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Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
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DOI：10.1109/MNET.2024.3352257.png)
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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.
Keyword:
Knowledge engineering
Wireless networks
Optimization
Neural networks
6G mobile communication
Training data
Data models
Deep learning
期刊
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6.3
论文数:
2.6K
被引数:
1.1W
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Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning.png)
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