Knowledge-Driven Machine Learning and Applications in Wireless Communications

The power of big data and machine learning has been drastically demonstrated in many fields during the past twenty years which somehow leads to the vague even false understanding that the huge amount of precious human knowledge accumulated to date seems to no longer matter. In this paper, we are pioneering to propose the knowledge-driven machine learning (KDML) model to exhibit that knowledge can play an important role in machine learning tasks. Compared with conventional machine learning, KDML contains a unique knowledge module based on specific domain knowledge, which is able to simplify the machine learning network structures, reduce the training overhead and improve interpretability. Channel estimation problem of wireless communication is taken as a case verification because such machine learning-based solutions face huge challenges in terms of accuracy, complexity, and reliability. We integrate the classical wireless channel estimation algorithms into different machine learning neural networks and propose KDML-based channel estimators in Orthogonal Frequency Division Multiplexing (OFDM) and Massive Multiple Input Multiple Output (MIMO) system. The experimental results in both communication systems validate the effectiveness of the proposed KDML-based channel estimators.

Automated summary

This paper proposes the knowledge-driven machine learning (KDML) model to demonstrate the importance of knowledge in machine learning tasks. Compared with conventional machine learning, KDML incorporates domain-specific knowledge to simplify networks, reduce training overhead, and improve interpretability. The effectiveness of KDML-based channel estimators is validated through experiments in the field of wireless communication.