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how we deploy and operate Open Radio Access Networks (O-RAN) by enhancing network analytics, anomaly detection, and code generation and significantly increasing the efficiency and reliability of a plethora of O-RAN tasks. In this paper, we present ORAN-Bench-13K, the first comprehensive benchmark designed to evaluate the performance of Large Language Models (LLMs) within the context of O-RAN. Our benchmark consists of 13,952 meticulously curated multiple-choice questions generated from 116 O-RAN specification documents. We leverage a novel three-stage LLM framework, and the questions are categorized into three distinct difficulties to cover a wide spectrum of ORAN-related knowledge. We thoroughly evaluate the performance of several state-of-the-art LLMs, including Gemini, Chat-GPT, and Mistral. Additionally, we propose ORANSight, a Retrieval-Augmented Generation (RAG)-based pipeline that demonstrates superior performance on ORAN-Bench-13K compared to other tested closed-source models. Our findings indicate that current popular LLM models are not proficient in O-RAN, highlighting the need for specialized models. We observed a noticeable performance improvement when incorporating the RAG-based ORANSight pipeline, with a Macro Accuracy of 0.784 and a Weighted Accuracy of 0.776, which was on average 21.55% and 22.59% better than the other tested LLMs. 

The advancement of 5G and NextG networks through Open Radio Access Network (O-RAN) architecture marks a transformative shift towards more virtualized, modular, and disaggregated configurations. A critical component within this O-RAN architecture is the RAN Intelligent Controller (RIC), which facilitates the management and control of the RAN through sophisticated machine learning-driven software microservices known as xApps. These xApps rely on accessing a diverse range of sensitive data from RAN and User Equipment (UE), stored in the near Real-Time RIC (Near-RT RIC) database. The inherent nature of this shared, multi-vendor, and open environment significantly raises the risk of unauthorized sensitive RAN/UE data exposure. In response to these privacy concerns, this paper proposes a privacy-preserving zero-trust RIC (dubbed as, ZT-RIC) framework that preserves RAN/UE data privacy within the RIC platform (i.e., shared RIC database, xApp, and E2 interface). The underlying idea is to employ a computationally efficient cryptographic technique called Inner Product Functional Encryption (IPFE) to encrypt the RAN/UE data at the base station, thus, preventing data leaks over the E2 interface and shared RIC database. Furthermore, ZT-RIC customizes the xApp’s inference model by leveraging the inner product operations on encrypted data supported by IPFE to enable xApp to make accurate inferences without data exposure. For evaluation purposes, we leverage a state-of-the-art InterClass xApp, which utilizes RAN key performance metrics (KPMs) to identify jamming signals within the wireless network. Prototyping on an LTE/5G O-RAN testbed demonstrates that ZT-RIC not only ensures data privacy/confidentiality but also guarantees a desired model accuracy, evidenced by a 97.9% accuracy in detecting jamming signals as well as meeting stringent sub-second timing requirement with a round-trip time (RTT) of 0.527 

This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a MApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%. Comprehensive evaluations, including emulations and over-the-air experiments, demonstrate the significant performance gains achieved through SPARC, showcasing it as a promising solution for optimizing resource efficiency and network performance in NextG cellular networks. 

This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a xApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%. Comprehensive evaluations, including emulations and over-the-air experiments, demonstrate the significant performance gains achieved through SPARC, showcasing it as a promising solution for optimizing resource efficiency and network performance in NextG cellular networks. 

Spectrum sharing between terrestrial 5G and incumbent networks in the satellite bands presents a promising avenue to satisfy the ever-increasing bandwidth demand of the next-generation wireless networks. However, protecting incumbent operations from harmful interference poses a fundamental challenge in accommodating terrestrial broadband cellular networks in the satellite bands. State-of-the-art spectrum-sharing policies usually consider several worst-case assumptions and ignore site-specific contextual factors in making spectrum-sharing decisions, and thus, often results in under-utilization of the shared band for the secondary licensees. To address such limitations, this paper introduces CAT3S (Context-Aware Terrestrial-Satellite Spectrum Sharing) framework that empowers the coexisting terrestrial 5G network to maximize utilization of the shared satellite band without creating harmful interference to the incumbent links by exploiting the contextual factors. CAT3S consists of the following two components: (i) context-acquisition unit to collect and process essential contextual information for spectrum sharing and (ii) context-aware base station (BS) control unit to optimize the set of operational BSs and their operation parameters (i.e., transmit power and active beams per sector). To evaluate the performance of the CAT3S, a realistic spectrum coexistence case study over the 12 GHz band is considered. Experiment results demonstrate that the proposed CAT3S achieves notably higher spectrum utilization than state-of-the-art spectrum-sharing policies in different weather contexts. 

This paper introduces ASCENT (context Aware Spectrum Coexistence Design and Implementation) toolset, an advanced context-aware terrestrial satellite spectrum sharing toolset designed for researchers, policymakers, and regulators. It serves two essential purposes (a) evaluating the potential for harmful interference to primary users in satellite bands and (b) facilitating the analysis, design, and implementation of diverse regulatory policies on spectrum usage and sharing. Notably, ASCENT implements a closed-loop feedback system that allows dynamic adaptation of policies according to a wide range of contextual factors (e.g., weather, buildings, summer/winter foliage, etc.) and feedback on the impact of these policies through realistic simulation. Specifically, ASCENT comprises the following components (i) interference evaluation tool for evaluating interference at the incumbents in a spectrum-sharing environment while taking the underlying contexts, (ii) dynamic spectrum access (DSA) framework for providing context-aware instructions to adapt networking parameters and control secondary terrestrial network's access to the shared spectrum band according to context aware prioritization, (iii) Context broker to acquire essential and relevant contexts from external context information providers; and (iv) DSA Database to store dynamic and static contexts and the regulator's policy information. The closed-loop feedback system of ASCENT is implemented by integrating these components in a modular software architecture. A case study of sharing the lower 12 GHz Ku band (12.2-12.7 GHz) with the 5G terrestrial cellular network is considered, and the usability of ASCENT is demonstrated by dynamically changing exclusion zone's radius in different weather conditions.