Nameer Hashim Qasim

Background: Telecommunication infrastructure requires regular maintenance and upkeep for its networks’ matrices, but existing approaches have been associated with issues such as time consumption and concern costs, as well as safety hazards. Newer developments in drone technology present progressive opportunity through the improvement of current maintenance processes by means of automation, predictability, and real time computation. Objective: The article seeks to assess whether the use of drone in telecommunication maintenance enhances the operational productivity through increasing the efficiency, reducing cost, safety, environmental and scalability and in different terrains. Methods: The methods followed included the conduct of experimental surveys with drone operations in five different telecommunication settings. These areas of interest were inspection efficiency, the accuracy of condition-based maintenance, signal received signal power, delay reduction through edge computing, and energy consumption. Sophisticated numerical computations, like Kalman filters and various frameworks of edge computing, were used in this context to draw analytical insights on the collected data. Results: The methods that used drones lowered the time needed for inspections by ¾ and cut the expenses by 49.3% and increased safety and quality of the coverage. Predictive maintenance was found to have achieved 89.7% accuracy with the system response time being 246ms at different site. The results of energy consumption model depicted the errors under 2% confirming this approach’s suitability for operational planning. Conclusion: By evaluating the applicability of drones in telecoms maintenance, the paper shows that the notion of drones in this context is promising both now and in the future. These results signal existing and potential applications of drones is to incorporate drone technology into infrastructural management solutions to address emerging needs in the industry.

Background: The advances in use of resilient telecommunication networks have shown the possible use of autonomous drones to support connectivity in unpredictable and complex terrains. Current network infrastructures have limitations in delivering optimized service in areas like traffic congestion, area of sparseness, disasters etc., which requires some form of innovation. Objective: The article is meant to propose a framework for using autonomous drones in practical telecommunication systems, with emphasis on the energy consumption, scalability, dependability, and flexibility of the solution for various situations. Methods: The study also uses other state-of-the-art approaches such as trajectory optimization, swarm coordination, dynamic spectrum management, and machine learning based resource allocation. Various slips were used on urban, rural, and disaster-sensitive scenarios to assess performance indices including energy input, network connectivity, signal strength, and lag time. The simulation results were supported by field experiments providing insights into various circumstances. Results: The simulation results of the actually proposed framework show network scalability enhancements, where coverage area involves up to 50 km² and power saving higher than 15%. The performance improvement included near perfect trajectory anticipation at a rate of 98%, while the utilization of resources was also optimized. Dynamic spectrum management was useful in reducing interference and increasing efficiency especially in areas of high density. Conclusion: The article promotes the use of UAV based telecommunication networks where challenging questions on scalability and reliability are raised and solved. Through the work presented, strong theoretical and empirical assumptions are made to foster concepts that will solidify next generation communication network.

Background: Smart city evolution is fast-paced, and imposes severe demands on telecom infrastructures: it must be highly flexible and scalable for coping with bursty traffic loads and heterogeneous service needs. Legacy network systems are not well suited to handle the changing requirements of smart city environments with autonomous cars, IoT, and public safety systems. Objective : The study to offer an AI-native network slicing framework for 6G smart city networks in order to improve dynamic resource control and management. The framework aims to enhance the delay, energy, and resource performance metrics which are significant for smart city services. Method: To facilitate the real-time network resource orchestration depending on the changing traffic requirements and user preferences, the authors consider moving target defense adapted artificial intelligence with a Deep Reinforcement Learning (DRL) model. Simulations were carried out to compare the AI-native model to conventional and AI-supported slicing methods. Results : Simulation results validate that the AI-native network slicing framework outperforms current 5G solutions with 25% reduction in latency and 20% increase in energy efficiency. Furthermore, the model's online resource allocation scheme can enhance the utilization efficiency of the bandwidth and the energy by 15% compared with the traditional approaches. Such improvements especially in critical applications like traffic management, emergency response, and health care would be important. Conclusion: The presented results demonstrate that AI-native network slicing is a viable, flexible, and scalable solution for 6G smart city networks. The framework is designed to support the future sustainable and high-performance requirements of urban infrastructures, providing both energy-efficient real-time adaptability. This study provides an overarching front-to-end outlook to address the management issues of sophisticated resource systems, and puts AI-native network slicing at the base level of the emerging smart cities.