Proceedings ArticleDOI
Group-Based Uplink Scheduling for Machine-Type Communications in LTE-Advanced Networks
Tsung-Hui Chuang,Meng-Hsun Tsai,Chun-Yi Chuang +2 more
- pp 652-657
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TLDR
A combination of Machine-Type Communications and Device-to-Device (D2D)communications to design a group-based uplink scheduling algorithm that takes into account the fairness of inter-group and intra-group decisions and demonstrates the benefits compared to conventional approaches on resource allocation.Abstract:
The increasing adoption of Machine-Type Communication (MTC) applications over the Long Term Evolution-Advanced (LTE-Advanced) network brings new challenges for the traditional resources allocation. Particularly, we focus on the smart metering traffic, which is less time-sensitive and transmitting small data at infrequent periods. In this scenario, many MTC devices transmit access requests to the eNodeBat the same time. This situation causes a serious problem of access congestion. Unfortunately, existing scheduling algorithms are not be able to efficiently handle smart metering traffic at a heavily loaded eNodeB. Specifically, there may not be sufficient resource blocks (RBs) available at an eNodeB for smart metering traffic. In this paper, we utilize a combination of Machine-Type Communications and Device-to-Device (D2D)communications to design our group-based uplink scheduling algorithm. In addition, we also take into account the fairness of inter-group and intra-group. Our simulation results demonstrate the benefits of our proposed scheme compared to conventional approaches on resource allocation.read more
Citations
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Journal ArticleDOI
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
TL;DR: In this paper, the authors present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission in the mMTC scenario and provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem.
Journal ArticleDOI
A Cluster-Based Congestion-Mitigating Access Scheme for Massive M2M Communications in Internet of Things
TL;DR: Numerical results show that the proposed CCAS can significantly decrease collision probability, and increase the number of successfully received packets of the system without increasing average access delay.
Journal ArticleDOI
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
TL;DR: In this paper, a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission is presented, and a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks.
Proceedings ArticleDOI
Grouping-Based Random Access Collision Control for Massive Machine-Type Communication
Bin Han,Hans D. Schotten +1 more
TL;DR: In this article, an analytical model of grouped random access collision is proposed, and the impact of D2D reliability on the collision rate is investigated based on the analytical model, and an efficient grouped RA procedure is designed to extend the state-of-the-art with an efficient local group update mechanism against DL link exceptions.
Journal ArticleDOI
A Delay Optimal Multiclass Packet Scheduler for General M2M Uplink
TL;DR: This paper classify the M2M uplink data into multiple QoS classes and use sigmoidal function to map the delay requirements of each class onto utility functions and proposes a delay-optimal multiclass packet scheduler at AS obtained by iteratively searching for the optimal fraction of time-sharing between all preemptive priority scheduling policies.
References
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Proceedings ArticleDOI
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