# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 1 Introduction  
Traditional communication systems are primarily designed for reliable transmission of symbol (bit) streams, irrespective of specific requirements to support the task at the receiver. However, as communication technologies approach the Shannon capacity limit, traditional communication methods struggle to meet the growing demands for higher data volumes in 5G/6G networks and AI-driven applications [1]. Unlike traditional communication, semantic communication focuses on extracting and transmitting meaningful, task-critical features of the source information [2]. By preserving task-critical content while reducing redundancy, semantic communication shows higher efficiency and ensures better task performance at the receiver, making it a promising paradigm for next-generation intelligent communications [3]. Recent studies have demonstrated the effectiveness of semantic communication across various data modalities, including images [4, 5, 6, 7], texts [8, 9, 10, 11], videos [12, 13], and question-answering (QA) tasks [14, 15]. For image-based semantic communication, several works leverage joint source-channel coding (JSCC) to enhance transmission efficiency while preserving task-critical information. An attention-based DeepJSCC model is introduced in [4], which dynamically adapts to varying signal-to-noise ratio (SNR) levels during image transmission. Furthermore, the authors in [5] extend this approach to a multi-task semantic communication framework, enabling image recovery and classification simultaneously. For text-based semantic communication, deep learning-based methods have been widely adopted to enhance transmission reliability while reducing bandwidth usage. The authors in [8] introduce DeepSC, a semantic communication system designed to maximize semantic accuracy in text transmission. To further optimize transmission efficiency, a lightweight distributed semantic communication model is proposed in [9], which enables IoT devices to transmit textual information at the semantic level. For video-based semantic communication, the authors in [12] present an adaptive DeepJSCC framework tailored for wireless video delivery, ensuring high-quality transmission under varying network conditions. To reduce bandwidth requirement while maintaining perceptual quality, a novel video transmission framework, VISTA, is proposed in [13] to transmit the most relevant video semantics. In QA tasks, semantic communication systems are evolving toward human-like communication through contextual understanding. In [14], DeepSC-VQA is introduced as a transformer-enabled multi-user semantic communication model for visual QA, which can effectively integrate text and image modalities at the transmitter and receiver. Similarly, the authors in [15] propose a memory-aided semantic communication system that utilizes a memory queue to enhance context-awareness and semantic extraction at the receiver. These studies highlight the diverse applications of semantic communication across different data types, each involving distinct tasks at the receiver. To effectively support these tasks, semantic coding plays a crucial role in extracting task-critical information while reducing redundancy. To this end, several recent studies have focused on semantic coding methods tailored to various application scenarios [16; 17]. In [18], the authors propose a deep learning-based joint transmission-recognition scheme for IoT devices to optimize task-specific recognition. This approach significantly enhances classification performance, particularly in low SNR conditions. Reference [19] develops an adaptable, deep learning-based semantic compression approach, which can compress the semantics according to their importance relevant to the image classification task. In [20], deep learning-based feature extraction with JSCC is leveraged to achieve efficient image retrieval in edge computing environments.