In this paper, a new technique for generation of ultra-wide and flattened optical frequency comb (OFC) based on serial cascading of a phase modulator and dual-driven Lithium-Niobate Mach Zehnder modulator (DD-LiNbO 3-MZM) is proposed. Over 60 carriers were generated by carefully adjusting the RF switching voltage (RFSV) of the DD-LiNbO 3-MZM and the signal frequency of the sinusoidal wave (RF) source. A low power amplified RF source with a signal driving power of 16.9dBm is applied, and the power of CW laser is kept at 3dBm. The frequency spacing (FS) is kept at 20GHz for generating the maximum number of carriers. Nonetheless, the scheme is also tested for the FSs of 64GHz, and 32GHz. Each scenario is examined in simulation environment and the main outcomes are highlighted. The proposed scheme is comparatively simple and the FS varies with the applied RF source on the modulators. The achieved OFC lines have a tone-to-noise ratio (TNR) of over 45dB with an undesired side mode suppression ratio of approximately 20dB. The comb lines are almost flat with a varying power deviation of around 0dB-6dB which is optimized to nearly 0.12dB. Furthermore, the impact of the RFSV on the generated number of carriers is studied in detail. The scheme is analyzed in terms of cost efficiency, power deviations, TNR, optical signal to noise ratio, and number of achieved comb lines.

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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.

The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer's a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also emerged in the 5G network to provide a better quality of experience (QoE) to users with different QoS requirements. However, UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing, path planning, optimization, QoS assurance, mobility management, etc. The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging. So, an automated Artificial Intelligence (AI) enabled QoSaware solution is needed for trajectory planning and optimization. Therefore, this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network. It has an efficient Deep Reinforcement Learning (DRL) algorithm for real-time and proactive trajectory planning and optimization. It also fulfills QoS-aware service provisioning. A greedypolicy approach is used to maximize the long-term reward for serving more users with QoS. Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.