UE Antenna Panel Switching Using Reinforcement Learning
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Solution Overview
Problem
In 5G wireless communication systems, terminal devices with multiple antenna panels face challenges in efficiently switching active antenna panels for optimal signal quality, leading to potential radio link failures, beam failures, and handover failures due to limited hardware complexity and energy consumption, impacting user experience.
Innovation Solution
Implementing machine learning, specifically reinforcement learning, in user equipment (UE) to automatically determine when to perform measurements on other antenna panels, balancing key performance indicators such as throughput, latency, and failure rates, and guiding antenna panel switching through signaling with the base station to maximize long-term cumulative rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If terminal devices frequently switch between multiple antenna panels to maintain optimal signal quality, then serving cell quality is improved, but hardware complexity and energy consumption increase
Solution Approach 1:
The terminal device autonomously performs antenna panel switching based on machine learning predictions without requiring complex network control or manual intervention. The device self-manages the selection of antenna panels by predicting serving cell quality trends and autonomously switching panels to maintain optimal signal quality, thereby reducing the need for complex external control systems.
Solution Approach 2:
The system dynamically changes operational parameters (antenna panel selection) based on predicted serving cell quality. By using machine learning to forecast quality metrics, the terminal adjusts which antenna panel is active, transforming static hardware operation into dynamic parameter optimization that maintains reliability without proportionally increasing complexity.
2Reliability
If terminal devices switch antenna panels frequently to maintain optimal signal quality, then user experience is improved, but energy consumption increases
Solution Approach 1:
The machine learning model performs preliminary predictions of serving cell quality before actual signal degradation occurs. By forecasting quality trends in advance, the terminal can proactively switch antenna panels to prevent failures rather than reactively switching after quality drops, thereby maintaining user experience while reducing unnecessary switching operations and associated energy consumption.
Solution Approach 2:
The system implements a feedback loop where the terminal continuously monitors actual serving cell quality against machine learning predictions. This feedback mechanism allows the device to learn from past performance and optimize future switching decisions, ensuring energy is consumed only when panel switching actually improves user experience rather than through arbitrary or excessive switching.
3Productivity
If machine learning is implemented in UE to optimize antenna panel switching, then long-term cumulative rewards are maximized, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or rule-based antenna switching mechanisms with a machine learning-based predictive system. Instead of using complex hardware controllers or elaborate switching algorithms, the terminal employs software-based machine learning models that run on standard processing units, substituting mechanical complexity with intelligent software control that achieves better productivity with comparable or reduced overall device complexity.
Data Source
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AI summary
Disclosed is a method comprising using a machine learning algorithm to select an antenna panel from a plurality of antenna panels. A first long-term reward value associated with the selected antenna panel is determined based at least partly on one or more first signals received on the selected antenna panel. A second signal is then transmitted or received via the selected antenna panel, if the first long-term reward value exceeds one or more second long-term reward values associated with at least a subset of the plurality of antenna panels.