Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking-Reference-Cited by-同舟云学术

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Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking

Published:2015-09 Issue: Volume:88 Page:269-285
ISSN:1389-1286
Container-title:Computer Networks
language:en
Short-container-title:Computer Networks

Author:

Coutinho Nuno,Matos Ricardo,Marques Carlos,Reis André,Sargento Susana,Chakareski Jacob,Kassler Andreas

Funder

FCT

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference37 articles.

1. Cisco, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2012–2017, White Paper, Cisco, 2013.

2. User measures of quality of experience: why being objective and quantitative is important;Brooks;IEEE Netw.,2010

3. Wireless mesh networks: a survey;Akyildiz;Comput. Netw.,2005

4. Quality of experience-based routing in multi-service wireless mesh networks;Matos,2012

5. Distortion optimized multi-service scheduling for next-generation wireless mesh networks;Reis,2010

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1. The future of wireless mesh network in next-generation communication: a perspective overview;Evolving Systems;2024-04-23

2. A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook;Ad Hoc Networks;2022-05

3. Optimization of reinforcement routing for wireless mesh network using machine learning and high‐performance computing;Concurrency and Computation: Practice and Experience;2022-03-23

4. Artificial Neural Network in Operation Management Regarding Communication Issue;Research Anthology on Artificial Neural Network Applications;2022

5. Improving Data-Driven Reinforcement Learning in Wireless IoT Systems Using Domain Knowledge;IEEE Communications Magazine;2021-11

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