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

Exploring deep learning for adaptive energy detection threshold determination: A multistage approach

The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection.European Union’s H2020 Framework Programm

On the traffic offloading in Wi-Fi supported heterogeneous wireless networks

Heterogeneous small cell networks (HetSNet) comprise several low power, low cost (SBSa), (D2D) enabled links wireless-fidelity (Wi-Fi) access points (APs) to support the existing macrocell infrastructure, decrease over the air signaling and energy consumption, and increase network capacity, data rate and coverage. This paper presents an active user dependent path loss (PL) based traffic offloading (TO) strategy for HetSNets and a comparative study on two techniques to offload the traffic from macrocell to (SBSs) for indoor environments: PL and signal-to-interference ratio (SIR) based strategies. To quantify the improvements, the PL based strategy against the SIR based strategy is compared while considering various macrocell and (SBS) coverage areas and traffic–types. On the other hand, offloading in a dense urban setting may result in overcrowding the (SBSs). Therefore, hybrid traffic–type driven offloading technologies such as (WiFi) and (D2D) were proposed to en route the delay tolerant applications through (WiFi) (APs) and (D2D) links. It is necessary to illustrate the impact of daily user traffic profile, (SBSs) access schemes and traffic–type while deciding how much of the traffic should be offloaded to (SBSs). In this context, (AUPF) is introduced to account for the population of active small cells which depends on the variable traffic load due to the active users

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

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

Measurement Based Statistical Channel Characterization of Air-to-Ground Path Loss Model at 446 MHz for Narrow-Band Signals in Low Altitude UAVs

Powered by the advances in microelectronics technologies, unmanned aerial vehicles (UAVs) provide a vast variety of services ranging from surveillance to delivery in both military and civilian domains. It is clear that a successful operation in those services relies heavily on wireless communication technologies. Even though wireless communication techniques could be considered to reach a certain level of maturity, wireless communication links including UAVs should be regarded in a different way due to the peculiar characteristics of UAVs such as agility in 3D spatial domain and versatility in modes of operation. Such mobility characteristics in a vast variety of environmental diversity render links including UAVs different from those in traditional, terrestrial mobility scenarios. Furthermore, UAVs are critical instruments for network operators in order to provide basic voice and short messaging services for narrow band communication in and around disaster areas. It is obvious that such widespread use of UAVs under different scenarios and environments requires a better understanding the behavior of the communication links that include UAVs. Therefore, in this study, details of a measurement campaign designed to collect data for large-scale propagation characterization of air-to-ground links operated by UAVs at 446MHz under narrowband assumption are given. Data collection, post-processing, and measurement results are provided.Comment: This work is accepted to 2020 IEEE 91st Vehicular Technology Conference: VTC2020-Spring on January 11, 202

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

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