Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment

Moltchanov, Dmitri; Ometov, Aleksandr; Kustarev, Pavel; Evsutin, Oleg; Hošek, Jiří; Koucheryavy, Yevgeni

doi:10.3390/s20143880

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Moltchanov et al [12] studied the human skin permittivity models by using mmWave from the 10-60 GHz range. The researchers noted that the mmWaves lead to a solution for various crowded environments that are based on human body blockages.…”

Section: Related Workmentioning

confidence: 99%

Human body blockage effect on wireless network performance for outdoor coverage

Muttair

Mosleh

2023

IJECE

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The rapid development in the field of communications and growing numbers of the population every year stimulate telecommunications companies to develop communications systems to accommodate all users. In this paper, we will study the blockage effect of the student body on the propagation of the signals in the external wireless network. We took various numbers of the student density on the campus to know the extent it affects especially in crowded environments. The student body structure and buildings are designed in the college according to the real dimensions by Wireless InSite software. We compared scenarios for the different numbers of student density, we noticed that whenever an increase in the student density in the college will lead to increased path loss and delay spread time. In addition, note there is a gradual decrease in the received power (RP) if there is no student density highest RP is -28.2 dBm, when there are 300 students highest RP is -34.7 dBm, and when there are 600 students highest RP is -36.5 dBm. The reasons are that signals path spread inside the college will be passing through several collisions whether student body blockage or buildings that are built from different materials.

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Section: Related Workmentioning

confidence: 99%

Human body blockage effect on wireless network performance for outdoor coverage

Muttair

Mosleh

2023

IJECE

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“…The presented analysis of the propagation aspects of maritime wireless communication clearly shows why an attempt to evaluate TCP session performance using low-level parameters or even measured RTT and PER metrics of the network layer of the link can be very difficult. Although many numerical TCP models have been proposed [ 71 , 72 , 73 ], modeling link throughput using analytical formulas seems to be unreliable in real-word systems [ 74 ] due to a large number of parameters affecting this throughput. Moreover, since obtaining values of physical parameters comprehensively describing a wireless-link is difficult, the use of highly detailed analysis methods (which account for, e.g., dynamically changing TCP packet loss ratio [ 75 ]) become hard to implement in practical cases [ 76 ].…”

Section: Proposed Wireless Link Selection Based On Deep Learningmentioning

confidence: 99%

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

Hoeft

Gierłowski

Woźniak

2022

Sensors

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In recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-shore wireless access infrastructure. However, such an infrastructure is a heterogeneous one, managed by many independent operators and utilizing a number of different communication technologies. If a moving sea vessel is to maintain a reliable communication within such a system, it needs to employ a set of network mechanisms dedicated for this purpose. In this paper, we provide a short overview of such requirements and overall characteristics of maritime communication, but our main focus is on the link selection procedure—an element of critical importance for the process of changing the device/system which the mobile vessel uses to retain communication with on-shore networks. The paper presents the concept of employing deep neural networks for the purpose of link selection. The proposed methods have been verified using propagation models dedicated to realistically represent the environment of maritime communications and compared to a number of currently popular solutions. The results of evaluation indicate a significant gain in both accuracy of predictions and reduction of the amount of test traffic which needs to be generated for measurements.

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“…The research community has also recently focused on the evaluation of the end-to-end performance of 5th generation (5G) mmWave networks, with analysis [21], simulations [22], and experiments [23]. This has been recognized as fundamental to enable a proper design and validation of 5G technologies, given that new, unexplored technologies are being coupled with protocols and networks that have not been specifically designed for NR [11].…”

Section: B State Of the Artmentioning

confidence: 99%

Hybrid Beamforming in 5G mmWave Networks: a Full-stack Perspective

Gómez-Cuba¹,

Zugno²,

Kim³

et al. 2020

Preprint

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This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this work reveals the interplay between HBF techniques and the higher layers of the protocol stack. To this aim, prior work on full stack simulation of mmWave cellular network has been extended by including the modeling of MU-MIMO and HBF. Our results reveal novel relations between the networking layers and the HBF MU-MIMO performance in the physical layer. Particularly, throughput can be increased in 5G networks by means of Spatial Division Multiple Access (SDMA). However, in order to achieve such benefits it is necessary to take into account certain trade-offs and the implementation complexity of a full-stack HBF solution.

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Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment

Cited by 11 publications

References 19 publications

Human body blockage effect on wireless network performance for outdoor coverage

Human body blockage effect on wireless network performance for outdoor coverage

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

Hybrid Beamforming in 5G mmWave Networks: a Full-stack Perspective

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