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D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications


Core Concepts
The author proposes a novel framework of digital deep joint source-channel coding (D2-JSCC) targeting image transmission in SemCom, integrating digital source and channel codings to minimize end-to-end distortion.
Abstract
Semantic communications (SemCom) introduce a new paradigm for efficient data transmission using artificial intelligence algorithms. The D2-JSCC framework optimizes source and channel codings jointly, outperforming classic deep JSCC schemes. The content discusses the challenges faced by existing SemCom techniques and introduces the concept of D2-JSCC to address these issues. By combining digital source and channel coding, the framework aims to reduce communication overhead while improving efficiency. The proposed algorithm involves two steps: model selection and retraining to optimize the system's performance. Through simulations and experiments, the D2-JSCC framework is shown to outperform traditional methods, offering a promising solution for future communication systems. Key points include: Introduction of Semantic Communications (SemCom) for efficient data transmission. Proposal of D2-JSCC framework for optimized source-channel coding. Challenges faced by existing techniques and benefits of D2-JSCC. Algorithm involving model selection and retraining for optimal performance. Experimental results demonstrating the superiority of D2-JSCC over traditional methods.
Stats
Most existing SemCom techniques rely on deep neural networks (DNNs). Proposed algorithm minimizes end-to-end distortion through joint optimization. Experiments show that D2-JSCC outperforms classic deep JSCC schemes.
Quotes
"The proposed framework features digital source and channel codings that are jointly optimized to reduce end-to-end distortion." "D2-JSCC is found to be free from undesirable cliff effect and leveling-off effect."

Key Insights Distilled From

by Jianhao Huan... at arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07338.pdf
D$^2$-JSCC

Deeper Inquiries

How does the integration of digital source and channel coding improve communication efficiency

The integration of digital source and channel coding improves communication efficiency in several ways. Firstly, by jointly optimizing the source and channel codings, the end-to-end (E2E) distortion can be minimized, leading to higher data transmission quality. This optimization ensures that the semantic features extracted from the data are efficiently encoded and protected against channel errors, resulting in improved overall system performance. Additionally, integrating digital source and channel coding allows for a more seamless communication process as it eliminates compatibility issues between analog-based systems and modern digital communication architectures. By leveraging deep learning techniques for both source encoding and channel protection, the system can adapt to varying conditions and optimize its performance based on real-time feedback.

What are the implications of minimizing end-to-end distortion in semantic communications

Minimizing end-to-end distortion in semantic communications has significant implications for enhancing communication reliability and efficiency. By reducing E2E distortion, the system can achieve higher data accuracy during transmission, ensuring that the received information closely matches the original input data. This is crucial for applications where precise semantic information needs to be preserved throughout the communication process. Minimizing distortion also leads to better utilization of network resources by reducing redundant or erroneous data transmissions, ultimately improving overall system throughput and performance.

How can the proposed D2-JSCC framework be applied to other types of data transmission beyond image processing

The proposed D2-JSCC framework can be applied to various types of data transmission beyond image processing by adapting its architecture and algorithms to suit different data formats. For text-based communications, the deep source encoder could extract semantic features from textual inputs while employing suitable entropy encoding methods for efficient compression. Similarly, audio signals could undergo feature extraction using neural networks tailored for sound analysis before being transmitted through a digital channel coder designed specifically for audio data protection. By customizing the D2-JSCC framework's components according to each type of data being transmitted—such as adjusting quantization levels or error correction codes—the system can effectively handle diverse forms of information while maintaining high communication efficiencies across various applications such as IoT devices or multimedia streaming services.
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