Multi-User Channels With Large-Scale Antenna Arrays in a Subway Environment: Characterization and Modeling

Zhang, Bei; Zhang, Zhong; He, Ruisi; Ai, Bo; Dahman, Ghassan; Yang, Mi; Li, Jianzhi

doi:10.1109/access.2017.2764621

Cited by 10 publications

(4 citation statements)

References 57 publications

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“…We use the correlation between the channel estimate of the antennas on a UAV as a performance metric to quantify the system performance and efficiency of the antennas. For this purpose, we calculate intra-user correlation for different multi-antenna system configurations [30,31]. The correlation metric for the SIMO system can be described as the correlation between the receiver antenna elements and, mathematically, for the signals transmitted from the horizontal antenna of the transmitter, it can be written as follows:…”

Section: Correlationmentioning

confidence: 99%

Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

Syed,

Singh,

Camp

2023

Sensors

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UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance.

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Section: Correlationmentioning

confidence: 99%

Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

Syed,

Singh,

Camp

2023

Sensors

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“…We employ the channel matrix collinearity as metric for spatial user correlation, similar to other works in the context of correlation of MIMO channels [27], [41]- [43]. This collinearity measure is defined via (3) with the matrices A and B being two MIMO channel matrices [44].…”

Section: ) Spatial Correlationmentioning

confidence: 99%

“…To investigate propagation characteristics of real-world wireless channels in the context of massive MIMO, many measurements were conducted in the past years, see [8], [12], [21]- [28] to name just a few examples. Many of these measurement campaigns were performed entirely outdoors [8], [12], [21]- [24], or indoors [12], [25]- [27], while only few consider an outdoor-to-indoor scenario, for example [28].…”

mentioning

confidence: 99%

Measured User Correlation in Outdoor-to-Indoor Massive MIMO Scenarios

et al. 2020

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The performance of a multiuser multiple-input multiple-output (MIMO) system is mainly determined by the correlation of user channels. Applying channel models without spatial correlation or spatially inconsistent channel models for performance analysis leads to an underestimation of spatial correlation and therefore to overly optimistic system performance. This is especially pronounced when the wireless channel is modeled as i.i.d. Rayleigh fading for users in a rich scattering environment, for example, for indoor users. To analyze the spatial channel correlation in massive MIMO systems, we performed wireless channel measurements in three different scenarios. In each scenario, we measure 148 receiver positions, spread over a length of almost 9 m. Since the wireless channel statistics change with receiver position, we perform a stationarity analysis. We provide a statistical analysis of the measured channel in terms of amplitude distribution and user-side spatial correlation within the region of stationarity. This analysis shows that, even for a deep-indoor user location, the spatial correlation is significantly higher compared to a Rician channel model with the same K factor. We model the measured channel by means of a Rician channel model and a spatially consistent channel model, which is based on the scenario geometry, to provide an insight for the observed propagation phenomena. Results show that the user correlation is not negligible for massive MIMO in outdoor-to-indoor scenarios. The achievable spectral efficiency with linear precoding is 20% lower compared to the Rician channel model, even for large inter-user distances of 6 m. INDEX TERMS MIMO, mobile communications, channel measurements, channel modeling I. INTRODUCTION

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“…The results of these studies showed comparatively high exposure from civil communication bands and the major source of exposure in the subway was GSM900. Most previous studies focused on the coverage of the wireless system in subways or a high-speed railway environment [24], and little information has been published on electromagnetic exposure safety in the rail transit system, except for recent studies [8,25,26]. The research on the general public electromagnetic exposure of subway passengers, especially children, is limited.…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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The objective of this work is to evaluate the safety of adult and child passengers exposed to a radio frequency (RF) source, i.e., a leaky coaxial cable (LCX) on the subway platform. An adult model, a child model, and a LCX model have been numerically designed in COMSOL Multiphysics software. The distributions of the induced electric field (E-field), specific absorption rate (SAR), magnetic field (H-field) and the head temperature increase in adult and child passenger models were calculated at 900 MHz. The induced fields in the passengers were compared with that without screen doors. The results show that the E-field, SAR and H-field in the whole body of the child are 2.00×10 −2 V/m, 1.07×10 −7 W/kg, and 2.94 × 10 −4 A/m, respectively. The E-field, SAR and H-field in the central nervous system of the child are 1.00 × 10 −2 V/m, 2.44 × 10 −8 W/kg, and 2.41 × 10 −4 A/m, respectively. The maximum values of the E-field, SAR and H-field in the adult passenger are 1.49-2.34 times higher than those of the child. The E-field, SAR, and H-field in the passenger models without a screen door are larger than those with a screen door. The screen door has a partial shielding effect on the RF electromagnetic field. The values of the maximum temperature that increases in adult and child head tissue are 0.2114 and 0.2111 • C after waiting 6 minutes exposure, respectively. All calculated results are well below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) limits for general public exposure, indicating that RF electromagnetic exposure caused by the LCX on the subway platform is not a threat to passenger's health.

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Multi-User Channels With Large-Scale Antenna Arrays in a Subway Environment: Characterization and Modeling

Cited by 10 publications

References 57 publications

Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

Measured User Correlation in Outdoor-to-Indoor Massive MIMO Scenarios

Archives of Electrical Engineering

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