Base Station Antenna Tilt Control Using UE-Trained ML Models
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Solution Overview
Problem
Existing methods for controlling antenna tilt in radio base stations are infrequent and require careful consideration due to their impact on coverage, often relying on manual adjustments that do not account for real-time signal quality variations among numerous wireless communication devices.
Innovation Solution
A method utilizing machine learning models trained by wireless communication devices to predict signal quality based on their distance from the radio base station, allowing the base station to dynamically adjust antenna tilt for improved coverage and interference reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If antenna tilt is adjusted frequently to improve coverage and reduce interference, then network performance is improved, but system complexity and risk of incorrect adjustments increase
Solution Approach 1:
The system enables self-service by allowing wireless communication devices to autonomously train machine learning models using their own signal quality measurements and location data. Each device independently contributes to the collective intelligence without requiring manual configuration or complex centralized control, thereby improving network performance while avoiding the complexity of manual adjustment systems.
Solution Approach 2:
The patent replaces manual mechanical adjustment of antenna tilt with an automated electronic system based on machine learning. Instead of physically adjusting antenna positions based on operator intervention, the system uses ML models to compute optimal tilt values and electronically control the antenna positioning, eliminating the need for complex human-in-the-loop control mechanisms.
2Reliability
If manual adjustment of antenna tilt is performed to ensure careful consideration, then coverage impact is controlled, but adjustment frequency is reduced and real-time adaptability is lost
Solution Approach 1:
The system transitions from static manual adjustment to dynamic automated control. The machine learning models continuously receive real-time input data from wireless devices regarding signal quality and location, enabling the antenna tilt to be dynamically adjusted in response to changing network conditions while maintaining careful consideration through algorithmic analysis of coverage impact.
Solution Approach 2:
The patent implements feedback mechanisms where wireless communication devices continuously report signal quality measurements and location information back to the system. This feedback loop enables the machine learning models to learn from actual network performance and adjust antenna tilt accordingly, increasing adjustment frequency while ensuring each adjustment is based on measured outcomes rather than guesswork.
3Measurement precision
If location data is shared to train machine learning models for antenna tilt control, then model accuracy is improved, but user privacy is compromised
Solution Approach 1:
The system applies local quality by allowing each wireless communication device to train its own machine learning model using only its local signal quality measurements and location data. Each device processes its own data locally without sharing sensitive location information with other devices or central servers, thereby achieving accurate models while preserving user privacy through decentralized processing.
Solution Approach 2:
The patent introduces federated learning as an intermediary mechanism that enables collaborative model training without direct data sharing. The system uses aggregation of locally trained models through a privacy-preserving protocol, allowing the collection to benefit from multiple devices' data while preventing any single device from exposing its location information, thus balancing model accuracy with privacy protection.
Data Source
AI summary
The present disclosure relates to a method of a wireless communication device (103) of facilitating control of antenna tilt for a radio base station (101), a method of a radio base station (101) of facilitating control of antenna tilt at the radio base station (101) and further a wireless communication device (103) and a radio base station (101) performing the respective method. In a first aspect a method of a wireless communication device (103) of facilitating control of antenna tilt for a radio base station (101) is provided. The method comprises receiving (S202), from the radio base station (101), information indicating a location of the radio base station (101), determining (S203) a set of values of a measure of quality of a signal received from the radio base station and a distance of the wireless communication device (103) from the location of the radio base station (101) at which each value in the set is determined, supplying (S204) a machine learning model with the determined set of values of said measure of quality and the distance of the wireless communication device (103) from the location of the radio base station (101) for each value, thereby creating a trained machine learning model associating a distance of the wireless communication device (101) from the location of the radio base station (103) with a value of a measure of quality of a signal received from the radio base station (101), and transmitting (S205) the trained machine learning model to the radio base station (103), the trained machine learning model being used by the radio base station (101) to control antenna tilt.