A Novel Extreme-Learning-Machine Aided Receiver Design for THz-SM With Hardware Imperfections-Reference-Cited by-同舟云学术

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A Novel Extreme-Learning-Machine Aided Receiver Design for THz-SM With Hardware Imperfections

Published:2022-11 Issue:11 Volume:26 Page:2606-2610
ISSN:1089-7798
Container-title:IEEE Communications Letters
language:
Short-container-title:IEEE Commun. Lett.

Author:

Jiang Ke¹^ORCID,Yang Ping¹^ORCID,Zhang Bo²^ORCID,Liu Zilong³^ORCID,Fu Jialiang¹,Li Shaoqian¹

Affiliation:

1. National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China

2. Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing, China

3. School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.

Funder

National Key Research and Development Program of China

National Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/4234/9945064/09847214.pdf?arnumber=9847214

Reference26 articles.

1. Transmit Antenna Selection for Large-Scale MIMO GSM With Machine Learning

2. Efficient Selection on Spatial Modulation Antennas: Learning or Boosting

3. Transmit Antenna Selection for Full-Duplex Spatial Modulation Based on Machine Learning

4. A DNN Architecture for the Detection of Generalized Spatial Modulation Signals

5. SVM Aided Signal Detection in Generalized Spatial Modulation VLC System

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