Intesar Abbas

Background: As the demand for ultra-fast, low-latency communication continues to rise, Terahertz (THz) communication has emerged as a promising candidate for enabling next-generation 6G networks. However, environmental sensitivity and hardware challenges pose significant limitations. Objective: This study investigates the potential of THz communication to support ultra-high data transfer rates in 6G networks, with a focus on the impact of environmental conditions, hardware complexity, and modulation techniques. Method: Through simulation analysis under both optimal and adverse environmental conditions, the performance of THz communication was assessed. The study also explores emerging materials and adaptive technologies to mitigate performance degradation. Results: Under optimal conditions, THz communication demonstrated the ability to achieve data rates up to 8.5 Tbps with approximately 1 ms latency at 10 THz. However, in high humidity and non-line-of-sight (NLOS) scenarios, performance declined significantly, with the signal-to-noise ratio (SNR) dropping from 35 dB to 18 dB and the bit error rate (BER) increasing from 3×10⁻³ to 4×10⁻². Orthogonal Frequency Division Multiplexing (OFDM) outperformed Quadrature Amplitude Modulation (QAM) in BER under varying conditions. The integration of advanced materials such as graphene and photonic crystals, along with intelligent reflecting surfaces (IRS), showed promise in enhancing signal quality and thermal management. Conclusion: While THz communication exhibits strong potential for supporting the high-speed, low-latency demands of 6G, environmental vulnerabilities and hardware complexity remain key challenges. Future research should prioritize the development of cost-effective, scalable materials and adaptive technologies to improve performance and deployment feasibility in diverse conditions.

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.