# Semantic Communication with Distribution Learning through Sequential Observations

arXiv: https://arxiv.org/abs/2508.10350 · HTML: https://mlsanity.com/paper/2508.10350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [DeepWiVe: Deep-Learning-Aided Wireless Video Transmission](https://mlsanity.com/paper/2111.13034/) (2021)
- [Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity](https://mlsanity.com/paper/1907.02441/) (2019)
- [An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation](https://mlsanity.com/paper/2201.12477/) (2022)

## Abstract (arXiv)

Semantic communication aims to convey meaning rather than bit-perfect reproduction, representing a paradigm shift from traditional communication. This paper investigates distribution learning in semantic communication where receivers must infer the underlying meaning distribution through sequential observations. While semantic communication traditionally optimizes individual meaning transmission, we establish fundamental conditions for learning source statistics when priors are unknown. We prove that learnability requires full rank of the effective transmission matrix, characterize the convergence rate of distribution estimation, and quantify how estimation errors translate to semantic distortion. Our analysis reveals a fundamental trade-off: encoding schemes optimized for immediate semantic performance often sacrifice long-term learnability. Experiments on CIFAR-10 validate our theoretical framework, demonstrating that system conditioning critically impacts both learning rate and achievable performance. These results provide the first rigorous characterization of statistical learning in semantic communication and offer design principles for systems that balance immediate performance with adaptation capability.