Path Selection and Rate Allocation in Self-Backhauled mmWave Networks

Abstract : We investigate the problem of multi-hop scheduling in self-backhaul mmWave networks, owing to the high path loss of mmWave, multi-hop 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 to allocate rates over paths subject to latency constraints with a guaranteed probability?". To answer this question, we propose a new system design, which factors in channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint with a guaranteed probability and network stability. The studied problem is decoupled into path/route selection and 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, respectively.
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Trung Kien Vu, Chen-Feng Liu, Mehdi Bennis, Merouane Debbah, Matti Latva-Aho. Path Selection and Rate Allocation in Self-Backhauled mmWave Networks. 2018 IEEE Wireless Communications and Networking Conference (WCNC), Apr 2018, Barcelona, Spain. ⟨hal-01962088⟩

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