# Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks

arXiv: https://arxiv.org/abs/1805.07743 · HTML: https://mlsanity.com/paper/1805.07743/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2018.

## Built on by (most cited first, 3 of 3)

- [A Reinforcement Learning Approach for Scheduling in mmWave Networks](https://mlsanity.com/paper/2108.00548/) (2021)
- [Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework](https://mlsanity.com/paper/2302.02711/) (2023)
- [Proactive Resilient Transmission and Scheduling Mechanisms for mmWave Networks](https://mlsanity.com/paper/2211.09307/) (2022)

## Abstract (arXiv)

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.