Sixth-generation (6G) wireless networks have the potential to transform global connectivity by supporting ultra-high data rates, ultra-reliable low latency communication (uRLLC), and intelligent, adaptive networking. To realize this vision, 6G must incorporate groundbreaking technologies that enhance network efficiency, spectral utilization, and dynamic adaptability. Among them, unmanned aerial vehicles (UAVs), terahertz (THz) communication, and intelligent reconfigurable surfaces (IRSs) are three major enablers in redefining the architecture and performance of next-generation wireless systems. This survey provides a comprehensive review of these transformative technologies, exploring their potential, design challenges, and integration into future 6G ecosystems. UAV-based communication provides flexible, on-demand communication in remote, harsh areas and is a vital solution for disasters, self-driving, and industrial automation. THz communication taking place in the 0.1–10 THz band reveals ultra-high bandwidth capable of a data rate of multi-gigabits per second and can avoid spectrum bottlenecks in conventional bands. IRS technology based on programmable metasurface allows real-time wavefront control, maximizing signal propagation and spectral/energy efficiency in complex settings. The work provides architectural evolution, active current research trends, and practical issues in applying these technologies, including their potential contribution to the creation of intelligent, ultra-connected 6G networks. In addition, it presents open research questions, possible answers, and future directions and provides information for academia, industry, and policymakers.

A new technique for fiber faults events detection and monitoring in optical communication network systems is proposed. The fiber performance monitoring tool is a new proposed technique designed to detect, locate, and estimate the fiber faults without interrupting the data flow with efficient costs and to improve the availability and reliability of optical networks as it detects fiber faults remotely in real time. Instead of the traditional old method, the new proposed FPMT uses an optical time domain reflectometer to detect multiple types of fiber failures, e.g., fiber breaks, fiber end face contamination, fiber end face burning, large insertion losses on the connector and interconnection, or mismatches between two different types of fiber cables. The proposed technique methodology to detect the fiber failures depends on analyzing the feedback of the reflected signal and the pattern shape of the reflected signal over network fiber lines, supports a higher range of distance testing and performance monitoring, and can be performed inside an optical network in real time and remotely by integrating with an OSC board. The proposed technique detects fiber faults with an average accuracy of measurement up to 99.8%, the maximum distance to detect fiber line faults is up to 150 km, and it can improve the system power budget with a minimal insertion loss of 0.4 dB. The superiority of the suggested technique over real networks was verified with success by the Huawei labs’ infrastructure nodes in the simulation experiment results.

Fiber performance monitoring using modern online technologies in the next generation of intelligent optical networks allows for identifying the source of the degeneration and putting in protective steps to increase remote optical network stability & reliability. In this paper, the performance of the fiber performance monitoring tool (FPMT) technique was improved by integrating it with optical amplifier boards. In this regard, the improved technique detects optical layer events and all fiber soft and hard failures at the online remote rather than disrupting the data flow with a measurement accuracy for defect location of up to ~99.9%, small tolerance of up to ~1 m, the longest distance to detecting optical line defects of up to ~300km, and enhanced power budget for the system with optimum insertion-loss of up to ~0.0 dB. The proposed integration method provides better results with an excellent and efficient solution at fault location measurement & detection in real-time with good financial implications of the technique. The competitiveness of the improved technique over the actual optical networks has been successfully confirmed through application to Huawei labs infrastructure nodes and displayed experimental simulation results.

In Vehicular Ad Hoc Networks (VANETs), ensuring reliable, energy-efficient communication under varying traffic densities remains a major challenge, particularly when relying completely on conventional Radio Frequency (RF) communication. This study presents an integrated communication framework that combines Visible Light Communication (VLC) with RF to improve vehicular connectivity. Motivated by the limitations of fixed-power techniques in dynamic environments, we introduce two power allocation algorithms based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN): one using Fixed Power Allocation (DFPA) and another employing Dynamic Power Allocation (DDPA). Both approaches are evaluated using realistic traffic simulations based on Simulation of Urban Mobility (SUMO) and OpenStreetMap (OSM) data, with a focus on energy efficiency and communication coverage. Simulation results show that DDPA outperforms DFPA in cases with cluster radii below 1159 m, reducing power consumption by up to 20% and serving 90% of vehicles. Conversely, DFPA proves more efficient at larger radii, saving up to 12% power. These results establish a scalable, integrated VLC/RF framework for next-generation VANETs, adaptable to diverse density and coverage requirements.

Ensuring the security and privacy of confidential data during transmission is a critical challenge, necessitating advanced techniques to protect against unwarranted disclosures. Steganography, a concealment technique, enables secret information to be embedded in seemingly harmless carriers such as images, audio, and video. This work proposes two secure audio steganography models based on the least significant bit (LSB) and discrete wavelet transform (DWT) techniques for concealing different types of multimedia data (i.e., text, image, and audio) in audio files, representing an enhancement of current research that tends to focus on embedding a single type of multimedia data. The first model (secured model (1)) focuses on high embedding capacity, while the second model (secured model (2)) focuses on improved security. The performance of the two proposed secure models was tested under various conditions. The models’ robustness was greatly enhanced using convolutional encoding with binary phase shift keying (BPSK). Experimental results indicated that the correlation coefficient (Cr) of the extracted secret audio in secured model (1) increased by 18.88% and by 16.18% in secured model (2) compared to existing methods. In addition, the Cr of the extracted secret image in secured model (1) was improved by 0.1% compared to existing methods. The peak signal-to-noise ratio (PSNR) of the steganography audio of secured model (1) was improved by 49.95% and 14.44% compared to secured model (2) and previous work, respectively. Furthermore, both models were evaluated in an orthogonal frequency division multiplexing (OFDM) system over various wireless channels, i.e., Additive White Gaussian Noise (AWGN), fading, and SUI-6 channels. In order to enhance the system performance, OFDM was combined with differential phase shift keying (DPSK) modulation and convolutional coding. The results demonstrate that secured model (1) is highly immune to noise generated by wireless channels and is the optimum technique for secure audio steganography on noisy communication channels.

Direction-of-Arrival (DOA) estimation in the presence of coherent sources remains a challenging problem in array signal processing, particularly when dealing with rank-deficient covariance matrices due to limited snapshots. The reason behind this difficulty is primarily because coherent (i.e., highly correlated or multipath) sources make the sample covariance matrix rank-deficient, especially when there are limited snapshots or low Signal-to-Noise Ratios (SNRs). Thus, conventional subspace-based DOA estimation techniques such as MUSIC and ESPRIT fail to accurately estimate the angles of arrival since these methods are derived from the full-rank covariance matrix assumption and the uncorrelated sources' assumption. Additionally, techniques such as spatial smoothing, which are commonly used to deal with coherence, introduce additional computational complexity and reduce angular resolution. These limitations point to the significance of developing more robust and resilient methods that would be capable of maintaining a high-resolution DOA estimation under actual source coherence, low SNR, and sparsity levels of available data. This paper presents a novel adaptive sparse regularization framework that effectively addresses these challenges through three key innovations. First, an adaptive regularization scheme automatically adjusts to signal conditions. Second, a sparse weighting mechanism enhances the resolution for coherent sources. Finally, a computationally efficient implementation is suitable for real-time applications. The theoretical analysis establishes a modified Cramér-Rao Lower Bound that accounts for both coherent sources and regularization effects. Extensive simulations demonstrate that the proposed method achieves superior performance compared to existing approaches, with RMSE improvements of up to 40% under low SNR conditions (-20 to -15 dB) compared to traditional MUSIC, ESPRIT, and regularized least-squares methods. The method maintains robust performance even with coherent sources, achieving angular accuracy within 0.4° at high SNRs, while requiring computational complexity comparable to existing techniques. These results establish this approach as a practical solution to challenging DOA estimation scenarios in real-world applications.