# Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization arXiv: https://arxiv.org/abs/2305.13553 ยท HTML: https://mlsanity.com/paper/2305.13553/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 59 references that are in the map. - First posted on arXiv 22 May 2023. ## Builds on (most cited first, 30 of 35) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019) - [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015) - [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016) - [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016) - [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (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) - [Quantization Networks](https://mlsanity.com/paper/1911.09464/) (2019) - [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015) - [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021) - [BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems](https://mlsanity.com/paper/1910.14315/) (2019) - [Post-Training Piecewise Linear Quantization for Deep Neural Networks](https://mlsanity.com/paper/2002.00104/) (2020) - [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021) - [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020) - [BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1902.01000/) (2019) - [Wireless Image Retrieval at the Edge](https://mlsanity.com/paper/2007.10915/) (2020) - [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022) - [Value-aware Quantization for Training and Inference of Neural Networks](https://mlsanity.com/paper/1804.07802/) (2018) - [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021) - [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022) - [JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution](https://mlsanity.com/paper/1812.10027/) (2018) - [Improving the Interpretability of Deep Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/1812.10924/) (2018) - [DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.08100/) (2022) - [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021) - [Semantic-preserved Communication System for Highly Efficient Speech Transmission](https://mlsanity.com/paper/2205.12727/) (2022) - [Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning](https://mlsanity.com/paper/1903.03472/) (2019) ## Built on by (most cited first, 13 of 13) - [Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints](https://mlsanity.com/paper/2407.18468/) (2024) - [OFDM-Based Digital Semantic Communication with Importance Awareness](https://mlsanity.com/paper/2401.02178/) (2024) - [Joint Semantic-Channel Coding and Modulation for Token Communications](https://mlsanity.com/paper/2511.15699/) (2025) - [Vision Transformer-based Semantic Communications With Importance-Aware Quantization](https://mlsanity.com/paper/2412.06038/) (2024) - [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024) - [VideoQA-SC: Adaptive Semantic Communication for Video Question Answering](https://mlsanity.com/paper/2406.18538/) (2024) - [Multimodal LLM Integrated Semantic Communications for 6G Immersive Experiences](https://mlsanity.com/paper/2507.04621/) (2025) - [Importance-Aware Semantic Communication in MIMO-OFDM Systems Using Vision Transformer](https://mlsanity.com/paper/2508.07696/) (2025) - [Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion](https://mlsanity.com/paper/2503.17777/) (2025) - [Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission](https://mlsanity.com/paper/2506.03211/) (2025) - [Large Speech Model Enabled Semantic Communication](https://mlsanity.com/paper/2512.04711/) (2025) - [Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission](https://mlsanity.com/paper/2601.08135/) (2026) - [VQ-DSC-R: Robust Vector Quantized-Enabled Digital Semantic Communication With OFDM Transmission](https://mlsanity.com/paper/2602.15045/) (2026) ## Abstract (arXiv) Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Robust semantic features can be learned and transmitted in an analog fashion, but it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic communication system, which consists of an encoding network deployed on a resource-limited device and a decoding network deployed at the edge. To acquire better semantic representation for digital transmission, a novel non-linear quantization module is proposed to efficiently quantize semantic features with trainable quantization levels. Additionally, structured pruning is incorporated to reduce the dimension of the transmitted features. We also introduce a semantic learning loss (SLL) function to reduce semantic error. To adapt to various channel conditions and inputs under constraints of communication and computing resources, a policy network is designed to adaptively choose the split point and the dimension of the transmitted semantic features. Experiments using the CIFAR-10 and ImageNet dataset for image classification are employed to evaluate the proposed digital semantic communication network, and ablation studies are conducted to assess the proposed quantization module, structured pruning and SLL.