Nonlinear Transform Source-Channel Coding for Semantic Communications

• URL: http://arxiv.org/abs/2112.10961v1
• Date: Tue, 21 Dec 2021 03:30:46 GMT
• Title: Nonlinear Transform Source-Channel Coding for Semantic Communications
• Authors: Jincheng Dai, Sixian Wang, Kailin Tan, Zhongwei Si, Xiaoqi Qin, Kai Niu, Ping Zhang
• Abstract summary: We propose a new class of high-efficient deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features. Notably, the proposed NTSCC method can potentially support future semantic communications due to its vigorous content-aware ability.
• Score: 7.81628437543759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a new class of high-efficient deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel coding (NTSCC). In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features and provide side information for source-channel coding. Unlike existing conventional deep joint source-channel coding methods, the proposed NTSCC essentially learns both the source latent representation and an entropy model as the prior on the latent representation. Accordingly, novel adaptive rate transmission and hyperprior-aided codec refinement mechanisms are developed to upgrade deep joint source-channel coding. The whole system design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under established perceptual quality metrics. Across simple example sources and test image sources, we find that the proposed NTSCC transmission method generally outperforms both the analog transmission using the standard deep joint source-channel coding and the classical separation-based digital transmission. Notably, the proposed NTSCC method can potentially support future semantic communications due to its vigorous content-aware ability.

Related papers

• Learned Image Transmission with Hierarchical Variational Autoencoder [28.084648666081943]
  We introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission. Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical representations of the original image. Our proposed model outperforms existing baselines in rate-distortion performance and maintains robustness against channel noise.
  arXiv  Detail & Related papers  (2024-08-29T08:23:57Z)
• Multi-level Reliability Interface for Semantic Communications over Wireless Networks [5.9056146376982]
  Joint source-channel coding (JSCC) maps source messages directly into channel input symbols. We propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This work represents an important step towards realizing semantic communications in wireless networks.
  arXiv  Detail & Related papers  (2024-07-07T20:15:10Z)
• Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network [70.63240823677182]
  Near-space airship-borne communication network urgently needs reliable and efficient Airship-to-X link. This paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology.
  arXiv  Detail & Related papers  (2024-05-30T09:46:59Z)
• Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication [56.54422521327698]
  Recent studies on semantic communication rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC) Unlike traditional transceivers, these neural transceivers are trainable using actual source data and channels, enabling them to extract and communicate semantics. We propose a distributed learning based solution, which leverages split learning (SL) and partial NN fine-tuning techniques.
  arXiv  Detail & Related papers  (2023-10-13T20:29:55Z)
• Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel [0.0]
  We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC) We introduce a novel joint image compression and transmission scheme, where the devices send their compressed image representations in a non-orthogonal manner. We show significant improvements in terms of the quality of the reconstructed images compared to transmission employing current DeepJSCC approaches.
  arXiv  Detail & Related papers  (2022-11-17T22:36:03Z)
• Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding [11.101344530143303]
  We propose an online learned joint source and channel coding approach that leverages the deep learning model's overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the model or representations to an individual data or channel state instance.
  arXiv  Detail & Related papers  (2022-11-08T16:00:27Z)
• Denoising Diffusion Error Correction Codes [92.10654749898927]
  Recently, neural decoders have demonstrated their advantage over classical decoding techniques. Recent state-of-the-art neural decoders suffer from high complexity and lack the important iterative scheme characteristic of many legacy decoders. We propose to employ denoising diffusion models for the soft decoding of linear codes at arbitrary block lengths.
  arXiv  Detail & Related papers  (2022-09-16T11:00:50Z)
• Wireless Deep Video Semantic Transmission [14.071114007641313]
  We propose a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. Our framework is collected under the name deep video semantic transmission (DVST)
  arXiv  Detail & Related papers  (2022-05-26T03:26:43Z)
• Error Correction Code Transformer [92.10654749898927]
  We propose to extend for the first time the Transformer architecture to the soft decoding of linear codes at arbitrary block lengths. We encode each channel's output dimension to high dimension for better representation of the bits information to be processed separately. The proposed approach demonstrates the extreme power and flexibility of Transformers and outperforms existing state-of-the-art neural decoders by large margins at a fraction of their time complexity.
  arXiv  Detail & Related papers  (2022-03-27T15:25:58Z)
• Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission [132.72277692192878]
  An adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality.
  arXiv  Detail & Related papers  (2022-03-12T17:44:02Z)
• Neural Data-Dependent Transform for Learned Image Compression [72.86505042102155]
  We build a neural data-dependent transform and introduce a continuous online mode decision mechanism to jointly optimize the coding efficiency for each individual image. The experimental results show the effectiveness of the proposed neural-syntax design and the continuous online mode decision mechanism.
  arXiv  Detail & Related papers  (2022-03-09T14:56:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.