Search Results for author: Tongyang Xu

Found 7 papers, 0 papers with code

A Low-Cost Multi-Band Waveform Security Framework in Resource-Constrained Communications

no code implementations1 Feb 2024 Tongyang Xu, Zhongxiang Wei, Tianhua Xu, Gan Zheng

In this paper, we propose a multi-carrier based multi-band waveform-defined security (WDS) framework, independent from CSI and RF chains, to defend against AI eavesdropping.

Index Modulation Pattern Design for Non-Orthogonal Multicarrier Signal Waveforms

no code implementations18 Apr 2022 Yinglin Chen, Tongyang Xu, Izzat Darwazeh

Spectral efficiency improvement is a key focus in most wireless communication systems and achieved by various means such as using large antenna arrays and/or advanced modulation schemes and signal formats.

An Experimental Proof of Concept for Integrated Sensing and Communications Waveform Design

no code implementations9 Feb 2022 Tongyang Xu, Fan Liu, Christos Masouros, Izzat Darwazeh

This experimental work focuses on a dual-functional radar sensing and communication framework where a single radiation waveform, either omnidirectional or directional, can realize both radar sensing and communication functions.

Waveform-Defined Security: A Low-Cost Framework for Secure Communications

no code implementations21 Dec 2021 Tongyang Xu

Communication security could be enhanced at physical layer but at the cost of complex algorithms and redundant hardware, which would render traditional physical layer security (PLS) techniques unsuitable for use with resource-constrained communication systems.

Wavelet Classification for Over-the-Air Non-Orthogonal Waveforms

no code implementations21 Jun 2020 Tongyang Xu, Izzat Darwazeh

Composite statistical features are investigated and the wavelet enabled two-dimensional time-frequency feature grid is further simplified into a one-dimensional feature vector via proper statistical transform.

Classification General Classification

Deep Learning for Over-the-Air Non-Orthogonal Signal Classification

no code implementations14 Nov 2019 Tongyang Xu, Izzat Darwazeh

Experimental results indicate that transfer learning based CNN can efficiently distinguish different signal formats in both line-of-sight and non-line-of-sight scenarios with great accuracy improvement relative to the non-transfer-learning approaches.

General Classification Transfer Learning

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