AI-DRIVEN FRAMEWORK FOR CHANNEL STATE INFORMATION PROCESSING

Abstract

ABSTRACT AI-DRIVEN FRAMEWORK FOR CHANNEL STATE INFORMATION PROCESSING The present disclosure introduces AI-driven framework for channel state information processing 100 that enhances wireless communication performance by utilizing advanced machine learning techniques. It comprises of data acquisition module 102 to collect real-time channel state information and machine learning processing unit 104 that processes the CSI data for accurate channel estimation. The other components are feature extraction module 106, resource management and optimization layer 108, feedback loop mechanism 110, real-time prediction engine 112, contextual awareness module 114, scalability and modularity system 116, predictive resource allocation module 118, security and privacy mechanisms 120, dynamic feature extraction 122, automated hyperparameter optimization module 124, collaborative learning module 126, user-centric adaptation module 128, multi-layered predictive modeling 130, multi-access edge computing (MEC) integration 132, visual analytics dashboard 134, cross-domain learning capability 136, channel prediction and interference mitigation module 138. Reference Fig 1

Specification

Description:AI-Driven Framework for Channel State Information Processing
TECHNICAL FIELD
[0001] The present innovation relates to an AI-driven framework for processing Channel State Information (CSI) in wireless communication systems to enhance network performance and resource optimization.

BACKGROUND

[0002] In modern wireless communication systems, Channel State Information (CSI) plays a vital role in managing and optimizing network performance. CSI provides data on the quality of the communication channel, which is essential for adjusting transmission parameters, improving signal quality, and managing interference. However, traditional methods of CSI processing rely on predefined models and assumptions about channel behavior, which are often inadequate in dynamic and complex environments such as 5G networks. These conventional methods fail to adapt quickly to rapid changes in channel conditions caused by factors like multipath fading, interference, and user mobility, leading to suboptimal network performance, inefficient resource allocation, and degraded user experiences.

[0003] Available options for processing CSI typically involve basic signal processing techniques or static algorithms that do not leverage the real-time data analysis capabilities required in fast-evolving networks. While some systems integrate machine learning, they often focus on isolated components of network optimization, leaving gaps in the holistic processing of CSI. These approaches lack adaptability, fail to scale with complex networks, and do not effectively manage resource allocation in real-time.

[0004] The AI-driven framework proposed in this invention addresses these limitations by using advanced machine learning algorithms to process and interpret CSI dynamically. Unlike existing methods, this invention combines neural networks, reinforcement learning, and support vector machines to predict and adapt to changing channel conditions with high accuracy. The framework is capable of real-time CSI acquisition, adaptive feature extraction, and proactive resource management, ensuring optimized bandwidth usage, reduced interference, and improved signal quality.

[0005] The novelty of this invention lies in its ability to integrate AI-driven predictive modeling and real-time adaptation within a unified framework. Key features include dynamic learning from feedback, contextual awareness, and scalability across different wireless technologies, making it a robust and innovative solution for next-generation communication systems

OBJECTS OF THE INVENTION


[0006] The primary object of the invention is to enhance wireless communication performance by accurately estimating and processing Channel State Information (CSI) using advanced machine learning algorithms.

[0007] Another object of the invention is to provide a dynamic framework that adapts in real-time to changing channel conditions, improving resource allocation and reducing network interference.

[0008] Another object of the invention is to enable more efficient bandwidth utilization, thereby optimizing the quality of service in next-generation wireless networks, including 5G and beyond.

[0009] Another object of the invention is to integrate machine learning models, such as neural networks and reinforcement learning, for predictive CSI analysis and adaptive network management.

[00010] Another object of the invention is to offer a scalable and modular framework that can be deployed across various network environments, from localized wireless setups to large-scale cellular systems.

[00011] Another object of the invention is to minimize energy consumption and improve network sustainability by optimizing resource management and reducing unnecessary transmissions.

[00012] Another object of the invention is to provide a feedback loop that continuously refines machine learning models, ensuring that the system improves its predictive accuracy over time.

[00013] Another object of the invention is to facilitate the integration of emerging technologies, such as IoT and smart city infrastructure, by offering real-time CSI processing and optimization.

[00014] Another object of the invention is to improve user experiences by dynamically adjusting network parameters based on real-time user behavior and mobility patterns.

[00015] Another object of the invention is to overcome the limitations of traditional CSI processing methods by offering a comprehensive, AI-driven solution that combines real-time data analysis with advanced predictive capabilities

SUMMARY OF THE INVENTION

[00016] In accordance with the different aspects of the present invention, AI-driven framework for channel state information processing is presented. The system utilizes advanced machine learning algorithms to enhance network performance, resource allocation, and signal quality. The framework dynamically adapts to changing channel conditions, providing real-time CSI analysis and predictive modeling. It optimizes bandwidth usage, reduces interference, and improves user experiences in next-generation networks like 5G. The system is scalable, modular, and designed to support emerging technologies such as IoT and smart cities. This innovative approach addresses the limitations of traditional CSI processing methods and enhances network efficiency and sustainability.

[00017] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00018] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00019] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00020] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00021] FIG. 1 is component wise drawing for AI-driven framework for channel state information processing.

[00022] FIG 2 is working methodology of plates for AI-driven framework for channel state information processing.

DETAILED DESCRIPTION

[00023] 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.

[00024] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of AI-driven framework for channel state information processing 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.

[00025] 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.

[00026] 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.

[00027] 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.

[00028] 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.

[00029] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data acquisition module 102, machine learning processing unit 104, feature extraction module 106, resource management and optimization layer 108, feedback loop mechanism 110, real-time prediction engine 112, contextual awareness module 114, scalability and modularity system 116, predictive resource allocation module 118, security and privacy mechanisms 120, dynamic feature extraction 122, automated hyperparameter optimization module 124, collaborative learning module 126, user-centric adaptation module 128, multi-layered predictive modeling 130, multi-access edge computing (MEC) integration 132, visual analytics dashboard 134, cross-domain learning capability 136, channel prediction and interference mitigation module 138.

[00030] Referring to Fig. 1, the present disclosure provides details of AI-driven framework for channel state information processing 100 designed to enhance wireless communication performance using advanced machine learning and predictive analytics. It integrates multiple components, such as data acquisition module 102, machine learning processing unit 104, and feature extraction module 106, to enable accurate CSI estimation and resource management. The framework dynamically adapts to changing channel conditions through resource management and optimization layer 108 and real-time prediction engine 112, ensuring optimized bandwidth utilization and reduced interference. Key components like feedback loop mechanism 110 and predictive resource allocation module 118 facilitate continuous learning and proactive decision-making. Additionally, it incorporates contextual awareness module 114 and security and privacy mechanisms 120 to support robust and secure operations in dynamic environments.

[00031] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with data acquisition module 102, which collects real-time channel state information (CSI) from various network sources, including user devices and base stations. The data acquisition module 102 uses advanced sensors and signal processing techniques to capture essential CSI metrics like signal strength and interference levels. It interacts seamlessly with machine learning processing unit 104, ensuring that raw data is consistently fed into the system for analysis and prediction. This module plays a critical role in enabling real-time decision-making throughout the framework.

[00032] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with machine learning processing unit 104, which utilizes machine learning algorithms such as neural networks and reinforcement learning to analyze the CSI data collected by data acquisition module 102. This unit is responsible for identifying patterns, trends, and anomalies in the CSI data, enabling accurate channel estimation and resource optimization. The machine learning processing unit 104 works in close collaboration with feature extraction module 106 to ensure that only relevant features are used for model training and prediction.

[00033] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with feature extraction module 106, which extracts essential features from the preprocessed CSI data. This module captures both time-domain and frequency-domain characteristics, ensuring that the machine learning processing unit 104 can accurately model the behavior of the communication channel. The feature extraction module 106 works dynamically with resource management and optimization layer 108 to ensure that extracted features are used for real-time adaptation in wireless communication.

[00034] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with resource management and optimization layer 108, which adjusts network parameters such as transmission power, modulation schemes, and bandwidth allocation based on real-time CSI estimates. This layer plays a critical role in minimizing interference and maximizing network throughput by leveraging the predictions generated by machine learning processing unit 104. It continuously communicates with feedback loop mechanism 110 to ensure the system adapts effectively to changing channel conditions.

[00035] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with feedback loop mechanism 110, which captures performance metrics and resource allocation outcomes to refine the machine learning models. The feedback loop mechanism 110 plays a vital role in ensuring continuous learning and improvement by providing real-time feedback to the machine learning processing unit 104. This mechanism also works in conjunction with predictive resource allocation module 118 to dynamically adjust network resources based on real-time predictions and outcomes.
[00036] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with real-time prediction engine 112, which predicts future channel states based on the current CSI data processed by machine learning processing unit 104. The real-time prediction engine 112 enables proactive adjustments to network parameters, ensuring that the system adapts before issues like interference or congestion arise. It operates closely with resource management and optimization layer 108 to apply predictive insights for enhanced network performance.

[00037] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with contextual awareness module 114, which incorporates additional data such as user mobility patterns and environmental factors to enhance resource allocation decisions. By factoring in these contextual variables, the contextual awareness module 114 allows the system to make more informed decisions that go beyond pure CSI data. This module works in parallel with real-time prediction engine 112 to ensure that predictions and resource adjustments are contextually relevant.

[00038] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with scalability and modularity system 116, which enables the framework to scale seamlessly across different network environments, including small localized setups and large-scale cellular systems. The scalability and modularity system 116 ensures that the framework can be easily integrated into existing infrastructures without major reconfigurations. It works in conjunction with predictive resource allocation module 118 to efficiently handle varying network loads and user demands.

[00039] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with predictive resource allocation module 118, which uses machine learning insights to anticipate future resource requirements based on user behavior and network conditions. This module proactively allocates resources such as bandwidth and transmission power to minimize latency and optimize performance. The predictive resource allocation module 118 operates in close collaboration with feedback loop mechanism 110 to refine its allocation strategies over time.

[00040] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with security and privacy mechanisms 120, which safeguard sensitive data such as CSI during acquisition and processing. These mechanisms ensure that user data is protected through techniques like differential privacy and data anonymization. The security and privacy mechanisms 120 work alongside data acquisition module 102 and machine learning processing unit 104 to ensure that data handling is both secure and efficient.

[00041] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with dynamic feature extraction 122, which adapts its feature extraction techniques based on real-time channel conditions and data characteristics. This module ensures that the most relevant features are extracted for machine learning processing unit 104, improving the accuracy of the predictive models. Dynamic feature extraction 122 operates in tandem with real-time prediction engine 112 to ensure continuous feature refinement.

[00042] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with automated hyperparameter optimization module 124, which automatically tunes the parameters of the machine learning models to ensure peak performance. Using techniques such as Bayesian optimization or genetic algorithms, this module minimizes the need for manual tuning. Automated hyperparameter optimization module 124 works closely with machine learning processing unit 104 to ensure that models operate efficiently across different network environments.

[00043] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with collaborative learning module 126, which enables distributed learning across multiple base stations. This module allows base stations to share insights and collaboratively improve the accuracy of CSI predictions. Collaborative learning module 126 enhances overall network performance by promoting data sharing and distributed learning among various network components.

[00044] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with user-centric adaptation module 128, which prioritizes resource allocation based on real-time user behavior and service requirements. This module enhances the user experience by ensuring that resources are allocated in line with individual user demands. User-centric adaptation module 128 interacts closely with predictive resource allocation module 118 to ensure that user preferences are factored into network optimization strategies.

[00045] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with multi-layered predictive modeling 130, which employs multiple machine learning algorithms to provide a robust, layered approach to CSI estimation. This module allows the system to combine the strengths of different algorithms, such as neural networks and support vector machines, to improve the accuracy and reliability of predictions. Multi-layered predictive modeling 130 works in collaboration with feature extraction module 106 and machine learning processing unit 104 to enhance predictive capabilities.

[00046] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with multi-access edge computing (MEC) integration 132, which allows localized processing of CSI data, reducing latency and enhancing responsiveness. This module processes data closer to the end-user, improving real-time performance in communication networks. Multi-access edge computing integration 132 operates in tandem with real-time prediction engine 112 to provide localized insights for rapid decision-making.
[00047] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with visual analytics dashboard 134, which offers a user-friendly interface for network operators to monitor real-time CSI data and resource allocation. This dashboard provides insights and visual representations of network performance, enabling informed decision-making. Visual analytics dashboard 134 works closely with resource management and optimization layer 108 to present key performance indicators to the operator in an accessible format.

[00048] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with cross-domain learning capability 136, which allows the system to generalize learning across different communication scenarios, such as urban, rural, indoor, and outdoor environments. This module enhances the versatility and adaptability of the machine learning models, ensuring consistent performance across varied conditions. Cross-domain learning capability 136 works in conjunction with machine learning processing unit 104 to ensure that models can adapt to diverse channel conditions.

[00049] Referring to Fig. 1, AI-driven framework for channel state information processing 100 is provided with channel prediction and interference mitigation module 138, which forecasts future channel states and proactively mitigates interference. This module plays a crucial role in maintaining communication quality by identifying potential interference sources and adjusting network parameters accordingly. Channel prediction and interference mitigation module 138 works closely with resource management and optimization layer 108 and real-time prediction engine 112 to ensure that network performance remains optimal despite dynamic conditions


[00050] Referring to Fig 2, there is illustrated method 200 for AI-driven framework for channel state information processing 100. The method comprises:

At step 202, method 200 includes data acquisition module 102 collecting real-time channel state information (CSI) from various network elements such as user devices and base stations;

At step 204, method 200 includes machine learning processing unit 104 processing the acquired CSI data, applying machine learning algorithms to identify patterns and trends;

At step 206, method 200 includes feature extraction module 106 extracting relevant features from the CSI data, focusing on both time-domain and frequency-domain characteristics;

At step 208, method 200 includes real-time prediction engine 112 predicting future channel conditions based on the current CSI data and the extracted features;

At step 210, method 200 includes resource management and optimization layer 108 adjusting transmission parameters like power, modulation, and bandwidth based on the predicted channel states;

At step 212, method 200 includes feedback loop mechanism 110 capturing the outcomes of resource allocation decisions and feeding them back into the machine learning processing unit 104 to refine the models;

At step 214, method 200 includes predictive resource allocation module 118 proactively reallocating network resources based on user behavior patterns and network conditions to optimize performance;

At step 216, method 200 includes contextual awareness module 114 incorporating user mobility patterns and environmental data to further refine resource allocation decisions;

At step 218, method 200 includes multi-access edge computing (MEC) integration 132 performing localized processing of CSI data to reduce latency and enhance system responsiveness;

At step 220, method 200 includes visual analytics dashboard 134 providing real-time visual insights and performance metrics to network operators for effective decision-making


[00051] 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.

[00052] 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.

[00053] 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 processing 100 comprising of
data acquisition module 102 to collect real-time channel state information (CSI) from various network elements;
machine learning processing unit 104 to process CSI data using advanced machine learning algorithms;
feature extraction module 106 to extract relevant time-domain and frequency-domain features from CSI data;
resource management and optimization layer 108 to adjust transmission parameters based on CSI predictions;
feedback loop mechanism 110 to capture and provide real-time feedback for refining learning models;
real-time prediction engine 112 to predict future channel conditions for proactive resource adjustments;
contextual awareness module 114 to incorporate user mobility and environmental factors into decision-making;
scalability and modularity system 116 to ensure seamless integration and scalability across different network environments;
predictive resource allocation module 118 to reallocate network resources based on user behavior and network conditions;
security and privacy mechanisms 120 to safeguard sensitive CSI data during acquisition and processing;
dynamic feature extraction 122 to adapt feature extraction methods based on real-time channel conditions;
automated hyperparameter optimization module 124 to automatically tune machine learning model parameters for peak performance;
collaborative learning module 126 to enable distributed learning and data sharing across multiple base stations;
user-centric adaptation module 128 to prioritize resource allocation based on user needs and service requirements;
multi-layered predictive modeling 130 to improve CSI estimation accuracy using a layered machine learning approach;
multi-access edge computing (MEC) integration 132 to perform localized CSI processing and reduce latency;
visual analytics dashboard 134 to provide real-time insights and performance metrics for network operators;
cross-domain learning capability 136 to generalize learning across different communication environments; and
channel prediction and interference mitigation module 138 to forecast channel states and mitigate interference

2. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein data acquisition module 102 is configured to collect real-time channel state information (CSI) from various network elements including user devices and base stations, enabling continuous CSI data input for real-time processing.

3. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein machine learning processing unit 104 is configured to analyze the CSI data using machine learning algorithms, including neural networks and reinforcement learning, for accurate channel state estimation and pattern recognition.

4. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein feature extraction module 106 is configured to extract relevant time-domain and frequency-domain features from the CSI data, ensuring that the most critical data is processed for model training and prediction.

5. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein resource management and optimization layer 108 is configured to dynamically adjust transmission parameters such as power, modulation, and bandwidth based on real-time CSI predictions, optimizing network performance and minimizing interference.

6. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein real-time prediction engine 112 is configured to predict future channel conditions based on current CSI data and extracted features, enabling proactive adjustments to network parameters.

7. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein feedback loop mechanism 110 is configured to capture the outcomes of resource allocation decisions and feed them back into the machine learning processing unit 104, facilitating continuous learning and improvement of prediction accuracy.

8. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein predictive resource allocation module 118 is configured to anticipate future resource requirements based on user behavior patterns and network conditions, enabling efficient reallocation of network resources.

9. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein multi-access edge computing (MEC) integration 132 is configured to perform localized processing of CSI data, reducing latency and enhancing system responsiveness in real-time communication environments

10. The AI-driven framework for channel state information processing 100 as claimed in claim 1, wherein method comprises of
data acquisition module 102 collecting real-time channel state information (CSI) from various network elements such as user devices and base stations;

machine learning processing unit 104 processing the acquired CSI data, applying machine learning algorithms to identify patterns and trends;

feature extraction module 106 extracting relevant features from the CSI data, focusing on both time-domain and frequency-domain characteristics;

real-time prediction engine 112 predicting future channel conditions based on the current CSI data and the extracted features;

resource management and optimization layer 108 adjusting transmission parameters like power, modulation, and bandwidth based on the predicted channel states;

feedback loop mechanism 110 capturing the outcomes of resource allocation decisions and feeding them back into the machine learning processing unit 104 to refine the models;

predictive resource allocation module 118 proactively reallocating network resources based on user behavior patterns and network conditions to optimize performance;

contextual awareness module 114 incorporating user mobility patterns and environmental data to further refine resource allocation decisions;

multi-access edge computing (MEC) integration 132 performing localized processing of CSI data to reduce latency and enhance system responsiveness; and

visual analytics dashboard 134 providing real-time visual insights and performance metrics to network operators for effective decision-making

Documents