Path Selection and Rate Allocation in Self-Backhauled mmWave Networks

TL;DR

This paper presents a novel method for path selection and rate allocation in self-backhauled mmWave networks, optimizing latency and reliability by integrating reinforcement learning and convex optimization.

Contribution

It introduces a new system design that combines reinforcement learning for path selection with convex optimization for rate allocation, tailored for mmWave network dynamics.

Findings

Ensures 99.9999% reliable communication.

Reduces latency by over 50%.

Outperforms baseline methods in simulations.

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 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.