The article "Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G" by Ruijin Sun, Nan Cheng, Changle Li, Fangjiong Chen, and Wen Chen provides a comprehensive overview of knowledge-driven deep learning (KDL) in wireless network optimization. The authors highlight the challenges posed by the complexity and computational demands of 6G networks, which traditional model-based methods struggle to address. Data-driven deep learning (DDL) is noted for its fast online inference and universal approximation ability but lacks interpretability and requires abundant training data. To overcome these limitations, KDL integrates domain knowledge into neural networks, enhancing interpretability and reducing the need for extensive training data. The article proposes a holistic framework for KDL in wireless networks, encompassing knowledge sources, representation, integration, and application. It introduces a taxonomy of knowledge integration approaches, including knowledge-assisted, knowledge-fused, and knowledge-embedded DL. These approaches leverage domain knowledge to guide neural network selection, model design, and learning algorithm customization. A case study on resource management in device-to-device (D2D) networks demonstrates the effectiveness of KDL, showing superior performance, scalability, and fast inference compared to traditional methods. The authors also discuss open issues, such as handling complex constraints, theoretical analysis, and knowledge selection and aggregation, emphasizing the need for further research to fully realize the potential of KDL in 6G networks.The article "Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G" by Ruijin Sun, Nan Cheng, Changle Li, Fangjiong Chen, and Wen Chen provides a comprehensive overview of knowledge-driven deep learning (KDL) in wireless network optimization. The authors highlight the challenges posed by the complexity and computational demands of 6G networks, which traditional model-based methods struggle to address. Data-driven deep learning (DDL) is noted for its fast online inference and universal approximation ability but lacks interpretability and requires abundant training data. To overcome these limitations, KDL integrates domain knowledge into neural networks, enhancing interpretability and reducing the need for extensive training data. The article proposes a holistic framework for KDL in wireless networks, encompassing knowledge sources, representation, integration, and application. It introduces a taxonomy of knowledge integration approaches, including knowledge-assisted, knowledge-fused, and knowledge-embedded DL. These approaches leverage domain knowledge to guide neural network selection, model design, and learning algorithm customization. A case study on resource management in device-to-device (D2D) networks demonstrates the effectiveness of KDL, showing superior performance, scalability, and fast inference compared to traditional methods. The authors also discuss open issues, such as handling complex constraints, theoretical analysis, and knowledge selection and aggregation, emphasizing the need for further research to fully realize the potential of KDL in 6G networks.