Saleh Ahmed Alawsh

Beam splitting (or beam squint) is a critical impairment in wideband reconfigurable intelligent surface (RIS)-aided terahertz (THz) systems, where subcarriers focus in different spatial directions, causing substantial loss in beamforming gain. Conventional passive RIS (PRIS) not only suffers from this effect but also experiences severe double-hop path loss due to multiplicative fading. Active RIS (ARIS), equipped with joint amplitude and phase control, can overcome the path loss limitation and further mitigate beam splitting. While true time delay (TTD) units have been studied as a potential remedy to mitigate beam splitting effect, their large insertion loss and hardware complexity hinder practical deployment. In this letter, we propose a joint amplitude–phase optimization framework for ARIS that maximizes the minimum array gain across all subcarriers under a transmit power constraint, without requiring TTD hardware. Numerical results demonstrate that the proposed ARIS design achieves a significantly flatter array-gain profile and effectively suppresses beam splitting. Moreover, it doubles the array gain compared to the conventional passive-RIS baseline, which co-phases the cascaded channel at the central frequency. A passive-RIS based setup is also implemented with the same objective of maximizing the minimum array gain across all subcarriers; however, it achieves 33% lower array gain at the extreme subcarriers than our ARIS design. Both setups operate under the same total power budget to ensure a fair comparison. Finally, the role of the power allocation factor, which governs the allocation of power between the BS and active-RIS is also analyzed. Simulation results further show that the proposed ARIS consistently achieves higher achievable rates than both the passive-RIS and baseline schemes under the same power budget.

Recognizing phase-coded radar waveforms at low visibility is often handled through learning-based classifiers, but such methods require training data and tend to lose reliability when the noise statistics or operating conditions change. This work follows a different path and develops a deterministic algorithm that operates directly on the received pulse. A single carrier-bin trace is extracted from either the Short-Time Fourier Transform (STFT) or the wavelet transform, and two complementary descriptors are formed from its phase and magnitude. These descriptors are combined through an SNR-dependent weight selected by a simple margin-based optimization that does not rely on learned models or prior assumptions. Using four representative waveform families, the STFT version already offers clear gains over phase-only and magnitude-only decisions, while the wavelet front-end provides sharper chip localization and stronger separation between classes, especially at the lowest SNR values. The results show consistent improvements in accuracy, decision margins, and confusion matrices, and the overall procedure remains lightweight enough for real-time or embedded receivers.

This paper investigates the impact of Reconfigurable Intelligent Surface (RIS) orientation on the throughput performance of Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems. Specifically, we study how physical rotation of the RIS, through controlled azimuth and elevation adjustments, influences the effective channel and data rate. A UAV-mounted RIS enables directional alignment to serve ground users in scenarios where the direct Base Station (BS)-to-user path is blocked. Using the SimRIS channel simulator, we analyze the system under various rotation angles and present performance heatmaps that highlight optimal RIS orientations. The study shows that RIS alignment has a substantial effect on achievable rates, thereby motivating orientation-aware optimization in practical deployments.

Atmospheric ducts, formed by sharp refractive index gradients in the lower atmosphere, play a crucial role in signal propagation, particularly for next-generation communication systems operating in high-frequency bands (sub-6 GHz and beyond), where ducts can significantly modify signal paths. This study refines the standard ITU refractivity model through a region-specific correction and subsequently develops a multiple linear regression (MLR) framework to adapt the model using real meteorological data. The MLR model evaluates predictor significance, addresses multicollinearity, and applies statistical criteria to produce a localized refractivity estimate. To address residual nonlinear patterns not captured by MLR, a machine learning (ML) model is trained on the residuals and integrated with the statistical output to form a hybrid prediction. The region-specific (RS) model shows a systematic deviation from the ITU-based modified refractivity profile, with a mean difference of 4.67 M-units, a standard deviation of 3.49 M-units, and an RMSE of 5.83 M-units. These differences suggest that the ITU profile does not adequately represent local atmospheric conditions. Furthermore, the hybrid model reduces residual error relative to MLR alone. The results highlight the importance of incorporating RS adjustments and data-driven methods in modeling atmospheric ducts for propagation analysis.

Phase coding plays an important role in radar systems, affecting signal modulation, target detection, and interference rejection. This study assesses various phase codes, including Gold, Kasami, Barker, Frank, and Chaotic codes, by analyzing performance metrics like cross-correlation and peak-to-sidelobe ratio (PSLR). The study highlights certain limitations, suggesting the need for new waveform solutions. It integrates component-based and Kronecker product-based interpulse coding to combine different codes and improve overall system performance. By merging phase codes effectively, interpulse coding leverages the strengths of individual codes to produce a composite waveform with enhanced autocorrelation and cross-correlation properties, improving radar performance. An optimization problem is formulated using a weighted performance metric to identify the best waveform, considering factors such as cross-correlation and PSLR. The approach, utilizing simple Kronecker and element-wise multiplication techniques, offers significant benefits with minimal complexity. The Barker-Gold interpulse code stands out as highly effective for spread spectrum-based applications due to its favorable correlation properties.

The use of a reconfigurable intelligent surface (RIS) in radar systems significantly enhances target detection, particularly in challenging non-line-of-sight (NLoS) scenarios. In urban environments, where structures frequently obstruct line-of-sight (LoS) paths, the integration of RISs with existing radars can offer a viable solution for enhancing signal-to-noise ratio (SNR) and improving target detection. Approaches utilizing a single RIS can still fail in scenarios where a link cannot be established. This paper presents a novel approach for deriving a comprehensive expression for the received power, SNR and path loss (PL) in systems where multiple RISs assist a monostatic radar. We analyze the power received in dual RIS configurations and extend this to include additional RISs, demonstrating how each additional RIS placement affects the system's performance. Moreover, the analysis explores the impact of different Swerling target models on the SNR and PL, highlighting the optimal angles for target detection. This multi-RIS strategy offers a substantial performance boost over conventional radars and single RIS-assisted systems, particularly in environments with obstacles. Simulation results demonstrate a significant improvement in SNR with a dual RIS-assisted radar, with up to 14.42 dB gains observed when employing a $46 \times 46$ element RIS configuration at L-band and 65.47 dB gain when employing a $328 \times 328$ element RIS configuration at X-band, corresponding to a RIS size of $5\rm{m} \times 5\rm{m}$ at both frequencies, showing the efficacy of the proposed multi-RIS strategy.

This paper investigates the performance of hovering unmanned aerial vehicles (UAVs) using free space optical (FSO) communications with decode-and-forward (DF) relaying. Multi-hop UAV relays are considered. The channel is modeled considering the atmospheric attenuation due to the sandstorm and relative humidity (RH), in addition to the atmospheric turbulence, angle-of-arrival (AOA) fluctuations, and pointing errors (PE). We first obtain a simplified expression for end-to-end (E2E) outage probability considering atmospheric attenuation conditions, then the impact of various parameters such as transmitted power, field of view (FOV) and optical beamwidth on E2E outage probability is incorporated. We also derive a closed form expression for the ergodic capacity and characterize the effect of different system parameters. Monte Carlo simulation is utilized to prove the analytical results. The asymptotic bound for E2E outage probability is validated to offer insights into the effects of different channel coefficients. Analysis of the results indicates that the impact of low visibility due to sandstorms is comparable to that of high RH on FSO links.

Metallic materials such as brass, copper, and aluminum are used in numerous applications, including industrial manufacturing. The vibration characteristics of these objects are unique and can be used to identify these objects from a distance. This research presents a methodology for detecting and classifying these metallic objects using the vibration dynamics induced by their micro-Doppler signatures. The proposed approach utilizes image processing techniques to extract pivotal features from spectrograms. These spectrograms originate from micro-Doppler signatures of data collected during controlled laboratory experiments where signals were transmitted towards vibrating metal sheets, and the ensuing reflections were recorded using a software-defined radio (SDR). The spectrogram data was augmented using geometric transformation to train a convolutional neural network (CNN) based machine learning model for object classification. The results indicate that the proposed CNN model achieved an accuracy of more than 95% in classifying metals into brass, copper, and aluminum. This research could be used to understand the foundations of classifying spectrogram images using micro-Doppler signatures for its applications towards enhancing the sensing capabilities in industrial and defense applications.

In two-dimensional direction of arrival (2D-DOA) estimation, planar arrays can estimate the elevation and azimuth angles simultaneously. However, many planar array topologies such as billboard, L-shaped, T-shaped, and 2D nested arrays suffer from mutual coupling that results from the small separation between the physical sensors (antennas), which limits the estimation capability of the sensor array. In an attempt to reduce mutual coupling between sensors, this paper proposes sparse billboard and T-shaped arrays in which the number of closely separated sensors is significantly reduced. In addition to extending the CRB for fourth order coarray, this paper also derives closed-form expressions for the sensor locations and the number of consecutive lags or the uniform degrees of freedom (uDOF), in the fourth-order difference coarray (FODC). Simulation results demonstrate the robustness of the proposed sparse arrays against mutual coupling.