4.3k citations
- National Taiwan UniversityTW74 papers
- Southeast UniversityCN65 papers
- Princeton UniversityUS59 papers
- Center for Astrophysics Harvard & SmithsonianUS58 papers
- National Radio Astronomy ObservatoryUS58 papers
- Chinese Academy of SciencesCN56 papers
- Institute of Astronomy and Astrophysics, Academia SinicaTW54 papers
- National Taiwan Normal UniversityTW51 papers
- The University of TokyoJP51 papers
- University College LondonGB51 papers
- Max Planck Institute for Radio AstronomyDE47 papers
- Peking UniversityCN47 papers
33 papers · 1 filter
Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI
Yu Han, Wankai Tang, Shi Jin +2
Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS…
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
Peiwen Jiang, Tianqi Wang, Bin Han +5
Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brou…
Dynamical precession of spin in the two-dimensional spin-orbit coupled systems
Tsung-Wei Chen, Zhi-Yang Huang, Dah-Wei Chiou
We investigate the spin dynamics in the two-dimensional spin-orbit coupled system subject to an in-plane (- plane) constant electric field, which is assumed to be turned on a…
Colouring of generalized signed planar graphs
Ligang Jin, Tsai-Lien Wong, Xuding Zhu
Assume is a graph. We view as a symmetric digraph, in which each edge of is replaced by a pair of opposite arcs and . Assume is an inve…
ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers
Xuanxuan Gao, Shi Jin, Chao-Kai Wen +1
In this article, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing…
A Model-Driven Deep Learning Network for MIMO Detection
Hengtao He, Chao-Kai Wen, Shi Jin +1
In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding…