Machine Learning Assisted Design of Wireless Access Systems for Reliable and Low-Latency Financial and Smart Commerce Services

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 qu