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1. Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis

   Kouchaki, Mohammadreza; Zhang, Minglong; Abdalla, Aly S; Lan, Guangchen; Brinton, Christopher G; Marojevic, Vuk (December 2024, 2024 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt))

   In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum access and management. This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder for the detection of anomalous spectral activities in 5G networks at the waveform level. Our approach, grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoen-coders, enabling a more nuanced understanding of temporal dependencies and contextual relationships within the RF spectrum. Utilizing a simulated 5G Radio Access Network (RAN) test-bed constructed with srsRAN 5G and Software Defined Radios (SDRs), we generated a comprehensive stream of data that reflects real-world RF spectrum conditions and attack scenarios. The model is trained to reconstruct standard signal behavior, establishing a normative baseline against which deviations, indicative of security threats, are identified. The proposed architecture is designed to balance between detection precision and computational efficiency, so the LSTM network, enriched with self-attention, continues to optimize for minimal execution latency and power consumption. Conducted on a real-world SDR-based testbed, our results demonstrate the model's improved performance and accuracy in threat detection. 

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2. AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities

   https://doi.org/10.1109/MWC.001.2200213  

   Tang, Bo; Shah, Vijay K.; Marojevic, Vuk; Reed, Jeffrey H. (March 2023, IEEE Wireless Communications)

   Openness and intelligence are two enabling features to be introduced in next generation wireless networks, for example, Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms. 

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3. COSMOS: Optical Architecture and Prototyping

   Gutterman, Craig; Minakhmetov, Artur; Yu, Jiakai; Chen, Tingjun; Zhu, Shengxiang; Zussman, Gil; Seskar, Ivan; Raychaudhuri, Dipankar; Kilper, Dan (January 2019, ACM SIGCOMM 2019 Workshop on Optical Systems Design)

   ABSTRACT The Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multiuser advanced wireless testbed that is being deployed in New York City [1]. Open APIs and programmability across all the technology components and protocol layers in COSMOS will enable researchers to explore 5G technologies in a real world environment. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies as well as experimentation directly in the optical physical layer. A paper on the COSMOS optical architecture was previously presented in [2]. In this talk, we briefly introduce COSMOS’ optical x-haul network with SDN control, and its integration with the software-defined radio (SDR) and mobile edge cloud. 

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4. 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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5. O-RAN Performance Analyzer: Platform Design, Development, and Deployment

   https://doi.org/10.1109/MCOM.002.2300822  

   Kouchaki, Mohammadreza; Natanzi, Seyed_Bagher Hashemi; Zhang, Minglong; Tang, Bo; Marojevic, Vuk (September 2024, IEEE Communications Magazine)

   The open radio access network (O-RAN) represents a paradigm shift in RAN architecture, integrating intelligence into communication networks via xApps -- control applications for managing RAN resources. This integration facilitates the adoption of AI for network optimization and resource management. However, there is a notable gap in practical network performance analyzers capable of assessing the functionality and efficiency of xApps in near real-time within operational networks. Addressing this gap, this article introduces a comprehensive network performance analyzer, tailored for the near-real time RAN intelligent controller. We present the design, development, and application scenarios for this testing framework, including its components, software, and tools, providing an end-to-end solution for evaluating the performance of xApps in O-RAN environments. 

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