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1. Physics-Constrained Taylor Neural Networks for Learning and Control of Dynamical Systems

   Nguyen, Nam T; Tique, Juan C; Nghiem, Truong X (July 2025, IEEE)

   Data-driven approaches are increasingly popular for identifying dynamical systems due to improved accuracy and availability of sensor data. However, relying solely on data for identification does not guarantee that the identified systems will maintain their physical properties or that the predicted models will generalize well. In this paper, we propose a novel method for data-driven system identification by integrating a neural network as the first-order derivative of the learned dynamics in a Taylor series instead of learning the dynamical function directly. In addition, for dynamical systems with known monotonic properties, our approach can ensure monotonicity by constraining the neural network derivative to be non-positive or non-negative to the respective inputs, resulting in Monotonic Taylor Neural Networks (MTNN). Such constraints are enforced by either a specialized neural network architecture or regularization in the loss function for training. The proposed method demonstrates better performance compared to methods without the physics-based monotonicity constraints when tested on experimental data from an HVAC system and a temperature control testbed. Furthermore, MTNN shows good performance in a control application of a model predictive controller for a nonlinear MIMO system, illustrating the practical application of our method. 

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2. 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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3. Demo: Decoding Control Information Passively from Standalone 5G Network

   https://doi.org/10.1145/3636534.3698840  

   Wan, Haoran; Cao, Xuyang; Marder, Alexander; Jamieson, Kyle (December 2024, ACM)

   5G New Radio cellular networks are designed to provide high Quality of Service for application on wirelessly connected devices. However, changing conditions of the wireless last hop can degrade application performance, and the applications have no visibility into the 5G Radio Access Network (RAN). Most 5G network operators run closed networks, limiting the potential for co-design with the wider-area internet and user applications. This paper demonstrates NR-Scope, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that can passively measure fine-grained RAN capacity, latency, and retransmission information. Application servers can take advantage of the measurements to achieve better millisecond scale, application-level decisions on offered load and bit rate adaptation than end-to-end latency measurements or end-to-end packet losses currently permit. We demonstrate the performance of NR-Scope by decoding the downlink control information (DCI) for downlink and uplink traffic of a 5G Standalone base station in real-time. 

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4. O-RAN Perspective on Industrial Internet of Things: A SWOT Analysis

   https://doi.org/10.1109/ICIT58465.2023.10143032  

   Rahman, Talha Faizur; Zhang, Minglong; Marojevic, Vuk (June 2023, 2023 IEEE International Conference on Industrial Technology (ICIT))

   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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5. Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach

   https://doi.org/10.1109/ICRA48506.2021.9561417  

   Shen, Xu; Zhu, Edward L.; Sturz, Yvonne R.; Borrelli, Francesco (May 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA))

   We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV’s position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases. 

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