Kudaiberdieva Gulmira Karimovna

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: Contemporary multi-protocol networks necessitate scalability, reliability, energy efficiency, and security due to the increasing number of devices and the diversification of network traffic. Conventional network management methods are inadequate to meet these demands, necessitating sophisticated solutions. Artificial intelligence (AI) has emerged as a significant field, offering advanced methods including predictive maintenance, anomaly detection, and intelligent resource management. Objective: This article aims to critically evaluate the effectiveness, flexibility, and productivity of AI-based applications in addressing major challenges in network management, including performance, scalability, energy consumption, threat detection rates, and cost. Methods: The study employs simulations and modeled datasets to assess AI-oriented solutions across various network environments, such as industrial IoT, smart cities, and telecommunications. The evaluation encompasses factors including Mean Time Between Failure (MTBF), resource utilization, delay minimization, and operating cost reduction. Digital twins, intelligent routing algorithms, and self-attention-based anomaly detection models are utilized, and the overall performance of these integrated technologies is analyzed. Results: The analysis demonstrates that AI-powered systems achieve near-optimal performance across all evaluated indicators. Specifically, the Manufacturing and Automotive Knowledge (MAK) sector observed a 52% increase in MTBF, the Banking, Financial Services, and Insurance (BFSI) sector noted a 32.39% improvement in energy efficiency, and the Defense and Public Enterprise (DPE) sector experienced a 94% increase in advanced threat detection. Conclusion: The findings indicate that AI solutions can effectively address many of the challenges present in current networks, offering cost-efficient and secure methods for implementing new communication networks with vast potential. Nonetheless, further empirical research is necessary to generalize these results and validate their applicability in real-world scenarios.

Background: Telecommunication system plays a crucial role in fast development of energy demand growth and carbon dioxide emissions. As sustainability becomes part of corporate goals green telecommunications strive to bring innovation in energy efficiency. Objective: As part of examining the state of art developments in energy-efficient networking technologies and approaches to minimize power consumption in telecommunication facilities, the important global task of using green telecommunication for sustainable development goals is highlighted. Methods: A literature review and analysis were successfully performed to examine the use of advanced hardware technologies, SDN technology, NFV, and intelligent renewable energy integration. Some of the green telecommunication’s solutions that were implemented are explained with case studies in this article. Results: The studies reveal that new practices including energy-sensitive algorithms, state-of-art cooling solutions and integration of renewable power into Telecommunications networks have improved the energy efficiency standards. In addition, SDN and NFV also improve resource allocation of data centers, which also boosts energy efficiency. Conclusion: Green telecoms offer available strategies for cutting back energy use in telecoms sector. Mitigation of the environmental impacts can therefore be achieved through incorporation of Energy Efficiency measures and Renewable Energy Source technology to utility services without necessarily compromising quality of service delivery hence catalyzing the Advancement of the progress of sustainability.

Background: Recent advancements in Natural Language Processing (NLP) have been significantly influenced by transformer models. However, challenges related to scalability, discrepancies between pretraining and finetuning, and suboptimal performance on tasks with diverse and limited data remain. The integration of Reinforcement Learning (RL) with transformers has emerged as a promising approach to address these limitations. Objective: This article aims to evaluate the performance of a transformer-based NLP model integrated with RL across multiple tasks, including translation, sentiment analysis, and text summarization. Additionally, the study seeks to assess the model's efficiency in real-time operations and its fairness. Methods: The hybrid model's effectiveness was evaluated using task-oriented metrics such as BLEU, F1, and ROUGE scores across various task difficulties, dataset sizes, and demographic samples. Fairness was measured based on demographic parity and equalized odds. Scalability and real-time performance were assessed using accuracy and latency metrics. Results: The hybrid model consistently outperformed the baseline transformer across all evaluated tasks, demonstrating higher accuracy, lower error rates, and improved fairness. It also exhibited robust scalability and significant reductions in latency, enhancing its suitability for real-time applications. Conclusion: This article illustrates that the proposed hybrid model effectively addresses issues related to scale, diversity, and fairness in NLP. Its flexibility and efficacy make it a valuable tool for a wide range of linguistic and practical applications. Future research should focus on improving time complexity and exploring the use of deep unsupervised learning for low-resource languages.