Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
Author:
Affiliation:
1. National Mobile Communications Research Laboratory, Southeast University, Nanjing, China
2. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K
Funder
National Natural Science Foundation of China
Scientific Research Foundation of Graduate School of Southeast University
Scholarship from the China Scholarship Council
National Science and Technology Council of Taiwan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
http://xplorestaging.ieee.org/ielx7/7693/10064475/09900133.pdf?arnumber=9900133
Reference57 articles.
1. Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs
2. Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels
3. Efficient channel estimation for millimeter wave MIMO with limited RF chains
4. Analog-to-Digital Converters: Digitizing the Analog World
5. Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems
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