Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916117766733824 |
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| author | Sun, Ruijin Cheng, Nan Li, Changle Chen, Fangjiong Chen, Wen |
| author_facet | Sun, Ruijin Cheng, Nan Li, Changle Chen, Fangjiong Chen, Wen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G Sun, Ruijin Cheng, Nan Li, Changle Chen, Fangjiong Chen, Wen Networking and Internet Architecture Machine Learning Signal Processing 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. |
| title | Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G |
| topic | Networking and Internet Architecture Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2402.01665 |