# Semantic Multi-Resolution Communications

arXiv: https://arxiv.org/abs/2308.11604 · HTML: https://mlsanity.com/paper/2308.11604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback](https://mlsanity.com/paper/1911.11174/) (2019)
- [WITT: A Wireless Image Transmission Transformer for Semantic Communications](https://mlsanity.com/paper/2211.00937/) (2022)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM](https://mlsanity.com/paper/2101.03909/) (2021)
- [Successive Refinement of Images with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/1903.06333/) (2019)
- [Multiresolution Convolutional Autoencoders](https://mlsanity.com/paper/2004.04946/) (2020)
- [Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks](https://mlsanity.com/paper/2212.10438/) (2022)
- [Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks](https://mlsanity.com/paper/2212.11205/) (2022)

## Built on by (most cited first, 2 of 2)

- [Transformer-Aided Semantic Communications](https://mlsanity.com/paper/2405.01521/) (2024)
- [Efficient Semantic Communication Through Transformer-Aided Compression](https://mlsanity.com/paper/2412.01817/) (2024)

## Abstract (arXiv)

Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets.