A novel learning based solution for efficient data transport in heterogeneous wireless networks
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Some RL-based approaches for updating CWND are designed for resource-constrained networks including WANETs and IoT, while others address wider network architectures such as satellite, cellular, and data center networks.
“Some of these approaches are particularly designed for resourceconstrained networks, including WANETs [29,219,379,380] and IoT [271], while others address a wider range of network architectures [30,122,477], such as satellite, cellular, and data center networks.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [30]
The CWND update mechanism of TCP is not suitable for the diverse characteristics of different network technologies.
“the CWND update mechanism of TCP is not suitable for the diverse characteristics of different network technologies [30,122].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [30]
UL-TCP is slightly more complex than Learning-TCP due to the storage and usage of more parameters for computing the states.
“However, UL-TCP is slightly more complex than Learning-TCP due to the storage and usage of more parameters for computing the states.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [30]
Simulations of single-hop-wireless networks in ns-2 show that UL-TCP achieves significantly better packet loss and fairness than TCP-NewReno and TCP-ATL.
“Simulations of single-hop-wireless networks in ns-2 show that UL-TCP achieves significantly better packet loss and fairness than TCP-NewReno and TCP-ATL [10] (a non-ML TCP variant designed for single-hopwireless networks).”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [30]
For multi-hop-wireless networks, UL-TCP compared to TCP-NewReno and TCP-FALA exhibits similar results to Learning-TCP.
“For multi-hop-wireless networks (i.e.WANETs), UL-TCP is compared to TCP-NewReno and TCP-FALA, and exhibit similar results to Learning-TCP.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [30]
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
- Authors
- Badarla, V., & Siva Ram Murthy, C.
- Affiliations
- National University of Ireland, Maynooth (IE)
- Corresponding author
- Badarla, Venkataramana
- Venue
- Wireless Networks
- Published
- 2009-11-24
- Volume
- 16
- Issue
- 6
- Pages
- 1777-1798
- ISSN
- 1022-0038 (ISSN-L)
- 1572-8196
- OpenAlex
- W2022548994
- Cited by
- 6
- References
- 28
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 2 · 2021: 1 · 2018: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Wireless Networks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- congestion control, multi-hop ad hoc networks, single-hop wireless networks, wireless LAN, cellular networks, satellite networks, flow fairness, packet loss reduction, goodput
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Statistics and arithmetic are recomputed, not judged.
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