A directed information learning framework for event-driven M2M traffic prediction
Ali, Samad; Saad, Walid; Rajatheva, Nandana (2018-08-31)
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
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2018122051408
Tiivistelmä
Abstract
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.
Kokoelmat
- Avoin saatavuus [38618]