Stochastic Geometry Analysis of Large Intelligent Surface-Assisted Millimeter Wave Networks

Journal article


Zhu, Y (2020). Stochastic Geometry Analysis of Large Intelligent Surface-Assisted Millimeter Wave Networks. IEEE Journal on Selected Areas in Communications.
AuthorsZhu, Y
Abstract

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Reliable and efficient networks are the trend for next-generation wireless communications. Recent improved hardware technologies -- known as Large Intelligent Surfaces (LISs) -- have decreased the energy consumption of wireless networks, while theoretically being capable of offering an unprecedented boost to the data rates and energy efficiency (EE). In this paper, we use stochastic geometry to provide performance analysis of a realistic two-step user association based millimeter wave (mmWave) networks consisting of multiple users, transmitters and one-hop reflection from a LIS. All the base stations (BSs), users and LISs are equipped with multiple uniform linear antenna arrays. The results confirm that LIS-assisted networks significantly enhance capacity and achieve higher optimal EE as compared to traditional systems \textcolor{black}{when the density of BSs is not large}. Moreover, there is a trade-off between the densities of LIS and BS when there is a total density constraint. It is shown that the LISs are excellent supplements for traditional cellular networks, which enormously enhance the average rate and area spectral efficiency (ASE) of mmWave networks. However, when the BS density is higher than the LIS density, the reflected interference and phase-shift energy consumption will limit the performance of LIS-assisted networks, so it is not necessary to employ the LIS devices.

KeywordsLarge intelligent surface; millimeter wave; stochastic geometry; uniform linear array
Year2020
JournalIEEE Journal on Selected Areas in Communications
PublisherInstitute of Electrical and Electronics Engineers
ISSN0733-8716
Digital Object Identifier (DOI)doi:10.1109/JSAC.2020.3000806
Publication process dates
Accepted15 Feb 2020
Deposited09 Jun 2020
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
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License: CC BY 4.0
File access level: Open

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