FRAMEWORK FOR CHANNEL STATE INFORMATION UTILIZING ARTIFICIAL INTELLIGENCE

Abstract

ABSTRACT Framework for Channel State Information Utilizing Artificial Intelligence The present disclosure introduces an AI-driven framework for channel state information management 100, enhancing wireless communication systems through efficient CSI processing and predictive analysis. This system comprises of data collection and preprocessing module 102 to gather and filter CSI data, and an AI-based CSI analysis module 104 utilizing machine learning models for real-time CSI prediction. Network adjustments are handled by the resource optimization and control module 106, while the AI model training and adaptation engine 108 continuously updates model accuracy. The framework incorporates an edge-AI and cloud integration platform 110 for scalable CSI processing, and a CSI data compression and bandwidth optimization unit 112 to conserve bandwidth in resource-limited settings. Security and anomaly detection are managed by the anomaly detection and security system 114, ensuring data integrity, while the multi-channel interference mitigation module 116 reduces interference, optimizing communication quality in multi-user environments. Reference Fig 1

Specification

Description:DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of framework for channel state information utilising artificial intelligence and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, framework for channel state information utilising artificial intelligence 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data collection and preprocessing module 102, AI-based CSI analysis module 104, resource optimization and control module 106, AI model training and adaptation engine 108, edge-AI and cloud integration platform 110, CSI data compression and bandwidth optimization unit 112, anomaly detection and security system 114, multi-channel interference mitigation module 116, context-aware prediction and adaptation system 118, cross-layer optimization mechanism 120.

[00029] Referring to Fig. 1, the present disclosure provides details of an AI-driven framework for channel state information management 100 in wireless communication systems. This invention optimizes CSI prediction and resource allocation, significantly improving network performance and adaptability. In one embodiment, the framework includes key components such as data collection and preprocessing module 102, AI-based CSI analysis module 104, and resource optimization and control module 106 for real-time CSI processing and adjustment. The system integrates AI model training and adaptation engine 108 and edge-AI and cloud integration platform 110 for scalable and efficient operations. It also features CSI data compression and bandwidth optimization unit 112 to conserve resources and anomaly detection and security system 114 to maintain data integrity. Additional components such as multi-channel interference mitigation module 116 and context-aware prediction and adaptation system 118 further enhance the system's adaptability and responsiveness in complex network environments.

[00030] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with data collection and preprocessing module 102, which is responsible for gathering CSI data from various sources, including antennas, base stations, and user devices. This module preprocesses data by filtering noise and normalizing signals, ensuring that high-quality data is available for analysis. The data collection and preprocessing module 102 works in conjunction with AI-based CSI analysis module 104 to streamline data flow, setting the foundation for accurate CSI predictions across the system.

[00031] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with AI-based CSI analysis module 104, which utilizes machine learning models like deep neural networks and convolutional neural networks to predict and analyze channel states in real-time. This module is continuously trained and adapted by the AI model training and adaptation engine 108 to maintain high predictive accuracy even in dynamic conditions. The AI-based CSI analysis module 104 directly supports resource optimization and control module 106 by providing predictive insights needed for real-time network adjustments.

[00032] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with resource optimization and control module 106, which optimizes network parameters such as transmission power, modulation schemes, and frequency allocations based on CSI predictions. This module relies on real-time data from AI-based CSI analysis module 104 to ensure efficient use of network resources. The resource optimization and control module 106 also collaborates with the edge-AI and cloud integration platform 110 to distribute control tasks across the network infrastructure for scalability.

[00033] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with AI model training and adaptation engine 108, responsible for the initial training and continuous updating of the AI models used in CSI prediction. This engine utilizes historical and real-time CSI data to ensure models are up-to-date with current network conditions. The AI model training and adaptation engine 108 enhances the accuracy of AI-based CSI analysis module 104 and enables the system to adapt dynamically, supporting both centralized and edge-based processing through the edge-AI and cloud integration platform 110.

[00034] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with edge-AI and cloud integration platform 110, which enables distributed processing of CSI data by deploying AI models across edge and cloud servers. This platform reduces latency and supports high-density environments by bringing data processing closer to the user devices. The edge-AI and cloud integration platform 110 works closely with resource optimization and control module 106 to ensure real-time adjustments can be made quickly across large networks, enhancing overall system responsiveness.

[00035] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with CSI data compression and bandwidth optimization unit 112, which compresses CSI data to reduce transmission overhead and conserve network bandwidth. This unit employs advanced algorithms to retain critical information while minimizing data size, which is especially valuable in bandwidth-constrained environments. The CSI data compression and bandwidth optimization unit 112 works in coordination with data collection and preprocessing module 102 to ensure that only essential, compressed data is transmitted to AI-based CSI analysis module 104 for efficient processing.

[00036] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with anomaly detection and security system 114, which safeguards the CSI data's integrity and detects any potential security threats within the network. This system includes encryption protocols and secure access controls, providing data protection and monitoring for unusual patterns that may signal interference or unauthorized access. The anomaly detection and security system 114 operates closely with AI-based CSI analysis module 104 to detect and mitigate threats in real-time, ensuring a secure and reliable communication environment.

[00037] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with multi-channel interference mitigation module 116, which identifies and minimizes interference across multiple communication channels. This module actively analyzes CSI data from AI-based CSI analysis module 104 to detect overlapping frequencies or potential interference and adjusts network parameters accordingly. By reducing cross-channel interference, the multi-channel interference mitigation module 116 enhances overall network performance, especially in environments with high user density and device interaction.
[00038] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with context-aware prediction and adaptation system 118, which incorporates contextual factors such as user mobility, geographic location, and time of day to improve CSI prediction accuracy. This system leverages insights from AI model training and adaptation engine 108 to adapt predictions based on environmental changes, enhancing the reliability of AI-based CSI analysis module 104. The context-aware prediction and adaptation system 118 allows the network to dynamically adjust and maintain optimal performance in varying conditions.

[00039] Referring to Fig. 1, the AI-driven framework for channel state information management 100 is provided with cross-layer optimization mechanism 120, which enables seamless coordination of CSI-driven adjustments across multiple network layers, including the physical, MAC, and network layers. This mechanism uses data from AI-based CSI analysis module 104 and resource optimization and control module 106 to make informed adjustments across protocol layers, enhancing network reliability, data routing, and error handling. The cross-layer optimization mechanism 120 ensures that the AI-driven CSI framework operates efficiently and effectively, aligning lower and higher network layers for a robust communication experience.

[00040] Referring to Fig 2, there is illustrated method 200 for AI-driven framework for channel state information management 100. The method comprises:

At step 202, method 200 includes the data collection and preprocessing module 102 collecting CSI data from various network sources, including antennas, base stations, and user devices;
At step 204, method 200 includes the data collection and preprocessing module 102 filtering out noise and normalizing signals to prepare the data for further analysis;
At step 206, method 200 includes the data collection and preprocessing module 102 sending the preprocessed data to the AI-based CSI analysis module 104, where machine learning models analyze and predict channel state information in real time;
At step 208, method 200 includes the AI-based CSI analysis module 104 providing real-time CSI predictions to the resource optimization and control module 106, which then adjusts network parameters for efficient resource allocation based on these predictions;
At step 210, method 200 includes the AI model training and adaptation engine 108 continuously updating AI models with historical and real-time data to enhance predictive accuracy over time;
At step 212, method 200 includes the edge-AI and cloud integration platform 110 distributing the CSI processing load, enabling decentralized and scalable operations in high-density network environments;
At step 214, method 200 includes the CSI data compression and bandwidth optimization unit 112 compressing CSI data to conserve bandwidth while retaining critical information, particularly beneficial in bandwidth-limited environments;
At step 216, method 200 includes the anomaly detection and security system 114 monitoring CSI data for anomalies and potential security threats, protecting data integrity and maintaining network security;
At step 218, method 200 includes the multi-channel interference mitigation module 116 analyzing CSI data to detect and minimize interference across multiple communication channels, optimizing communication quality and reducing signal overlap;
At step 220, method 200 includes the context-aware prediction and adaptation system 118 adapting CSI predictions based on contextual factors like user mobility and geographic location, enhancing prediction reliability in variable environments;
At step 222, method 200 includes the cross-layer optimization mechanism 120 performing adjustments across multiple protocol layers based on AI-driven CSI predictions, optimizing overall network performance and maintaining communication quality.


[00041] The AI-driven framework for channel state information (CSI) management offers transformative applications across various wireless communication technologies. In one of the embodiments, in 5G networks the framework optimizes Massive MIMO systems by enhancing beamforming and spatial multiplexing, which improves spectral efficiency and reduces interference, a critical requirement in high-density usage areas.

[00042] In another embodiment, in IoT networks the invention ensures reliable data transmission for low-power devices by dynamically adapting to changing channel conditions, maintaining efficiency in challenging environments. This technology is also highly beneficial for smart cities, where it strengthens communication networks amidst high user density and frequent interference.

[00043] In another embodiment, autonomous vehicles can leverage the framework for reliable, low-latency communication with surrounding infrastructure, essential for safe and responsive operation.

[00044] In another embodiment for industrial automation the system enhances wireless network performance by mitigating interference and adapting to fluctuating conditions common in factory settings, ensuring stable and efficient machine-to-machine communication.

[00045] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00046] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00047] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. An AI-driven framework for channel state information management 100 comprising of
data collection and preprocessing module 102 to collect and prepare CSI data from network sources for analysis;
AI-based CSI analysis module 104 to analyze and predict channel state information in real time;
resource optimization and control module 106 to adjust network parameters based on CSI predictions for efficient resource use;
AI model training and adaptation engine 108 to continuously update AI models with historical and real-time data;
edge-AI and cloud integration platform 110 to distribute CSI processing load for scalable, decentralized operations;
CSI data compression and bandwidth optimization unit 112 to compress CSI data, conserving bandwidth without losing critical information;
anomaly detection and security system 114 to monitor CSI data for security threats and maintain data integrity;
multi-channel interference mitigation module 116 to detect and minimize interference across communication channels;
context-aware prediction and adaptation system 118 to enhance prediction accuracy based on environmental context; and
cross-layer optimization mechanism 120 to perform adjustments across protocol layers for optimal network performance;
2. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein data collection and preprocessing module 102 is configured to collect CSI data from diverse network sources, filter noise, and normalize signals in real time to provide high-quality data for predictive analysis.

3. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein AI-based CSI analysis module 104 is configured to utilize machine learning models, including deep neural networks, for dynamic and accurate prediction of channel state information, enabling real-time network adjustments.

4. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein resource optimization and control module 106 is configured to adjust network parameters, including transmission power, modulation schemes, and frequency allocations, based on AI-predicted CSI, ensuring optimal resource allocation and improved data throughput.

5. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein AI model training and adaptation engine 108 is configured to continuously update machine learning models with historical and real-time CSI data, facilitating model adaptability and accuracy under dynamic network conditions.

6. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein edge-AI and cloud integration platform 110 is configured to distribute CSI processing loads across edge and cloud environments, providing scalable and low-latency data processing for dense network environments.

7. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein CSI data compression and bandwidth optimization unit 112 is configured to compress CSI data without significant information loss, optimizing bandwidth usage and supporting effective communication in bandwidth-limited settings.

8. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein anomaly detection and security system 114 is configured to monitor CSI data for unusual patterns and potential security threats, maintaining data integrity and preventing unauthorized access through encryption and secure access controls.

9. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein multi-channel interference mitigation module 116 is configured to detect and minimize cross-channel interference in real time, enhancing communication quality and reliability in multi-user and high-density network environments.

10. The AI-driven framework for channel state information management 100 as claimed in claim 1, wherein method comprises of
data collection and preprocessing module 102 collecting CSI data from various network sources, including antennas, base stations, and user devices;
data collection and preprocessing module 102 filtering out noise and normalizing signals to prepare the data for further analysis;
data collection and preprocessing module 102 sending the preprocessed data to the AI-based CSI analysis module 104, where machine learning models analyze and predict channel state information in real time;
AI-based CSI analysis module 104 providing real-time CSI predictions to the resource optimization and control module 106, which then adjusts network parameters for efficient resource allocation based on these predictions;
AI model training and adaptation engine 108 continuously updating AI models with historical and real-time data to enhance predictive accuracy over time;
edge-AI and cloud integration platform 110 distributing the CSI processing load, enabling decentralized and scalable operations in high-density network environments;
CSI data compression and bandwidth optimization unit 112 compressing CSI data to conserve bandwidth while retaining critical information, particularly beneficial in bandwidth-limited environments;
anomaly detection and security system 114 monitoring CSI data for anomalies and potential security threats, protecting data integrity and maintaining network security;
multi-channel interference mitigation module 116 analyzing CSI data to detect and minimize interference across multiple communication channels, optimizing communication quality and reducing signal overlap;
context-aware prediction and adaptation system 118 adapting CSI predictions based on contextual factors like user mobility and geographic location, enhancing prediction reliability in variable environments;
cross-layer optimization mechanism 120 performing adjustments across multiple protocol layers based on AI-driven CSI predictions, optimizing overall network performance and maintaining communication quality.

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