Deep Reinforcement Learning Empowered Rate Selection of XP-HARQ-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Empowered Rate Selection of XP-HARQ

Published:2023-09 Issue:9 Volume:27 Page:2363-2367
ISSN:1089-7798
Container-title:IEEE Communications Letters
language:
Short-container-title:IEEE Commun. Lett.

Author:

Wu Da¹,Feng Jiahui¹,Shi Zheng¹^ORCID,Lei Hongjiang²^ORCID,Yang Guanghua¹^ORCID,Ma Shaodan³^ORCID

Affiliation:

1. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China

2. Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

3. State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China

Funder

National Natural Science Foundation of China

Chongqing Key Laboratory of Mobile Communications Technology

Basic and Applied Basic Research Foundation of Guangdong Province

Zhuhai Basic and Applied Basic Research Foundation

Major Talent Program of Guangdong Provincial

Science and Technology Development Fund, Macau SAR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/4234/10247108/10194952.pdf?arnumber=10194952

Reference12 articles.

1. Effect of Feedback Delay on Amplify-and-Forward Relay Networks With Beamforming

2. Throughput Maximization for Polar Coded IR-HARQ Using Deep Reinforcement Learning

3. Asymptotic Outage Analysis of HARQ-IR Over Time-Correlated Nakagami- $m$ Fading Channels

4. The throughput of hybrid-ARQ protocols for the Gaussian collision channel

5. Effective Capacity for Renewal Service Processes With Applications to HARQ Systems

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