AI Antenna Selection for Multi-Antenna UE Grip and AoA Uncertainty
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Solution Overview
Problem
Existing cellular network technologies face challenges in selecting optimal antennas for data transmission and reception, particularly when the angle of arrival (AoA) of signals is unknown, leading to suboptimal performance and increased power consumption.
Innovation Solution
An artificial intelligence (AI) model-based approach is used to select the optimal antenna and tuner state for data transmission and reception, considering factors such as user grip, frequency band, duplexing technology, and reference signal measurements, along with predefined antenna data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional antenna selection methods are used without AI models, then device complexity remains lower, but data throughput is reduced and power consumption increases
Solution Approach 1:
The patent replaces traditional mechanical/electrical antenna selection methods with an AI-based system that uses machine learning models to predict optimal antenna configurations. The AI model processes multiple input parameters (user grip, frequency band, duplexing technology, reference signal measurements) to automatically determine optimal antenna selection and tuner state, substituting complex manual optimization processes with intelligent automation that achieves higher data throughput while managing device complexity through software-based decision making
2Use of energy by moving object
If AI models are used to select optimal antennas, then data throughput increases and power consumption decreases, but device complexity increases
Solution Approach 1:
The AI model enables the system to self-optimize antenna selection and tuner state configuration automatically. The model processes input parameters including user grip detection, frequency band identification, duplexing technology determination, and reference signal measurements to autonomously determine optimal antenna configurations without requiring manual intervention or complex external optimization systems, thereby reducing power consumption while managing device complexity through integrated self-service functionality
3Reliability
If antenna selection is performed without considering user grip and operating conditions, then device complexity remains lower, but antenna performance deteriorates
Solution Approach 1:
The patent systematically varies and processes multiple parameters including user grip state, frequency band, duplexing technology, and reference signal measurements to determine optimal antenna selections. The AI model dynamically adjusts antenna configuration based on changes in these parameters, ensuring optimal antenna performance under different operating conditions while managing device complexity through structured parameter processing and machine learning-based decision making
Data Source
AI summary
The present application relates to selecting an antenna optimization parameter of a UE. In an example, grip data indicating a user grip of the UE is generated. The grip data is used in a look-up of antenna data indicating an antenna selection from the plurality of antennas and/or a tuner state from a plurality of tuner states. In another example, reference signal measurements can be generated, each corresponding to one of the antennas. The reference signal measurements can be input to artificial intelligence model that outputs the antenna selection, the tuner state, or a predicted angle of arrival. The predicted angle of arrival can be used in a look-up of antenna data to determine the antenna selection and/or the tuner state.