Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G

• URL: http://arxiv.org/abs/2402.01665v1
• Date: Mon, 15 Jan 2024 07:47:30 GMT
• Title: Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
• Authors: Ruijin Sun, Nan Cheng, Changle Li, Fangjiong Chen, Wen Chen
• Abstract summary: knowledge-driven deep learning aims to integrate proven domain knowledge into the construction of neural networks. This article provides a systematic review of knowledge-driven DL in wireless networks.
• Score: 28.84906559528008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the sixth-generation (6G) networks, newly emerging diversified services of massive users in dynamic network environments are required to be satisfied by multi-dimensional heterogeneous resources. The resulting large-scale complicated network optimization problems are beyond the capability of model-based theoretical methods due to the overwhelming computational complexity and the long processing time. Although with fast online inference and universal approximation ability, data-driven deep learning (DL) heavily relies on abundant training data and lacks interpretability. To address these issues, a new paradigm called knowledge-driven DL has emerged, aiming to integrate proven domain knowledge into the construction of neural networks, thereby exploiting the strengths of both methods. This article provides a systematic review of knowledge-driven DL in wireless networks. Specifically, a holistic framework of knowledge-driven DL in wireless networks is proposed, where knowledge sources, knowledge representation, knowledge integration and knowledge application are forming as a closed loop. Then, a detailed taxonomy of knowledge integration approaches, including knowledge-assisted, knowledge-fused, and knowledge-embedded DL, is presented. Several open issues for future research are also discussed. The insights offered in this article provide a basic principle for the design of network optimization that incorporates communication-specific domain knowledge and DL, facilitating the realization of intelligent 6G networks.

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