Deep Learning-Based Channel Estimation for Wideband Hybrid MmWave Massive MIMO-Reference-Cited by-同舟云学术

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Deep Learning-Based Channel Estimation for Wideband Hybrid MmWave Massive MIMO

Published:2023-06 Issue:6 Volume:71 Page:3679-3693
ISSN:0090-6778
Container-title:IEEE Transactions on Communications
language:
Short-container-title:IEEE Trans. Commun.

Author:

Gao Jiabao¹,Zhong Caijun¹^ORCID,Li Geoffrey Ye²^ORCID,Soriaga Joseph B.³,Behboodi Arash⁴^ORCID

Affiliation:

1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

2. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K

3. Qualcomm Technologies Inc., San Diego, CA, USA

4. Qualcomm Technologies Netherlands B.V., Amsterdam, The Netherlands

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/26/10153794/10075639.pdf?arnumber=10075639

Reference47 articles.

1. Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems

2. Deep Learning based Channel Estimation for Massive MIMO with Hybrid Transceivers

3. Estimation of Broadband Multiuser Millimeter Wave Massive MIMO-OFDM Channels by Exploiting Their Sparse Structure

4. Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement Matrices

5. FDD Massive MIMO Channel Estimation With Arbitrary 2D-Array Geometry

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2. Deep unfolding based channel estimation for wideband terahertz near-field massive MIMO systems;Frontiers of Information Technology & Electronic Engineering;2024-08

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4. Model-Based Deep Learning for Massive Access in mmWave Cell-Free Massive MIMO System;2024 IEEE International Conference on Communications Workshops (ICC Workshops);2024-06-09

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