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Separate Source Channel Coding Is Still What You Need:An LLM-Based Rethinking 被引量:3
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作者 REN Tianqi LI Rongpeng +5 位作者 ZHAO Mingmin CHEN Xianfu LIU Guangyi YANG Yang ZHAO Zhifeng ZHANG Honggang 《ZTE Communications》 2025年第1期30-44,共15页
Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ... Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication systems.In other words,separate source channel coding is still what we need. 展开更多
关键词 separate source channel coding(sscc) joint source channel coding(JSCC) end-to-end communication system Large Language Model(LLM) lossless text compression Error Correction Code Transformer(ECCT)
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大模型逐像素预测赋能的图像语义通信:一种分离信源信道编码的视角
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作者 任天骐 李荣鹏 《信号处理》 北大核心 2025年第10期1657-1669,共13页
随着6G愿景的展开,语义通信成为核心技术。当前主流的基于深度学习的联合信源信道编码(Joint Source-Channel Coding, JSCC)方案虽在特定条件下性能优异,但固有的兼容性差、泛化能力弱和设计灵活性低等问题限制了其广泛应用。为应对这... 随着6G愿景的展开,语义通信成为核心技术。当前主流的基于深度学习的联合信源信道编码(Joint Source-Channel Coding, JSCC)方案虽在特定条件下性能优异,但固有的兼容性差、泛化能力弱和设计灵活性低等问题限制了其广泛应用。为应对这些挑战,本文回归分离式信源信道编码(Separate Source-Channel Coding, SSCC)范式,提出一种基于视觉大模型的分离信源信道编码框架(Large Visual Model-based Separate Source-Channel Coding Framework, LVM-SSCC)。该框架创新性地利用视觉大模型(如ImageGPT)进行自回归像素预测,并结合算术编码实现对信源的高效无损压缩;同时,在信道编码端引入纠错码Transformer(Error Correction Code Transformer,ECCT)来增强低密度奇偶校验(Low-Density Parity-Check, LDPC)码的译码鲁棒性。为实现公平比较,本文提出了统一能耗信噪比(Unified Energy Consumption-based Signal-to-Noise Ratio, SNRunified)评估基准。在CIFAR-10数据集上的大量仿真实验表明,无论在加性高斯白噪声(Additive White Gaussian Noise, AWGN)还是瑞利衰落信道下,本文提出的方案在图像重建质量(峰值信噪比(Peak Signal-to-Noise Ratio, PSNR)和结构相似性指数(Structural Similarity Index, SSIM))方面,尤其是在中高信噪比区域,均显著优于DeepJSCC和SparseSBC等主流JSCC方案,在保持与数字通信系统完全兼容的同时,于其优势信噪比区间内实现了逼近无损的极高保真度重建。本研究为分离式编码范式在未来图像语义通信中的应用提供了强有力的实证,并凸显了其在性能、兼容性与灵活性上的综合优势。 展开更多
关键词 语义通信 无损图像传输 分离信源信道编码(sscc) 大型视觉模型(LVM) 纠错码Transformer(ECCT)
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