Knowledge-Centric Deep Learning Frameworks for System-Level Intelligence in 6G Wireless Networks

The development of the sixth-generation (6G) wireless networks requires smart system-level decision-making models that can accommodate the ultra-low latency, enormous connectivity, combined sensing and communication, and ultra-reliable network models. Whereas there is high ability in modeling data-driven deep learning methodologies, they are usually characterized by high sample complexity, low interpretability, and poor generalization in non-stationary and highly dynamic wireless settings. In this paper, a new knowledge-based deep learning (KCDL) framework is presented that incorporates domain knowledge, expert-developed policies, physically-based model, and network semantics within the deep learning model architecture, training, and inference phase of the network to support statistically robust system-level intelligence in 6G networks. The most important feature of the presented framework is the hybrid modeling method that is unified and makes prior knowledge a structural constraint and semantic guidance in learning modules, which makes undesired exploration less essential and decision stability better. Thorough statistical analysis of the proposed KCDL model using extensive simulation of representative 6G scenarios, such as intelligent radio resource management, network slicing, edge intelligence, and integrated sensing and communication, has shown that convergence in the proposed KCDL framework is 40 - 45 times faster, end-to-end latency is reduced by 20-25 percent, spectrum utilization efficiency is increased by 15 - 20 percent, and performance deterioration under unseen network conditions is less than 8 percent relative to over 25 percent with conventional data-driven baselines. These statistically significant advantages affirm the fact that knowledge-based deep learning offers scalable, power efficiency, interpretability, and reliability of the autonomous, resilient, and context-aware 6G network architectures.