A stochastic geometry-based performance analysis of a UAV corridor-assisted IoT network

The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks

A Survey on Machine-Learning Techniques for UAV-Based Communications

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security

Outage probability analysis in multi-user FSO/RF and UAV-enabled MIMO communication networks

This paper considers a mixed free-space optical/radio frequency (FSO/RF) system, which facilitates the communication between a ground central unit (CU) and multiple ground users (GUs) via a hovering unmanned aerial vehicle (UAV) acting as a decode-and-forward (DF) aerial relay. It is considered that the CU employs multiple transmit apertures and is interconnected with the aerial relay via an FSO link with transmit aperture selection (TAS), whereas the relay communicates with the GUs over multiple input multiple-output (MIMO) RF links with orthogonal space-time block coding (OSTBC) transmission. It is assumed that the optical channels are mainly affected by atmospheric attenuation as well as geometric and misalignment loss (GML). Besides, the atmospheric turbulence is assumed weak for short-range links. Also, fully correlated shadowed conditions are considered in the RF links and the MIMO channels are modeled using the Nakagami/Inverse Gamma (IG) composite fading distribution. Opportunistic GU scheduling is applied in the downlink and novel closed-form mathematical formulas for the outage probability (OP) are derived. The results demonstrate the theoretical derivations and the performance benefits of the proposed approach. (C) 2021 Elsevier B.V. All rights reserved

Performance Analysis of a Class of GSC Receivers over Nonidentical Weibull Fading Channels

The performance of a class of generalized-selection combining (GSC) receivers operating over independent but nonidentically distributedWeibull fading channels is studied.We consider the case where the two branches with the largest instantaneous signal-to-noise ratio (SNR), from a total of L available, GSC(2, L) are selected. By introducing a novel property for the product of moments of ordered Weibull random variables, convenient closed form expressions for the moments of the GSC(2,L) outputSNRare derived. Using these expressions, important performance criteria, such as average output SNR and amount of fading, are obtained in closed form. Furthermore, employing the Pade\ub4approximants theory and themoment-generating function approach, outage and bit-error rate performance are studied. An attempt is also made to identify the equivalency between the Weibull and the Rice fading channel, which is typically used to model the mobile satellite channel. We present various numerical performance evaluation results for different modulation formats and channel conditions. These results are complemented by equivalent computer simulated resultswhich validate the accuracy of the proposed analysis

Triple-branch MRC diversity in Weibull fading channels

In this paper a performance analysis of triple-branch maximal ratio combining (MRC) diversity receivers operating over an arbitrarily correlated Weibull fading environment is presented. For the trivariate Weibull distribution infinite series representations for the joint probability density function (PDF) and the cumulative distribution function (CDF) are derived. Moreover, a novel analytical expression for the joint moment-generating function (MGF) is presented. It is assumed that the arbitrarily correlated variates do not necessarily have identical fading parameters and average powers. These series representations are readily applicable to the performance analysis of a triple-branch MRC receiver. Furthermore, the average bit error probability (ABEP) is then derived and analytically studied. The proposed mathematical analysis is accompanied by various numerical results, with parameters of interest the fading severity, the correlation and the power decay factor

Human fall detection using mmWave radars: a cluster-assisted experimental approach

Accurate and timely human fall detection is a strong requirement either for the surveillance of critical infrastructures or for ships. Indeed, sea-faring vessels are one of the most important means for maintaining the marine economy in many countries by transporting goods or people. However, unfortunate tragic accidents on-board ships involving people, either a member of the ship’s crew or a passenger who has fallen off the ship may take place, which is known by the term “man overboard” (MOB). Accordingly, the use of radar sensors for human safety monitoring applications is vital and is of special interest since it is proven that radar sensors are less influenced by environmental conditions (e.g. fog, rain, temperature) compared to other systems like video cameras. Consequently, human fall detection from either sea or ground infrastructures is easier to be identified using radars compared to the conventional methods. This paper focuses in the description of a real experimental approach based on multiple long-range millimeter-wave band radar sensors for human fall detection. The stream(s) of information collected by the system, are processed using clustering techniques. The clustering results are evaluated in terms of the ability to detect and track real human fall scenarios. The results reveal that the measure of velocity plays a key role in the detection procedure