Yaser Issam Hamodi Aljanabi

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: Machine learning (ML) has developed significantly over the years, changing several industries through the use of automation and Big Data. By building better next-generation machine learning models, AI’s future has the potential of improving on existing problematic methods such as scalability, interpretability, and generalization. Objective: This article examines about how new generation of ML models are developed and used to explain about the capabilities of AI in different fields. In particular, it is focused on changes in structural models, certain methods of training them, and the application of brand-new technologies as quantum computing. Methods: A review of the state of the art and several case studies were carried out with regard to the latest work being done on different types of ML algorithms such as transformer models, reinforcement learning, and Neural Architecture Search. Moreover, the given models were tested in experiments concerning the applicability of these models in tasks including image recognition, natural language processing, and in autonomous systems. Results: The next-gen models, thereby outperformed the traditional models in terms of accuracy, computational speed, and flexibility. The identified benefits were decreased training time, better interpretability, and better performance with multi-modal and cross-domain tasks. Conclusion: These new generation of ML models are the game changers in AI development solving previous challenges while providing opportunities across numerous sectors. In this vein, further research in this field is needed to achieve AI’s solving of problems.

Background: The telecommunications industry is at the crossroad of change seemingly precipitated by the use of Artificial Intelligence (AI) and Machine Learning (ML). These technologies have yielded new features like network automation, prescriptive analytics, and contextual-consumer engagement, solving traditional dilemmas in service delivery and operationalization. Objective: The current article seeks to understand how AI and ML has positively affected customer experience and service provision in the telecommunication industry. The research objectives focus on how to increase KPIs to service latencies, network reliability, and customer retention while at the same time establishing the problems associated with big data large-scale implementation. Methods: Samples were gathered using systematic reviews of the current literature, meta-analysis of case studies, and assessment of industry datasets. This concerned artificial intelligence enabled operations such as dynamic resource management, real-time customer emotions analysis and real-time fault detection. Regression analysis and time series models were used in order for measuring performance indices. Results: AI and ML integration led to multifaceted advancements: a decrease of average service latency by 55%, reduction of network downtime by 70%, and an increase of maintenance predictions accuracy by 35%. The customer retention rate which had improved to 25% was also credited to better personalization of the services as well as having proper service management. AI-equipped resource allocation also raised efficiency in bandwidth utilization by 60%. Conclusion: AI and ML are positively disrupting telecommunications as they deliver remarkable enhancements in the caliber of services and client satisfaction. With all the challenges in data governance and interoperability, it is clear that their adoption promises a great chance in enhancing the current standards within the telecommunications field and creating the basis for the development of a more sophisticated environment.