This matlab code package is related to our articles:
[A1] "Path Selection and Rate Allocation in Self-Backhauled mmWave Networks", Proc. IEEE Wireless Commun. Netw. Conf., pp. 2371-2376, 15-18 April 2018, Barcelona, Spain.
[A2] "Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks", DOI (identifier) 10.1109/TWC.2019.2904275, for publication in IEEE Transactions on Wireless Communications (2019).
[A3] "Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach." IEEE Communications Letters 22.4 (2018): 708-711.
Title: Path Selection and Rate Allocation in Self-Backhauled mmWave Networks
#Authors: Trung Kien Vu
The package contains a simulation environment, based on Matlab, that reproduces all the numerical results and figures in the article. We encourage you to also perform reproducible research!
#Abstract of Article
Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, we investigate the problem of path selection and rate allocation for multi-hop self-backhaul millimeter wave (mmWave) networks. Enabling multi-hop mmWave transmissions raises a potential issue of increased latency, and thus, in this work we aim 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, we propose a new system design, which exploits multiple antenna diversity, mmWave bandwidth, and traffic splitting techniques to improve the downlink transmission. The studied problem is cast as a network utility maximization, subject to an upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: path selection and rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a nonconvex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, we provide a comprehensive performance analysis and convergence proofs for the proposed solution. Numerical results show that our 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 trade-off between latency and network arrival rate.
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Lyapunov Optimization
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Reinforcement Learning
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Non-convex Optimization Techinique
System requirement
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MATLAB version: 9.1 (R2016b) https://www.mathworks.com/downloads/
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OS: Windows 7 amd64 version 6.1
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Java version: 1.7.0_60
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Yalmip lastest version https://yalmip.github.io/?n=Main.Download
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Mosek solver version 7 at least : https://www.mosek.com/
Solver Name ---- Status ------------ Versionn ------Location
Mosek ------ selected,default -------- 7.1.0.12 ---- {cvx}\mosek\w64
SDPT3 ------ --------------------------4.0 ------------ {cvx}\sdpt3
SeDuMi ------------------------------ 1.34 -------- {cvx}\sedumi
Content of Code Package
The paper contains three simulation figures:
Figure 2 is generated by the Matlab script main.m or main_0.m
See each file for further documentation. Note that this package constains a simple procedure/function that allows to learn the path/route and allocate the transmit power in our paper.
Acknowledgements This research has been financially supported by the Academy of Finland 6Genesis Flagship (grant 318927). The Academy of Finland funding via the grant 307492 and the CARMA grants 294128 and 289611, and the Nokia Foundation are also acknowledged.
License and Referencing
This code package is licensed under the GPLv3 license. If you in any way use this code for research that results in publications, please cite our original article listed above.