# Proactive Resilient Transmission and Scheduling Mechanisms for mmWave Networks

arXiv: https://arxiv.org/abs/2211.09307 · HTML: https://mlsanity.com/paper/2211.09307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Millimeter Wave Cellular Wireless Networks: Potentials and Challenges](https://mlsanity.com/paper/1401.2560/) (2014)
- [Directional Initial Access for Millimeter Wave Cellular Systems](https://mlsanity.com/paper/1511.06483/) (2015)
- [On the Temporal Effects of Mobile Blockers in Urban Millimeter-Wave Cellular Scenarios](https://mlsanity.com/paper/1705.08037/) (2017)
- [Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks](https://mlsanity.com/paper/1805.07743/) (2018)
- [A Reinforcement Learning Approach for Scheduling in mmWave Networks](https://mlsanity.com/paper/2108.00548/) (2021)
- [Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications](https://mlsanity.com/paper/1907.12616/) (2019)

## Abstract (arXiv)

This paper aims to develop resilient transmission mechanisms to suitably distribute traffic across multiple paths in an arbitrary millimeter-wave (mmWave) network. The main contributions include: (a) the development of proactive transmission mechanisms that build resilience against network disruptions in advance, while achieving a high end-to-end packet rate; (b) the design of a heuristic path selection algorithm that efficiently selects (in polynomial time in the network size) multiple proactively resilient paths with high packet rates; and (c) the development of a hybrid scheduling algorithm that combines the proposed path selection algorithm with a deep reinforcement learning (DRL) based online approach for decentralized adaptation to blocked links and failed paths. To achieve resilience to link failures, a state-of-the-art Soft Actor-Critic DRL algorithm, which adapts the information flow through the network, is investigated. The proposed scheduling algorithm robustly adapts to link failures over different topologies, channel and blockage realizations while offering a superior performance to alternative algorithms.