An adaptive system for interference reduction based on artificial intelligence

Abstract

An artificial intelligence-based adaptive interference reduction system (100), comprising: a signal acquisition unit (102) configured to acquire and digitize raw signals from an environment via one or more sensors; an interference detection module (104) comprising a processor configured to implement a Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model for identifying and categorizing interference patterns based on frequency, amplitude and spectral properties; an adaptive filter control module (106) configured to implement reinforcement learning-based adaptive filtering by dynamically adjusting the filter parameters based on detected interference conditions; a machine learning module (108) configured to implement a hierarchical deep neural network (DNN) model trained on historical interference patterns to predict and prevent interference in real time; and a feedback control module (110) configured to monitor and evaluate the interference reduction performance and to provide iterative adjustment signals to the adaptive filter control module (106).

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to an adaptive interference mitigation system based on artificial intelligence, and in particular to a system that autonomously detects and suppresses interference in multiple technical domains. BACKGROUND OF THE INVENTION

[0002] Interference such as ambient noise, electromagnetic sources, and other signals impairs the performance of various technical systems and therefore requires a robust interference mitigation solution. Existing interference suppression techniques predominantly use static filtering algorithms and predefined threshold parameters, which have inherent limitations in addressing dynamic interference scenarios and complex interference from multiple sources. Existing techniques lack the flexibility, adaptability, and effectiveness to mitigate system interference.

[0003] The integration of artificial intelligence and machine learning algorithms represents a paradigm shift in interference mitigation technology. Deep learning architectures, particularly convolutional neural networks (CNNs) and reinforcement learning models, offer promising solutions for real-time adaptation to dynamic interference environments. These advanced computational models demonstrate superior performance in pattern recognition and adaptive parameter optimization compared to traditional static filtering methods. The implementation of hierarchical deep neural networks (DNNs) enables systems to not only respond to current interference conditions but also predict and preemptively optimize filter parameters based on learned patterns, representing a significant advance in interference suppression technology.

[0004] In light of the preceding discussion, it is clear that the implementation of artificial intelligence offers a novel approach to interference mitigation. Therefore, the present invention provides an artificial intelligence-based adaptive interference mitigation system that autonomously detects and suppresses interference in multiple technical domains. SUMMARY OF THE INVENTION

[0005] This disclosure relates to an adaptive interference mitigation system based on artificial intelligence that autonomously detects and suppresses interference in multiple technical domains. The proposed system utilizes machine learning techniques to classify and identify interference for mitigation. The system includes components that utilize deep neural networks and reinforcement learning to continuously monitor interference sources and dynamically adjust system parameters to optimize signal clarity. The proposed system demonstrated improved resilience to interference from multiple sources, resulting in improved signal processing and communication quality by mitigating the effects of noise, interference, and other external factors or technical systems.

[0006] The present disclosure aims to provide an adaptive interference mitigation system based on artificial intelligence. The system includes: a signal acquisition unit configured to acquire and digitize raw signals from an environment via one or more sensors; an interference detection module including a processor configured to implement a convolutional neural network (CNN) and support vector machine (SVM) model for identifying and categorizing interference patterns based on frequency, amplitude, and spectral properties; an adaptive filter control module configured to implement reinforcement learning-based adaptive filtering by dynamically adjusting the filter parameters based on detected interference conditions;a machine learning module configured to implement a hierarchical deep neural network (DNN) model trained on historical interference patterns to predict and prevent interference in real time; and a feedback control module configured to monitor and evaluate interference mitigation performance and provide iterative adjustment signals to the adaptive filter control module.

[0007] An object of the present invention is to provide an adaptive interference mitigation system based on artificial intelligence.

[0008] Another aim of the present invention is to autonomously detect and suppress disturbances in several technical areas.

[0009] Another object of the present invention is to provide a system that utilizes machine learning techniques, including deep neural networks and reinforcement learning, to continuously monitor interference sources and dynamically adjust system parameters to optimize signal clarity.

[0010] Another objective of the present disclosure is to provide a system that can eliminate disturbances in real time and adapt through self-optimization in real time.

[0011] To further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof, which are illustrated in the accompanying drawings. It should be noted that these drawings represent only typical embodiments of the invention and are therefore not to be considered limiting its scope. The invention will be described and explained in additional detail and in greater detail with reference to the accompanying drawings. SHORT DESCRIPTION OF THE FIGURE

[0012] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Where: Fig. 1 shows a block diagram of an artificial intelligence-based adaptive interference mitigation system according to an embodiment of the present disclosure.

[0013] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. DETAILED DESCRIPTION

[0014] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation on the scope of the invention is intended thereby, since such changes and further modifications of the illustrated system, and such further applications of the principles of the invention as illustrated therein, are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0015] It will be understood by one skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0016] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.

[0017] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0019] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0020] The functional units described in this specification have been referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may contain executable code and may, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct.However, the executable file of an identified device need not be physically located together, but may comprise different instructions stored in different locations which, when logically linked together, constitute the device and fulfill the stated purpose of the device.

[0021] Indeed, executable code of a device or module may be a single instruction or multiple instructions, and may even be distributed across several different code segments, among different applications, and across multiple storage devices. Similarly, operational data may be identified and represented within the device and embodied in any suitable form and organized in any suitable type of data structure. The operational data may be captured as a single set of data or distributed across different locations, including different storage devices, and may exist, at least in part, as electronic signals in a system or network.

[0022] References in this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the appearances of the phrases "a selected embodiment," "in an embodiment," or "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0023] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the disclosed subject matter. However, one of ordinary skill in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details, or with different methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosed subject matter.

[0024] According to the example embodiments, the disclosed computer programs or modules may be executed in many example ways, for example, as an application stored in the memory of a device or as a hosted application running on a server and communicating with the device application or browser using a variety of standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in example programming languages ​​that execute from memory on the device or from a hosted server, for example, BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0025] Some of the disclosed embodiments comprise or otherwise involve data transmission over a network, such as communicating various inputs or files over the network. The network may comprise, for example, one or more of the following: the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and / or any other delivery or tunneling mechanism for transmitting data. The network may comprise multiple networks or subnetworks, each of which may comprise, for example, a wired or wireless data path. The network may comprise a circuit-switched voice network, a packet-switched data network, or any other network capable of transmitting electronic communications.For example, the network may include networks based on Internet Protocol (IP) or Asynchronous Transfer Mode (ATM), and may support voice using, for example, VoIP, Voice over ATM, or other comparable protocols used for voice data communications. In one implementation, the network includes a cellular network configured for the exchange of text or SMS messages.

[0026] Examples of the network include, but are not limited to, a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), and so on.

[0027] Fig. 1 shows a block diagram of an artificial intelligence-based adaptive interference mitigation system (100) according to an embodiment of the present disclosure.

[0028] With reference to Fig.the system (100) comprises: a signal acquisition unit (102) configured to acquire and digitize raw signals from an environment via one or more sensors; an interference detection module (104) comprising a processor configured to implement a convolutional neural network (CNN) and support vector machine (SVM) model for identifying and categorizing interference patterns based on frequency, amplitude, and spectral properties; an adaptive filter control module (106) configured to implement reinforcement learning-based adaptive filtering by dynamically adjusting the filter parameters based on detected interference conditions; a machine learning module (108) configured to implement a hierarchical deep neural network (DNN) model trained on historical interference patterns to predict and prevent interference in real time;and a feedback control module (110) configured to monitor and evaluate interference mitigation performance and provide iterative adjustment signals to the adaptive filter control module (106);

[0029] In one embodiment, the signal acquisition unit (102) comprises: a sensor array (102a) comprising at least one of the following elements: antennas, microphones, and optical sensors; and an analog-to-digital converter (102b) configured to digitize the acquired raw signals for processing.

[0030] In one embodiment, the interference detection module (104) further comprises: a feature extraction sub-module (104a) configured to extract primary features of interference patterns from the digitized signals; and a classification sub-module (104b) configured to categorize detected interference based on the extracted features using the CNN and SVM models.

[0031] In one embodiment, the adaptive filter control module (106) comprises: a parameter optimization sub-module (106a) configured to determine optimal filter configurations based on reinforcement learning algorithms; a real-time adaptation sub-module (106b) configured to implement the determined filter configurations; and a performance monitoring sub-module (106c) configured to evaluate filter effectiveness.

[0032] In one embodiment, the machine learning module (108) comprises: a training sub-module (108a) configured to train the hierarchical DNN model using historical interference pattern data sets; a prediction sub-module (108b) configured to generate real-time interference predictions; and a filter configuration sub-module (108c) configured to propose pre-configured filter responses based on predicted interference patterns.

[0033] In one embodiment, the feedback control module (110) comprises: a performance evaluation sub-module (110a) configured to evaluate the effectiveness of interference mitigation; an optimization sub-module (110b) configured to generate recommendations for parameter adjustment; and a feedback loop controller (110c) configured to provide adjustment signals to the adaptive filter control module (106).

[0034] In one embodiment, the machine learning module (108) further comprises: a self-learning component configured to update the hierarchical DNN model based on newly encountered interference patterns during system operation; and an adaptation component configured to change model parameters based on changing environmental conditions.

[0035] In one embodiment, the adaptive filter control module (106) implements AI-driven self-tuning routines comprising: a dynamic parameter adjustment mechanism configured to change filter parameters based on historical interference data; a real-time performance optimization mechanism configured to adapt to current interference conditions; and a predictive optimization mechanism configured to preemptively adjust filter parameters based on predicted interference patterns.

[0036] The present invention provides a system that uses artificial intelligence and machine learning techniques, including deep neural networks and reinforcement learning, to continuously monitor interference sources and dynamically adjust system parameters to optimize signal clarity. The system mainly includes a signal monitoring module configured to continuously monitor incoming signals for interference; an adaptive filtering module configured to use machine learning techniques and reinforcement learning to adapt filter parameters to identified interference characteristics; a feedback loop including a real-time feedback mechanism for fine-tuning the response, thereby improving the accuracy of interference suppression; and a machine learning module using a deep neural network to detect interference patterns and suggest optimal filtering techniques.

[0037] The artificial intelligence model used by the proposed system is pre-trained on datasets containing various interference scenarios. This data is continuously updated through on-device learning to ensure adaptability to real-world environments. The AI-driven adaptive interference mitigation system is particularly effective in high-noise applications such as telecommunications, radar systems, and sensor networks.

[0038] The invention relates to an adaptive interference mitigation system that integrates advanced components to address real-time signal processing challenges. The system architecture comprises several modules that work together to effectively detect, classify, and suppress interference. The signal acquisition unit serves as a starting point and captures raw signals from the environment using suitable sensors such as antennas, microphones, or optical sensors, depending on the specific application. These signals are then digitized for further processing. The interference detection module, which utilizes convolutional neural networks (CNN) and support vector machines (SVM), identifies patterns indicative of interference and categorizes them based on key spectral properties such as frequency and amplitude.The Adaptive Filter Control module uses reinforcement learning to dynamically adjust filter parameters in response to interference conditions, enabling real-time suppression. A machine learning model structured as a hierarchical deep neural network (DNN) is trained using historical interference patterns to predict and prevent interference by suggesting preconfigured filter responses.

[0039] First, the system performs signal acquisition and analysis, capturing signals and analyzing noise sources in real time. Feature extraction identifies the core characteristics of the noise. This analysis informs the system's dynamic adaptation component, which configures adaptive filter parameters based on real-time data. Reinforcement learning ensures that the system continues to learn and optimizes settings for different noise and interference types. The system is also equipped with a feedback mechanism that continuously monitors the effectiveness of the noise mitigation. This iterative process allows the system to fine-tune its parameters for improved performance.

[0040] The invention also incorporates sophisticated learning and adaptation mechanisms. The machine learning models within the system are trained using comprehensive datasets covering various interference scenarios. During operation, the system adapts to new interference patterns through self-training, ensuring its effectiveness in dynamic environments. The filter algorithms feature AI-driven self-optimization, which adaptively reconfigures the filter parameters based on historical and real-time data, allowing the system to efficiently handle interference in a range of scenarios.

[0041] The present invention is relevant for various applications, such as telecommunications applications, where the system mitigates interference from overlapping network signals and enables clearer communication channels. In sensor networks, particularly in industrial environments with high levels of electromagnetic interference, the system improves signal reliability. For radar and military applications, the system reduces radar interference, thus improving target detection accuracy and performance in defense missions.

[0042] This invention offers significant advantages over conventional methods. Its adaptive real-time interference mitigation outperforms static filtering techniques and can effectively handle complex and dynamic interference environments. Continuous learning and adaptation ensure sustained effectiveness, even under changing conditions. Potential applications are diverse and include telecommunications, sensor networks, industrial automation, radar systems, defense, and any area requiring robust signal processing under high-interference conditions.

[0043] The system's intelligent design, based on AI algorithms, deep learning, and adaptive response mechanisms, represents a significant advance in interference management. Its ability to operate precisely and responsively in demanding environments makes it a transformative solution for numerous engineering applications.

[0044] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0045] Advantages, other benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage or solution to occur or become more apparent are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 An artificial intelligence-based adaptive system for interference mitigation. 102 Signal acquisition unit 102a Sensor arrangement 102b analog-to-digital converter 104 Interference detection module 104a Submodule for feature extraction 104b Submodule Classification 106 Adaptive Filter Control Module 106a Submodule Parameter Optimization 106b Submodule for real-time adjustment 106c Submodule Performance Monitoring 108 Machine Learning Module 108a Training sub-module 108b Prediction submodule 108c Filter configuration submodule 110 Feedback control module 110a Submodule Performance Assessment 110b Optimization submodule 110c feedback loop controller

Claims

[1] An artificial intelligence-based adaptive interference mitigation system (100) comprising: a signal acquisition unit (102) configured to acquire and digitize raw signals from an environment via one or more sensors; an interference detection module (104) comprising a processor configured to implement a convolutional neural network (CNN) and support vector machine (SVM) model for identifying and categorizing interference patterns based on frequency, amplitude, and spectral properties; an adaptive filter control module (106) configured to implement reinforcement learning-based adaptive filtering by dynamically adjusting the filter parameters based on detected interference conditions; a machine learning module (108) configured to implement a hierarchical deep neural network (DNN) model trained on historical interference patterns to predict and prevent interference in real time; and a feedback control module (110) configured to monitor and evaluate interference mitigation performance and provide iterative adjustment signals to the adaptive filter control module (106). [2] System (100) according to claim 1, wherein the signal acquisition unit (102) comprises: a sensor array (102a) comprising at least one of the following elements: antennas, microphones, and optical sensors; and an analog-to-digital converter (102b) configured to digitize the acquired raw signals for processing. [3] The system (100) of claim 1, wherein the interference detection module (104) further comprises: a feature extraction sub-module (104a) configured to extract primary features of interference patterns from the digitized signals; and a classification sub-module (104b) configured to categorize detected disturbances based on the extracted features using the CNN and SVM models. [4] The system (100) of claim 1, wherein the adaptive filter control module (106) comprises: a parameter optimization sub-module (106a) configured to determine optimal filter configurations based on reinforcement learning algorithms; a real-time adaptation submodule (106b) configured to implement the determined filter configurations; and a performance monitoring sub-module (106c) configured to evaluate filter effectiveness. [5] The system (100) of claim 1, wherein the machine learning module (108) comprises: a training sub-module (108a) configured to train the hierarchical DNN model using historical interference pattern data sets; a prediction submodule (108b) configured to generate real-time interference predictions; and a filter configuration sub-module (108c) configured to suggest pre-configured filter responses based on predicted interference patterns. [6] The system (100) of claim 1, wherein the feedback control module (110) comprises: a performance evaluation sub-module (110a) configured to evaluate the effectiveness of the interference mitigation; an optimization sub-module (110b) configured to generate recommendations for parameter adjustment; and a feedback loop controller (110c) configured to provide adjustment signals to the adaptive filter control module (106). [7] The system (100) of claim 1, wherein the machine learning module (108) further comprises: a self-learning component configured to update the hierarchical DNN model based on newly encountered interference patterns during system operation; and an adaptation component configured to change model parameters based on changing environmental conditions. [8] The system (100) of claim 1, wherein the adaptive filter control module (106) implements AI-driven self-optimization routines comprising: a dynamic parameter adjustment mechanism configured to change the filter parameters based on historical interference data; Real-time performance optimization mechanism configured to adapt to current disturbance conditions; and Predictive optimization mechanism configured to preemptively adjust filter parameters based on predicted interference patterns.

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