Semi-Deterministic Dynamic Millimeter-Wave Channel Modeling Based on an Optimal Neural Network Approach-Reference-Cited by-同舟云学术

Semi-Deterministic Dynamic Millimeter-Wave Channel Modeling Based on an Optimal Neural Network Approach

Published:2022-06 Issue:6 Volume:70 Page:4082-4095
ISSN:0018-926X
Container-title:IEEE Transactions on Antennas and Propagation
language:
Short-container-title:IEEE Trans. Antennas Propagat.

Author:

Zhao Xiongwen¹^ORCID,Fu Zihao¹^ORCID,Fan Wei²^ORCID,Zhang Yu¹^ORCID,Geng Suiyan¹,Du Fei¹^ORCID,Qin Peng¹^ORCID,Zhou Zhenyu¹^ORCID,Zhang Lei³

Affiliation:

1. State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China

2. Department of Electronic Systems, Aalborg University, Aalborg, Denmark

3. Shandong Electric Power Research Institute, State Grid Corporation of China, Jinan, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8/9794936/09695951.pdf?arnumber=9695951

Reference43 articles.

1. Channel Measurements, Modeling, Simulation and Validation at 32 GHz in Outdoor Microcells for 5G Radio Systems

2. 5G Millimeter Wave Channel Modeling and Simulations for a High-Voltage Substation

3. Optimization by Simulated Annealing

4. RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network

5. Playback of 5G and Beyond Measured MIMO Channels by an ANN-Based Modeling and Simulation Framework

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