AI 中文总结
针对分组擦除信道中的图像语义通信丢包问题，提出轻量级生成式JSCC框架LGSemCom，利用对抗学习和擦除感知加权策略实现高质量重建，推理速度提升一个数量级。
AI 中文摘要
本文针对由网络拥塞或信道波动引起的语义通信中的分组丢失问题。我们提出了LGSemCom，一种轻量级生成式分组级联合信源信道编码（JSCC）框架，用于在分组擦除信道上实现高效且鲁棒的图像传输。与传统的面向失真的恢复方法（这些方法在擦除区域产生模糊的平均结果）不同，LGSemCom将分组擦除下的图像恢复重新表述为条件生成任务。通过利用对抗性学习，所提出的解码器能够合成合理的细节，而不会增加推理时的复杂度。我们框架的一个关键创新是擦除感知加权（EAW）策略，该策略优先在擦除区域进行生成，同时在正确接收的区域保持像素保真度。为确保计算效率，LGSemCom采用基于高效长距离注意力块（ELABs）的全卷积编解码器，以低复杂度捕获全局语义依赖。大量实验表明，在严重分组丢失情况下，与现有基准相比，LGSemCom实现了优越的感知重建质量，同时实现了快一个数量级的推理速度。这些特性使LGSemCom非常适合资源受限边缘设备上的延迟敏感应用。
英文摘要
This paper addresses packet loss in semantic communication caused by network congestion or channel fluctuations. We propose LGSemCom, a lightweight generative packet-level joint source-channel coding (JSCC) framework for efficient and robust image transmission over packet erasure channels. Unlike conventional distortion-oriented recovery methods that yield blurry averages over erased regions, LGSemCom reformulates image recovery under packet erasures as a conditional generative task. By leveraging adversarial learning, the proposed decoder synthesizes plausible details without increasing complexity during inference. A key innovation of our framework is an erasure-aware weighting (EAW) strategy, which prioritizes generation in erased regions while preserving pixel fidelity in correctly received areas. To ensure computational efficiency, LGSemCom employs a fully convolutional codec based on efficient long-range attention blocks (ELABs) that capture global semantic dependencies with low complexity. Extensive experiments show that LGSemCom achieves superior perceptual reconstruction quality compared with existing benchmarks under severe packet loss, while achieving an order of magnitude faster inference. These attributes make LGSemCom highly suitable for latency-sensitive applications on resource-constrained edge devices.
