6G

Background: The rapid evolution from 4G to 5G has transformed the telecommunications landscape, but as technological demands continue to grow, the shift toward 6G is gaining attention. 6G aims to address the limitations of 5G, such as latency and bandwidth constraints, while introducing new capabilities like terahertz communication and ubiquitous AI integration. Objective: This article explores the development roadmap of 6G, highlighting its applications across industries and addressing key challenges in its deployment. Methods: A comprehensive review of current literature on 5G advancements and emerging 6G technologies was conducted. Comparative analyses were performed on the theoretical frameworks of 6G’s core capabilities, including network architecture, spectrum management, and AI integration. Results: The study identified key applications for 6G, such as smart cities, autonomous transportation, healthcare, and industrial automation. It also highlighted the anticipated improvements in data transmission speed, reliability, and connectivity. Conclusion: 6G represents a pivotal evolution in telecommunications, offering transformation in numerous sectors. However, challenges such as infrastructure development, regulatory frameworks, and energy efficiency must be addressed.

Background: The emergence of 6G networks requires new approaches to extend coverage, increase network availability and optimize performance in difficult conditions, including urban and rural areas. Thus, UAVs or UAV systems have developed as a powerful candidate to counter these problems by offering on-demand contingent coverage and differing communication services.   Objective: The opportunity of the development of UAVs’ application in the extension of the network’s coverage is studied in the context of energy efficiency, latency, and Inter-UE interference in high-density 6G environment. Methods: A three-layered optimization architecture was devised, including multi-agent reinforcement learning (MARL) for interference control, trajectory optimization techniques, and energy-aware deployment schemes. Small scale scenarios including urban, suburban and rural environment were considered and the results were analyzed based on the network coverage, energy efficiency, end to end latency and interference encountered on UAVs. Results: The outcome significantly revealed the enhancements in the spatial coverage of the network; UAVs prevented considerable gaps and offered enhancements of network coverage in rural and suburban regions. These achievements include up to 30.5% energy efficiency enhancement, more than 50% latency minimization and interference management that enabled 35.4% enhancement of SINR. Conclusion: Integrating of drones in 6G network is invaluable in enhancing coverage in the networks by providing massive coverage while at the same time providing scalable solutions to problems of coverage gaps, power demands and real-time network adjustments. In future studies, researchers should channel their efforts toward increasing real-time dynamism and energy consumption that suit large-scale executions.

Background: In the article, the author explores the possibilities of Open Radio Access Network (Open RAN) as a revolutionary idea to democratize telecom networks. Objective: The study aims to compare the efficiency, cost, flexibility, scalability, and performance of Open RAN against conventional RAN systems. Methods: The study used simulation, cost modeling and execution of real-world case studies with support from Rakuten Mobile, Vodafone, Telefónica, MTN, and DISH Network. The approach also employed prescriptive analytics to evaluate the deployment of relatively new paradigms like blockchain and AI into Open RAN environments. Results: The study shows that Open RAN leads to substantial CAPEX and OPEX cost saving with a further enhancement in the key network performance metric such as latency by 20% and throughputs by 25%. Additional improvements of 30% demonstrate that Open RAN is also an environmentally friendly solution. The validations also showed how it could expand to both heavily populated large cities and sparsely populated rural areas to improve both coverage and mobility. Conclusion: However, some of the disadvantages that surfaced include; the problem of compatibility, high costs of implementation in the initial stages, and compliance with set regulatory standards. These underscore the need for standardized and coherent protocols and frameworks to enable widespread implementation. Open RAN is highly transformative in modern telecommunications due to the fact that it is affordable, expandable and eco-friendly. Due to its Flexible/Modular design in combination with advanced technologies, it acts as key enabler for future networks such as 5G, 6G and more and tackles Global connectivity and efficiency problems.

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.