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.

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，它将变分潜在建模与信道自适应传输相结合。与隐式吸收信道变化的基于学习的JSCC方法不同，Robust-NTC通过变分目标函数显式建模逐元素潜在分布，其中使用高斯代理来近似量化和信道噪声，使编码器-解码器无需针对特定信道训练即可捕获潜在不确定性。基于学习到的统计量，Robust-NTC还能实现率失真优化，根据实时信道条件自适应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，通过统一的资源分配框架联合优化潜在量化、比特分配、调制阶数和功率分配，在保证学习失真目标的同时最小化传输时延。仿真结果表明，对于实际OFDM系统，基于Robust-NTC的方案相较于传统分离编码方案和数字JSCC基线，在各种信道条件下均能实现更优的率失真效率和稳定的重建保真度。