Performance analysis for IRS-assisted MEC networks with unit selection-Reference-Cited by-同舟云学术

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Performance analysis for IRS-assisted MEC networks with unit selection

Published:2022-12 Issue: Volume:55 Page:101869
ISSN:1874-4907
Container-title:Physical Communication
language:en
Short-container-title:Physical Communication

Author:

Lu Jinrong,Lai Shiwei,Xia Junjuan,Tang Maobin,Fan Chengyuan,Ou Jiangtao,Fan Dahua

Funder

NSFC

Guangzhou Science and Technology Program key projects

Guangzhou University

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering

Reference26 articles.

1. Distributed machine learning for multiuser mobile edge computing systems;Guo;IEEE J. Sel. Top. Signal Process.,2022

2. Toward optimally efficient search with deep learning for large-scale MIMO systems;He;IEEE Trans. Commun.,2022

3. DQN-based mobile edge computing for smart internet of vehicle;Zhang;EURASIP J. Adv. Signal Process.,2022

4. On the impact of time-correlated fading for downlink NOMA;Cai;IEEE Trans. Commun.,2019

5. Energy-efficient resource allocation for secure NOMA-enabled mobile edge computing networks;Wu;IEEE Trans. Commun.,2020

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1. A survey on reconfigurable intelligent surfaces assisted multi-access edge computing networks: State of the art and future challenges;Computer Science Review;2024-11

2. On the performance of multiple‐IRS aided wireless networks over Nakagami‐m$$ m $$ fading channels;International Journal of Communication Systems;2024-08-03

3. A Survey on Resource Allocation and Energy Efficient Maximization for IRS-Aided MIMO Wireless Communication;IEEE Access;2024

4. Collaborative framework for UAVs-assisted mobile edge computing: a proximity policy optimization approach;The Journal of Supercomputing;2023-12-27

5. Enhancing Computation Offloading by Integrating MEC and IRS;2023 IEEE Future Networks World Forum (FNWF);2023-11-13

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