# Effective Communication with Dynamic Feature Compression

arXiv: https://arxiv.org/abs/2401.16236 · HTML: https://mlsanity.com/paper/2401.16236/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Learning to Schedule Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1902.01554/) (2019)
- [InfoBot: Transfer and Exploration via the Information Bottleneck](https://mlsanity.com/paper/1901.10902/) (2019)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Semantic Communications in Networked Systems: A Data Significance Perspective](https://mlsanity.com/paper/2103.05391/) (2021)
- [Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity](https://mlsanity.com/paper/1907.02441/) (2019)
- [Semantic Information Recovery in Wireless Networks](https://mlsanity.com/paper/2204.13366/) (2022)
- [Urgency of Information for Context-Aware Timely Status Updates in Remote Control Systems](https://mlsanity.com/paper/2002.07987/) (2020)
- [Rate-Constrained Remote Contextual Bandits](https://mlsanity.com/paper/2204.12620/) (2022)
- [Zero-Delay Rate Distortion via Filtering for Vector-Valued Gaussian Sources](https://mlsanity.com/paper/1810.00298/) (2018)
- [Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression](https://mlsanity.com/paper/2301.05901/) (2023)
- [Multi-Agent Reinforcement Learning for Pragmatic Communication and Control](https://mlsanity.com/paper/2302.14399/) (2023)
- [Goal-oriented Policies for Cost of Actuation Error Minimization in Wireless Autonomous Systems](https://mlsanity.com/paper/2303.04908/) (2023)
- [Semantic Communication of Learnable Concepts](https://mlsanity.com/paper/2305.08126/) (2023)

## Built on by (most cited first, 6 of 6)

- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [RIS-aided Latent Space Alignment for Semantic Channel Equalization](https://mlsanity.com/paper/2507.16450/) (2025)
- [Robust and Efficient Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2511.11393/) (2025)
- [Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications](https://mlsanity.com/paper/2508.08479/) (2025)
- [Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification](https://mlsanity.com/paper/2503.11954/) (2025)
- [GO-GenZip: Goal-Oriented Generative Sampling and Hybrid Compression](https://mlsanity.com/paper/2603.20109/) (2026)

## Abstract (arXiv)

The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may overload even high-capacity connections. Consequently, solving the effective communication problem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to a robot controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches.