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Early Data Transmit in Cellular-IoT: A Supervised Learning Approach to Optimized Resource Pool Allocation
KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab). (Techno Economic)ORCID iD: 0000-0002-5164-3597
Luleå University of Technology.
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).ORCID iD: 0000-0003-0125-2202
Luleå University of Technology.
Show others and affiliations
2020 (English)In: Article in journal (Refereed) Submitted
Abstract [en]

3GPP introduced Early Data Transmit (EDT) protocol in release-15 to address Internet of things (IoT) services signaling cost and connection delay due to RRC connectivity procedure. EDT enables data transmission in MSG3 wherein traditional RACH procedure, a collision is realized at MSG3 reception. Traditional preventive approach (access class barring) may control the device access rate, but that does not always adequate to solve hidden collision due to the receiver’s limitation and devices power control mechanism. The present work aims at solving the problem by allocating extra resources pool for potential collided devices. The allocated resource of the pool is optimized based on an artificial neural network (ANN) based learning algorithm. The performance evaluation result shows that the learning-based resource allocation radically increases the data transmission success rate with EDT.

Place, publisher, year, edition, pages
Dublin, Ireland: IEEE Communications Society, 2020.
Keywords [en]
Resource Allocation, EDT, LTE-M, Machine Learning, Random Access, Neural Network.
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-256521OAI: oai:DiVA.org:kth-256521DiVA, id: diva2:1346199
Conference
IEEE International Conference on Communications
Note

QC 20190903

Available from: 2019-08-27 Created: 2019-08-27 Last updated: 2019-09-03Bibliographically approved
In thesis
1. Cellular-Internet-of-Things Enablers:: A Techno-Economic Study of Wide Area Networks Connectivity and Platform Solutions
Open this publication in new window or tab >>Cellular-Internet-of-Things Enablers:: A Techno-Economic Study of Wide Area Networks Connectivity and Platform Solutions
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Internet of Things (IoT) services are gradually attaining the expected service growth rate estimated by market actors. New connectivity paradigms, like the low-power wide-area network (LPWAN), have emerged to address the immediate challenges of IoT connectivity service. In addition, a plethora of new connectivity and application platforms have been developed to support IoT services. Until now, the majority of IoT services have been small scale deployments or trials. The overall cost-effectiveness and scalability aspects of IoT service provisioning are still not well understood. Hence, the growth of IoT services requires attention from a multidisciplinary perspective to address the cost-efficient scalability of IoT communication and service platforms.The technical part of this thesis focuses on the impact of multiplicity on the physical random access channel (PRACH) performance. We investigate the performance limitations of the initial access resource allocation, considering the multiplicity effect on physical layer signature detection to ensure uniform accessibility of devices. The performance evaluation reveals that MAC-protocol designed for PRACH needs to consider the realistic impact of multiplicity on signature detection. Then, we propose an efficient algorithm to detect multiplicity with a higher confidence factor. We also investigate the trade-off of random-access collision and resource allocation utilisation to meet the IoT resource utilisation requirements. We propose a pool-based resource allocation procedure that uses supervised learning to optimise the performance of early data transmission (EDT). Our analysis suggests that with this approach, EDT can handle delay constraint IoT services efficiently.The economic part of the thesis addresses the cost-structure and scalability aspects of both connectivity and platform solutions. The overall research question is: "What factors are driving the costs of IoT connectivity and platform services and why?" We have developed a framework for cost structure analysis of IoT services. We present cost structure breakdown analysis for both IoT connectivity and IoT platform services. The evaluation results discuss conditions when a platform service provider should choose a platform as a service (PaaS), and when on-premises platform deployment is viable.The technical study contributes to shaping the assessment metrics of the random-access algorithm selection. This study proposes solutions to support heterogeneous IoT solutions in cellular-IoT systems. Furthermore, the study demonstrates the potential of supervised learning to optimise resource allocation. The proposed algorithm assures service scalability in terms of user density for massive-IoT, and delay constraint IoT use cases.The economic study is helpful for telecom managers and IoT service providers to understand the cost breakdown of IoT connectivity and platform solutions under a different scenario. The cost driver of different IoT communication technologies like LPWAN, LPLAN, and C-IoT can be estimated at a high level. The framework provides a comparison base which is helpful for the actors in the IoT domain to analyse and compare different service provisioning options.

Place, publisher, year, edition, pages
Stockholm: Kungliga Tekniska högskolan, 2019. p. 140
Series
TRITA-EECS-AVL ; 2019:64
Keywords
IoT, LPWAN, 5G, PRACH Contention Resolution, Multiplicity detection, Resource Allocation, Cost Structure.
National Category
Communication Systems
Research subject
Telecommunication; Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-256522 (URN)978-91-7873-276-0 (ISBN)
Public defence
2019-09-27, Sal-C, Electrum 229, SE-164 40 Kista, Sweden, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20190905

Available from: 2019-09-05 Created: 2019-08-27 Last updated: 2019-09-10Bibliographically approved

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Hossain, Mohammad IstiakAzari, AminMarkendahl, Jan

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