بررسي روش‌هاي بهبود كيفيت سرويس‌دهي در سرويس‌هاي با قابليت اطمينان بالا و تأخير كم در شبكه‌هاي نسل پنجم و بعد از آن

Ultra-Reliable Low-Latency Communication (URLLC) is a pivotal component of fifth-generation (5G) mobile networks designed to meet the stringent demands of various critical applications, including healthcare, autonomous driving, and industrial automation. This thesis presents a comprehensive analysis and eva‎luation of several statistical methodologies and tools employed in understanding and optimizing URLLC systems. The study encompasses a range of topics such as reliability theory, short packet communications, risk assessment techniques, rare event simulation, queuing theory, data freshness, and the utilization of machine learning for system enhancement. The essence of URLLC lies in its ability to ensure high reliability while maintaining minimal latency for data transmission. The reliability is quantified as the probability of successful data delivery within a prescribed time limit, reflecting the inherent trade-off between achieving low latency and high reliability. Various strategies for enhancing latency include mechanisms like short code utilization, time-slot scheduling, edge computing, and optimized low-overhead protocols. Concurrently, measures to bolster reliability encompass multiple connection strategies, data redundancy, Automatic Repeat request (ARQ) protocols, Multiple-Input Multiple-Output (MIMO) configurations, and limited block-length coding. Key theoretical frameworks utilized in this research include sophisticated mathematical models that facilitate the prediction and optimization of URLLC performance metrics. The application of reliability functions within Markov models provides valuable insights into system behavior under different failure scenarios. Additionally, rare event simulation techniques further bolster understanding by eva‎luating performance across a spectrum of operational conditions, allowing for comprehensive assessment of various parameters that influence reliability and latency. Machine learning is identified as a transformative approach that can significantly enhance the design, operation, and optimization of URLLC systems. It is particularly effective in large-scale optimization tasks, learning from sparse data samples, time series forecasting, and executing risk-aware resource allocation strategies, which are crucial for the effective implementation of URLLC capabilities. The study also investigates alternative methodologies, including diversity repetition, random linear network coding, queuing theory, and limited block-length coding. Here, diversity repetition is primarily focused on augmenting reliability, while random linear network coding addresses latency reduction effectively. Limited block-length coding is studied as a solution for mitigating delays while enhancing reliability in short packet frameworks.. In summary, the implications of URLLC are expansive, serving critical functions in a variety of fields that demand immediate and reliable communication. In healthcare, for example, the necessity for real-time data transmission underscores the gravity of latency and reliability, as delays may have life-threatening consequences. Similarly, in autonomous vehicle operations, timely and reliable communication is essential for instantaneous decision-making. Ultimately, this thesis encapsulates the fundamental aspects of URLLC by integrating theoretical and practical perspectives. Its findings aim to propel future advancements in URLLC technology, ensuring that communication systems can effectively adapt to the requirements of an increasingly interconnected and data-driven society. As the landscape of cellular technology evolves, the insights provided in this analysis will serve as a foundation for fostering advancements in reliability, efficiency, and overall performance of URLLC systems. Keywords 1-URLLC 2-Reliability 3-Low Latency 4-5G

مهدي مهدوي

حسين سعيدي , مسعودرضا هاشمي