Source-channel Coding Research Articles

Deep Joint Source-Channel Coding for Adaptive Image Transmission Over MIMO Channels

Deep Joint Source-Channel Coding for Adaptive Image Transmission Over MIMO Channels

Channel-Blind Joint Source-Channel Coding for Wireless Image Transmission.

Joint source-channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder-decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model's performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.

Rate-Distortion-Perception Optimized Neural Speech Transmission System for High-Fidelity Semantic Communications.

We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate-distortion-perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source-channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.

Joint Task and Data-Oriented Semantic Communications: A Deep Separate Source-Channel Coding Scheme

Joint Task and Data-Oriented Semantic Communications: A Deep Separate Source-Channel Coding Scheme

Engineering a Lightweight Deep Joint Source-Channel Coding Based Semantic Communication System

Engineering a Lightweight Deep Joint Source-Channel Coding Based Semantic Communication System

Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications

Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications

Joint Source-Channel Coding System for 6G Communication: Design, Prototype and Future Directions

Joint Source-Channel Coding System for 6G Communication: Design, Prototype and Future Directions

SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding

SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding

An Effecient Joint Source-Channel Coding Scheme for Wireless Hierarchical Federated Learning and Its Information-Theoretic Analysis

An Effecient Joint Source-Channel Coding Scheme for Wireless Hierarchical Federated Learning and Its Information-Theoretic Analysis

Content Adaptive Distributed Joint Source-Channel Coding for Image Transmission With Hyperprior

Content Adaptive Distributed Joint Source-Channel Coding for Image Transmission With Hyperprior

A New Joint Source-Channel Coding for Short-Packet Communications

A New Joint Source-Channel Coding for Short-Packet Communications

Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization

Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization

Lightweight Joint Source-Channel Coding for Semantic Communications

Lightweight Joint Source-Channel Coding for Semantic Communications

Joint Source-Channel Coding for a Multivariate Gaussian Over a Gaussian MAC Using Variational Domain Adaptation

Joint Source-Channel Coding for a Multivariate Gaussian Over a Gaussian MAC Using Variational Domain Adaptation

Deep Joint Source-Channel Coding for Image Transmission With Visual Protection

Deep Joint Source-Channel Coding for Image Transmission With Visual Protection

Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Models

The implementation of joint source-channel coding (JSCC) schemes using deep learning has accelerated the development of semantic communication research. Existing JSCC schemes based on deep learning (DL) are trained on a fixed signal-to-noise ratio (SNR); however, these trained models are not designed for scenarios in which the SNR is dynamic. Therefore, a novel semantic adaptive model for semantic communication—called joint source-channel coding with adaptive models (AMJSCC)—that has a semantic adaptive model selection (SAMS) module is proposed. The joint source-channel encoding (JSCE) model and the joint source-channel decoding (JSCD) model adapt according to both real-time channel conditions and system available computational power resources. Furthermore, residual networks with different layers are investigated to further improve the accuracy of information recovery. Simulation results demonstrate that our model can achieve higher recovery similarity and is more robust and adaptive to the SNR and communication resources. Meanwhile, compared to the state-of-the-art deep JSCC methods, it reduces storage space and communication resource consumption.

Energy-Limited Joint Source-Channel Coding of Gaussian Sources over Gaussian Channels with Unknown Noise Level.

We consider the problem of transmitting a Gaussian source with minimum mean square error distortion over an infinite-bandwidth additive white Gaussian noise channel with an unknown noise level and under an input energy constraint. We construct a universal joint source-channel coding scheme with respect to the noise level, that uses modulo-lattice modulation with multiple layers. For each layer, we employ either analog linear modulation or analog pulse-position modulation (PPM). We show that the designed scheme with linear layers requires less energy compared to existing solutions to achieve the same quadratically increasing distortion profile with the noise level; replacing the linear layers with PPM layers offers an additional improvement.

Joint Design of Source-Channel Codes With Linear Source Encoding Complexity and Good Channel Thresholds Based on Double-Protograph LDPC Codes

Joint Design of Source-Channel Codes With Linear Source Encoding Complexity and Good Channel Thresholds Based on Double-Protograph LDPC Codes

Deep Joint Source-Channel Coding for Semantic Communications

Deep Joint Source-Channel Coding for Semantic Communications

Excess Distortion Exponent Analysis for Semantic-Aware MIMO Communication Systems

In this paper, the analysis of excess distortion exponent for joint source-channel coding (JSCC) in semantic-aware communication systems is presented. By introducing an unobservable semantic source, we extend the classical results by Csiszar to semantic-aware communication systems. Both upper and lower bounds of the exponent for the discrete memoryless source-channel pair are established. Moreover, an extended achievable bound of the excess distortion exponent for MIMO systems is derived. Further analysis explores how the block fading and numbers of antennas influence the exponent of semantic-aware MIMO systems. Our results offer some theoretical bounds of error decay performance and can be used to guide future semantic communications with joint source-channel coding scheme.