Adaptive Antenna Array Reconfiguration for Real-Time Network Metrics
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Solution Overview
Problem
Existing antenna systems lack the ability to adaptively and efficiently adjust their configuration in real-time to optimize performance metrics such as data throughput, call drop rate, and energy consumption based on dynamic network conditions.
Innovation Solution
A system and method for controlling radiating elements of an antenna array using machine learning models to iteratively update configuration parameters, including position, azimuth, and elevation angles, to move performance metrics towards a target value, with real-time or near real-time reconfiguration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If antenna configuration parameters are adjusted manually or through traditional methods, then system complexity is reduced, but adaptability to dynamic network conditions deteriorates
Solution Approach 1:
The system implements a closed-loop feedback mechanism where performance metrics are continuously monitored and fed back to the machine learning model, which then adjusts configuration parameters accordingly. This enables automatic adaptation to changing network conditions without manual intervention, resolving the contradiction between adaptability and system complexity.
Solution Approach 2:
The machine learning model autonomously performs configuration optimization without requiring external control or manual adjustment. The system serves itself by automatically learning from performance data and making configuration changes, thereby achieving high adaptability while keeping the control system simple.
2Adaptability or versatility
If machine learning models are used to iteratively update configuration parameters in real-time, then adaptability improves, but processing time and computational energy consumption increase
Solution Approach 1:
The machine learning model is pre-trained offline using historical performance data to learn optimal configuration strategies. During real-time operation, the pre-trained model makes rapid predictions and adjustments without requiring extensive iterative computation, thus achieving real-time adaptability with minimal processing time.
Solution Approach 2:
Instead of retraining the complete model in real-time, the system performs partial updates or uses the pre-trained model for direct predictions. This selective application of computational resources maintains adaptability while significantly reducing processing time and energy consumption.
3Productivity
If configuration parameters are frequently updated to optimize performance metrics, then network efficiency improves, but system stability deteriorates
Solution Approach 1:
The system uses feedback from performance metrics to determine when configuration changes are actually beneficial. By monitoring whether changes improve or degrade performance, the system can stabilize configurations when further changes would not provide benefit, thus maintaining stability while optimizing efficiency.
Solution Approach 2:
The system incorporates stabilization mechanisms that prevent excessive or erratic configuration changes. By preparing for potential instability through controlled update rates and validation checks, the system maintains configuration stability while still achieving network efficiency improvements through targeted optimizations.
Data Source
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AI summary
An apparatus, method and computer program product is disclosed. The apparatus may comprise means for receiving a performance metric for an antenna array comprised of a plurality of radiating elements, the performance metric being based on performance data associated with the antenna array, the antenna array having a radiating configuration represented by configuration parameters. The apparatus may also comprise means for updating the configuration parameters dependent on the received performance metric by means of estimating new configuration parameters for moving the performance metric towards a target value. The apparatus may also comprise means for re-configuring the radiating configuration of the antenna array based on the updated configuration parameters such that the physical geometry of the antenna array is changed.