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 condition. 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 digital JSCC baselines across wide-ranging SNR conditions.

翻译：本文提出鲁棒非线性变换编码（Robust-NTC），这是一种可泛化的数字联合信源信道编码（JSCC）框架，它将变分隐变量建模与信道自适应传输相结合。与那些隐式吸收信道变化的基于学习的JSCC方法不同，Robust-NTC通过一个以高斯代理模型处理量化与信道噪声的变分目标，显式地建模逐元素的隐变量分布，使得编码器-解码器能够捕获隐变量不确定性，而无需进行信道特定的训练。利用学习到的统计量，Robust-NTC还支持率失真优化，以根据在线信道条件自适应地选择逐元素量化器和比特深度。为支持实际部署，Robust-NTC被集成到正交频分复用（OFDM）系统中，其中统一的资源分配框架联合优化隐变量量化、比特分配、调制阶数和功率分配，以最小化传输延迟，同时保证学习到的失真目标。仿真结果表明，在实际OFDM系统中，与数字JSCC基线方法相比，Robust-NTC在广泛的信噪比条件下实现了更优的率失真效率和稳定的重建保真度。