University of Oulu

S. Ali, W. Saad and N. Rajatheva, "A Directed Information Learning Framework for Event-Driven M2M Traffic Prediction," in IEEE Communications Letters, vol. 22, no. 11, pp. 2378-2381, Nov. 2018. doi: 10.1109/LCOMM.2018.2868072

A directed information learning framework for event-driven M2M traffic prediction

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Author: Ali, Samad1; Saad, Walid2; Rajatheva, Nandana1
Organizations: 1Center for Wireless Communications, University of Oulu
2Bradley Department of Electrical and Computer Engineering, Virginia Tech.
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2018-12-20


Burst of transmissions stemming from event-driven traffic in machine-type communication (MTC) can lead to congestion of random access resources, packet collisions, and long delays. In this letter, a directed information (DI) learning framework is proposed to predict the source traffic in event-driven MTC. By capturing the history of transmissions during past events by a sequence of binary random variables, the DI between different machine-type devices (MTDs) is calculated and used for predicting the set of possible MTDs that are likely to report an event. Analytical and simulation results show that the proposed DI learning method can reveal the correlation between transmission from different MTDs that report the same event, and the order in which they transmit their data. The proposed algorithm and the presented results show that DI can be used to implement effective predictive resource allocation for event-driven MTC.

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Series: IEEE communications letters
ISSN: 1089-7798
ISSN-E: 2373-7891
ISSN-L: 1089-7798
Volume: 22
Issue: 11
Pages: 2378 - 2381
DOI: 10.1109/LCOMM.2018.2868072
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the by Academy of Finland 6Genesis Flagship under Grant 318927 and, in part, by the Office of Naval Research (ONR) under Grant N00014-15-1-2709 and, in part, by the U.S. National Science Foundation under Grant CNS-1739642.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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