AI-Controlled Reconfigurable Surface for Self-Healing Beam Steering
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing beam direction and signal strength in reconfigurable intelligent surfaces, particularly in adapting to environmental changes and self-healing from failures.
Innovation Solution
A reconfigurable intelligent surface controlled by an AI/ML model adjusts its geometry to optimize beam direction and strength, utilizing decentralized learning and federated learning to enhance beamforming and signal transmission efficiency, and self-heals by altering the tile geometry to compensate for failing subarrays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the reconfigurable intelligent surface uses fixed tile geometry, then the manufacturing and deployment are simpler, but the system cannot adapt to environmental changes or self-heal from failures
Solution Approach 1:
The patent applies the dynamics principle by enabling the tile geometry to change from a fixed configuration to a dynamically reconfigurable one. The intelligent surface can alter the arrangement and configuration of tiles based on real-time environmental conditions and system state, allowing adaptation to environmental changes while maintaining the ability to self-heal from failures through geometric reconfiguration.
2Reliability
If the system manually monitors and repairs failing subarrays, then the self-healing capability is simpler to implement, but the response time and productivity are reduced
Solution Approach 1:
The patent implements the self-service principle by enabling the reconfigurable intelligent surface to automatically detect and respond to failing subarrays without manual intervention. The system monitors its own state, identifies failures, and executes geometric reconfiguration to compensate for the failures, achieving self-healing functionality that reduces response time and eliminates the need for manual repair operations.
Solution Approach 2:
The patent applies the feedback principle by incorporating continuous monitoring of the intelligent surface's operational state and using this information to trigger automatic reconfiguration. The system receives feedback about failing subarrays and responds by adjusting the tile geometry to maintain functionality, creating a closed-loop self-healing mechanism that minimizes downtime.
3Productivity
If the reconfigurable intelligent surface uses AI/ML models for control, then the beamforming and signal transmission efficiency are improved, but the computational complexity and energy consumption increase
Solution Approach 1:
The patent applies the preliminary action principle by pre-training AI/ML models to optimize beamforming and signal transmission before actual operation. The models are trained in advance on various environmental conditions and failure scenarios, enabling the system to make intelligent control decisions rapidly during operation without requiring continuous heavy computational processing, thus improving efficiency while managing energy consumption.
Data Source
AI summary
The technology described herein is directed towards a reconfigurable intelligent surface that is controlled by an artificial intelligence/machine learning (AI/ML) model of a local tile controller. Adaptive shaping of a reconfigurable intelligent surface's geometry by the model produces a desired coverage pattern, including signal strength determined by a model-determined aperture of subarrays of unit cells, and beam direction via controlled phase shifts of the unit cells. Such on-demand reconfiguration adapts the surface for different operating conditions. Further, the model can repair (self-heal) a reconfigurable intelligent surface, by selecting a different aperture that does not include a failing subarray. Each model is locally trained based on local data, as well as federated learning data obtained from other models and aggregated at a centralized controller that learns a global model from the aggregated data. Model optimization via retraining is an ongoing process for continued model improvement.