arXiv subjects

Vijay K. Shah

Publications and source records attributed to Vijay K. Shah.

At least 37 records · Page 2Linked to original sources

arXiv2024

ZT-RIC:A Zero Trust RIC Framework for ensuring data Privacy and Confidentiality in Open RAN

The advancement of 5G and NextG networks through Open Radio Access Network (O-RAN) architecture enables a shift toward virtualized, modular, and disaggregated configurations. A core component of O-RAN is the RAN Intelligent Controller (RIC), which manages RAN using machine learning-driven xApps that access sensitive data from RAN and User Equipment (UE), stored in the near Real-Time RIC (Near-RT RIC) database. This shared, open environment increases the risk of unauthorized data exposure. To address these concerns, this paper proposes a zero-trust RIC (ZT-RIC) framework that preserves data privacy across the RIC platform, including the RIC database, xApps, and E2 interface. ZT-RIC employs Inner Product Functional Encryption (IPFE) to encrypt RAN/UE data at the base station, preventing leaks through the E2 interface and shared database. Additionally, ZT-RIC enables xApps to perform inference on encrypted data without exposing sensitive information. For evaluation, a state-of-the-art InterClass xApp, which detects jamming signals using RAN key performance metrics (KPMs), is implemented. Testing on an LTE/5G O-RAN testbed shows that ZT-RIC preserves data confidentiality while achieving 97.9% accuracy in jamming detection and meeting sub-second latency requirements, with a round-trip time (RTT) of 0.527 seconds.

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arXiv2024

ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networks

Large Language Models (LLMs) can revolutionize 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.

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arXiv2024

ASCENT: A Context-Aware Spectrum Coexistence Design and Implementation Toolset for Policymakers in Satellite Bands

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.

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arXiv2024

Context-Aware Spectrum Coexistence of Terrestrial Beyond 5G Networks in Satellite Bands

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.

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arXiv2024

System-level Analysis of Adversarial Attacks and Defenses on Intelligence in O-RAN based Cellular Networks

While the open architecture, open interfaces, and integration of intelligence within Open Radio Access Network technology hold the promise of transforming 5G and 6G networks, they also introduce cybersecurity vulnerabilities that hinder its widespread adoption. In this paper, we conduct a thorough system-level investigation of cyber threats, with a specific focus on machine learning (ML) intelligence components known as xApps within the O-RAN's near-real-time RAN Intelligent Controller (near-RT RIC) platform. Our study begins by developing a malicious xApp designed to execute adversarial attacks on two types of test data - spectrograms and key performance metrics (KPMs), stored in the RIC database within the near-RT RIC. To mitigate these threats, we utilize a distillation technique that involves training a teacher model at a high softmax temperature and transferring its knowledge to a student model trained at a lower softmax temperature, which is deployed as the robust ML model within xApp. We prototype an over-the-air LTE/5G O-RAN testbed to assess the impact of these attacks and the effectiveness of the distillation defense technique by leveraging an ML-based Interference Classification (InterClass) xApp as an example. We examine two versions of InterClass xApp under distinct scenarios, one based on Convolutional Neural Networks (CNNs) and another based on Deep Neural Networks (DNNs) using spectrograms and KPMs as input data respectively. Our findings reveal up to 100% and 96.3% degradation in the accuracy of both the CNN and DNN models respectively resulting in a significant decline in network performance under considered adversarial attacks. Under the strict latency constraints of the near-RT RIC closed control loop, our analysis shows that the distillation technique outperforms classical adversarial training by achieving an accuracy of up to 98.3% for mitigating such attacks.

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arXiv2023

Adaptive RRI Selection Algorithms for Improved Cooperative Awareness in Decentralized NR-V2X

Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle's sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error. Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle's sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error.

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arXiv2023

A Practical AoI Scheduler in IoT Networks with Relays

Internet of Things (IoT) networks have become ubiquitous as autonomous computing, communication and collaboration among devices become popular for accomplishing various tasks. The use of relays in IoT networks further makes it convenient to deploy IoT networks as relays provide a host of benefits, like increasing the communication range and minimizing power consumption. Existing literature on traditional AoI schedulers for such two-hop relayed IoT networks are limited because they are designed assuming constant/non-changing channel conditions and known (usually, generate-at-will) packet generation patterns. Deep reinforcement learning (DRL) algorithms have been investigated for AoI scheduling in two-hop IoT networks with relays, however, they are only applicable for small-scale IoT networks due to exponential rise in action space as the networks become large. These limitations discourage the practical utilization of AoI schedulers for IoT network deployments. This paper presents a practical AoI scheduler for two-hop IoT networks with relays that addresses the above limitations. The proposed scheduler utilizes a novel voting mechanism based proximal policy optimization (v-PPO) algorithm that maintains a linear action space, enabling it be scale well with larger IoT networks. The proposed v-PPO based AoI scheduler adapts well to changing network conditions and accounts for unknown traffic generation patterns, making it practical for real-world IoT deployments. Simulation results show that the proposed v-PPO based AoI scheduler outperforms both ML and traditional (non-ML) AoI schedulers, such as, Deep Q Network (DQN)-based AoI Scheduler, Maximal Age First-Maximal Age Difference (MAF-MAD), MAF (Maximal Age First) , and round-robin in all considered practical scenarios.

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arXiv2023

Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks

The growing number of devices using the wireless spectrum makes it important to find ways to minimize interference and optimize the use of the spectrum. Deep learning models, such as convolutional neural networks (CNNs), have been widely utilized to identify, classify, or mitigate interference due to their ability to learn from the data directly. However, there have been limited research on the complexity of such deep learning models. The major focus of deep learning-based wireless classification literature has been on improving classification accuracy, often at the expense of model complexity. This may not be practical for many wireless devices, such as, internet of things (IoT) devices, which usually have very limited computational resources and cannot handle very complex models. Thus, it becomes important to account for model complexity when designing deep learning-based models for interference classification. To address this, we conduct an analysis of CNN based wireless classification that explores the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty: namely, interference classification, heterogeneous transmitter classification, and homogeneous transmitter classification. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model, providing important insights into the use of CNNs in computationally constrained applications.

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arXiv2022

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

Openness and intelligence are two enabling features to be introduced in next generation wireless networks, e.g. 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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arXiv2022

Optimizing Number, Placement, and Backhaul Connectivity of Multi-UAV Networks

Multi-Unmanned Aerial Vehicle (UAV) Networks is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) devices in farmlands), where the traditional cellular networks are sparse or unavailable. A key challenge in such networks is the 3D placement of all UAV base stations such that the formed Multi-UAV Network (i) utilizes a minimum number of UAVs while ensuring -- (ii) backhaul connectivity directly (or via other UAVs) to the nearby terrestrial base station, and (iii) wireless coverage to all ground users in the area of operation. This joint Backhaul-and-coverage-aware Drone Deployment (BoaRD) problem is largely unaddressed in the literature, and, thus, is the focus of the paper. We first formulate the BoaRD problem as Integer Linear Programming (ILP). However, the problem is NP-hard, and therefore, we propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Our simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches -- backhaul-aware greedy and random algorithm, respectively by up to 17% and 95% in utilizing fewer UAVs while ensuring 100% ground user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting.

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arXiv2022

Prototyping Next-Generation O-RAN Research Testbeds with SDRs

Open RAN (O-RAN) defines an emerging cellular radio access network (RAN) architecture for future 6G wireless networks, emphasizing openness and intelligence which are considered the foundations of future 6G wireless networks. While the inherent complexity and flexibility of the RAN give rise to many new research problems, progress in developing solutions is hampered due to the lack of end-to-end, fully developed platforms that can help in pursuing use cases in realistic environments. This has motivated the formation of open-source frameworks available to the wireless community. However, the rapid evolution of dedicated platforms and solutions utilizing various software-based technologies often leaves questions regarding the interoperability and interactions between the components in the framework. This article shows how to build a software-defined radio testbed featuring an open-source 5G system that can interact with the near-real-time (near-RT) RAN intelligent controller (RIC) of the O-RAN architecture through standard interfaces. We focus on the O-RAN E2 interface interactions and outline the procedure to enable a RAN system with E2 capabilities. We demonstrate the working of two xApps on the testbed with detailed E2 message exchange procedures and their role in controlling next-generation RANs.

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arXiv2022

Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!

The open radio access network (O-RAN) describes an industry-driven open architecture and interfaces for building next generation RANs with artificial intelligence (AI) controllers. We circulated a survey among researchers, developers, and practitioners to gather their perspectives on O-RAN as a framework for 6G wireless research and development (R&D). The majority responded in favor of O-RAN and identified R&D of interest to them. Motivated by these responses, this paper identifies the limitations of the current O-RAN specifications and the technologies for overcoming them. We recognize end-to-end security, deterministic latency, physical layer real-time control, and testing of AI-based RAN control applications as the critical features to enable and discuss R&D opportunities for extending the architectural capabilities of O-RAN as a platform for 6G wireless.

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arXiv2021

AoI-minimizing Scheduling in UAV-relayed IoT Networks

Due to flexibility, autonomy and low operational cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as \textit{relays} to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions, i.e., error-free channels and generate-at-will traffic generation at IoT devices. On the contrary, for realistic conditions, we propose a Deep-Q-Networks (DQN) based scheduler. Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10's of IoT devices). However, it does not scale well with network size whereas MAF-MAD outperforms all other schedulers under all considered scenarios for larger networks.

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arXiv2021

Power Systems Performance under 5G Radio Access Network in a Co-Simulation Environment

Communication can improve control of important system parameters by allowing different grid components to communicate their states with each other. This information exchange requires a reliable and fast communication infrastructure. 5G communication can be a viable means to achieve this objective. This paper investigates the performance of several smart grid applications under a 5G radio access network. Different scenarios including set point changes and transients are evaluated, and the results indicate that the system maintains stability when a 5Gnetwork is used to communicate system states.

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arXiv2021

3D Placement and Orientation of mmWave-based UAVs for Guaranteed LoS Coverage

Unmanned aerial vehicles (UAVs), as aerial base stations, are a promising solution for providing wireless communications, thanks to their high flexibility and autonomy. Moreover, emerging services, such as extended reality, require high-capacity communications. To achieve this, millimeter wave (mmWave), and recently, terahertz bands have been considered for UAV communications. However, communication at these high frequencies requires a line-of-sight (LoS) to the terminals, which may be located in 3D space and may have extremely limited direct-line-of-view (LoV) due to blocking objects, like buildings and trees. In this paper, we investigate the problem of determining 3D placement and orientation of UAVs such that users have guaranteed LoS coverage by at least one UAV and the signal-to-noise ratio (SNR) between the UAV-user pairs are maximized. We formulate the problem as an integer linear programming(ILP) problem and prove its NP-hardness. Next, we propose a low-complexity geometry-based greedy algorithm to solve the problem efficiently. Our simulation results show that the proposed algorithm (almost) always guarantees LoS coverage to all users in all considered simulation settings.

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arXiv2021

RAN Slicing in Multi-MVNO Environment under Dynamic Channel Conditions

With the increasing diversity in the requirement of wireless services with guaranteed quality of service(QoS), radio access network(RAN) slicing becomes an important aspect in implementation of next generation wireless systems(5G). RAN slicing involves division of network resources into many logical segments where each segment has specific QoS and can serve users of mobile virtual network operator(MVNO) with these requirements. This allows the Network Operator(NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining number of users and therefore, number of MVNOs that a NO can support. In this work, we study the problem of Modulation and Coding Scheme(MCS) aware RAN slicing(MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel Quality Indicator(CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of user being served. For this reason, we consider the channel conditions to be dynamic where the choice of MCS level varies at each time instant. We model the MaRS problem as a Non-Linear Programming problem and show that it is NP-Hard. Next, we propose a solution based on greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of proposed solution by comparing against the upper bound under various channel and network configurations.

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arXiv2021

Adaptive Semi-Persistent Scheduling for Enhanced On-road Safety in Decentralized V2X Networks

Decentralized vehicle-to-everything (V2X) networks (i.e., Mode-4 C-V2X and Mode 2a NR-V2X), rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive information (e.g., vehicle position) and has the potential to improve on-road safety. For BSM scheduling, decentralized V2X networks utilize sensing-based semi-persistent scheduling (SPS), where vehicles sense radio resources and select suitable resources for BSM transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). In this paper, we show that such a BSM scheduling (with a fixed RRI) suffers from severe under- and over- utilization of radio resources under varying vehicle traffic scenarios; which severely compromises timely dissemination of BSMs, which in turn leads to increased collision risks. To address this, we extend SPS to accommodate an adaptive RRI, termed as SPS++. Specifically, SPS++ allows each vehicle -- (i) to dynamically adjust RRI based on the channel resource availability (by accounting for various vehicle traffic scenarios), and then, (ii) select suitable transmission opportunities for timely BSM transmissions at the chosen RRI. Our experiments based on Mode-4 C-V2X standard implemented using the ns-3 simulator show that SPS++ outperforms SPS by at least $50\%$ in terms of improved on-road safety performance, in all considered simulation scenarios.

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arXiv2021

Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks

We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based IA process by utilizing only a subset of the available beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of beams to the beam that is best oriented to the receiver. In both line of sight (LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and outperforms the conventional IA's beam prediction accuracy. We show that the beam prediction accuracy of DeepIA saturates with the number of beams used for IA and depends on the particular selection of the beams. In LoS conditions, the selection of the beams is consequential and improves the accuracy by up to 70%. In NLoS situations, it improves accuracy by up to 35%. We find that, averaging multiple RSS snapshots further reduces the number of beams needed and achieves more than 95% accuracy in both LoS and NLoS conditions. Finally, we evaluate the beam prediction time of DeepIA through embedded hardware implementation and show the improvement over the conventional beam sweeping.

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