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1. Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges

   Polese, M ; Bonati, L ; D'Oro, S ; Basagni, S ; Melodia, T. ( January 2023 , IEEE Communications Surveys & Tutorials)

   The Open Radio Access Network (RAN) and its embodiment through the O-RAN Alliance specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes virtualized RANs where disaggregated components are connected via open interfaces and optimized by intelligent controllers. The result is a new paradigm for the RAN design, deployment, and operations: O-RAN networks can be built with multi-vendor, interoperable components, and can be programmatically optimized through a centralized abstraction layer and data-driven closed-loop control. Therefore, understanding O-RAN, its architecture, its interfaces, and workflows is key for researchers and practitioners in the wireless community. In this article, we present the first detailed tutorial on O-RAN. We also discuss the main research challenges and review early research results. We provide a deep dive of the O-RAN specifications, describing its architecture, design principles, and the O-RAN interfaces. We then describe how the O-RAN RAN Intelligent Controllers (RICs) can be used to effectively control and manage 3GPP-defined RANs. Based on this, we discuss innovations and challenges of O-RAN networks, including the Artificial Intelligence (AI) and Machine Learning (ML) workflows that the architecture and interfaces enable, security, and standardization issues. Finally, we review experimental research platforms that can be used to design and test O-RAN networks, along with recent research results, and we outline future directions for O-RAN development. 

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2. Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis

   https://doi.org/10.1109/GCWkshps56602.2022.10008713  

   Kouchaki, Mohammadreza ; Marojevic, Vuk ( December 2022 , 2022 IEEE Globecom Workshops (GC Wkshps))

   Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy network controllers. xApps are the applications that can leverage machine learning (ML) algorithms for near-real time control. Despite the opportunities provided by this new architecture, the progress of practical artificial intelligence (AI)-based solutions for network control and automation has been slow. There is a lack of end-to-end solutions for designing, deploying, and testing AI-based xApps in production-like network settings. This paper introduces an end-to-end O-RAN design and evaluation procedure using the latest O-RAN architecture and interface releases. We provide details on the development of a reinforcement learning (RL)-based xApp, considering two RL approaches and present numerical results to validate the xApp. 

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3. 3D-O-RAN: Dynamic Data Driven Open Radio Access Network Systems

   https://doi.org/10.1109/MILCOM55135.2022.10017706  

   Restuccia, Francesco ; Blasch, Erik ; Ashdown, Andrew ; Ashdown, Jonathan ; Turck, Kurt ( November 2022 , MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM))

   This position paper introduces a Dynamic Data Driven Open Radio Access Network System (3D-O-RAN). The key objective of 3D-O-RAN is to support congested, contested and contaminated tactical settings where multimedia sensors, application constraints and operating wireless conditions may frequently change over space, time and frequency. 3D-O-RAN is compliant with the O-RAN specification for beyond 5G cellular systems to reduce costs and guarantee interoperability among vendors. Moreover, 3D-O-RAN integrates computational, sensing, and cellular networking components in a highly-dynamic, feedback-based, data-driven control loop. Specifically, 3D-O-RAN is designed to incorporate heterogeneous data into the network control loop to achieve a system-wide optimal operating point. Moreover, 3D-O-RAN steers the multimedia sensor measurement process in real time according to the required application needs and current physical and/or environmental constraints. 3D-O-RAN uses (i) a semantic slicing engine, which takes into account the semantic of the application to optimally compress the multimedia stream without losing in classification accuracy; (ii) a dynamic data driven neural network certification system that translates mission-level constraints into technical-level constraints on neural network latency/accuracy, and occupation of hardware/software resources. Realistic use-case scenarios of 3D-O-RAN in a tactical context demonstrate system performance. 

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4. OpenAI dApp: An Open AI Platform for Distributed Federated Reinforcement Learning

   Kouchaki, Mohammadreza ; Abdalla, Aly S. ; Marojevic, Vuk ( November 2023 , IEEE Future Networks Word Forum (FNWF))

   The evolution of open architectures for Radio Ac- cess Networks (RANs) is revolutionizing network management and optimization. This transformation, fostered by O-RAN, expedites data acquisition and examination by exploiting newly established open interfaces. Moreover, it has led to the rise of near real-time RAN Intelligent Controllers (RICs), instigating a wave of AI-driven applications, or xApps, that employ Artificial Intelligence (AI)/Machine Learning (ML) methods. Nevertheless, deploying xApps as centralized applications presents substantial challenges, such as handling vast data transactions, potential delays, and security vulnerabilities, which are notably prominent within the multifaceted, decentralized, multivendor, and trustless nature of open networks. To alleviate these predicaments, a transition from centralized apps operating in near real-time to distributed real-time apps is imperative for augmented security and efficiency. This paper addresses these complexities by introducing an open platform that integrates a federated reinforcement learning algorithm to operate as distributed Apps (dApps) within the next-generation O-RAN architecture. We present evaluation results in a specific test environment. 

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5. Toward Secure and Efficient O-RAN Deployments: Secure Slicing xApp Use Case

   Moore, Joshua ; Adhikari, Nisha ; Abdalla, Aly S. ; Marojevic, Vuk ( November 2023 , IEEE Future Networks World Forum (FNWF))

   The open radio access network (O-RAN) is rec- ognized for its modularity and adaptability, facilitating swift responses to emerging applications and technological advance- ments. However, this architecture’s disaggregated nature, cou- pled with support from various vendors, introduces new security challenges. This paper proposes an innovative approach to bolster the security of future O-RAN deployments by leveraging RAN slicing principles. Central to this security enhancement is the concept of secure slicing. We introduce SliceX, an xApp designed to safeguard RAN resources while ensuring strict throughput and latency requirements are met for legitimate users. Leveraging the open artificial intelligence cellular re- search (OAIC) platform, we observed that the network latency averages around ten microseconds in a default configuration without SliceX. The latency escalates to over seven seconds in the presence of a malicious user equipment (UE) flooding the net- work with requests. SliceX intervenes, restoring network latency to normal levels, with a maximum latency of approximately 2.3 s. These and other numerical findings presented in this paper affirm the tangible advantages of SliceX in mitigating security threats and ensuring that O-RAN deployments meet stringent performance requirements. Our research demonstrates the real- world effectiveness of secure slicing, making SliceX a valuable tool for military, government, and critical infrastructure opera- tors reliant on public wireless communication networks to fulfill their security, resiliency, and performance objectives. 

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