# Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective

arXiv: https://arxiv.org/abs/2208.08342 · HTML: https://mlsanity.com/paper/2208.08342/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 17 Aug 2022.

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

- [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)
- [Deep Joint Source-Channel Coding for Wireless Image Transmission](https://mlsanity.com/paper/1809.01733/) (2018)
- [DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.08100/) (2022)
- [Federated Edge Learning with Misaligned Over-The-Air Computation](https://mlsanity.com/paper/2102.13604/) (2021)
- [OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels](https://mlsanity.com/paper/2109.05194/) (2021)
- [Channel-Adaptive Wireless Image Transmission with OFDM](https://mlsanity.com/paper/2205.02417/) (2022)
- [KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning](https://mlsanity.com/paper/2108.12920/) (2021)
- [AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications](https://mlsanity.com/paper/2205.14955/) (2022)
- [Learning OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2110.10987/) (2021)
- [Flexible Subcarrier Allocation for Interleaved Frequency Division Multiple Access](https://mlsanity.com/paper/2002.06552/) (2020)

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

- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things](https://mlsanity.com/paper/2403.00321/) (2024)
- [Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems](https://mlsanity.com/paper/2311.00207/) (2023)
- [Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence](https://mlsanity.com/paper/2502.15472/) (2025)
- [Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding](https://mlsanity.com/paper/2504.03690/) (2025)
- [Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications](https://mlsanity.com/paper/2501.16726/) (2025)

## Abstract (arXiv)

Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come from the additional freedom brought by the high-PAPR continuous-amplitude signal. In this paper, we address this question by exploring three PAPR reduction techniques in the application of image transmission. We confirm that the superior image reconstruction performance of DeepJSCC-based semantic communications can be retained while the transmitted PAPR is suppressed to an acceptable level. This observation is an important step towards the implementation of DeepJSCC in practical semantic communication systems.