Path selection and rate allocation in self-backhauled mmWave networks

Vu, Trung Kien; Liu, Chen-Feng; Bennis, Mehdi; Debbah, Mérouane; Latva-aho, Matti (2018-06-11)
 
Avaa tiedosto
Lataukset: 

URL:
https://doi.org/10.1109/WCNC.2018.8377239

Vu, Trung Kien
Liu, Chen-Feng
Bennis, Mehdi
Debbah, Mérouane
Latva-aho, Matti
Institute of Electrical and Electronics Engineers
11.06.2018

T. K. Vu, C. Liu, M. Bennis, M. Debbah and M. Latva-aho, "Path selection and rate allocation in self-backhauled mmWave networks," 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, 2018, pp. 1-6. doi: 10.1109/WCNC.2018.8377239

https://rightsstatements.org/vocab/InC/1.0/
© 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/
doi:https://doi.org/10.1109/WCNC.2018.8377239
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2018090334429
Tiivistelmä

Abstract

We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks. Owing to the high path loss and blockage of mmWave links, multi-hop paths/routes between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: “how to select the best paths and how to allocate rates over these paths subject to latency constraints?” To answer this question, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path/route selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.

Kokoelmat