Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm
Wei Huang, Tianfu Qi, Yundi Guan, Qihang Peng, Jun Wang

TL;DR
This paper introduces a novel knowledge graph-based paradigm for waveform design in communication systems, enabling efficient, intelligent recommendations of waveform candidates through structured semantic and numerical data integration.
Contribution
It proposes a new paradigm utilizing a communication waveform knowledge graph and an intelligent recommendation system with advanced feature extraction and evaluation methods.
Findings
High reliability in waveform recommendation demonstrated in simulations
Effective integration of semantic knowledge and numerical parameters
Enhanced efficiency over traditional waveform design methods
Abstract
Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis, which is usually laborious and time-consuming. In this paper, we investigate the waveform design from a novel perspective and propose a new waveform design paradigm with the knowledge graph (KG)-based intelligent recommendation system. The proposed paradigm aims to improve the design efficiency by structural characterization and representations of existing waveforms and intelligently utilizing the knowledge learned from them. To achieve this goal, we first build a communication waveform knowledge graph (CWKG) with a first-order neighbor node, for which both structured semantic knowledge and numerical parameters of a waveform are integrated by representation learning. Based on the developed CWKG, we further propose an intelligent communication…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Cognitive Computing and Networks
