Massive Random Access of Machine-to-Machine Communications in LTE Networks : Throughput Optimization With a Finite Data Transmission Rate

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

11 Scopus Citations
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Detail(s)

Original languageEnglish
Article number8830384
Pages (from-to)5749-5763
Journal / PublicationIEEE Transactions on Wireless Communications
Volume18
Issue number12
Online published10 Sept 2019
Publication statusPublished - Dec 2019

Abstract

This is a sequel of our previous work [20] on access throughput optimization of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. By incorporating a finite data transmission rate, this paper aims to characterize the effect of data transmission on the optimal access performance of Machine-Type Devices (MTDs). Specifically, both the maximum access throughput and the corresponding optimal Access Class Barring (ACB) factor are obtained as explicit functions of the data transmission rate, which show that even with the ACB factor optimally tuned, the access throughput may deteriorate as the number of MTDs increases, and even drop to zero if the data transmission rate is too small. To boost the data transmission rate, more resources should be allocated to data transmission, which, however, leads to fewer chances for access. In light of the tradeoff between the data transmission rate and the access frequency, the time slot length is further optimized for maximizing the normalized maximum access throughput. Simulation results corroborate that by properly choosing the time slot length, substantial gains can be achieved over the default setting in various scenarios.

Research Area(s)

  • data transmission, LTE, Machine-to-Machine (M2M) communications, optimization, random access, throughput

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