University of Oulu

T. K. Vu, M. Bennis, M. Debbah and M. Latva-Aho, "Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mm-wave Networks," in IEEE Transactions on Wireless Communications, vol. 18, no. 4, pp. 2431-2445, April 2019. doi: 10.1109/TWC.2019.2904275

Joint path selection and rate allocation framework for 5G self-backhauled mm-wave networks

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Author: Vu, Trung Kien1; Bennis, Mehdi1; Debbah, Mérouane2,3;
Organizations: 1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Large Networks and System Group (LANEAS), CentraleSupélec, Université Paris-Saclay, 91192 Gif-sur-Yvette, France
3Mathematical and Algorithmic Sciences Laboratory, Huawei France Research and Development, 92100 Paris, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 11 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019041712667
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-04-17
Description:

Abstract

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter-wave (mm-wave) networks. Enabling multi-hop mm-wave transmissions raises a potential issue of increased latency, and thus, this paper aims at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, a new system design, which exploits multiple antenna diversity, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution. The numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99.9999% and reduces latency by 50.64% and 92.9% as compared to baseline models. Furthermore, the results showcase the key tradeoff between latency and network arrival rate.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 18
Issue: 4
Pages: 2431 - 2445
DOI: 10.1109/TWC.2019.2904275
OADOI: https://oadoi.org/10.1109/TWC.2019.2904275
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
5G
Funding: This work was supported in part by the Academy of Finland 6Genesis Flagship under Grant 318927, in part by the Academy of Finland under Grant 307492, in part by the Context-Aware Resource Management in 5G Heterogeneous Networks (CARMA) under Grants 294128 and 289611, in part by the Nokia Foundation, in part by the Tauno Tönning Foundation, and in part by theTekniikan Edistamissäätiö.
Academy of Finland Grant Number: 318927
307492
294128
289611
Detailed Information: 318927 (Academy of Finland Funding decision)
307492 (Academy of Finland Funding decision)
294128 (Academy of Finland Funding decision)
289611 (Academy of Finland Funding decision)
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