Machine Learning Assisted Design of Wireless Access Systems for Reliable and Low-Latency Financial and Smart Commerce Services
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P115Keywords:
Machine Learning, Wireless Access Systems, Low Latency Communication, Ultra-Reliable Communication, Smart Commerce, Financial Services, Queue-Aware Scheduling, Lyapunov Optimization, Cross-Layer Design, Data-Driven Network OptimizationAbstract
The fast process of digitalization of financial services and the development of intelligent exchanges have put in front of wireless access systems their strict demands, especially ultra-low latency, high reliability, and strong quality-of-service (QoS) guarantees. Mobile banking, high-frequency trading, contactless payments, real-time fraud detection, smart retail systems (among others) are all becoming reliant on wireless communication infrastructures capable of providing deterministic performance in the face of the most dynamically varying traffic and channel conditions. The conventional design methods of wireless access based on intensive use of static optimization and/or model-based methods tend to fail to deliver high latency and reliability as requested by these mission-critical services. The aim of the paper at hand is to elaborate a detailed research on machine learning-aided design of wireless access system in black and white to reliable and low-latency financial and smart commerce services. The designed framework combines the learning-based decision and control solutions with the scheduling that is latency-conscious, managing resources across the layers, and optimizing queues. Machine learning models based on a combination of historical and real-time network data can be used to do predictive analytics on network performance, adaptive resource allocation, and proactive mitigation of congestion. The framework focuses on the common interest of latency and reliability measurements based on data-driven strategies and reducing the natural uncertainty of wireless environments and variability. The systematic integration of the Lyapunov optimization with control policies based on learning to achieve queue stability and with the rigid delay and reliability constraints is one of the main contributions of this work. To make financial and commerce-based traffic have heterogeneous service-level agreements, deadline limited scheduling and queue conscious admission control are used to prioritize traffic. In addition, the paper discusses how supervised learning, reinforcement learning, and hybrid learning paradigm can be used to increase system adaptability and resilience. Through considerable analytical discourse and comparative analogy, it is seen that the machine learning-enabled wireless access systems outperform traditional strategies by a substantial margin in end-to-end latency alleviation, dependability in packet transmission, as well as efficacy in resource utilization. The results emphasize how the optimization of AI-based systems could be used as an enabling underlying technology of future financial and intelligent commerce services in wireless networks.
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