In goal-oriented communications, the objective of the receiver is often to apply a Deep-Learning model, rather than reconstructing the original data. In this context, direct learning over compressed data, without any prior decoding, holds promise for enhancing the time-efficient execution of inference models at the receiver. However, conventional entropic-coding methods like Huffman and Arithmetic break data structure, rendering them unsuitable for learning without decoding. In this paper, we propose an alternative approach in which entropic coding is realized with Low-Density Parity Check (LDPC) codes. We hypothesize that Deep Learning models can more effectively exploit the internal code structure of LDPC codes. At the receiver, we leverage a specific class of Recurrent Neural Networks (RNNs), specifically Gated Recurrent Unit (GRU), trained for image classification. Our numerical results indicate that classification based on LDPC-coded bit-planes surpasses Huffman and Arithmetic coding, while necessitating a significantly smaller learning model. This demonstrates the efficiency of classification directly from LDPC-coded data, eliminating the need for any form of decompression, even partial, prior to applying the learning model.

翻译：在目标导向通信中，接收端的目标通常是应用深度学习模型，而非重建原始数据。在此背景下，无需任何先验解码即可直接对压缩数据进行学习，有望提升接收端推理模型的时间高效执行能力。然而，传统的熵编码方法（如哈夫曼编码和算术编码）会破坏数据结构，导致其不适合无需解码的学习场景。本文提出一种替代方案，即利用低密度奇偶校验（LDPC）码实现熵编码。我们假设深度学习模型能够更有效地利用LDPC码的内部结构。在接收端，我们采用一类特定的循环神经网络（RNN），即门控循环单元（GRU），并针对图像分类任务进行训练。数值结果表明，基于LDPC码编码位平面的分类性能优于哈夫曼编码和算术编码，同时所需的学习模型规模显著更小。这证明了直接从LDPC码编码数据中进行分类的有效性，无需在应用学习模型前进行任何形式的解压缩（即使是部分解压缩）。