计算机科学
可解释性
领域知识
推论
知识整合
知识抽取
人工智能
无线网络
知识表示与推理
开放式知识库连接
基于知识的系统
数据科学
分布式计算
机器学习
知识管理
无线
个人知识管理
组织学习
电信
作者
Ruijin Sun,Nan Cheng,Changle Li,Fangjiong Chen,Wen Chen
出处
期刊:IEEE Network
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-1
被引量:1
标识
DOI:10.1109/mnet.2024.3352257
摘要
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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