The rapid advancement of fifth-generation (5G)-and-beyond networks coupled with unmanned aerial vehicles (UAVs) has opened up exciting possibilities for diverse applications and cutting-edge technologies, revolutionizing the way connections, communications, and innovations unfold in the digital age. This paper presents a comprehensive survey of the deployment scenarios, applications, emerging technologies, regulatory aspects, research trends, and challenges associated with the use of UAVs in 5G-and-beyond networks. It begins with a succinct background and motivation, followed by a systematic UAV classification and a review of relevant works. The survey covers UAV deployment scenarios, including single and multiple UAV configurations. The categorization of UAV applications in 5G is presented, along with investigations into emerging technologies for enhancing UAV communications. Regulatory considerations encompassing flight guidelines, spectrum allocation, privacy, and safety are discussed. Moreover, light is shed on the latest research trends and open challenges in the field, with promising directions for future investigations identified, concluding with a summary of key findings and contributions. This survey serves as a valuable resource for researchers, practitioners, and policymakers in the UAV and communication domains. Additionally, it offers a comprehensive foundation for informed decision-making, fostering collaboration, and driving advancements in UAV and communication technologies to address the evolving needs of our interconnected world.

Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone’s free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones’ characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques.

Tropospheric ducts, characterized by anomalous positive vertical refractive index gradients, significantly influence radio wave propagation by enhancing signal strength and extending communication ranges. While beneficial in some contexts, these phenomena can disrupt communication systems, particularly in maritime and coastal regions where ducting conditions are prevalent. This paper addresses the challenge of accurately predicting and mitigating the effects of tropospheric ducts on radio propagation, which is critical for the reliability of next-generation communication systems. We present a comprehensive review of radio propagation in tropospheric ducts, encompassing fundamental concepts, theoretical models, and estimation techniques. To enhance the classification of anomalous propagation in the troposphere, including ducting, we develop machine learning models based on real-world data obtained from King Fahd University of Petroleum and Minerals (KFUPM). Our findings demonstrate that machine learning models, particularly support vector machines, can effectively classify ducting conditions, offering superior predictive performance compared to other models. The advancements presented in this paper address the challenges faced by next-generation radio systems and contribute to the development of effective mitigation strategies. The insights gained pave the way for further research and optimization of communication system performance in duct-prone environments.

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.

This paper presents miniature dual-band cylindrical dielectric resonator antenna (CDRA) based on button antenna for wireless body area network (WBAN) applications. A button antenna is designed by stacking two CDRAs (low and high permittivity) fed by coaxial probe through the ground plane and penetrates the bottom CDRA. The prime contributions of using DRAs are to enhance the performance and to provide a compact size of antenna. However, the coaxial probe realizes two radiation patterns, monopole and broadside patterns. A comprehensive validation using CST microwave studio is carried out to determine the characteristics of the proposed button antenna. In addition, to ensure that the results are acceptable to the practitioner in this field, a prototype was fabricated and tested. The superiority of the proposed antenna is confirmed by possessing 39% more compact size compared to previously reported studies, efficiency of more than 63% and bandwidth of 4.9% and 6.6% for lower and upper band, respectively. This proposed design is a promising candidate to benefit on/off body communication devices operating at 2.4 and 5.6 GHz.

Atmospheric ducting significantly influences radio wave propagation, especially in maritime and desert regions. Rapid refractive-index variations critically affect signal propagation, especially for next-generation systems operating in the sub-6 GHz and higher frequency bands. Existing atmospheric duct models are often limited in scope, typically focusing on single-output prediction tasks and relying on synthetic or surface-level inputs, which do not fully capture the vertical refractivity structure required for flight-level duct characterization. To address this gap, this paper introduces a multi-task learning (MTL) framework that combines convolutional neural networks (CNNs) with bidirectional long short-term memory (BiLSTM) networks to sequentially detect duct occurrence, classify duct types, and predict essential duct characteristics. The model is trained and evaluated using two years of vertical profile data for a coastal region, with stratified 5-fold cross-validation (CV) and independent hold-out test set. In addition to point-estimate performance, the framework is validated using uncertainty-aware Monte Carlo test-time stochasticity on the test data to quantify predictive uncertainty and assess robustness of duct characterization. Results demonstrate superior duct detection accuracy, reliable duct-type classification, and robust duct-parameter prediction compared with denser MTL-CNN and MTL-LSTM baselines. The proposed framework provides a practical basis for real-time atmospheric duct assessment and has the potential to enhance propagation forecasting for advanced radar and high-frequency communication systems.