Analog joint source-channel coding (JSCC) has demonstrated superior performance for semantic communications through graceful degradation across channel conditions. However, a fundamental hardware-software mismatch prevents deployment on modern digital physical layers (PHYs): analog JSCC generates continuous-valued symbols requiring infinite waveform diversity, while digital PHYs produce a finite set of discrete waveforms and employ non-differentiable operations that break end-to-end gradient flow. Existing solutions either fundamentally limit representation granularity or require impractical white-box PHY access. We introduce D2AJSCC, a novel framework enabling high-fidelity analog JSCC deployment on standard digital PHYs. Our approach exploits orthogonal frequency-division multiplexing's parallel subcarrier structure as a waveform synthesizer: computational PHY inversion determines input bitstreams that orchestrate subcarrier amplitudes and phases to emulate ideal analog waveforms. To enable end-to-end training despite non-differentiable PHY operations, we develop ProxyNet-a differentiable neural surrogate of the communication link that provides uninterrupted gradient flow while preventing JSCC degeneration. Simulation results for image transmission over WiFi PHY demonstrate that our system achieves near-ideal analog JSCC performance with graceful degradation across SNR conditions, while baselines exhibit cliff effects or catastrophic failures. By enabling next-generation semantic transmission on legacy infrastructure without hardware modification, our framework promotes sustainable network evolution and bridges the critical gap between analog JSCC's theoretical promise and practical deployment on ubiquitous digital hardware.

翻译：模拟联合信源信道编码（JSCC）通过在不同信道条件下实现性能的优雅降级，已在语义通信中展现出卓越性能。然而，硬件与软件之间的根本性不匹配阻碍了其在现代数字物理层上的部署：模拟JSCC生成连续值符号，需要无限的波形多样性，而数字物理层仅产生有限离散波形集合，并采用不可微操作，破坏了端到端的梯度流。现有解决方案要么从根本上限制了表示粒度，要么需要不切实际的白盒物理层访问权限。本文提出D2AJSCC——一种能够在标准数字物理层上实现高保真模拟JSCC部署的新型框架。该方法利用正交频分复用的并行子载波结构作为波形合成器：通过计算物理层反演确定输入比特流，从而协调子载波的幅度与相位以模拟理想的模拟波形。为在物理层不可微操作下实现端到端训练，我们开发了ProxyNet——通信链路的可微分神经代理模型，该模型在提供不间断梯度流的同时防止JSCC性能退化。基于WiFi物理层的图像传输仿真结果表明，所提系统在多种信噪比条件下均能实现接近理想的模拟JSCC性能并保持优雅降级特性，而基线方案则呈现悬崖效应或灾难性失效。该框架使得无需硬件改造即可在现有基础设施上实现下一代语义传输，既促进了网络的可持续演进，也弥合了模拟JSCC的理论优势与在泛在数字硬件上实际部署之间的关键鸿沟。