Knowledge-Driven Deep Learning Paradigms for Wireless Network
  Optimization in 6G
Ruijin Sun Nan Cheng Changle Li Fangjiong Chen Wen Chen
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.