Machine Learning/AI for IoT, M2M, and Computer Communication-Reference-Cited by-同舟云学术

Machine Learning/AI for IoT, M2M, and Computer Communication

Published:2019-09 Issue:9 Volume:30 Page:
ISSN:2161-3915
Container-title:Transactions on Emerging Telecommunications Technologies
language:en
Short-container-title:Trans Emerging Tel Tech

Author:

Yalew Zelalem Jembre¹,Ahmed Syed Hassan²,Choi Young‐June³,Lloret Jaime⁴

Affiliation:

1. Department of Electronic Engineering Keimyung University Daegu South Korea

2. Department of Computer Science University of Central Florida Orlando Florida

3. Department of Information and Computer Engineering Ajou University Suwon South Korea

4. Department of CommunicationsPolytechnic University of Valencia València Spain

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.3757

Reference19 articles.

1. Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications;Ortega YR;Trans Emerging Tel Tech,2019

2. On the machine learning‐based smart beamforming for wireless virtualization with large‐scale MIMO system;Sapavath NN;Trans Emerging Tel Tech,2019

3. Improving positioning accuracy based on self-organizing map and inter‐vehicular communication;Ma S;Trans Emerging Tel Tech,2019

4. An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network;Khuntia P;Trans Emerging Tel Tech,2019

5. Reinforcement learning algorithm for 5G indoor device‐to‐device communications;Sreedevi AG;Trans Emerging Tel Tech,2019

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5. A 6LoWPAN OPNET simulation model for machine‐to‐machine communications;Transactions on Emerging Telecommunications Technologies;2020-09-08