Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm

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 waveform recommendation system (CWRS) to generate waveform candidates. In the CWRS, an improved involution1D operator, which is channel-agnostic and space-specific, is introduced according to the characteristics of KG-based waveform representation for feature extraction, and the multi-head self-attention is adopted to weigh the influence of various components for feature fusion. Meanwhile, multilayer perceptron-based collaborative filtering is used to evaluate the matching degree between the requirement and the waveform candidate. Simulation results show that the proposed CWKG-based CWRS can automatically recommend waveform candidates with high reliability.