핵심 개념
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.
초록
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.
통계
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.
인용구
"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."