Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning

Published:2023-04 Issue:4 Volume:72 Page:5407-5412
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

López-Sánchez Maria¹,Villena-Rodríguez Alejandro¹^ORCID,Gómez Gerardo¹^ORCID,Martín-Vega Francisco J.¹^ORCID,Aguayo-Torres Mari Carmen¹^ORCID

Affiliation:

1. Communications and Signal Processing Lab, Instituto Universitario de Investigación en Telecomunicación (TELMA), Universidad de Málaga, Málaga, Spain

Funder

Junta de Andalucía

European Union and the European Fund for Regional Development FEDER

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10104191/09961944.pdf?arnumber=9961944

Reference13 articles.

1. Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

2. Radio Resource Allocation Algorithms for the Downlink of Multiuser OFDM Communication Systems

3. Human-level control through deep reinforcement learning;mnih;Nature,2015

4. Effect of average-throughput window size on proportional fair scheduling for radio resources in LTE-A networks;musleh;J Theor Appl Inf Technol,2015

5. Deep Reinforcement Learning-Based Joint Scheduling of eMBB and URLLC in 5G Networks

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1. A Q-learning-based downlink scheduling in 5G systems;Wireless Networks;2023-11-13

2. Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler;Applied Sciences;2023-03-07

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