Robust Nonlinear Transform Coding: A Framework for Generalizable Joint Source-Channel Coding

TL;DR

This paper introduces Robust-NTC, a flexible joint source-channel coding framework that models uncertainty explicitly and adapts to channel conditions, improving efficiency and fidelity in OFDM systems.

Contribution

It presents a novel variational latent modeling approach for robust nonlinear transform coding that explicitly captures uncertainty and enables adaptive rate-distortion optimization.

Findings

Robust-NTC outperforms traditional schemes in rate-distortion efficiency.

It maintains stable reconstruction quality across diverse channel conditions.

The integrated resource allocation optimizes latency and distortion targets.

Abstract

This paper proposes robust nonlinear transform coding (Robust-NTC), a generalizable digital joint source-channel coding (JSCC) framework that couples variational latent modeling with channel-adaptive transmission. Unlike learning-based JSCC methods that implicitly absorb channel variations, Robust-NTC explicitly models element-wise latent distributions via a variational objective with a Gaussian proxy for quantization and channel noise, allowing encoder-decoder to capture latent uncertainty without channel-specific training. Using the learned statistics, Robust-NTC also facilitates rate-distortion optimization to adaptively select element-wise quantizers and bit depths according to online channel conditions. To support practical deployment, Robust-NTC is integrated into an orthogonal frequency-division multiplexing (OFDM) system, where a unified resource allocation framework jointly optimizes latent quantization, bit allocation, modulation order, and power allocation to minimize transmission latency while guaranteeing learned distortion targets. Simulation results demonstrate that for practical OFDM systems, Robust-NTC achieves superior rate-distortion efficiency and stable reconstruction fidelity compared to both a conventional separated coding scheme and digital JSCC baselines across various channel conditions.