Future mobile networks (5G and beyond) are expected to lead to a big revolution, enabling ubiquitous and enhanced broadband services, smart/autonomous vehicles, intelligent transport, and complex human–machine interactions (e.g., extended reality), as well as the Internet of Things (IoT), which will lay the foundations of future smart cities, Industry 4.0 and e-health environments, and allow a massive number of machine-type devices to connect to the network edge.
Planning, developing and managing such a complex and massive system of devices is a hard task, and machine learning (ML) is emerging as the technology of choice. Indeed, ML techniques can achieve outstanding performances for wireless communication applications thanks to their online learning and optimization capabilities. ML methods decrease the complexity of algorithm computations, enabled by data learning and interactions with the environment. Hence, they accelerate the convergence in finding sub-optimal solutions compared to conventional optimization techniques.
This Special Issue is addressed to all the types of ML-based algorithms enabling intelligent mobile network management and optimization.
Special Issue Editor
Dr. Paolo Dini Website
Centre Tecnológic de Telecomunicacions de Catalunya, Castelldefels, Spain
Interests: sustainable networking and computing; multi-agent systems; decision-making processes; distributed optimization and optimal control; machine learning; data mining and analytics
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