A Holistic Investigation on Terahertz Propagation and Channel Modeling Toward Vertical Heterogeneous Networks

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 is 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 orbit (GEO), and low-earth orbit (LEO) satellites (e.g., CubeSats), and networked flying platforms (NFPs) along with terrestrial communication links. In this study, we propose the Terahertz (THz) communications to enable the elements of V-HetNets to function in harmony. As THz links offer a 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 paper, the insights obtained through our measurement campaigns are highlighted, to reveal the true potential of THz communications in V-HetNets.Comment: It has been accepted for the publication in IEEE Communications Magazin

Reconfigurable Intelligent Surfaces in Action for Non-Terrestrial Networks

Next-generation communication technology will be fueled on the cooperation of terrestrial networks with nonterrestrial networks (NTNs) that contain mega-constellations of high-altitude platform stations and low-Earth orbit satellites. On the other hand, humanity has embarked on a long road to establish new habitats on other planets. This deems the cooperation of NTNs with deep space networks (DSNs) necessary. In this regard, we propose the use of reconfigurable intelligent surfaces (RISs) to improve and escalate this collaboration owing to the fact that they perfectly match with the size, weight, and power restrictions of the operational environment of space. A comprehensive framework of RIS-assisted non-terrestrial and interplanetary communications is presented by pinpointing challenges, use cases, and open issues. Furthermore, the performance of RIS-assisted NTNs under environmental effects such as solar scintillation and satellite drag is discussed through simulation results.Comment: 7 pages, 6 figure

Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks

In this paper, graph attention network (GAT) is firstly utilized for the channel estimation. In accordance with the 6G expectations, we consider a high-altitude platform station (HAPS) mounted reconfigurable intelligent surface-assisted two-way communications and obtain a low overhead and a high normalized mean square error performance. The performance of the proposed method is investigated on the two-way backhauling link over the RIS-integrated HAPS. The simulation results denote that the GAT estimator overperforms the least square in full-duplex channel estimation. Contrary to the previously introduced methods, GAT at one of the nodes can separately estimate the cascaded channel coefficients. Thus, there is no need to use time-division duplex mode during pilot signaling in full-duplex communication. Moreover, it is shown that the GAT estimator is robust to hardware imperfections and changes in small-scale fading characteristics even if the training data do not include all these variations.Comment: This paper has been accepted for the presentation in IEEE ICC'202

Modeling and Analysis of sub-Terahertz Communication Channel via Mixture of Gamma Distribution

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 test to show the difference between the proposed model and the actual probability density functions (PDFs) that are obtained via the designed test environment. According to WMRD error metrics, KL-divergence, and KS test results, PDFs generated by the mixture of Gamma distributions fit the actual histogram of the measurement data. It is shown that instead of taking pseudo-average characteristics of sub-bands in the wideband, using the mixture models allows for determining channel parameters more precisely.Comment: This paper has been accepted for publication in IEEE Transactions on Vehicular Technolog

Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function

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 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 overthe-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

Statistical channel modeling for short range line-of-sight terahertz communication

Underutilized spectrum constitutes a major concern in wireless communications especially in the presence of legacy systems and the prolific need for high-capacity applications as well as consumer expectations. From this perspective, Terahertz frequencies provide a new paradigm shift in wireless communications since they have been left unexplored until recently. Such a vast frequency spectrum region extending all the way up to visible light and beyond points out significant opportunities from dramatic data rates on the order of tens of Gbps to a variety of inherent security and privacy mechanisms, and techniques that are not available in the traditional systems. Thus, in this paper, we investigate statistical parameters for short-range line- of-sight channels of Terahertz communication. Short-range measurement campaign within the interval of [3cm, 20cm] are carried out between 275GHz to 325GHz range. Path loss model is examined for different frequencies and distances to provide the insight regarding the effect of the operating frequency. Measurement results are provided with relevant discussions and future directions

Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines

ABSTRACT: Radio air interface identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) signal identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto-correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over-the-air real-world measurements are taken to show that wireless signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in-phase/quadrature (I/Q) samples, training set size, or signal-to-noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well-known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/F-1-scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing

Graph Attention Network-Based Single-Pixel Compressive Direction of Arrival Estimation

In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT)-based deep-learning framework. The physical layer compression is achieved using a coded-aperture technique, probing the spectrum of far-field sources that are incident on the aperture using a set of spatio-temporally incoherent modes. This information is then encoded and compressed into the channel of the coded-aperture. The coded-aperture is based on a metasurface antenna design and it works as a receiver, exhibiting a single-channel and replacing the conventional multichannel raster scan-based solutions for DoA estimation. The GAT network enables the compressive DoA estimation framework to learn the DoA information directly from the measurements acquired using the coded-aperture. This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to achieve DoA estimation. We show that the presented GAT integrated single-pixel radar framework can retrieve high fidelity DoA information even under relatively low signal-to-noise ratio (SNR) levels.Comment: 5 pages, 4 figure

Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications

Direct-to-satellite (DtS) communication has gained importance recently to support globally connected Internet of things (IoT) networks. However, relatively long distances of densely deployed satellite networks around the Earth cause a high path loss. In addition, since high complexity operations such as beamforming, tracking and equalization have to be performed in IoT devices partially, both the hardware complexity and the need for high-capacity batteries of IoT devices increase. The reconfigurable intelligent surfaces (RISs) have the potential to increase the energy-efficiency and to perform complex signal processing over the transmission environment instead of IoT devices. But, RISs need the information of the cascaded channel in order to change the phase of the incident signal. This study evaluates the pilot signal as a graph and incorporates this information into the graph attention networks (GATs) to track the phase relation through pilot signaling. The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. Moreover, bit error rate performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation based on the proposed method. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible.Comment: 11 pages, 13 figure

Replication Data for:"Real-World Considerations for Deep Learning in Wireless Signal Identification Based on Spectral Correlation Function"

The dataset includes spectral correlation function (SCF) estimations by FFT accumulation method (FAM) for totally 4500 signals with 20000 I/Q samples. The signals belong to three different cellular communication standards: GSM, WCDMA, and LTE. The signals have been received from the different channels with multipath, fading, and noise. The dataset can be used to validate the designed classifier model aiming to identify cellular communication signals. For each signal, the dimension of SCF estimate is 8193*16. There are two train sets which must be used together (concatenate train_data_wo_mapping1 and train_data_wo_mapping2 ). Two train sets have 3000 signals totally, and the test set has 1500. The label of the cellular communication standards are given in dataset as follows: WCDMA -> 0 LTE -> 1 GSM -> 2 The dataset includes: 1. SCFDatatrain1.mat 2. SCFDatatrain2.mat 3. SCFDatatest.mat The contents of .mat files: train_class : denotes class labels of the train set, its dimension is 3000*1 double train_data_wo_mapping1 : includes the first half of the training data, its dimension 1500*1 cell train_data_wo_mapping2 : includes the second half of the training data, its dimension 1500*1 cell *Note, concatenate two cells given above (ie [train_data_wo_mapping1; train_data_wo_mapping2]) test_class : denotes class labels of the train set, its dimension is 1500*1 double test_data_without_mapping : includes the test data, its dimension 1500*1 cell Each cell contains 1500 SCF estimates (8193*16) . The dataset has been used for the paper "Real-World Considerations for Deep Learning in Wireless Signal Identification Based on Spectral Correlation Function" submitted for possible publication in IEEE Wireless Communication Letters. Please cite this paper, if you use the dataset