HIERARCHICAL IRS CONFIGURATION FRAMEWORK FOR ENHANCED PERFORMANCE IN SPATIALLY CORRELATED, MULTI-CHANNEL NETWORKS

Abstract

The invention provides a Hierarchical IRS Configuration Framework designed to enhance performance in spatially correlated, multi-channel networks by dynamically configuring Intelligent Reflecting Surfaces (IRS). This framework divides the IRS into clusters managed by localized controllers, creating a hierarchical structure that enables targeted, adaptive configurations based on spatial proximity and real-time channel conditions. Each cluster operates semi-independently, minimizing inter-cluster interference and optimizing signal propagation. A machine learning-based algorithm further refines IRS configurations by analyzing metrics like channel state information (CSI), spatial correlation, and network load. This real-time adaptability ensures efficient resource use, improved data throughput, reduced latency, and heightened energy efficiency. The hierarchical design also offers scalability, allowing seamless management of large IRS arrays in complex environments, such as dense urban areas, industrial IoT networks, and high-capacity public networks. This framework addresses critical limitations in current IRS systems, making it ideal for modern wireless communication applications.

Specification

Description:The Hierarchical IRS Configuration Framework is designed to enhance performance in spatially correlated, multi-channel networks by organizing IRS (Intelligent Reflecting Surfaces) into a structured, layered configuration.
[015] IRS arrays, composed of numerous passive reflecting elements, are strategically clustered within the framework, with each cluster managed by a local controller.
[016] This hierarchical structure enables localized control of IRS elements, which is crucial for addressing the challenges posed by spatial correlation in high-density environments, where signal interference and degradation are common.
[017] The framework integrates machine learning algorithms to dynamically adjust IRS configurations in response to real-time network conditions. These algorithms analyze critical network metrics, such as spatial correlation and channel state information, to optimize reflection parameters in each IRS cluster. [018] This adaptive approach allows the framework to respond to changes in user location, network traffic, and channel fading, maintaining optimal performance even in dynamic settings.
[019] By dividing IRS elements into clusters, the framework minimizes inter-cluster interference, which enhances signal quality and data throughput across the network.
[020] Each cluster operates independently, with its controller making localized adjustments, which collectively contribute to improved overall network efficiency. This localized control structure also helps in balancing the computational load, reducing the energy consumption associated with real-time IRS configuration updates.
[021] The hierarchical design ensures scalability, making it suitable for large-scale IRS arrays in dense urban or industrial IoT networks. As network demands increase, additional clusters can be added without compromising system performance.
[022] This flexibility provides a sustainable and efficient solution for modern, high-capacity communication networks, where managing spatial correlation and channel interference is critical.

[023] This approach not only enhances the signal strength and quality but also ensures that the system can adapt in real time to the ever-changing conditions of the wireless environment, thereby maintaining optimal performance. The integration of machine learning in this process significantly boosts the efficiency and reliability of mmWave communications, making it a highly effective solution for modern wireless networks.
, Claims:1) The invention encompasses a method and system for enhancing performance in spatially correlated, multi-channel networks through a Hierarchical IRS Configuration Framework. This framework involves dividing an IRS array into multiple clusters, each managed by a localized controller, to facilitate targeted signal optimization. A central control unit coordinates configuration across clusters and incorporates a machine learning module to monitor real-time channel state information (CSI), spatial correlation, and traffic load. Based on this data, the system dynamically adjusts reflection parameters for each cluster, minimizing interference and enhancing performance metrics, including data throughput, signal quality, and energy efficiency. The system's hierarchical structure enables each IRS cluster to self-adapt based on localized network conditions and central control directives, effectively reducing computational complexity while maintaining real-time adaptability. This scalable architecture is designed to support network expansion and high-density deployments, ensuring robust performance even in dynamic, multi-channel network environments.

2) According to claim1# the invention is based on spatial correlation and proximity, with each cluster of IRS elements configured to optimize reflection characteristics tailored to the localized propagation environment, thereby improving signal-to-interference-plus-noise ratio (SINR) in spatially correlated multi-channel networks.


3) According to claim1,2# the invention is to analyzes real-time channel state information (CSI) and spatial correlation metrics, and subsequently provides adaptive control signals to each cluster's localized controller, facilitating dynamic adjustment of reflection coefficients to sustain optimal performance across varying network conditions.

4) According to claim1,2# wherein the localized controllers independently manage IRS element configurations within their respective clusters, enabling localized interference reduction and enhanced signal quality by isolating interference control to cluster-specific reflection parameter adjustments, thereby enhancing data throughput and latency in the network.

5) According to claim1# the invention further comprising an energy-efficient control mechanism where hierarchical clustering reduces computational complexity, limiting the IRS configuration adjustment processes to the necessary clusters, thus optimizing power consumption and reducing overall system energy requirements.

6) According to claim1,2,3,4# wherein the central control unit coordinates with each localized controller in real-time, allowing for scalability and modular expansion of the IRS array by enabling additional clusters to be seamlessly integrated, maintaining performance efficiency and network adaptability in high-density or dynamically changing network environments.

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