scholarly journals A Reinforcement Learning Approach to Access Management in Wireless Cellular Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jihun Moon ◽  
Yujin Lim
Keyword(s):  
Reinforcement Learning ◽  
Data Transmission ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Collision Rate ◽  
Access Management ◽  
Machine Type ◽  
Access Delay ◽  
Machine Type Communication ◽  
3Rd Generation Partnership Project

In smart city applications, huge numbers of devices need to be connected in an autonomous manner. 3rd Generation Partnership Project (3GPP) specifies that Machine Type Communication (MTC) should be used to handle data transmission among a large number of devices. However, the data transmission rates are highly variable, and this brings about a congestion problem. To tackle this problem, the use of Access Class Barring (ACB) is recommended to restrict the number of access attempts allowed in data transmission by utilizing strategic parameters. In this paper, we model the problem of determining the strategic parameters with a reinforcement learning algorithm. In our model, the system evolves to minimize both the collision rate and the access delay. The experimental results show that our scheme improves system performance in terms of the access success rate, the failure rate, the collision rate, and the access delay.

scholarly journals Reinforcement-Learning Approach Guidelines for Energy Management

2019 ◽  
Vol 15 (3) ◽  
pp. 283-293 ◽  
Author(s):  
Yohann Rioual ◽  
Johann Laurent ◽  
Jean-Philippe Diguet
Keyword(s):  
Reinforcement Learning ◽  
Energy Management ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Autonomous Systems ◽  
Difficult Problem ◽  
Learning Approach ◽  
Neural Network Approach ◽  
Q Learning ◽  
Energy Harvesting Devices

IoT and autonomous systems are in charge of an increasing number of sensing, processing and communications tasks. These systems may be equipped with energy harvesting devices. Nevertheless, the energy harvested is uncertain and variable, which makes it difficult to manage the energy in these systems. Reinforcement learning algorithms can handle such uncertainties, however selecting the adapted algorithm is a difficult problem. Many algorithms are available and each has its own advantages and drawbacks. In this paper, we try to provide an overview of different approaches to help designer to determine the most appropriate algorithm according to its application and system. We focus on Q-learning, a popular reinforcement learning algorithm and several of these variants. The approach of Q-learning is based on the use of look up table, however some algorithms use a neural network approach. We compare different variants of Q-learning for the energy management of a sensor node. We show that depending on the desired performance and the constraints inherent in the application of the node, the appropriate approach changes.

scholarly journals Fog Fragment Cooperation on Bandwidth Management Based on Reinforcement Learning

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6942
Author(s):  
Motahareh Mobasheri ◽  
Yangwoo Kim ◽  
Woongsup Kim
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Learning Approach ◽  
Data Generation ◽  
Bandwidth Management ◽  
Q Learning ◽  
Decision Making Problem ◽  
Iot Devices ◽  
The Internet Of Things

The term big data has emerged in network concepts since the Internet of Things (IoT) made data generation faster through various smart environments. In contrast, bandwidth improvement has been slower; therefore, it has become a bottleneck, creating the need to solve bandwidth constraints. Over time, due to smart environment extensions and the increasing number of IoT devices, the number of fog nodes has increased. In this study, we introduce fog fragment computing in contrast to conventional fog computing. We address bandwidth management using fog nodes and their cooperation to overcome the extra required bandwidth for IoT devices with emergencies and bandwidth limitations. We formulate the decision-making problem of the fog nodes using a reinforcement learning approach and develop a Q-learning algorithm to achieve efficient decisions by forcing the fog nodes to help each other under special conditions. To the best of our knowledge, there has been no research with this objective thus far. Therefore, we compare this study with another scenario that considers a single fog node to show that our new extended method performs considerably better.

scholarly journals INFLUENCE LEARNING FOR MULTI-AGENT SYSTEM BASED ON REINFORCEMENT LEARNING

2014 ◽  
pp. 39-44
Author(s):  
Anton Kabysh ◽  
Vladimir Golovko ◽  
Arunas Lipnickas
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Learning Approach ◽  
Multi Agent System ◽  
Agent System ◽  
Additional Information ◽  
Optimal Behavior ◽  
Learning Technique ◽  
Multi Agent

This paper describes a multi-agent influence learning approach and reinforcement learning adaptation to it. This learning technique is used for distributed, adaptive and self-organizing control in multi-agent system. This technique is quite simple and uses agent’s influences to estimate learning error between them. The best influences are rewarded via reinforcement learning which is a well-proven learning technique. It is shown that this learning rule supports positive-reward interactions between agents and does not require any additional information than standard reinforcement learning algorithm. This technique produces optimal behavior of multi-agent system with fast convergence patterns.

Analysis of machine-type communication data transmission by multicasting technology in 5G wireless networks

2019 ◽  
Author(s):  
Vitalii Beschastnyi ◽  
Valeria Savich ◽  
Daria Ostrikova ◽  
Irina Gudkova ◽  
Giuseppe Araniti ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Data Transmission ◽  
Machine Type ◽  
Machine Type Communication

scholarly journals Resource Allocation for Machine-Type Communication of Energy-Harvesting Devices in Wi-Fi HaLow Networks

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2449
Author(s):  
Dmitry Bankov ◽  
Evgeny Khorov ◽  
Andrey Lyakhov ◽  
Jeroen Famaey
Keyword(s):  
Energy Harvesting ◽  
Data Transmission ◽  
Access Point ◽  
Optimal Number ◽  
Time Interval ◽  
Data Delivery ◽  
Machine Type ◽  
Machine Type Communication ◽  
Energy Harvesting Devices ◽  
The Internet Of Things

The recent Wi-Fi HaLow technology focuses on adopting Wi-Fi for the needs of the Internet of Things. A key feature of Wi-Fi HaLow is the Restricted Access Window (RAW) mechanism that allows an access point to divide the sensors into groups and to assign each group to an exclusively reserved time interval where only the stations of a particular group can transmit. In this work, we study how to optimally configure RAW in a scenario with a high number of energy harvesting sensor devices. For such a scenario, we consider a problem of device grouping and develop a model of data transmission, which takes into account the peculiarities of channel access and the fact that the devices can run out of energy within the allocated intervals. We show how to use the developed model in order to determine the optimal duration of RAW intervals and the optimal number of groups that provide the required probability of data delivery and minimize the amount of consumed channel resources. The numerical results show that the optimal RAW configuration can reduce the amount of consumed channel resources by almost 50%.

Sign in / Sign up

Export Citation Format

Share Document