# Signal Shaping for Semantic Communication Systems with A Few Message Candidates

arXiv: https://arxiv.org/abs/2202.02072 · HTML: https://mlsanity.com/paper/2202.02072/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2022.

## Built on by (most cited first, 3 of 3)

- [Transceiver Cooperative Learning-aided Semantic Communications Against Mismatched Background Knowledge Bases](https://mlsanity.com/paper/2301.03133/) (2023)
- [Emergent communication for AR](https://mlsanity.com/paper/2308.07342/) (2023)
- [Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels](https://mlsanity.com/paper/2406.16878/) (2024)

## Abstract (arXiv)

Semantic communications target to reliably convey the semantic meaning of messages. It is different from existing communication systems focusing on reliable bit transmission. To achieve the goal of semantic communications, we propose a signal shaping method by minimizing the semantic loss, which is measured by the pretrained bidirectional encoder representation from transformers (BERT) model. The signal set optimization problem for semantic communication systems with a few message candidates is investigated. We propose an efficient projected gradient descent method to solve the problem and prove its convergence. Simulation results show that the proposed method outperforms existing signal shaping methods in minimizing the semantic loss.