Resource allocation scheme to reduce computing energy consumption of uRLLC and eMBB services in MEC scenarios-Reference-Cited by-同舟云学术

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Resource allocation scheme to reduce computing energy consumption of uRLLC and eMBB services in MEC scenarios

Published:2024-05-24 Issue: Volume: Page:1584-1589
ISSN:
Container-title:2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)
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Author:

Wang Pu¹,Jiang Chengling²,Mao Zhen¹,Chen Yunfan¹,Liu Feng³

Affiliation:

1. Nanjing Power Supply Company,State Grid Jiangsu Electric Power CO., Ltd,Nanjing,China

2. State Grid Jiangsu Electric Power CO., Ltd,Nanjing,China

3. NARI Information & Communication Technology Co.Ltd,State Grid Electric Power Research Institute (NARI Group Co., Ltd),Nanjing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10574856/10574899/10574936.pdf?arnumber=10574936

Reference17 articles.

1. eMBB-URLLC Resource Slicing: A Risk-Sensitive Approach

2. A Deep-Reinforcement-Learning-Based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR

3. Contract-Based Scheduling of URLLC Packets in Incumbent EMBB Traffic

4. 5G Multi-RAT URLLC and eMBB Dynamic Task Offloading With MEC Resource Allocation Using Distributed Deep Reinforcement Learning

5. Energy Efficient Communication and Computation Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond

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