Search

In this paper, the outage performance of wireless networks with unstructured topologies is investigated. The network reliability perspective of graph theory is used to obtain the network outage polynomial of generalized wireless networks for both uncorrelated and correlated wireless channels. A relationship is established between the max-flow min-cut theorem and key communication performance indicators. The diversity order is equal to the size of the minimum cut-set between source and destination, and the coding gain is the number of cut-sets with size equal to the minimum cut. An ergodic capacity analysis of arbitrary network topologies based on the network outage polynomial is also presented. Numerical results are used to illustrate the technical definitions and verify the derivations. [ABSTRACT FROM AUTHOR]

The ubiquity of many different networking options with diversified wireless radio access technologies creates an exciting opportunity to use heterogeneous networks (HetNets) for localization systems. Toward this motivation, in this paper, we propose two different inter-network localization frameworks based on maximum likelihood (ML) estimator for HetNets with multi-connectivity by considering the propagation characteristics of individual network tiers in a realistic manner. The first is inter-network noncooperative (INN) based on the weighted superposition of the location estimates of individual network tiers along with their reliability. The second is inter-network cooperative (INC) grounded on the joint utilization of raw received power measurements from all network tiers. We also derive Cramér-Rao bound (CRB) expressions for both frameworks as a theoretical performance benchmark. Furthermore, we obtain another performance criteria called as spatial quantization error bound to analytically determine the effect of grid search algorithm, which is used for solving the INN and INC ML cost functions, on localization performance. Extensive simulations validate our proposed localization frameworks. [ABSTRACT FROM AUTHOR]

In recent years, satellite communication (SatCom) systems have been widely used for navigation, broadcasting application, disaster recovery, weather sensing, and even spying on the Earth. As the number of satellites is highly increasing and with the radical revolution in wireless technology, eavesdropping on SatCom will be possible in next-generation networks. In this context, we introduce the satellite eavesdropping approach, where an eavesdropping spacecraft can intercept optical communications established between a low Earth orbit satellite and a high altitude platform station (HAPS). Specifically, we propose two practical eavesdropping scenarios for satellite-to-HAPS (downlink) and HAPS-to-satellite (uplink) optical communications, where the attacker spacecraft can eavesdrop on the transmitted signal or the received signal. To quantify the secrecy performance of the scenarios, the average secrecy capacity and secrecy outage probability expressions are derived and validated with Monte Carlo simulations. Moreover, the secrecy throughput of the proposed models is investigated. We observe that turbulence-induced fading significantly impacts the secrecy performance of free-space optical communication. [ABSTRACT FROM AUTHOR]

With the recent developments on opening the terahertz (THz) spectrum for experimental purposes by the Federal Communications Commission, transceivers operating in the range of 0.1THz-10THz, which are known as THz bands, will enable ultra-high throughput wireless communications. However, actual implementation of the high-speed and high reliability THz band communication systems should start with providing extensive knowledge in regards to the propagation channel characteristics. Considering the huge bandwidth and the rapid changes in the characteristics of THz wireless channels, ray tracing and one-shot statistical modeling are not adequate to define an accurate channel model. In this work, we propose Gamma mixture based channel modeling for the THz band via the expectation-maximization (EM) algorithm. First, maximum likelihood estimation (MLE) is applied to characterize the Gamma mixture model parameters, and then EM algorithm is used to compute MLEs of the unknown parameters of the measurement data. The accuracy of the proposed model is investigated by using the Weighted relative mean difference (WMRD) error metrics, Kullback-Leibler (KL)-divergence, and Kolmogorov-Smirnov (KS) test to show the difference between the proposed model and the actual probability density functions (PDFs) that are obtained via the designed test environment. To efficiently evaluate the performance of the proposed method in more realistic scenarios, all the analysis is done by examining measurement data from a measurement campaign in the 240 GHz to 300 GHz frequency range, using a well-isolated anechoic chamber. According to WMRD error metrics, KL-divergence, and KS test results, PDFs generated by the mixture of Gamma distributions fit to the actual histogram of the measurement data. It is shown that instead of taking pseudo-average characteristics of sub-bands in the wide band, using the mixture models allows for determining channel parameters more precisely. [ABSTRACT FROM AUTHOR]

The use of real-time software-controlled reconfigurable intelligent surface (RIS) units is proposed to increase the reliability of vehicle-to-everything (V2X) communications. The optimum placement problem of the RIS units is formulated by considering their sizes and operating modes. The solution of the problem is given, where it is shown that the optimal placement of the RIS depends on the locations of the transmitter and the receiver. The proposed RIS-supported highway deployment can combat the high path loss experienced by the use of higher frequency bands, including the millimeter-wave and the terahertz bands, that are expected to be used in the next-generation wireless networks. By using the proposed RIS-powered architecture the existing base station deployment plans can remain operational while providing reliable and energy-efficient connectivity for autonomous vehicles. [ABSTRACT FROM AUTHOR]

In the recent years, there has been an explosive growth of smart devices applications with high computational demands and critical latency. The Internet of Things (IoT) multi-access edge computing (MEC) framework offers a lower latency and a higher speed to the users, by offloading the cloud computing capabilities at the nearest edge of the mobile network. In this operating scenario, the proper allocation of limited resources is one of the biggest challenges, and security is becoming vital as the number of devices in an IoT network tends to billions. According to recent studies, even authorized edge devices may be a significant threat for IoT networks (i.e., selfish behavior), as a result of mixed service structures with a wide range of different requirements. Thus, this work proposes a novel two-fold method to allocate resources and then identify attackers (selfish IoT malicious devices) by means of a reputation-based stable matching policy. The devices are categorized in three different states, namely honest, suspicious, and malicious states, according to their reputation indices. Our algorithm allows to move the devices between the three states, in order to exclude malicious devices and to rehabilitate users identified as unintentional attackers (due to bad propagation conditions). Theoretical and simulation results confirm the validity and effectiveness of such approach for identifying malicious IoT devices in MEC networks. [ABSTRACT FROM AUTHOR]

Modeling the variations in the local mean received power, the shadow fading is a relatively understudied effect in the literature. The inaccuracy of the universally accepted lognormal model is shown in many works. However, proposing other statistical distributions, such as gamma, which are not stemmed from the natural underlying physical process, cannot provide sufficient insights. Conceding the physical process of multiple reflections generating the lognormal distribution, in this paper, we propose a generalized mixture model that can address the modeling inaccuracies observed with a single lognormal distribution that may not correctly represent empirical data sets. To show that lognormal mixture model can be used under any shadow fading condition, we prove that an arbitrary probability density function can accurately be represented by a mixture of lognormal random variables (RVs). One of the main issues associated with mixture models is the determination of the mixture components. Here, we propose two solutions. Our first solution is a Dirichlet-process-mixture-based estimation technique that can provide the optimum number of components. Our second solution is an expectation–maximization (EM) algorithm-based technique for a more practical implementation. The proposed model and solution approaches are applied to our empirical data set, where the accuracy of the mixture model is verified by using both confidence-based and error-vector-norm-based techniques. Concluding this paper, we provide outage and cellular coverage probability expressions, where we show that more accurate shadow fading models yield more realistic performance estimates. [ABSTRACT FROM PUBLISHER]

Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function (SCF) which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and signal identification. The proposed method classifies wireless signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of signals beforehand. Implementation of the method on the measured over-the-air real-world signals in cellular bands indicates important performance gains when compared to the signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular signals, the proposed approach can be extended to the detection and classification of any signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development. [ABSTRACT FROM AUTHOR]