Enhancing spectral efficiency in non-line-of-sight (NLoS) environments is essential as 5G networks evolve, surpassing 4G systems with high information rates and minimal interference. Instead of relying on traditional Orthogonal Multiple Access (OMA) systems to tackle issues caused by NLoS, advanced wireless networks adopt innovative models like Non-Orthogonal Multiple Access (NOMA), cooperative relaying, Multiple Input Multiple Output (MIMO), and intelligent reflective surfaces (IRSs). Therefore, this study comprehensively analyzes these techniques for their potential to improve communication reliability and spectral efficiency in NLoS scenarios. Specifically, it encompasses an analysis of cooperative relaying strategies for their potential to improve reliability and spectral efficiency in NLoS environments through user cooperation. It also examines various MIMO configurations to address NLoS challenges via spatial diversity. Additionally, it investigates IRS settings, which can alter signal paths to enhance coverage and reduce interference and analyze the role of Unmanned Aerial Vehicles (UAVs) in establishing flexible communication infrastructure in difficult environments. This paper also surveys effective energy harvesting (EH) strategies that can be integrated with NOMA for efficient and reliable energy-communication networks. Our findings show that incorporating these technologies with NOMA not only enhances connectivity and spectral efficiency but also promotes a stable and environmentally sustainable data communication system.

Securing Industrial Control Systems (ICSs) is critical, but it is made challenging by the constant evolution of cyber threats and the scarcity of labeled attack data in these specialized environments. Standard intrusion detection systems (IDSs) often fail to adapt when transferred to new networks with limited data. To address this, this paper introduces an adaptive intrusion detection framework that combines a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model with a novel transfer learning strategy. We employ a Reinforcement Learning (RL) agent to intelligently guide the fine-tuning process, which allows the IDS to dynamically adjust its parameters such as layer freezing and learning rates in real-time based on performance feedback. We evaluated our system in a realistic data-scarce scenario using only 50 labeled training samples. Our RL-Guided model achieved a final F1-score of 0.9825, significantly outperforming a standard neural fine-tuning model (0.861) and a target baseline model (0.759). Analysis of the RL agent’s behavior confirmed that it learned a balanced and effective policy for adapting the model to the target domain. We conclude that the proposed RL-guided approach creates a highly accurate and adaptive IDS that overcomes the limitations of static transfer learning methods. This dynamic fine-tuning strategy is a powerful and promising direction for building resilient cybersecurity defenses for critical infrastructure.

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same time. Furthermore, students often make multiple visits to the university’s administration, make multiple calls, or write emails about their concerns, which makes it difficult to respond to their queries promptly. AI-powered chatbots can act as virtual assistants that promptly help students in addressing their simple and complex queries. Most of the research work has focused on chatbots supporting the English language, and significant improvement is needed for implementing chatbots in the Arabic language. Existing studies supporting the Arabic language have either employed rule-based models or built custom deep learning models for chatbots. Rule-based models lack understanding of diverse contexts, whereas custom-built deep learning models, besides needing huge datasets for effective training, are difficult to integrate with other platforms. In this work, we leverage the services offered by IBM Watson to develop a chatbot that assists university students in both English and Arabic. IBM Watson employs natural language understanding and deep learning techniques to build a robust dialog and offers a more scalable, integrable, and customizable solution for enterprises. The chatbot not only provides information about the university’s general services but also customizes its response based on the individual needs of the students. The chatbot has been deployed at Sultan Qaboos University (SQU), Oman, and tested by the university’s staff and students. User testing shows that the chatbot achieves promising results. This first bilingual AI chatbot at SQU supports English and Arabic and offers secure, personalized services via OTP and student email verification. SQUbot delivers both general and individualized academic support. Pilot testing showed 84.9% intent recognition accuracy. Most unidentified queries were due to dialectal variation or out-of-scope inputs, which were addressed through fallback prompts and dataset refinement.

Propagation Pathloss models are key elements when planning communication networks where improper selection leads to high co-channel interference as well power and resources waste. This is an important element in the energy budget of each mobile cells. For this reason, it is necessary to estimate or measure path loss with such precision and accuracy as to provide effective and stable connection in the coverage area. The empirical models are used in the frequency range (1700- 2100 MHz) which is necessary for the implementation of 4G LTE networks worldwide. In this study, the propagation models are studied for the frequencies used for 4G-LTE cells. The models are then compared with the experimentally collected data sets for different cells. This modelling process has been achieved through four main stages. Firstly, a rigorous drive test was conducted. Secondly, the measured path loss data has been handled using developed MATLAB scripts and compared to the predicted data of selected propagation models utilizing RMSE. Thirdly, the generalized equations of path loss models have been generated for each area and technology using the average RMSE value for each studied field and use of neural network for prediction.

Efficient power amplifiers are essential in portable battery-operated systems such as mobile phones. Also, the power amplifier (PA) is the most power-consuming building block in the transmitter of a portable system. This paper investigates how the efficiency of the power amplifier (which is beneficial for multiple applications in communcation sector) can be improved by increasing the efficiency of switching mode class E power amplifiers for frequencies of 900 MHz and 1800 MHz. The paper tackles modeling, design improvements and verification through simulation for higher efficiencies. This is the continuation of previous work by the authors. These nonlinear power amplifiers can only amplify constant-envelope RF signals without introducing significant distortion. Mobile systems such as Advanced Mobile Phone System (AMPS) and Global System for Mobile communications (GSM) use modulation schemes which generate constant amplitude RF outputs in order to use efficient but nonlinear power amplifiers. Improvements in designs are suggested and higher efficiencies are achieved, to the tune of 67.1% (for 900 MHz) and 67.0% (1800 MHz).