Model-based Customer-Relationship Management System and Strategic Board Game: Analogical Training

The pandemic has caused much mental, social and financial stress. Policies and standard operating procedures have helped to alleviate some stress and create systematicity, expectancy, and predictability. This paper aims to alleviate some of the stress through model-based social customer relationship management system, an Augmented Reality board game, and two mini tourism games. The first aims to increase collaboration among stakeholders and to increase more effective and efficient resource management. The second phases players through the preparation, recruitment, attack and observation phases at a slower pace than competitive gaming. The third and fourth project introduce places and trigger memories through mini games respectively. These provide analogical training in social customer relationship management and in developing more systematic thinking, expectations, fun and mindfulness. AI-assistance would enable personalization/intelligent context-aware recommendations

RAN Intelligent Controller (RIC): From open-source implementation to real-world validation

Open Radio Access Network (RAN) is an important architecture design shift for 5G and next generation telecommunications networks. With open RAN, mobile network operators would be able to mix and match multi-vendor RAN solutions as long as the solutions comply with open standards. O-RAN Alliance is the global leader in standardizing the open RAN architecture, where RAN Intelligent Controller (RIC) is positioned centrally as the brain of a RAN to manage and optimize the RAN operations. Open-source projects play a key role in accelerating the adoption of open RAN architecture, especially the use of RIC. However, the fast pace of development and the lack of documentation in open-source projects create steep learning curve for beginners. In this paper, we first provide an overview of widely used open-source RIC projects and discuss their pros and cons. We then share our first-hand experience to use RIC in our campus 5G network that consists of commercial-grade RAN solutions. In particular, we developed a suite of three RAN control applications (i.e., energy efficiency, interference management, and predictive maintenance) on an open-source RIC, and we deploy and evaluate them on a commercial-grade 5G network in a university campus. For these RIC applications, we design and evaluate different ML models based on real-world data collected from our 5G network, which we publish together with this paper. Our experimental results show that AI-based RIC applications can achieve more than 90% of accuracy in inferring the situation of the RAN for each given task. Our energy-saving RIC application can reduce 65% of energy consumption of the RAN over a simulated period of one year. Our project also validates the feasibility to interfacing an open-source RIC with existing commercial-grade 5G solutions