Saif Kamil Shnain

Background: The availability of advanced digital technology and evolving need for high speed and low latency connections have put pressures on the existing optical fiber networks. New technologies like the Wavelength Division Multiplexing (WDM), Photonic Integrated Circuits (PICs), Mode Division Multiplexing (MDM) and Quantum Communication will be valuable towards the achievement of these demands. Objective: The study examines the capability, expansiveness, and cost-effectiveness of current and emerging optical fiber systems for the development of future Internet technology. The research also seeks to assess these formations to improve data transmission rates, network response time, secure and efficient networks’ solutions. Methods: This is a mixed methods study where both experimental and computational data were collected and analyzed accompanied by theoretical insight. The results that were compared included transmission rate, spectral efficiency, signal integrity and lifecycle costs. Specific work was done on multi-band WDM, PIC-based systems, optical QKD along with simulation studies on large scalable multi-core and mode-division architectures. Results: The article samples acknowledge improved network capabilities with increased transits per watt by 300% in multi-band WDM and reduction of latency levels by employing edge computing. The tested PIC-based systems were shown to be more efficient than the comparable existing systems and quantum communication proved to be reliable method for transmitting data over short to medium distances. Conclusion: Today, it can be stated that the advanced optical fiber technologies are of great value for the construction of high speed, large bandwidth and secure Internet connection. Their integration can reportedly conquer future connectivity issues but new development is required to come over the barriers of deployment and sustainability.

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.