no code implementations • 7 Jun 2024 • Tongyang Xu, Shuangyang Li, Jinhong Yuan
This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924, with the aim of enhancing performance in industrial Internet of things (IIoT).
no code implementations • 1 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.
no code implementations • 18 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.
no code implementations • 9 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.
no code implementations • 21 Dec 2021 • Tongyang Xu
Wireless signals are commonly used for communications.
no code implementations • 21 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.
no code implementations • 21 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.
no code implementations • 14 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.