# Path Selection and Rate Allocation in Self-Backhauled mmWave Networks

arXiv: https://arxiv.org/abs/1802.02085 · HTML: https://mlsanity.com/paper/1802.02085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Using Deep Reinforcement Learning for mmWave Real-Time Scheduling](https://mlsanity.com/paper/2210.01423/) (2022)

## Abstract (arXiv)

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 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 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.