arXiv:2403.07338v1 Announce Type: cross
Abstract: Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding (D$^2$-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive density model is designed to encode semantic features according to their distributions. Second, digital channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates via the analysis of the Bayesian model and Lipschitz assumption on the DNNs. Then to minimize the E2E distortion, a two-step algorithm is proposed to control the source-channel rates for a given channel signal-to-noise ratio. Simulation results reveal that the proposed framework outperforms classic deep JSCC and mitigates the cliff and leveling-off effects, which commonly exist for separation-based approaches.
Semantic Communications and the Need for D$^2$-JSCC
In the era of sixth-generation applications, semantic communications (SemCom) have emerged as a crucial paradigm. SemCom involves transmitting the semantic features of data using artificial intelligence algorithms to achieve efficient communication. However, most existing SemCom techniques rely on deep neural networks (DNNs) for analog source-channel mappings, which are incompatible with digital communication architectures.
This is where the novel framework of digital deep joint source-channel coding (D$^2$-JSCC) comes into play. It is designed specifically for image transmission in SemCom and addresses the issue of integrating digital source and channel coding to reduce end-to-end (E2E) distortion.
The Framework of D$^2$-JSCC
The framework of D$^2$-JSCC leverages two components: deep source coding with an adaptive density model and digital channel coding. These components are jointly optimized to minimize E2E distortion.
Deep source coding is responsible for encoding semantic features based on their distributions. The adaptive density model allows for efficient encoding by adjusting to the characteristics of the data. On the other hand, digital channel coding protects the encoded features against channel distortion.
Characterizing E2E Distortion and Joint Design
One of the key aspects of the D$^2$-JSCC framework is characterizing the E2E distortion as a function of the source and channel rates. This is achieved through an analysis of the Bayesian model and the Lipschitz assumption on the DNNs.
By understanding the relationship between the source and channel rates, the two-step algorithm proposed in this paper controls the rates to minimize the E2E distortion for a given channel signal-to-noise ratio.
Advantages and Potential Applications
The simulation results demonstrate that the proposed D$^2$-JSCC framework outperforms classic deep JSCC and effectively mitigates the cliff and leveling-off effects commonly observed in separation-based approaches.
From a multidisciplinary perspective, the concepts presented in this paper have implications for a wide range of fields. In the domain of multimedia information systems, the integration of SemCom and digital deep source-channel coding opens up new possibilities for efficient and reliable transmission of multimedia content.
Furthermore, the D$^2$-JSCC framework has significant relevance to the fields of animations, artificial reality, augmented reality, and virtual realities. These immersive technologies heavily rely on the transmission of rich visual content, and the proposed framework can enhance the quality and fidelity of such content.
In conclusion, the introduction of the D$^2$-JSCC framework offers a promising approach to enable efficient and optimized transmission of semantic features in SemCom. Its joint design of digital source and channel coding, along with the characterization of E2E distortion, sets the stage for advancements in multimedia information systems and immersive technologies. This research paves the way for improved communication efficiencies and enhanced user experiences in the era of sixth-generation applications.