold-based beam selection with a machine learning classification system. The ML model processes beam strength measurements and contextual information to make probabilistic judgments about optimal beam selection, reducing sensitivity to small strength differences while maintaining rapid response through efficient pattern recognition in training data.
Data Source
AI summary
An intelligent computing device detects a plurality of candidate beams irradiated from a base station, clusters at least one of the plurality of candidate beams into at least one candidate cluster using a clustering algorithm, selects an optimal beam based on a received signal strength of each candidate beam in the at least one candidate cluster; and receives a signal using the optimal beam, and thus can reduce a beam failure rate by learning user's movement pattern of a UE through machine learning and performing a beam tracking function based on this. At least one of the base station, the UE, the intelligent computing device, and a server can be associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, etc.