Quantifying the millimeter wave new radio base stations density for network slicing with prescribed SLAs

2021 ◽  
Vol 174 ◽  
pp. 13-27
Author(s):  
Yevgeni Koucheryavy ◽  
Ekaterina Lisovskaya ◽  
Dmitri Moltchanov ◽  
Roman Kovalchukov ◽  
Andrey Samuylov
Keyword(s):  
Millimeter Wave ◽  
Base Stations ◽  
Network Slicing ◽  
New Radio

scholarly journals A Monte Carlo Analysis of Actual Maximum Exposure From a 5G Millimeter-Wave Base Station Antenna for EMF Compliance Assessments

2022 ◽  
Vol 9 ◽  
Author(s):  
Bo Xu ◽  
David Anguiano Sanjurjo ◽  
Davide Colombi ◽  
Christer Törnevik
Keyword(s):  
Monte Carlo ◽  
Millimeter Wave ◽  
Near Field ◽  
Multiple Input Multiple Output ◽  
Monte Carlo Analysis ◽  
Base Station ◽  
Base Stations ◽  
Far Field ◽  
Wave Radio ◽  
Maximum Exposure

International radio frequency (RF) electromagnetic field (EMF) exposure assessment standards and regulatory bodies have developed methods and specified requirements to assess the actual maximum RF EMF exposure from radio base stations enabling massive multiple-input multiple-output (MIMO) and beamforming. Such techniques are based on the applications of power reduction factors (PRFs), which lead to more realistic, albeit conservative, exposure assessments. In this study, the actual maximum EMF exposure and the corresponding PRFs are computed for a millimeter-wave radio base station array antenna. The computed incident power densities based on near-field and far-field approaches are derived using a Monte Carlo analysis. The results show that the actual maximum exposure is well below the theoretical maximum, and the PRFs similar to those applicable for massive MIMO radio base stations operating below 6 GHz are also applicable for millimeter-wave frequencies. Despite the very low power levels that currently characterize millimeter-wave radio base stations, using the far-field approach can also guarantee the conservativeness of the PRFs used to assess the actual maximum exposure close to the antenna.

scholarly journals Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986588 ◽  
Author(s):  
Shan Meng ◽  
Xin Dai ◽  
Bicheng Xiao ◽  
Yimin Zhou ◽  
Yumei Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicle ◽  
Millimeter Wave ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Base Stations ◽  
Communication Link ◽  
Multiple Sensors ◽  
Channel Tracking ◽  
Aerial Vehicle

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

scholarly journals Performance Analysis of Millimeter-Wave UAV Swarm Networks under Blockage Effects

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4593
Author(s):  
Haejoon Jung ◽  
In-Ho Lee
Keyword(s):  
Millimeter Wave ◽  
Channel Model ◽  
High Mobility ◽  
Base Stations ◽  
Shape Theory ◽  
High Data ◽  
Mobile Cellular ◽  
Seamless Connectivity ◽  
Mobile Cellular Networks ◽  
Uav Swarm

Due to their high mobility, unmanned aerial vehicles (UAVs) can offer better connectivity by complement or replace with the existing terrestrial base stations (BSs) in the mobile cellular networks. In particular, introducing UAV and millimeter wave (mmWave) technologies can better support the future wireless networks with requirements of high data rate, low latency, and seamless connectivity. However, it is widely known that mmWave signals are susceptible to blockages because of their poor diffraction. In this context, we consider macro-diversity achieved by the multiple UAV BSs, which are randomly distributed in a spherical swarm. Using the widely used channel model incorporated with the distance-based random blockage effects, which is proposed based on stochastic geometry and random shape theory, we investigate the outage performance of the mmWave UAV swarm network. Further, based on our analysis, we show how to minimize the outage rate by adjusting various system parameters such as the size of the UAV swarm relative to the distance to the receiver.

scholarly journals Deep Learning-Based Cell-Level and Beam-Level Mobility Management System

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7124
Author(s):  
Roman Klus ◽  
Lucie Klus ◽  
Dmitrii Solomitckii ◽  
Jukka Talvitie ◽  
Mikko Valkama
Keyword(s):  
Mobility Management ◽  
Management System ◽  
Network Models ◽  
Mobility Model ◽  
Base Stations ◽  
Dense Layer ◽  
Cell Level ◽  
Neural Network Models ◽  
Received Power ◽  
New Radio

The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.

scholarly journals Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3947
Author(s):  
Ehab Mahmoud Mohamed ◽  
Sherief Hashima ◽  
Abdallah Aldosary ◽  
Kohei Hatano ◽  
Mahmoud Ahmed Abdelghany
Keyword(s):  
Millimeter Wave ◽  
Base Stations ◽  
Superior Performance ◽  
Total System ◽  
Average Data ◽  
Data Rates ◽  
Terrestrial Network ◽  
Upper Confidence Bound ◽  
Selection Algorithms ◽  
Post Disaster

Recently, unmanned aerial vehicle (UAV)-based communications gained a lot of attention due to their numerous applications, especially in rescue services in post-disaster areas where the terrestrial network is wholly malfunctioned. Multiple access/gateway UAVs are distributed to fully cover the post-disaster area as flying base stations to provide communication coverage, collect valuable information, disseminate essential instructions, etc. The access UAVs after gathering/broadcasting the necessary information should select and fly towards one of the surrounding gateways for relaying their information. In this paper, the gateway UAV selection problem is addressed. The main aim is to maximize the long-term average data rates of the UAVs relays while minimizing the flights’ battery cost, where millimeter wave links, i.e., using 30~300 GHz band, employing antenna beamforming, are used for backhauling. A tool of machine learning (ML) is exploited to address the problem as a budget-constrained multi-player multi-armed bandit (MAB) problem. In this setup, access UAVs act as the players, and the arms are the gateway UAVs, while the rewards are the average data rates of the constructed relays constrained by the battery cost of the access UAV flights. In this decentralized setting, where information is neither prior available nor exchanged among UAVs, a selfish and concurrent multi-player MAB strategy is suggested. Towards this end, three battery-aware MAB (BA-MAB) algorithms, namely upper confidence bound (UCB), Thompson sampling (TS), and the exponential weight algorithm for exploration and exploitation (EXP3), are proposed to realize gateways selection efficiently. The proposed BA-MAB-based gateway UAV selection algorithms show superior performance over approaches based on near and random selections in terms of total system rate and energy efficiency.

scholarly journals Analytical Blind Beamforming for a Multi-Antenna UAV Base-Station Receiver in Millimeter-Wave Bands

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6561
Author(s):  
Pingchuan Liu ◽  
Kuangang Fan ◽  
Yuhang Chen
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Antenna Arrays ◽  
Base Station ◽  
Base Stations ◽  
Signal Recovery ◽  
Constant Modulus ◽  
Signal Space ◽  
Target User ◽  
Blind Beamforming

Over the last decade, unmanned aerial vehicles (UAVs) with antenna arrays have usually been employed for the enhancement of wireless communication in millimeter-wave bands. They are commonly used as aerial base stations and relay platforms in order to serve multiple users. Many beamforming methods for improving communication quality based on channel estimation have been proposed. However, these methods can be resource-intensive due to the complexity of channel estimation in practice. Thus, in this paper, we formulate an MIMO blind beamforming problem at the receivers for UAV-assisted communications in which channel estimation is omitted in order to save communication resources. We introduce one analytical method, which is called the analytical constant modulus algorithm (ACMA), in order to perform blind beamforming at the UAV base station; this relies only on data received by the antenna. The feature of the constant modulus (CM) is employed to restrict the target user signals. Algebraic operations, such as singular value decomposition (SVD), are applied to separate the user signal space from other interferences. The number of users in the region served by the UAV can be detected by exploring information in the measured data. We seek solutions that are expressible as one Kronecker product structure in the signal space; then, the beamformers that correspond to each user can be successfully estimated. The simulation results show that, by using this analytically derived blind method, the system can achieve good signal recovery accuracy, a reasonable system sum rate, and acceptable complexity.

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