Current communication networks use design methodologies that prevent the realization of maximum network efficiency. In the first place, while users' perception of satisfactory service diverges widely, current networks are designed to be a "universal fit," where they are generally over-engineered to deliver services appealing to all types of users. Also, current networks lack user-level data cognitive intelligence that would enable fast personalized network decisions and actions through automation. Thus, in this article, we propose the utilization of AI, big data analytics, and real-time non-intrusive user feedback in order to enable the personalization of wireless networks. Based on each user's actual QoS requirements and context, a multi-objective formulation enables the network to micro-manage and optimize the provided QoS and user satisfaction levels simultaneously. Moreover, in order to enable user feedback tracking and measurement, we propose a user satisfaction model based on the zone of tolerance concept. Furthermore, we propose a big-data-driven and AI-based personalization framework to integrate personalization into wireless networks. Finally, we implement a personalized network prototype to demonstrate the proposed personalization concept and its potential benefits through a case study. The case study shows how personalization can be realized to enable the efficient optimization of network resources such that certain requirement levels of user satisfaction and revenue in the form of saved resources are achieved.

翻译：当前通信网络采用的设计方法阻碍了最大网络效率的实现。首先，用户对满意服务的认知存在显著差异，而现有网络被设计为"普适适配"模式，通常过度工程化以迎合所有类型用户的需求。其次，现有网络缺乏用户级数据认知智能，无法通过自动化实现快速的个性化网络决策与行动。为此，本文提出利用人工智能、大数据分析与实时非侵入式用户反馈来实现无线网络的个性化。基于每个用户的实际服务质量需求与情境，多目标优化框架使网络能够同时微调并优化提供的服务质量与用户满意度水平。此外，为支持用户反馈追踪与测量，我们基于容忍区间概念提出了用户满意度模型，并进一步设计了大数据驱动与人工智能赋能的个性化框架以将个性化机制集成至无线网络。最后，通过案例研究实施个性化网络原型，展示了所提个性化概念及其潜在效益。案例研究表明，个性化能够实现网络资源的高效优化，从而在节省资源的形式下达到特定的用户满意度与收益要求水平。