Mmimo Antenna Sub-array Steering via Machine Learning Prediction
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Solution Overview
Problem
In 5G millimeter wave backhaul networks, antennas in arrays face significant lagging time when focusing on client devices, leading to inefficiencies in SSB (Synchronization Signal Block) and reduced network capacity and coverage, which affects signal strength and data throughput.
Innovation Solution
A Machine Learning (ML) model predicts probable client device locations, providing spatial coordinates to steer unallocated antenna sub-arrays, optimizing focus direction and reducing lagging time without affecting existing infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If antennas in arrays focus on client devices using conventional methods, then signal transmission is achieved, but lagging time increases and network capacity decreases
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict client device locations before actual communication begins. The system pre-determines optimal beam directions and antenna configurations based on predicted locations, allowing the antenna array to be pre-positioned and pre-oriented. This eliminates the need for reactive adjustment after client devices are detected, thereby reducing lagging time and increasing network capacity by preparing the system in advance.
2Area of stationary object
If antennas steer focus direction dynamically, then coverage is improved, but thermal performance deteriorates
Solution Approach 1:
The system performs preliminary thermal management by predicting client locations and determining optimal beam directions before active transmission begins. This allows the antenna array to be configured in advance with proper cooling pathways and thermal dissipation routes already established, rather than dynamically adjusting during operation. The pre-planned configuration reduces thermal stress during dynamic steering operations.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model continuously refines predictions based on actual client device locations and system performance. This feedback loop allows the system to adjust beam steering configurations optimally, reducing unnecessary thermal generation from incorrect or excessive steering movements while maintaining comprehensive coverage.
3Measurement precision
If machine learning model predicts client locations, then focus accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge the gap between raw sensor data and optimal antenna configuration. These models act as intelligent mediators that process complex patterns from client device behavior and environmental data to produce accurate location predictions. The intermediary nature of these models allows the system to achieve high measurement precision without directly implementing complex real-time optimization algorithms in the antenna control path.
Solution Approach 2:
By performing location prediction and beam direction determination in advance through machine learning, the system reduces the computational burden during actual communication operations. The complex analysis is completed beforehand, allowing simpler, faster execution during active transmission. This preliminary action approach manages system complexity by shifting computational tasks to appropriate time windows.
Data Source
AI summary
An antenna may be provided. First, a Machine Learning (ML) model may be used, at a predetermined time, to predict a probable location of at least one of a plurality of client devices. Next, spatial coordinates may be obtained for the probable location from the ML model. Then an antenna sub-array of an antenna array may be steered toward the spatial coordinates of the probable location. The antenna sub-array may be unallocated.