Massive swarms of low Earth orbit (LEO) satellites are poising to serve for high-speed and low-latency ubiquitous connectivity with almost global coverage. Broadband inter-satellite communication is one of the key elements of satellite communication systems that orchestrate massive satellite swarms in cooperation. Thanks to technological advancements in microelectronics and micro-systems, the terahertz (THz) band has emerged as a strong candidate for inter-satellite links (ISLs) due to its promise of wideband communication. Especially, multi-antenna systems can improve the system performance along with the wideband supported by the THz band. However, multi-antenna systems should be reconsidered due to their size, weight, and price/power (SWaP) constraints. On the other hand, as a state-of-art multi-antenna technology, reconfigurable intelligent surface (RIS) is able to relax SWaP constraints because of its passive component-based structures. However, as similar reflection characteristic throughout wideband is difficult to meet, it is possible to observe beam misalignment. In this work, we first provide an assessment of the use of the THz band for ISLs and quantify the impact of misalignment fading on error performance. Then, in order to compensate for the high path loss associated with high carrier frequencies, and to further improve the signal–to–noise ratio (SNR), we propose the use of RISs mounted on neighboring satellites to enable signal propagation. Based on a mathematical analysis of the problem, we present the error rate expressions for RIS-assisted ISLs with misalignment fading. Also, numerical results show that RIS can leverage the error rate performance and achievable capacity of THz ISLs.

The use of satellites to provide ubiquitous coverage and connectivity for densely deployed Internet of Things (IoT) networks is expected to be a reality in emerging 6G networks. Yet the low battery capacity of IoT nodes constitutes a problem for their direct connectivity to satellites, which are located at altitudes of up to 2000 km. In this article, we propose a novel architecture involving the use of reconfigurable intelligent surface (RIS) units to mitigate the path loss associated with long transmission distances. These RIS units can be placed on satellite reflectarrays, and, when used in broadcasting and beamforming, they can provide significant gains in signal transmission. This study shows that RIS-assisted satellites can provide up to 10⁵ times higher downlink and achievable uplink rates for IoT networks.

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.

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 240GHz to 300GHz 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.

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/F1–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.