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Creators/Authors contains: "Zhang, Minglong"

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  1. This demonstration explores the security concerns in 5G and beyond networks within open radio access network (O-RAN) deployments, focusing on active attacks disrupting cellular communications. An xApp developed on the open artificial intelligence cellular (OAIC) platform enables on-the-fly creation and management of network slices to mitigate such attacks. The xApp is hosted in the near-real time RAN intelligent controller (RIC) and establishes secure slices for the software radio network it controls. This solution presents a practical approach for resilient and secure network management in dynamic environments. 
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    Free, publicly-accessible full text available November 1, 2024
  2. This research proposes a dynamic resource allocation method for vehicle-to-everything (V2X) communications in the six generation (6G) cellular networks. Cellular V2X (C-V2X) communications empower advanced applications but at the same time bring unprecedented challenges in how to fully utilize the limited physical-layer resources, given the fact that most of the applications require both ultra low latency, high data rate and high reliability. Resource allocation plays a pivotal role to satisfy such requirements as well as guarantee quality of service (QoS). Based on this observation, a novel fuzzy-logic-assisted Q learning model (FAQ) is proposed to intelligently and dynamically allocate resources by taking advantage of the centralized allocation mode. The proposed FAQ model reuses the resources to maximize the network throughput while minimizing the interference caused by concurrent transmissions. The fuzzy-logic module expedites the learning and improves the performance of the Q-learning. A mathematical model is developed to analyze the network throughput considering the interference. To evaluate the performance, a system model for V2X communications is built for urban areas, where various V2X services are deployed in the network. Simulation results show that the proposed FAQ algorithm can significantly outperform deep reinforcement learning, Q learning and other advanced allocation strategies regarding the convergence speed and the network throughput. 
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    Free, publicly-accessible full text available July 14, 2024
  3. Internet of Things (IoT) is becoming increasingly popular due to its ability to connect machines and enable an ecosystem for new applications and use cases. One such use case is industrial loT (1IoT) that refers to the application of loT in industrial settings especially engaging instrumentation and control of sensors and machines with Cloud technologies. Industries are counting on the fifth generation (5G) of mobile communications to provide seamless, ubiquitous and flexible connectivity among machines, people and sensors. The open radio access network (O-RAN) architecture adds additional interfaces and RAN intelligent controllers that can be leveraged to meet the IIoT service requirements. In this paper, we examine the connectivity requirements for IIoT that are dominated by two industrial applications: control and monitoring. We present the strength, weakness, opportunity, and threat (SWOT) analysis of O-RAN for IIoT and provide a use case example which illustrates how O-RAN can support diverse and changing IIoT network services. We conclude that the flexibility of the O-RAN architecture, which supports the latest cellular network standards and services, provides a path forward for next generation IIoT network design, deployment, customization, and maintenance. It offers more control but still lacks products-hardware and software-that are exhaustively tested in production like environments. 
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