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The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services will burden network operators with rising infrastructure costs. Recently, the Open Radio Access Network (O-RAN) has been introduced as a solution for growing financial and operational burdens in Beyond 5G (B5G) and 6G networks. O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs. By disaggregating conventional Base Band Units (BBUs) into O-RAN Distributed Units (O-DU) and O-RAN Centralized Units (O-CU), O-RAN offers greater flexibility for upgrades and network automation. However, this openness introduces new security challenges compared to traditional RANs. Many existing studies overlook these security requirements of the O-RAN networks. To gain deeper insights into the O-RAN system and security, this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G applications. We then delve into specifications of O-RAN security threats and requirements, aiming to mitigate security vulnerabilities effectively. By providing a comprehensive understanding of O-RAN architecture, use cases, and security considerations, this work serves as a valuable resource for future research in O-RAN and its security. [ABSTRACT FROM AUTHOR]

The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting multiple access (RSMA) has emerged as a promising interference management scheme in multi-user communication systems. In this paper, we investigate an RSMA-enhanced FlyBS downlink communication system and formulate an optimization problem to maximize the sum-rate of users, taking into account the three-dimensional FlyBS trajectory and RSMA parameters. To address this continuous non-convex optimization problem, we propose a TD3-RFBS optimization framework based on the twin-delayed deep deterministic policy gradient (TD3). This framework overcomes the limitations associated with the overestimation issue encountered in the deep deterministic policy gradient (DDPG), a well-known deep reinforcement learning method. Our simulation results demonstrate that TD3-RFBS outperforms existing solutions for FlyBS downlink communication systems, indicating its potential as a solution for future wireless networks. [ABSTRACT FROM AUTHOR]