SYSTEM AND METHOD FOR CONFIGURATION AND OPTIMIZATION OF WIRELESS MULTI-CARRIER NETWORK

Abstract

ABSTRACT SYSTEM AND METHOD FOR CONFIGURATION AND OPTIMIZATION OF WIRELESS MULTI-CARRIER NETWORK The present disclosure introduces a system and method for configuration and optimization of wireless multi-carrier networks 100. It comprises of dynamic carrier configuration framework 102, which allocates carrier frequencies based on real-time conditions, and advanced resource allocation algorithms 104, which predict traffic demand and optimize sub-carrier assignments. Interference mitigation unit 106 manages interference sources, while adaptive modulation and coding (AMC) module 108 adjusts modulation schemes to maximize throughput. Energy-aware transmission control 110 optimizes power usage, and QOS metrics system 112 prioritizes resources based on user needs. The integrated performance monitoring system 114 tracks network metrics, feeding data into the feedback loop mechanism 120 for adaptive optimization. Additional components include context-aware adaptive algorithms 118, user behaviour prediction engine 126, cross-layer optimization unit 116, multi-dimensional resource allocation module 128, collaborative network learning framework 134, and multi-access edge computing 132. Reference Fig 1

Specification

Description:SYSTEM AND METHOD FOR CONFIGURATION AND OPTIMIZATION OF WIRELESS MULTI-CARRIER NETWORK
TECHNICAL FIELD
[0001] The present innovation relates to system and method for the configuration and optimization of wireless multi-carrier communication systems to enhance data transmission, bandwidth efficiency, and overall system performance.

BACKGROUND

[0002] The rapid advancement in wireless communication technologies has led to a surge in demand for high-speed, reliable data transmission across a wide range of applications. Traditional single-carrier systems face limitations in bandwidth efficiency, spectral utilization, and data throughput, particularly in environments with high user density and dynamic channel conditions. To address these issues, multi-carrier systems, such as Orthogonal Frequency Division Multiplexing (OFDM) and Multi-Carrier Code Division Multiple Access (MC-CDMA), have been developed, allowing data to be transmitted across multiple frequency carriers simultaneously. However, current multi-carrier systems often rely on static configurations or limited adaptive methods, which do not fully exploit available spectrum resources or address the complexities of dynamic wireless environments.

[0003] Existing solutions struggle with adapting to variable channel conditions, interference from neighbouring devices, and fluctuating user demands. The static nature of many current approaches results in inefficient use of resources and inconsistent user experiences, as they cannot dynamically adjust to optimize carrier allocation or manage interference effectively. Furthermore, with increasing emphasis on sustainability, traditional methods lack energy-efficient optimization, contributing to higher operational costs and environmental impact.
[0004] This invention offers a dynamic, adaptive approach to configure and optimize wireless multi-carrier systems in real-time. By incorporating advanced algorithms for resource allocation, interference management, adaptive modulation and coding, and energy efficiency, the invention overcomes the drawbacks of existing systems. It can adjust carrier configurations based on real-time network conditions, enabling optimal performance even in complex, high-demand scenarios. Novel features include a machine-learning-based predictive engine, real-time optimization algorithms, and energy-aware transmission controls, which ensure efficient use of resources while minimizing power consumption. These features differentiate the invention from existing options, delivering higher data rates, reduced latency, and sustainable operation, making it a robust, scalable solution for modern wireless communication networks

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to provide a dynamic configuration and optimization system for wireless multi-carrier networks, enhancing data transmission rates and bandwidth efficiency.

[0006] Another object of the invention is to enable real-time resource allocation in wireless networks, ensuring optimal carrier allocation based on varying user demands and network conditions.

[0007] Another object of the invention is to incorporate advanced interference management techniques that minimize the impact of neighbouring devices and other communication systems, improving data reliability and signal quality.

[0008] Another object of the invention is to support adaptive modulation and coding, allowing the system to adjust modulation schemes and coding rates in response to real-time channel conditions.
[0009] Another object of the invention is to introduce energy-efficient algorithms that minimize power consumption during data transmission, contributing to sustainable operation and reduced environmental impact.

[00010] Another object of the invention is to utilize predictive algorithms that analyze historical data and traffic patterns to forecast network demands and proactively allocate resources.

[00011] Another object of the invention is to support diverse applications, including IoT networks, telecommunications, and smart cities, by providing a scalable and robust system for managing multi-carrier wireless communication.

[00012] Another object of the invention is to improve user experience by reducing latency and increasing data throughput, particularly in high-density urban environments and areas with fluctuating network demands.

[00013] Another object of the invention is to facilitate seamless integration with existing wireless communication standards ensuring compatibility and ease of deployment.

[00014] Another object of the invention is to enhance the Quality of Service (QoS) and Quality of Experience (QoE) for users, offering reliable, high-performance wireless communication tailored to individual service requirements.

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, system and method for configuration and optimisation of wireless multi carrier is presented. It provides a system for configuring and optimizing wireless multi-carrier networks to enhance data transmission, bandwidth efficiency, and system capacity. It dynamically allocates resources, manages interference, and adapts modulation based on real-time network conditions. Using advanced algorithms, the invention ensures optimal performance while reducing power consumption for sustainable operation. It supports diverse applications, including IoT and telecommunications, offering scalability and seamless integration with existing standards. This approach enhances user experience by improving data rates, latency, and reliability across dynamic wireless environments.

[00016] 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.

[00017] 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
[00018] 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.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for system and method for configuration and optimisation of wireless multi carrier network.

[00021] FIG 2 is working methodology of system and method for configuration and optimisation of wireless multi carrier network.


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 system and method for configuration and optimisation of wireless multi carrier network 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, system and method for configuration and optimisation of wireless multi carrier network 100 is disclosed, in accordance with one embodiment of the present invention. It comprises dynamic carrier configuration framework 102, advanced resource allocation algorithms 104, interference mitigation unit 106, adaptive modulation and coding (AMC) module 108, energy-aware transmission control 110, user-centric quality of service (QOS) metrics system 112, integrated performance monitoring system 114, cross-layer optimization unit 116, context-aware adaptive algorithms 118, feedback loop mechanism 120, real-time user prioritization system 122, hybrid resource management model 124, user behavior prediction engine 126, multi-dimensional resource allocation module 128, integrated security features 130, support for multi-access edge computing (MEC) 132, collaborative network learning framework 134, dynamic bandwidth aggregation system 136, virtualized network functions (VNFS) 138.

[00029] Referring to Fig. 1, the present disclosure provides details of a system and method for configuration and optimization of wireless multi-carrier networks 100. This invention enhances wireless communication by dynamically allocating resources, managing interference, and optimizing modulation schemes in real-time. Key components include dynamic carrier configuration framework 102, advanced resource allocation algorithms 104, and interference mitigation unit 106, all working together to maximize data throughput and reliability. The system also includes adaptive modulation and coding module 108 and energy-aware transmission control 110, which optimize data rates and reduce power consumption. Additional components, such as integrated performance monitoring system 114 and user-centric quality of service metrics system 112, ensure high service quality tailored to specific user needs. By leveraging these components, the invention addresses dynamic network conditions, enhances scalability, and supports energy-efficient operation in wireless multi-carrier systems.

[00030] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with dynamic carrier configuration framework 102, which enables real-time reconfiguration of carrier frequencies based on channel conditions and user demands. This framework assesses environmental factors such as interference and user density to dynamically allocate carriers, optimizing resource utilization. The dynamic carrier configuration framework 102 works closely with advanced resource allocation algorithms 104 to ensure sub-carriers are assigned in a manner that balances network load and enhances data throughput.
[00031] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with advanced resource allocation algorithms 104, which predict traffic patterns and optimize resource distribution. These algorithms utilize machine learning to analyze user behaviour and network demand, proactively adjusting sub-carrier and power levels. In collaboration with dynamic carrier configuration framework 102, these algorithms enhance network responsiveness, enabling efficient utilization of available resources while meeting user demands.

[00032] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with interference mitigation unit 106, which dynamically manages interference from neighbouring devices. This unit applies techniques such as adaptive power control and interference alignment to maintain high signal quality. The interference mitigation unit 106 integrates with adaptive modulation and coding module 108 to ensure that data transmission remains reliable and interference is minimized, thereby improving overall network performance.

[00033] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with adaptive modulation and coding (AMC) module 108, which adjusts modulation schemes and coding rates based on real-time channel quality. This module selects optimal modulation techniques (e.g., QPSK, 16-QAM) to balance data rate and reliability, adapting to fluctuating conditions. Working in tandem with interference mitigation unit 106 and advanced resource allocation algorithms 104, the AMC module 108 maintains high throughput and ensures reliable communication.

[00034] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with energy-aware transmission control 110, which reduces power consumption by optimizing transmission strategies based on network activity. This component schedules transmission to minimize energy usage, especially during low-demand periods. By coordinating with advanced resource allocation algorithms 104 and dynamic carrier configuration framework 102, energy-aware transmission control 110 contributes to sustainable operation while maintaining performance.

[00035] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with user-centric quality of service (QOS) metrics system 112, which customizes network configurations to meet individual user requirements. This system prioritizes specific service needs, such as low latency for gaming or high throughput for streaming, by dynamically adjusting network resources. Working in close connection with advanced resource allocation algorithms 104 and adaptive modulation and coding module 108, the QOS metrics system 112 enhances the user experience by ensuring optimal performance tailored to various applications.

[00036] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with integrated performance monitoring system 114, which continuously tracks network operational metrics and offers real-time feedback for optimization. This monitoring system identifies potential performance issues before they impact users, enabling pre-emptive adjustments. By coordinating with dynamic carrier configuration framework 102 and feedback loop mechanism 120, the integrated performance monitoring system 114 ensures reliable and efficient network operation.

[00037] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with cross-layer optimization unit 116, which coordinates functions across multiple protocol layers, including physical, data link, and network layers. This unit facilitates efficient use of network resources by analyzing interdependencies across layers and adjusting configurations accordingly. In combination with user-centric QOS metrics system 112 and advanced resource allocation algorithms 104, the cross-layer optimization unit 116 contributes to enhanced system performance and resource efficiency.

[00038] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with context-aware adaptive algorithms 118, which dynamically adjust network configurations based on environmental factors such as user location, mobility, and application type. These algorithms enable the system to tailor performance to specific scenarios, enhancing user satisfaction. Working with feedback loop mechanism 120 and dynamic carrier configuration framework 102, the context-aware adaptive algorithms 118 optimize network adaptability and responsiveness.

[00039] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with feedback loop mechanism 120, a closed-loop system that collects performance data from network components and user devices to refine optimization algorithms continually. This mechanism uses real-time insights to adjust configurations dynamically, ensuring the network adapts to changing conditions. In cooperation with integrated performance monitoring system 114 and context-aware adaptive algorithms 118, the feedback loop mechanism 120 enhances network responsiveness and efficiency.

[00040] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with real-time user prioritization system 122, which allocates resources based on user importance or service level. This prioritization ensures that critical users, such as emergency responders or enterprise clients, receive guaranteed service levels during peak demand periods. It interworks with user-centric QOS metrics system 112 and advanced resource allocation algorithms 104 to maintain service quality for high-priority users.

[00041] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with hybrid resource management model 124, a flexible model that switches between centralized and decentralized management strategies based on network topology and traffic load. This adaptability ensures efficient resource use and maintains network stability. In conjunction with dynamic carrier configuration framework 102 and feedback loop mechanism 120, the hybrid resource management model 124 enhances system scalability and resilience.

[00042] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with user behaviour prediction engine 126, which uses historical data and machine learning to anticipate user behaviour and network demands. By forecasting peak times and potential congestion, this engine allows for pre-emptive resource allocation. It integrates with advanced resource allocation algorithms 104 and feedback loop mechanism 120 to proactively optimize configurations for uninterrupted service.

[00043] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with multi-dimensional resource allocation module 128, which prioritizes resource types such as bandwidth, power, and latency according to user needs and network conditions. This module maximizes efficiency by considering diverse resource demands, working with cross-layer optimization unit 116 and adaptive modulation and coding module 108 to ensure optimal allocation across all metrics.

[00044] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with integrated security features 130, which dynamically adjust encryption methods and security protocols based on data sensitivity and threat levels. This component ensures secure data transmission without compromising performance. Working alongside user-centric QOS metrics system 112 and context-aware adaptive algorithms 118, integrated security features 130 protect network integrity while maintaining high-quality communication.

[00045] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with support for multi-access edge computing (MEC) 132, which brings computational resources closer to end-users, reducing latency and improving processing efficiency. This component is essential for applications requiring real-time data analysis. Collaborating with hybrid resource management model 124 and user behaviour prediction engine 126, MEC 132 supports low-latency services and enhances the system's scalability.

[00046] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with collaborative network learning framework 134, which facilitates shared learning among network nodes by exchanging performance data and optimization strategies. This collaborative approach enables the system to learn from collective experiences, improving configuration efficiency. It interworks with feedback loop mechanism 120 and user behaviour prediction engine 126 to enhance adaptability and system intelligence.

[00047] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with dynamic bandwidth aggregation system 136, which combines bandwidth from multiple carriers to create a larger virtual bandwidth pool. This capability enables enhanced data throughput and improved user experience during high-demand periods. By coordinating with dynamic carrier configuration framework 102 and advanced resource allocation algorithms 104, dynamic bandwidth aggregation system 136 ensures optimal data delivery.

[00048] Referring to Fig. 1, the system and method for configuration and optimization of wireless multi-carrier networks 100 is provided with virtualized network functions (VNFS) 138, which enable on-demand deployment and scaling of network functions without dedicated hardware. This flexibility reduces operational costs and enhances network agility. The VNFS 138 component integrates with multi-dimensional resource allocation module 128 and support for multi-access edge computing (MEC) 132 to optimize resource management and facilitate seamless network adaptation.

[00049] Referring to Fig 2, there is illustrated method 200 for system and method for configuration and optimization of wireless multi-carrier networks 100. The method comprises:
At step 202, method 200 includes dynamic carrier configuration framework 102 assessing network conditions to allocate optimal carrier frequencies;
At step 204, method 200 includes advanced resource allocation algorithms 104 forecasting demand and adjusting allocations set by dynamic carrier configuration framework 102;
At step 206, method 200 includes interference mitigation unit 106 detecting interference, signaling adaptive modulation and coding module 108 to adjust modulation rates;
At step 208, method 200 includes adaptive modulation and coding module 108 optimizing modulation schemes based on interference levels from interference mitigation unit 106;
At step 210, method 200 includes energy-aware transmission control 110 adjusting power settings from usage patterns provided by advanced resource allocation algorithms 104;
At step 212, method 200 includes QOS metrics system 112 prioritizing resources to meet application-specific needs and relaying adjustments to dynamic carrier configuration framework 102;
At step 214, method 200 includes integrated performance monitoring system 114 tracking network metrics, informing feedback loop mechanism 120;
At step 216, method 200 includes cross-layer optimization unit 116 aligning protocol layers based on data from integrated performance monitoring system 114;
At step 218, method 200 includes context-aware adaptive algorithms 118 adjusting settings based on user mobility, coordinating with cross-layer optimization unit 116;
At step 220, method 200 includes feedback loop mechanism 120 refining configurations using data from performance monitoring and context-aware adaptive algorithms 118;
At step 222, method 200 includes real-time user prioritization system 122 allocating resources to priority users, coordinating with hybrid resource management model 124;
At step 224, method 200 includes hybrid resource management model 124 adapting centralized or decentralized control based on real-time demand from prioritization system 122;
At step 226, method 200 includes user behaviour prediction engine 126 forecasting demand spikes, guiding hybrid resource management model 124 for pre-emptive adjustments;
At step 228, method 200 includes multi-dimensional resource allocation module 128 distributing resources based on QOS metrics system 112 data;
At step 230, method 200 includes integrated security features 130 adjusting encryption dynamically, informed by QOS metrics system 112;
At step 232, method 200 includes multi-access edge computing 132 deploying local computation to reduce latency, coordinated with hybrid resource management model 124;
At step 234, method 200 includes collaborative network learning framework 134 sharing insights for continuous improvement with feedback loop mechanism 120;
At step 236, method 200 includes dynamic bandwidth aggregation system 136 pooling bandwidth, guided by advanced resource allocation algorithms 104;
At step 238, method 200 includes virtualized network functions 138 scaling functions as needed, integrated with multi-access edge computing 132 and multi-dimensional resource allocation module 128.

[00050] 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.

[00051] 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.

[00052] 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. A system and method for configuration and optimization of wireless multi-carrier networks 100 comprising of
dynamic carrier configuration framework 102 to allocate optimal carrier frequencies based on real-time conditions;
advanced resource allocation algorithms 104 to forecast network demand and adjust carrier allocations proactively;
interference mitigation unit 106 to detect and manage interference from neighbouring devices;
adaptive modulation and coding module 108 to adjust modulation and coding schemes for optimized data transmission;
energy-aware transmission control 110 to minimize power consumption during low-demand periods;
QOS metrics system 112 to prioritize resources according to specific application needs;
integrated performance monitoring system 114 to track and assess network performance metrics continuously;
cross-layer optimization unit 116 to coordinate functions across protocol layers for resource efficiency;
context-aware adaptive algorithms 118 to adjust settings based on user location and mobility;
feedback loop mechanism 120 to refine system configurations based on real-time data;
real-time user prioritization system 122 to allocate resources based on user priority levels;
hybrid resource management model 124 to dynamically switch between centralized and decentralized management;
user behaviour prediction engine 126 to anticipate network demand and optimize resource allocation;
multi-dimensional resource allocation module 128 to distribute resources like bandwidth and power as needed;
integrated security features 130 to adjust encryption and security protocols dynamically;
multi-access edge computing 132 to provide local computational resources, reducing latency for real-time applications;
collaborative network learning framework 134 to share insights across network nodes for improved optimization;
dynamic bandwidth aggregation system 136 to combine bandwidth across carriers for enhanced data throughput;
virtualized network functions 138 to enable on-demand scaling and deployment of network functions flexibly.

2. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the dynamic carrier configuration framework 102 is configured to assess real-time network conditions and allocate carrier frequencies dynamically, optimizing bandwidth efficiency and data transmission rates based on user demands and environmental factors.

3. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the advanced resource allocation algorithms 104 are configured to predict network demand through machine learning, proactively adjusting sub-carrier assignments and power levels, enhancing network responsiveness and resource utilization under varying traffic conditions.

4. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the interference mitigation unit 106 is configured to detect interference sources and dynamically adjust transmission settings through adaptive power control and interference alignment, maintaining high signal quality and reducing transmission errors in congested environments.

5. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the adaptive modulation and coding (AMC) module 108 is configured to adjust modulation schemes and coding rates in response to real-time channel quality, selecting optimal modulation types to maximize data throughput while ensuring reliable communication.

6. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the energy-aware transmission control 110 is configured to optimize power consumption by dynamically adjusting transmission power and scheduling transmissions during low-demand periods, achieving energy-efficient operation without compromising network performance.

7. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the QOS metrics system 112 is configured to prioritize resource allocation based on user-specific service requirements, such as low latency or high bandwidth, ensuring quality of service and enhanced user experience across diverse applications.

8. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the feedback loop mechanism 120 is configured to collect real-time network performance data, refining system configurations and optimization algorithms continuously, enabling adaptive responses to changing network conditions.

9. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein the user behaviour prediction engine 126 is configured to forecast demand spikes by analyzing historical user data and behaviour patterns, enabling pre-emptive resource adjustments to mitigate potential congestion and enhance network reliability.

10. The system and method for configuration and optimization of wireless multi-carrier networks 100 as claimed in claim 1, wherein method comprises of
dynamic carrier configuration framework 102 assessing network conditions to allocate optimal carrier frequencies;
advanced resource allocation algorithms 104 forecasting demand and adjusting allocations set by dynamic carrier configuration framework 102;
interference mitigation unit 106 detecting interference, signaling adaptive modulation and coding module 108 to adjust modulation rates;
adaptive modulation and coding module 108 optimizing modulation schemes based on interference levels from interference mitigation unit 106;
energy-aware transmission control 110 adjusting power settings from usage patterns provided by advanced resource allocation algorithms 104;
QOS metrics system 112 prioritizing resources to meet application-specific needs and relaying adjustments to dynamic carrier configuration framework 102;
integrated performance monitoring system 114 tracking network metrics, informing feedback loop mechanism 120;
cross-layer optimization unit 116 aligning protocol layers based on data from integrated performance monitoring system 114;
context-aware adaptive algorithms 118 adjusting settings based on user mobility, coordinating with cross-layer optimization unit 116;
feedback loop mechanism 120 refining configurations using data from performance monitoring and context-aware adaptive algorithms 118;
real-time user prioritization system 122 allocating resources to priority users, coordinating with hybrid resource management model 124;
hybrid resource management model 124 adapting centralized or decentralized control based on real-time demand from prioritization system 122;
user behaviour prediction engine 126 forecasting demand spikes, guiding hybrid resource management model 124 for pre-emptive adjustments;
multi-dimensional resource allocation module 128 distributing resources based on qos metrics system 112 data;
integrated security features 130 adjusting encryption dynamically, informed by QOS metrics system 112;
multi-access edge computing 132 deploying local computation to reduce latency, coordinated with hybrid resource management model 124;
collaborative network learning framework 134 sharing insights for continuous improvement with feedback loop mechanism 120;
dynamic bandwidth aggregation system 136 pooling bandwidth, guided by advanced resource allocation algorithms 104;
virtualized network functions 138 scaling functions as needed, integrated with multi-access edge computing 132 and multi-dimensional resource allocation module 128.

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