[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125025-en":3,"doc-seo-125025-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125025,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Next Generation Wireless Networks - Optimisation and Resource Allocation Using Machine Learning","The emergence of 5G and the upcoming 6G wireless networks motivates new approaches to achieve very high data rates, massive device connectivity, and ultra-low latency. Efficient resource utilization and capability enhancement remain difficult under dynamic traffic, mobility, interference, and energy constraints. This research examines machine learning methods for smart resource allocation, dynamic spectrum management, interference cancellation, and energy management, including deep, reinforcement, and federated learning.","Next Generation Wireless Networks: Optimisation and Resource Allocation Using Machine Learning  \nNabil Y. M. Salih  \nHigher Institute of Science and Technology, Al-Garabulli  \nAbstract  \nThe emergence of 5G and the upcoming 6G wireless networks set the foundation for a new paradigm of wireless communications to offer very high data rate, huge number of devices and very low latency. But to fine-tune resource utilization as well as augment the capability of the network within such settings remains a difficult feat on its own. This research explores how it is possible to solve these issues through the use of new generation of machine learning (ML) techniques; dynamic spectrum management, interference cancellation, and energy management. In our research we investigate different types of the ML approaches, such as deep, reinforcement, and federated learning to design smart resource allocation frameworks. These frameworks are envisaged to be self-configurable to adapt to the variations in the network, demanded traffic and interference thereby achieving higher spectrally efficiency and low energy utilization. Real-time data analysis is an important part of our study; the research also uses predictive mathematical techniques to determine the ideal distribution of resources based on the forecast of network traffic. We also study the real-time application of ML-based interference management measures including but not limited to, beam forming and dynamic power control to improve signal quality and minimize cross-system interference. They are tested and evaluated using detailed simulations and actual test beds where the enhancements in the throughput, delay and reliability of the networks are actualized. By focusing on the enhanced applicability of machine learning in defining the new characteristics of nextgeneration wireless networks, this research underlines the capacity of novel technologies in making the future communication systems more efficient, reliable, and environmentally friendly. These findings are well useful for the network operators, policymakers, and researchers, and bring out the significance of AI in the development of the next generation of wireless communication.  \n1.0: Introduction  \n1.2 Background of the Study  \nThe dramatic evolution of wireless technologies has resulted in the demand for lower latency, relatively higher data rates, and heightened connectivity. This has influenced the development of next-generation wireless networks, such as 5G and the anticipated 6G (Sun et al., 2019) . Tentatively, the networks are playing a fundamental role in supporting a myriad of applications sparking from ultra-reliable low-latency communication (URLLC) through massive machinetype communication (mMTC), calling for the need for complex resource management and optimisation approaches (Lien et al., 2017; Zhao et el., 2019) . However, the inherent complexity coupled with the intrinsic mobility of such networks poses numerous issues in the ability to control for interference, or resource management and power control (Jiang et al., 2016) .  \nTherefore, Machine learning (ML) has emerged to be one of the most effective tools suitable for handling such challenges. In the opinion of Li and Pan (2006) and Alwarafy et al. (2021), it is possible that the utilisation of ML approaches can contribute majorly to the establishment of intelligent systems having the feature capability of learning from data and the ability to work optimally in conditions of variability in network besides, the optimisation of performance in real-time. The study aims to investigate the applicability of applying ML in the enhancement of resource control and interference in next-generation wireless networks with an emphasis on the 5G and 6G networks.  \n1.2 Problem Statement  \nResource allocation is basic performance optimisation of the next-generation wireless network is an imperative requirement in the area of concern due to the constantly growing complex network e","cbCaios88djC13vZ","https://ap.wps.com/l/cbCaios88djC13vZ","pdf",381552,1,11,"English","en",105,"# Abstract\n# Introduction\n## Background of the Study\n## Problem Statement\n## Objectives\n# Literature Review\n## Interference Mitigation","[{\"question\":\"Why are 5G and 6G wireless networks challenging for resource allocation?\",\"answer\":\"They require very low latency, higher data rates, and support for many devices, while network mobility, interference, and rapidly changing traffic and channel conditions make optimization difficult in real time.\"},{\"question\":\"Which machine learning approaches are investigated for resource allocation?\",\"answer\":\"The study investigates deep learning, reinforcement learning, and federated learning to build adaptive resource allocation frameworks.\"},{\"question\":\"How does the research evaluate the proposed ML-based techniques?\",\"answer\":\"It uses detailed simulations and actual test beds, focusing on improvements in throughput, delay, and reliability while applying real-time interference management actions such as beamforming and dynamic power control.\"}]","Next Generation Wireless Networks - 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