Uncrewed aerial vehicles (UAVs) have become essential in advancing multiaccess edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RISs) with UAV-based MEC systems further elevates these capabilities, delivering significant gains in computational power, energy efficiency (EE), and physical-layer security (PLS). However, managing the complexity of RIS within UAV networks requires sophisticated optimization strategies. This survey offers a comprehensive analysis of the fundamentals of RIS, UAVs, and MEC, followed by an in-depth examination of RIS configurations in UAV-based MEC systems, including static, dynamic, and hybrid models. We evaluate the benefits and challenges of RIS integration, such as improved communication, enhanced computational efficiency, optimized energy use, better task management, and strengthened security. In addition, the survey explores the latest advancements in RIS-assisted UAVs for MEC, focusing on boosting computational capacity, minimizing delay, maximizing EE, and enhancing security. To provide a thorough exploration of these topics, detailed summary tables are included, offering a comparative analysis of methodologies, performance metrics, and scenarios from recent studies. Furthermore, the survey presents key lessons learned from current research and identifies future research directions crucial for fully realizing the potential of RIS-enhanced UAV-based MEC systems in next-generation networks.

The integration of fifth-generation (5G) and unmanned aerial vehicle (UAV) technologies has become a promising solution for providing seamless communication in applications, such as disaster management, because of its bandwidth availability, cost-efficacy, and mobile nature. The state-of-the-art research in UAV communication concentrates on effective positioning and path planning. Despite this, these systems performed poorly due to a lack of dynamic control and external factors, such as weather. The solution presented in this paper addresses the problems listed above by using dynamic positioning and energy-efficient path planning for disaster scenarios in the 5G-assisted multi-UAV environments (Dynamic-UAV) to maximize the performance metrics. The lightweight gated recurrent unit (LGRU) is used for weather and event prediction to determine the disaster and non-disaster area and the context of the disaster. The density-based optics clustering (DBOC) algorithm is used to achieve reliability during communication with cluster IoT devices in disaster and non-disaster regions. The satellite determines the number of UAVs required and positions the UAVs using the dynamic positioning-based soft actor–critic (DPSAC) algorithm to achieve maximum throughput. Moreover, the UAVs’ path planning is performed using the shuffled shepherd optimization with dynamic-window method (SSO-DWM) to reduce energy consumption. The proposed approach is simulated using the NS 3.26 simulator and validated by comparing the results with existing techniques regarding the quality of service (QoS), reliability, and energy efficiency. Experimental results indicate that the proposed method achieved maximum throughput (1.59 bit/s), packet delivery ratio (0.88), coverage probability (0.82), number of collected packets (7109–5875), energy efficiency (1.544), minimum delay (16.4 ms–18.5 ms), and energy consumption (7.48 KJ).

Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarchic federated learning (AFL) and other learning algorithms to enhance data rate, use rate, and resource allocation efficiency. Initially, the MEC–UAVs are optimally positioned based on the MU density using the beluga whale optimization (BLWO) algorithm. Optimal clustering is performed in terms of splitting and merging using the triple-mode density peak clustering (TM-DPC) algorithm based on user mobility. Moreover, the trajectory, altitude, and hovering time of MEC–UAVs are predicted and optimized using the self-simulated inner attention long short-term memory (SSIA-LSTM) algorithm. Finally, the MUs and MEC–UAVs play auction games based on the classified requests, using an AFL-based cross-scale attention feature pyramid network (CSAFPN) and enhanced deep Q-learning (EDQN) algorithms for dynamic resource allocation. To validate the proposed approach, our system model has been simulated in Network Simulator 3.26 (NS-3.26). The results demonstrate that the proposed work outperforms the existing works in terms of connectivity, energy efficiency, resource allocation, and data rate.

Unmanned aerial vehicles (UAVs) play a crucial role in resource management and offer advantages in fifth-generation (5G) and beyond networks, such as enhanced coverage, on-demand deployment, and flexible connectivity for managing data traffic spikes. However, limited power supply restricts operational time and demands efficient energy management. Real-time coordination of UAVs with ground networks and efficient data transmission further complicate resource management. Traditional methods often fail to provide the control and processing needed to optimize energy, resulting in higher operational costs, as UAVs require frequent recharging due to limited onboard energy. To address these limitations, this paper proposes a solution that combines the Double Exponential Crayfish Optimization Algorithm (DECOA) with Deep Belief Networks (DBN). DECOA merges the Crayfish Optimization Algorithm (COA) with Double Exponential Smoothing (DES) to enhance optimization for efficient resource allocation in UAV-assisted networks. DBN is applied to reduce overall network energy consumption while ensuring reliable connectivity by predicting energy consumption, enabling more accurate and optimized decision-making. The simulation results show that the proposed DECOA-based DBN model achieved an average energy efficiency of 0.80, a throughput of 0.75 Mb/s, and a computational time of 1434.5 seconds using 10 UAVs, thereby outperforming traditional methods by 26.5% in energy efficiency, 35.99% in computational time, and 35.08% in throughput. These metrics reflect a substantial reduction in energy consumption, quicker processing times, and higher data transmission rates, proving the effectiveness of the proposed DECOA-based DBN approach in UAV-assisted wireless networks.

This paper introduces an ultra-wideband (UWB) reflective metasurface that exhibits the characteristics of a linear to circular (LTC) polarization conversion. The LTC polarization conversion is an orthotropic pattern comprising two equal axes, v and u, which are mutually orthogonal. Additionally, it possesses a 45${}^{°}$ rotation with respect to the y-axis which extends vertically. The observed unit cell of the metasurface resembles a basic dipole shape. The converter has the capability to transform LP (linear polarized) waves into CP (circular polarized) waves within the frequency range 15.41–25.23 GHz. The band that contains its 3dB axial ratio lies within 15.41–25.23 GHz, which corresponds to an axial ratio (AR) bandwidth of 49.1%, and the resulting circular polarized wave is specifically a right-hand circular polarization (RHCP). Additionally, an LTC polarization conversion ratio (PCR) of over 98% is achieved within the frequency range between 15 and 24 GHz. A thorough theoretical investigation was performed to discover the underlying mechanism of the LTC polarization conversion. The phase difference $\Delta {\phi }_{\mu \nu }$ among the reflection coefficients of both the v- as well as the u-polarized incidences is approximately ±90${}^{°}$ that is accurately predictive of the AR of the reflected wave. This study highlights that the reflective metasurfaces can be used as an efficient LTC polarization conversion when the $\Delta {\phi }_{\mu \nu }$ approaches ±90${}^{°}$. The performance of the proposed metasurface enables versatile applications, especially in antenna design and polarization devices, through LTC polarization conversion.