Performance Analysis and Deep Learning Evaluation of URLLC Full-Duplex Energy Harvesting IoT Networks over Nakagami-m Fading Channels-Reference-Cited by-同舟云学术

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Performance Analysis and Deep Learning Evaluation of URLLC Full-Duplex Energy Harvesting IoT Networks over Nakagami-m Fading Channels

Published:2023-07-02 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Statistical Signal Processing Workshop (SSP)
language:
Short-container-title:

Author:

Nguyen Toan-Van¹,Huynh-The Thien²,Quoc Bao Vo Nguyen³

Affiliation:

1. International University-VNU,School of Computer Science and Engineering,HCMC,Vietnam

2. HCMC University of Technology and Education,Department of Computer and Communication Engineering,Vietnam

3. Posts and Telecommunications Institute of Technology,Ho Chi Minh,Vietnam,710372

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10207925/10207926/10207990.pdf?arnumber=10207990

Reference13 articles.

1. Physical-Layer Security in Full-Duplex Multi-Hop Multi-User Wireless Network With Relay Selection

2. Design and analysis of full-duplex massive antenna array systems based on wireless power transfer;mohammadi;IEEE Trans Commun,2020

3. Performance of Full-Duplex AF Relaying in the Presence of Residual Self-Interference

4. A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Transfer Learning-Based Performance Prediction of URLLC in Independent and Not Necessarily Identically Distributed Interference Networks;IEEE Access;2024

2. Secure Short-Packet Communications in Dual-Hop Cooperative IoT Networks with Multiple Eavesdroppers;2023 RIVF International Conference on Computing and Communication Technologies (RIVF);2023-12-23

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