Radio resource management for data transmission in low power wide area networks integrated with large scale cyber physical systems

2017 ◽  
Vol 20 (2) ◽  
pp. 1831-1842 ◽  
Author(s):  
Dae-Young Kim ◽  
Seokhoon Kim ◽  
Houcine Hassan ◽  
Jong Hyuk Park
Keyword(s):  
Resource Management ◽  
Low Power ◽  
Data Transmission ◽  
Large Scale ◽  
Radio Resource Management ◽  
Cyber Physical Systems ◽  
Wide Area ◽  
Wide Area Networks ◽  
Radio Resource ◽  
Physical Systems

Radio Resource Management for QoS Guarantees in Cyber-Physical Systems

2012 ◽  
Vol 23 (9) ◽  
pp. 1752-1761 ◽  
Author(s):  
Shao-Yu Lien ◽  
Shin-Ming Cheng ◽  
Sung-Yin Shih ◽  
Kwang-Cheng Chen
Keyword(s):  
Resource Management ◽  
Radio Resource Management ◽  
Cyber Physical Systems ◽  
Radio Resource ◽  
Physical Systems ◽  
Qos Guarantees

Distributed Resources Management in Wireless LANs

2011 ◽  
pp. 293-304
Author(s):  
Ghassan Kbar ◽  
Wathiq Mansoor
Keyword(s):  
Resource Management ◽  
Large Scale ◽  
Radio Resource Management ◽  
Management Technique ◽  
Radio Resource ◽  
Distributed Resources ◽  
Dynamic Channel Assignment ◽  
Resources Management ◽  
New Radio ◽  
Dynamic Channel

This paper introduces a new radio resource management technique based on distributed dynamic channel assignment, and sharing load among Access Points (AP). Deploying wireless LANs (WLAN) on a large scale is mainly affected by reliability, availability and performance. These parameters will be a concern for most managers who want to deploy WLANs. In order to address these concerns, a new radio resource management technique can be used in a new generation of wireless LAN equipment. This technique would include distributed dynamic channel assignment, and load sharing among Access Points (AP), which improves the network availability and reliability compared to centralized management techniques. In addition, it will help to increase network capacities and improve performance, especially in large-scale WLANs. Analysis results using normal and binomial distribution have been included which indicate an improvement of performance resulting from network balancing when implementing distributed resources management at WLANs.

scholarly journals A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 315
Author(s):  
Comsa ◽  
Zhang ◽  
Aydin ◽  
Kuonen ◽  
Trestian ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Resource Management ◽  
Large Scale ◽  
Radio Resource Management ◽  
Scheduling Problems ◽  
Radio Resource ◽  
Data Packets ◽  
Intelligent Decision Making ◽  
Fair Scheduler ◽  
Scale Control

Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this context, by making use of reinforcementlearning, we could actually determine, in each state, the most suitable scheduling rule to be employedthat could improve the quality of service provisioning. In this paper, we propose a reinforcementlearning-based framework to solve scheduling problems with the main focus on meeting the userfairness requirements. This framework makes use of feed forward neural networks to map momentarystates to proper parameterization decisions for the proportional fair scheduler. The simulation resultsshow that our reinforcement learning framework outperforms the conventional adaptive schedulersoriented on fairness objective. Discussions are also raised to determine the best reinforcement learningalgorithm to be implemented in the proposed framework based on various scheduler settings.

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