Abstract
The design and optimization of wireless networks have mostly been based on strong mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the era of 5G and beyond, unprecedented levels of complexity will be encountered in the design and optimization of the network. As a result, the use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization due to the flexibility and adaptability it offers in solving extremely complex problems in real-time. One of the main future applications of AI is enabling user-level personalization for numerous use cases. AI will revolutionize the way we interact with computers in which computers will be able to sense commands and emotions from humans in a non-intrusive manner, making the entire process transparent to users. By leveraging this capability, and accelerated by the advances in computing technologies, wireless networks can be redesigned to enable the personalization of network services to the user level in real-time. While current wireless networks are being optimized to achieve a predefined set of quality requirements, the personalization technology advocated in this article is supported by an intelligent big data-driven layer designed to micro-manage the scarce network resources. This layer provides the intelligence required to decide the necessary service quality that achieves the target satisfaction level for each user. Due to its dynamic and flexible design, personalized networks are expected to achieve unprecedented improvements in optimizing two contradicting objectives in wireless networks: saving resources and improving user satisfaction levels. This article presents some foundational background on the proposed network personalization technology and its enablers. Then, an AI-enabled big data-driven surrogate-assisted multi-objective optimization formulation is proposed and tested to illustrate the feasibility and prominence of this technology.
Highlights
O VER the past decade, the convergence of Internet of Things (IoT) and Ambient Intelligence (AmI) technologies have paved the way for more connected, adaptive, proactive, and smart environments
We proposed in [4] a dynamic user satisfaction model that is based on the notion of Zone of Tolerance (ZoT)
There are several advantages of Evolutionary Algorithms (EAs) that drive researchers to utilize them for solving various optimization problems, of which the most important is that they do not necessitate analytical modeling and formulation of the objectives and constraints functions associated with the optimization problem
Summary
O VER the past decade, the convergence of Internet of Things (IoT) and Ambient Intelligence (AmI) technologies have paved the way for more connected, adaptive, proactive, and smart environments. Some users may have lower QoS requirements, yet the network will always attempt to provide higher QoS levels, and charges users more for the unnecessary high-quality services This non-granular average-based singleobjective approach is currently adopted by all operators, it is far from optimum and it is costing the majority of users more money for the provided extra bandwidth they do not need or use. We present a review and categorization of datadriven Evolutionary MOO (EMOO) followed by a discussion on the benefits and challenges of employing EMOO in personalized wireless networks Another important aspect of the problem is integrating user satisfaction behavior into the optimization process. Varying the Number of Function Evaluations (NFEs) on the performance of the simulated algorithms and the quality of solutions
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