Unlocking High-Fidelity Analog Joint Source-Channel Coding on Standard Digital Transceivers
🤖 AI Summary
This work addresses the incompatibility of analog joint source-channel coding (Analog JSCC) with standard digital physical layers, which rely on discrete modulation and non-differentiable operations. To bridge this gap, the authors propose the D2AJSCC framework, which leverages the OFDM subcarrier structure as a waveform synthesizer. By computing an inverse physical-layer mapping to generate bitstreams that approximate ideal analog signals and introducing a differentiable proxy network (ProxyNet) to preserve end-to-end gradient flow, the method enables fully trainable Analog JSCC deployment on off-the-shelf digital transceivers—without requiring hardware modifications or white-box PHY access. Evaluated in Wi-Fi PHY-based image transmission simulations, the system closely approaches the performance of ideal Analog JSCC across varying SNR levels, exhibits graceful degradation, and significantly outperforms baseline methods that suffer from cliff effects.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
analog joint source-channel coding
digital transceivers
hardware-software mismatch
non-differentiable operations
semantic communications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Analog Joint Source-Channel Coding
Digital PHY Compatibility
OFDM Waveform Synthesis
Differentiable Proxy Model
Semantic Communication
🔎 Similar Papers
No similar papers found.
