Classification-Driven Discrete Neural Representation Learning for Semantic Communications
Classification-Driven Discrete Neural Representation Learning for Semantic Communications
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DOI：
10.1109/jiot.2024.3354312
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发表时间：
2024-05
影响因子：
10.6
通讯作者：
Wenhui Hua;Longhui Xiong;Sicong Liu;Lingyu Chen;Xuemin Hong;João F. C. Mota;Xiang Cheng
中科院分区：
文献类型：
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作者：
Wenhui Hua;Longhui Xiong;Sicong Liu;Lingyu Chen;Xuemin Hong;João F. C. Mota;Xiang Cheng
语义通信是物联网（IoT）的关键推动因素。通过专注于数据的语义含义而不是位级恢复，它允许智能代理以低得多的速率传递必要的信息。一种很有前途的语义通信技术是离散神经表征学习（DNRL）。其主要思想是从低层次、高维的感官数据中学习离散的符号，这样每个符号都被根植于感官领域中有意义的模式。本文提出了一种DNRL方案，将三种机制集成到一个连贯的框架中：1）对比学习; 2）稀疏编码; 3）神经索引量化。所提出的计划是适用于公共图像数据集的有损图像压缩与下游分类任务。结果表明，该方法产生了一个高度紧凑的连续的潜在表示和语义离散表示，与边际退化的分类精度。的可解释性和一致性的学习subsymbolic离散表示的神经网络解剖，神经网络可视化，和Maxillary- $K$分类测试，我们提出的一个概念，以评估分类性能的实验进行了验证极其压缩的信号。最后，离散表示是有用的速率自适应分布式传感应用中的低到中等的信噪比（SNR）。
Semantic communications is a key enabler of the Internet of Things (IoT). By focusing on the semantic meaning of data rather than bit-level recovery, it allows intelligent agents to communicate necessary information at much lower rates. A promising technique for semantic communications is discrete neural representation learning (DNRL). The main idea is to learn discrete symbols from low-level, high-dimensional sensory data, such that each symbol is grounded to a meaningful pattern in the sensory domain. This article proposes a DNRL scheme that integrates three mechanisms into a coherent framework: 1) contrastive learning; 2) sparse coding; and 3) neural index quantization. The proposed scheme is applied to public image data sets for lossy image compression with a downstream classification task. Results show that the proposed approach produces a highly compact continuous latent representation and a semantic discrete representation, with marginal degradation to the classification accuracy. The interpretability and consistency of the learned subsymbolic discrete representations are validated by experiments of neural-net dissection, neural-net visualization, and MaxAmp- $K$ classification test, a concept that we propose to evaluate classification performance of extremely compressed signals. Finally, the discrete representations are shown to be useful in rate-adaptive distributed sensing applications at the low-to-medium signal-to-noise ratios (SNRs).