Challenges on the Way of Implementing TCP Over 5G Networks
5G cellular communication, especially with its hugely available bandwidth provided by millimeter-wave, is a promising technology to fulfill the coming high demand for vast data rates. These networks can support new use cases such as Vehicle to Vehicle and augmented reality due to its novel features...
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Veröffentlicht in: | IEEE access 2020, Vol.8, p.176393-176415 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 5G cellular communication, especially with its hugely available bandwidth provided by millimeter-wave, is a promising technology to fulfill the coming high demand for vast data rates. These networks can support new use cases such as Vehicle to Vehicle and augmented reality due to its novel features such as network slicing along with the mmWave multi-gigabit-per-second data rate. Nevertheless, 5G cellular networks suffer from some shortcomings, especially in high frequencies because of the intermittent nature of channels when the frequency rises. Non-line of sight state, is one of the significant issues that the new generation encounters. This drawback is because of the intense susceptibility of higher frequencies to blockage caused by obstacles and misalignment. This unique characteristic can impair the performance of the reliable transport layer widely deployed protocol, TCP, in attaining high throughput and low latency throughout a fair network. As a result, the protocol needs to adjust the congestion window size based on the current situation of the network. However, TCP is not able to adjust its congestion window efficiently, and it leads to throughput degradation of the protocol. This paper presents a comprehensive analysis of reliable end-to-end communications in 5G networks. It provides the analysis of the effects of TCP in 5G mmWave networks, the discussion of TCP mechanisms and parameters involved in the performance over 5G networks, and a survey of current challenges, solutions, and proposals. Finally, a feasibility analysis proposal of machine learning-based approaches to improve reliable end-to-end communications in 5G networks is presented. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2020.3026540 |