Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Wireless Systems

2021 ◽  
pp. 648-660
Author(s):  
Shu Sun ◽  
Xiaofeng Li
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Wireless Systems ◽  
Next Generation

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>

scholarly journals Duplexing, resource allocation and inter-cell coordination: design recommendations for next generation wireless systems

2005 ◽  
Vol 5 (1) ◽  
pp. 77-93 ◽  
Author(s):  
A. Alexiou ◽  
D. Avidor ◽  
P. Bosch ◽  
S. Das ◽  
P. Gupta ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Wireless Systems ◽  
Next Generation ◽  
Design Recommendations ◽  
Coordination Design

ReCARL: Resource Allocation in Cloud RANs with Deep Reinforcement Learning

2020 ◽  
pp. 1-1
Author(s):  
Zhiyuan Xu ◽  
Jian Tang ◽  
Chengxiang Yin ◽  
Yanzhi Wang ◽  
Guoliang Xue ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning
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