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Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered to be a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large transmission delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges. [ABSTRACT FROM AUTHOR]

This paper presents a unified outage probability (OP) performance analysis of two hybrid antenna selection (AS) schemes, transmit antenna selection (TAS) and maximal ratio combining (MRC), and joint transmit and receive antenna selection (JTRAS) in multiple-input multiple-output non-orthogonal multiple access based downlink amplify-and-forward (AF) relaying network with channel estimation error (CEE) and feedback delay (FD). Since the communications in the first and second hops are kinds of single-user and multi-user communications, respectively the AS is done as optimal TAS/MRC or JTRAS is applied in the first hop while the suboptimal majority-based TAS/MRC or JTRAS is employed in the second hop. For both TAS/MRC and JTRAS schemes, the OP expressions are derived in single closed-form over Nakagami- ${m}$ fading channels in the practical and ideal cases. Moreover, in the practical case, the lower bound OP expressions are found and at high signal-to-noise ratio (SNR) values, the OP reaches an error floor value, which means zero-diversity order. In the ideal case, asymptotic OP expressions are obtained in high SNR regime and demonstrate achievable non-zero diversity and array gains. Finally, through simulations and software-defined radio-based real-time tests, the accuracy of theoretical analysis is validated. [ABSTRACT FROM AUTHOR]

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]

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]

User-centric and low-latency communications can be enabled not only by small cells but also through ubiquitous connectivity. Recently, the vertical heterogeneous network (V-HetNet) architecture has been proposed to backhaul/fronthaul a large number of small cells. Like an orchestra, the V-HetNet is a polyphony of different communication ensembles, including geostationary Earth orbit and low Earth orbit satellites (e.g., CubeSats), and networked flying platforms along with terrestrial communication links. In this study, we propose terahertz (THz) communications to enable the elements of V-HetNets to function in harmony. As THz links offer large bandwidth, leading to ultra-high data rates, it is suitable for backhauling and fronthauling small cells. Furthermore, THz communications can support numerous applications from inter-satellite links to in-vivo nanonetworks. However, to savor this harmony, we need accurate channel models. In this article, the insights obtained through our measurement campaigns are highlighted to reveal the true potential of THz communications in V-HetNets. [ABSTRACT FROM AUTHOR]

By introducing high randomness with reduced computational cost, physical layer (PHY) key generation is one of the candidate tools that can be used for the security of the Internet of Things applications. Nonidentical secret keys are one of the main problems of the physical layer (PHY) key generation schemes. In order to address this problem, key verification schemes, which are based on information reconciliation, are used in this article. In the current key verification techniques, the legitimate nodes reveal some information related to their secret keys to eliminate nonidentical bits. In this article, by jointly using PHY key generation with an embedded key, we propose a hybrid key generation and key verification scheme, where the revealed information during the key verification process is negligible, and the verified keys are identical. Numerical results and software-defined radio-based tests show that the proposed verification scheme achieves the requirements of the Industrial Internet of Things systems. [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]