S. Samarakoon, M. Bennis, W. Saad and M. Debbah, "Predictive Ultra-Reliable Communication: A Survival Analysis Perspective," in IEEE Communications Letters, vol. 25, no. 4, pp. 1221-1225, April 2021, doi: 10.1109/LCOMM.2020.3047446
Predictive ultra-reliable communication : a survival analysis perspective
|Author:||Samarakoon, Sumudu1; Bennis, Mehdi1; Saad, Walid2,3;|
1Centre for Wireless Communications, University of Oulu, FI-90014 Oulu, Finland
2Bradley Department of Electrical and Computer Engineering, Wireless@VT, Blacksburg, VA 24060 USA
3Department of Computer Science and Engineering, Kyung Hee University, Seoul 17104, South Korea
4CentraleSupélec, CNRS, Université Paris- Saclay, 91190 Gif-sur-Yvette, France
5Lagrange Mathematical and Computing Research Center, 75007 Paris, France
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021052030743
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-05-20
Ultra-reliable communication (URC) is a key enabler for supporting immersive and mission-critical 5G applications. Meeting the strict reliability requirements of these applications is challenging due to the absence of accurate statistical models tailored to URC systems. In this letter, the wireless connectivity over dynamic channels is characterized via statistical learning methods. In particular, model-based and data-driven learning approaches are proposed to estimate the non-blocking connectivity statistics over a set of training samples with no knowledge on the dynamic channel statistics. Using principles of survival analysis, the reliability of wireless connectivity is measured in terms of the probability of channel blocking events. Moreover, the maximum transmission duration for a given reliable non-blocking connectivity is predicted in conjunction with the confidence of the inferred transmission duration. Results show that the accuracy of detecting channel blocking events is higher using the model-based method for low to moderate reliability targets requiring low sample complexity. In contrast, the data-driven method yields a higher detection accuracy for higher reliability targets at the cost of 100× sample complexity.
IEEE communications letters
|Pages:||1221 - 1225|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported bybAcademy of Finland 6G Flagship (grant no. 318927) and project SMARTER, projects EU-ICT IntellIoT and EU-CHISTERA LearningEdge, Infotech-NOOR and NEGEIN, and the U.S. National Science Foundation under Grant CNS-1836802.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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