# Delay-Aware Scheduling over mmWave/Sub-6 Dual Interfaces: A Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2005.08668 · HTML: https://mlsanity.com/paper/2005.08668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

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

- [Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks](https://mlsanity.com/paper/2404.16920/) (2024)

## Abstract (arXiv)

We consider a transmitter with mmWave/sub6 dual interfaces. Due to the intermittency of mmWave channel, the transmitter must schedule packets wisely across the interfaces to minimize the average delay by observing the system state. We usethe well-known dynamic programming methods and Q-learning to find the optimal scheduling policy and investigate the influenceof observing CSI on the optimal policy under different levels of knowledge of the environment. We find that only when the channel state transition model is not available, the instantaneousCSI can help in reducing system delay