Maan Hameed

Background: Telecommunications has been rapidly moving from legacy systems to highly flexible modern architectures to accommodate the expanding demand on its services. This evolution is critical in providing the capacity needed for new technologies like 5G, IoT, and applications powered by AI. Objective: The study aims at establishing a literature review on the evolution from the more or less obsolete telecommunication structures to new generation digital structures, opportunity factors, technologies that facilitate this change as well as the value addition by this evolution. Methods: The literature review was followed by an examination of industry case studies of 50 telecommunications firms across the globe. The study looked at best practices including network resource utilization, operational price, and service delivery effectiveness, pre and post implementation of technologies like software-defined networking (SDN), network function virtualization (NFV), and cloud-native architectural strategies. Results: The analyses brought out the fact that with the new architectures, network scale up capabilities were enhanced by 70%, operation costs were brought down by up to 30% and service delivery rates were boosted by 40%. Nonetheless, 85% of the firms that implemented the software upgrade faced issues with system integration, which took fifteen months on average before the new system was fully incorporated, and the firms incurred an additional 20% in implementation costs in accommodating integration issues. Conclusion: Extension of telecommunication architectures towards digital landscape improves performance, capacity, and affordability thereby allowing the providers to address next generation applications. However, while making this transition, there are a number of risks that organizations have to face and it is very important to manage them in order to have maximum benefits from using new digital technologies.

Background: Enabling Edge Artificial Intelligence (Edge AI) to be implemented in autonomous systems and telecommunications can offer for improved real-time data, non-recurring latency, enhanced operational proficiency. Some empirical research suggests that Edge AI minimizes latency by 70%, enhances computing speed by 50%, and cuts bandwidth consumption by 30% in the most demanding cases. Objective: The purpose of this article is to investigate how Edge AI can serve as an enabling technology for the future of self-sustaining environments such as autonomous mobility and telecommunications in terms of measured utility and differentiation. Methods: Screening 120 refereed articles and 25 case studies connected to Edge AI application in telecoms and self-governing systems, this systematic looked-for patterns in the proximal research and promising agendas. The review encompassed research works concerned with latency minimization, bandwidth enhancement and enhancement in the processing capacity. Focus was made on application areas like self-driving cars, industrial IoT, and smart city platforms and performance analysis was made in these areas. Results: The current study prove that when employed in autonomous systems, Edge AI enhances decision making reaction time by 40-60%, while enhancing data traffic throughput within telecommunications networks by 35%. Further, Edge AI makes the overall energy consumption lower in IoT-based applications by cutting down the average usage by a quarter thus creating a sustainable network. Conclusion: Edge AI becomes a central tool in the development of self-driving cars and telecommunications, increased performance and ability to handle mass amount of data at a low latency. These developments place Edge AI at the base of the evolution of future intelligent systems as the basis for smarter and more responsive technological landscapes.