scholarly journals Radio Resource Allocation and Mobility Accessment of 5G Wireless Network Using Adaptive Q-LEACH Routing Protocol

2021 ◽  
Author(s):  
Premi.A ◽  
Rajakumar.S
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Wireless Network ◽  
Routing Protocol ◽  
Research Area ◽  
Maximum Speed ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Network Resources ◽  
Mobility Assessment

The implementation and operation of a Fifth Generation (5G) network aims to achieve a maximum speed, low potential, improved flexibility, and a change in requirements and technologies from service-oriented to user-oriented. The users need resource allocation and management that is effective. Established networks’ closed infrastructure and ossified services result in particularly in wireless networks, inefficient resource allocation and underutilized network resources. On the basis the standard of a service provider’s utility benefit then customer gratification, various allocation strategies are suggested. Wireless system based 5G another research area aimed at supply distribution and 5G access links is network. In this project, radio resource allocation and mobility assessment of 5G wireless network LEACH routing protocol is implemented. In terms of wireless networks, various architectural integrations of other wireless technologies such as 5G, LTE, Wi-MAX, and so on are highlighted. Furthermore, the project focuses on resource allocation approaches and strategies for cellular networks, as well as comprehensive criteria for future 5G networks

scholarly journals Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

2021 ◽  
Author(s):  
Abdulmalik Alwarafy ◽  
Mohamed Abdallah ◽  
Bekir Sait Ciftler ◽  
Ala Al-Fuqaha ◽  
Mounir Hamdi
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Reinforcement Learning ◽  
Large Scale ◽  
Heterogeneous Wireless Networks ◽  
Future Research ◽  
Radio Resource Allocation ◽  
Next Generation ◽  
Radio Resource ◽  
Next Generation Wireless Networks

<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

scholarly journals Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

2021 ◽  
Author(s):  
Abdulmalik Alwarafy ◽  
Mohamed Abdallah ◽  
Bekir Sait Ciftler ◽  
Ala Al-Fuqaha ◽  
Mounir Hamdi
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Reinforcement Learning ◽  
Large Scale ◽  
Heterogeneous Wireless Networks ◽  
Future Research ◽  
Radio Resource Allocation ◽  
Next Generation ◽  
Radio Resource ◽  
Next Generation Wireless Networks

<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

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