AI 中文总结
本文提出TS-JSCC框架，通过尾部结构化稀疏化与分阶段神经调节模块，实现单模型自适应无线图像传输，在多数据集信道场景下性能优于多数学习型JSCC基线。
AI 中文摘要
学习型联合信源信道编码（JSCC）通过在可微分信道模型上联合优化发射机与接收机，实现鲁棒的无线图像传输。对于带宽受限且时变的视觉链路，单一模型需支持用户可调的传输速率、适应变化的无线信道条件，同时根据空间内容动态分配资源。现有内容自适应或动态分配方案常依赖熵编码、上下文/概率预测、显式速率图或掩码，或辅助分配网络，这会增加编解码器流程的复杂度并提升边信息开销。本文提出TS-JSCC，一种具有尾部结构化稀疏化的单模型自适应JSCC框架。首先，基于L1的尾部结构化稀疏化目标鼓励每个token保留活跃特征通道前缀，同时抑制尾部通道，这可通过活跃前缀传输实现内容自适应的特征通道分配，且边信息紧凑。其次，轻量级的分阶段神经调节模块利用归一化稀疏度控制系数与信道信噪比（SNR）重新缩放中间特征，以实现单模型的传输速率与SNR自适应。在加性高斯白噪声（AWGN）和瑞利衰落信道下，针对CIFAR-10、Kodak和CLIC2021数据集的实验表明，TS-JSCC相较于最新的学习型JSCC基线实现了优异的率失真性能，且与所考虑的理想化分离基线相比仍具竞争力，同时保留了简单的一次性编解码器，无额外结构或计算开销。
英文摘要
Learned joint source-channel coding (JSCC) enables robust wireless image transmission by jointly optimizing the transmitter and receiver over differentiable channel models. For bandwidth-limited and time-varying visual links, a single model should support user-adjustable transmission rate and adapt to changing wireless channel conditions, while also dynamically allocating resources according to spatial content. Existing content-adaptive or dynamic allocation schemes often rely on entropy coding, context/probability prediction, explicit rate maps or masks, or auxiliary allocation networks, complicating the encoder-decoder pipeline and increasing side-information overhead. We propose TS-JSCC, a single-model adaptive JSCC framework with tail-structured sparsification. First, an L1-based tail-structured sparsification objective encourages each token to retain an active feature-channel prefix while suppressing trailing ones. This enables content-adaptive feature-channel allocation with compact side information through active-prefix transmission. Second, lightweight stage-wise neural regulating modules use a normalized sparsity-control coefficient and the channel signal-to-noise ratio (SNR) to rescale intermediate features for single-model transmission rate and SNR adaptation. Experiments on CIFAR-10, Kodak, and CLIC2021 under additive white Gaussian noise (AWGN) and Rayleigh fading show that TS-JSCC achieves strong rate-distortion performance against the latest learned-JSCC baselines and remains competitive with the considered idealized separation baselines, while retaining a simple one-shot encoder-decoder without extra structures or computations.
Comments16 pages, 13 figures, including supplementary material. v2: Incorporated the supplementary material into the PDF and refined cross-references and PDF metadata. The technical content and reported results in the main manuscript are unchanged
