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This paper introduces D2AJSCC, a framework that enables the deployment of high-fidelity analog joint source-channel coding (JSCC) on standard digital physical layers (PHYs) by addressing the hardware-software mismatch between continuous-valued analog JSCC and discrete digital PHYs. D2AJSCC uses orthogonal frequency-division multiplexing (OFDM) as a waveform synthesizer, employing computational PHY inversion to determine bitstreams that emulate analog waveforms and a differentiable neural surrogate (ProxyNet) for end-to-end training. Results show D2AJSCC achieves near-ideal analog JSCC performance for image transmission over WiFi PHY, demonstrating graceful degradation across SNR conditions compared to baseline methods.
Finally, analog joint source-channel coding can be deployed on standard digital transceivers, unlocking the potential of semantic communication on existing infrastructure.
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.
