# Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2305.13706 · HTML: https://mlsanity.com/paper/2305.13706/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 11 of 11)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Deep Lattice Networks and Partial Monotonic Functions](https://mlsanity.com/paper/1709.06680/) (2017)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems](https://mlsanity.com/paper/1809.05149/) (2018)
- [Remote State Estimation with Smart Sensors over Markov Fading Channels](https://mlsanity.com/paper/2005.07871/) (2020)
- [Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning](https://mlsanity.com/paper/2105.04207/) (2021)
- [DRL-based Resource Allocation in Remote State Estimation](https://mlsanity.com/paper/2205.12267/) (2022)
- [Structure-Enhanced DRL for Optimal Transmission Scheduling](https://mlsanity.com/paper/2212.12704/) (2022)
- [Optimal Downlink-Uplink Scheduling of Wireless Networked Control for Industrial IoT](https://mlsanity.com/paper/1907.07841/) (2019)
- [Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control](https://mlsanity.com/paper/2109.12562/) (2021)
- [Age of Incorrect Information in Semantic Communications for NOMA Aided XR Applications](https://mlsanity.com/paper/2305.09272/) (2023)

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

- [Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach](https://mlsanity.com/paper/2501.11921/) (2025)

## Abstract (arXiv)

For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance. Semantics here is a measure of the usefulness of information transmissions. Semantic-aware transmission scheduling of a large system often involves a large decision-making space, and the optimal policy cannot be obtained by existing algorithms effectively. In this paper, we first investigate the fundamental properties of the optimal semantic-aware scheduling policy and then develop advanced deep reinforcement learning (DRL) algorithms by leveraging the theoretical guidelines. Our numerical results show that the proposed algorithms can substantially reduce training time and enhance training performance compared to benchmark algorithms.