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%.

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.

Code-Division OFDM Joint Communication and Sensing System for 6G Machine-type Communication

2021 ◽  
pp. 1-1
Author(s):  
Xu Chen ◽  
Zhiyong Feng ◽  
Zhiqing Wei ◽  
Ping Zhang ◽  
Xin Yuan
Keyword(s):  
Machine Type ◽  
Sensing System ◽  
Machine Type Communication

scholarly journals Distributed Group Location Update Algorithm for Massive Machine Type Communication

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7336
Author(s):  
Mincheol Paik ◽  
Haneul Ko
Keyword(s):  
Outage Probability ◽  
Stochastic Game ◽  
Location Update ◽  
Response Dynamics ◽  
Small Energy ◽  
Machine Type ◽  
Best Response ◽  
Machine Type Communication ◽  
Iot Devices ◽  
The Individual

Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability.

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