用空闲频段传输压缩图像，实现低延迟任务感知通信
Low-Latency Task-Oriented Image Transmission with Opportunistic Spectrum Access
- 用VQ-VAE压缩图像，通过空闲频段发送离散潜在表示
- 相比传统编码，延迟降低79倍以上，准确率仅降5.7%
- 适合资源受限、信道恶劣下的实时任务系统
面向可靠数据重建的通信系统通常采用分离的源编码与信道编码，在频谱有限和信道衰落条件下延迟较高。为此，本文提出一种基于机会频谱接入的传输框架：发射端使用向量量化变分自编码器（VQ-VAE）学习图像的离散潜在表示，并通过标准数字调制在空闲授权频段上传输；接收端基于人工智能仍可重构任务相关信息。我们构建了跨层延迟模型，涵盖压缩、块错误、重传及随机信道接入等因素。在延迟-精度权衡测试中，该方案相较传统编码方法实现至少79倍和3.3倍的延迟降低，分类准确率仅下降5.7%和2.4%。该框架在频谱受限和恶劣信道下仍能实现低延迟通信与可靠任务执行。
原文摘要 · Abstract (English)
Communication systems designed for reliable data reconstruction, rather than task-oriented communication, typically rely on separate source and channel coding and incur high latency under limited spectrum availability and fading channels. To address this, we propose a transmission framework with opportunistic spectrum access, in which the transmitter sends discrete latent representations learned via a vector-quantized variational autoencoder (VQ-VAE) over idle licensed channels using standard digital modulation. The AI-powered receiver is still able to reconstruct task-related information from the heavily compressed data. We develop a cross-layer latency model that accounts for compression, block errors, retransmissions, and stochastic channel access. Results on latency-accuracy trade-offs show that the proposed scheme achieves at least 79- and 3.3-fold latency reductions with only 5.7% and 2.4% drops in classification accuracy compared to benchmarks using conventional source and channel coding. The framework enables low-latency communication and reliable task execution even under limited spectrum availability and challenging channel conditions.
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