arXiv subjects

Vuk Marojevic

Publications and source records attributed to Vuk Marojevic.

At least 37 records · Page 2Linked to original sources

arXiv2023

UAV Trajectory and Multi-User Beamforming Optimization for Clustered Users Against Passive Eavesdropping Attacks With Unknown CSI

This paper tackles the fundamental passive eavesdropping problem in modern wireless communications in which the location and the channel state information (CSI) of the attackers are unknown. In this regard, we propose deploying an unmanned aerial vehicle (UAV) that serves as a mobile aerial relay (AR) to help ground base station (GBS) support a subset of vulnerable users. More precisely, our solution (1) clusters the single-antenna users in two groups to be either served by the GBS directly or via the AR, (2) employs optimal multi-user beamforming to the directly served users, and (3) optimizes the AR's 3D position, its multi-user beamforming matrix and transmit powers by combining closed-form solutions with machine learning techniques. Specifically, we design a plain beamforming and power optimization combined with a deep reinforcement learning (DRL) algorithm for an AR to optimize its trajectory for the security maximization of the served users. Numerical results show that the multi-user multiple input, single output (MU-MISO) system split between a GBS and an AR with optimized transmission parameters without knowledge of the eavesdropping channels achieves high secrecy capacities that scale well with increasing the number of users.

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arXiv2023

End-to-End O-RAN Security Architecture, Threat Surface, Coverage, and the Case of the Open Fronthaul

O-RAN establishes an advanced radio access network (RAN) architecture that supports inter-operable, multi-vendor, and artificial intelligence (AI) controlled wireless access networks. The unique components, interfaces, and technologies of O-RAN differentiate it from the 3GPP RAN. Because O-RAN supports 3GPP protocols, currently 4G and 5G, while offering additional network interfaces and controllers, it has a larger attack surface. The O-RAN security requirements, vulnerabilities, threats, and countermeasures must be carefully assessed for it to become a platform for 5G Advanced and future 6G wireless. This article presents the ongoing standardization activities of the O-RAN Alliance for modeling the potential threats to the network and to the open fronthaul interface, in particular. We identify end-to-end security threats and discuss those on the open fronthaul in more detail. We then provide recommendations for countermeasures to tackle the identified security risks and encourage industry to establish standards and best practices for safe and secure implementations of the open fronthaul interface.

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arXiv2022

Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit

This paper introduces the deployment of unmanned aerial vehicles (UAVs) as lightweight wireless access points that leverage the fixed infrastructure in the context of the emerging open radio access network (O-RAN). More precisely, we propose an aerial radio unit that dynamically serves an under served area and connects to the distributed unit via a wireless fronthaul between the UAV and the closest tower. In this paper we analyze the UAV trajectory in terms of artificial intelligence (AI) when it serves both UEs and central units (CUs) at the same time in multi input multi output (MIMO) fading channel. We first demonstrate the nonconvexity of the problem of maximizing the overall network throughput based on UAV location, and then we use two different machine learning approaches to solve it. We first assume that the environment is a gridworld and then let the UAV explore the environment by flying from point A to point B, using both the offline Q-learning and the online SARSA algorithm and the achieved path-loss as the reward. With the intention of maximizing the average payoff, the trajectory in the second scenario is described as a Markov decision process (MDP). According to simulations, MDP produces better results in a smaller setting and in less time. In contrast, SARSA performs better in larger environments at the expense of a longer flight duration.

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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

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

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 RAN controllers. xApps are the applications which will take this responsibility by leveraging machine learning (ML) algorithms and acting in near-real time. 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. This is mostly because of the lack of an endto-end solution for designing, deploying, and testing AI-based xApps fully executable in real O-RAN network. In this paper we introduce an end-to-end O-RAN design and evaluation procedure and provide a detailed discussion of developing a Reinforcement Learning (RL) based xApp by using two different RL approaches and considering the latest released O-RAN architecture and interfaces.

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arXiv2022

Network and Physical Layer Attacks and countermeasures to AI-Enabled 6G O-RAN

Artificial intelligence (AI) will play an increasing role in cellular network deployment, configuration and management. This paper examines the security implications of AI-driven 6G radio access networks (RANs). While the expected timeline for 6G standardization is still several years out, pre-standardization efforts related to 6G security are already ongoing and will benefit from fundamental and experimental research. The Open RAN (O-RAN) describes an industry-driven open architecture and interfaces for building next generation RANs with AI control. Considering this architecture, we identify the critical threats to data driven network and physical layer elements, the corresponding countermeasures, and the research directions.

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arXiv2022

DDPG Learning for Aerial RIS-Assisted MU-MISO Communications

This paper defines the problem of optimizing the downlink multi-user multiple input, single output (MU-MISO) sum-rate for ground users served by an aerial reconfigurable intelligent surface (ARIS) that acts as a relay to the terrestrial base station. The deep deterministic policy gradient (DDPG) is proposed to calculate the optimal active beamforming matrix at the base station and the phase shifts of the reflecting elements at the ARIS to maximize the data rate. Simulation results show the superiority of the proposed scheme when compared to deep Q-learning (DQL) and baseline approaches.

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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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arXiv2022

Self Interference Management in In-Band Full-Duplex Systems

The evolution of wireless systems has led to a continuous increase in the demand for radio frequency spectrum. To address this issue, a technology that has received a lot of attention is In-Band Full-Duplex (IBFD). The interest in IBFD systems stems from its capability to simultaneously transmit and receive data in the same frequency. Cancelling the self interference (SI) from the transmitter to the collocated receiver plays a pivotal role in the performance of the system. There are two types of SI cancellation (SIC) approaches, passive and active. In this research, the focus is on active cancellation and, in particular, SIC in the digital domain. Among the direct and backscattered SI, the former has been studied for a long time; therefore, the backscatter is considered in this research and two SIC approaches are analyzed. The first achieves SIC through beamforming. This requires knowing the angle of the received SI to put the beam null-space in this direction. The second method removes SI by employing an Artificial Neural Networks (ANNs). Using an ANN, there is no need to know the direction of the SI. The neural network is trained with pilots which results in the network being able to separate the desired signal from the SI at the receiver. Bayesian Neural Networks show the importance of the weights and assign a parameter that facilitates ignoring the less significant ones. Through comparative simulations we demonstrate that the ANN-based SIC achieves equivalent bit error rate performance as two beamforming methods.

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arXiv2022

AI-Driven Demodulators for Nonlinear Receivers in Shared Spectrum with High-Power Blockers

Research has shown that communications systems and receivers suffer from high power adjacent channel signals, called blockers, that drive the radio frequency (RF) front end into nonlinear operation. Since simple systems, such as the Internet of Things (IoT), will coexist with sophisticated communications transceivers, radars and other spectrum consumers, these need to be protected employing a simple, yet adaptive solution to RF nonlinearity. This paper therefore proposes a flexible data driven approach that uses a simple artificial neural network (ANN) to aid in the removal of the third order intermodulation distortion (IMD) as part of the demodulation process. We introduce and numerically evaluate two artificial intelligence (AI)-enhanced receivers-ANN as the IMD canceler and ANN as the demodulator. Our results show that a simple ANN structure can significantly improve the bit error rate (BER) performance of nonlinear receivers with strong blockers and that the ANN architecture and configuration depends mainly on the RF front end characteristics, such as the third order intercept point (IP3). We therefore recommend that receivers have hardware tags and ways to monitor those over time so that the AI and software radio processing stack can be effectively customized and automatically updated to deal with changing operating conditions.

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arXiv2022

Handover Experiments with UAVs: Software Radio Tools and Experimental Research Platform

Mobility management is the key feature of cellular networks. When integrating unmanned aerial vehicles (UAVs) into cellular networks, their cell association needs to be carefully managed for coexistence with other cellular users. UAVs move in three dimensions and may traverse several cells on their flight path, and so may be subject to several handovers. In order to enable research on mobility management with UAV users, this paper describes the design, implementation, and testing methodology for handover experiments with aerial users. We leverage software-defined radios (SDRs) and implement a series of tools for preparing the experiment in the laboratory and for taking it outdoors for field testing. We use solely commercial off-the-shelf hardware, open-source software, and an experimental license to enable reproducible and scalable experiments. Our initial outdoor results with two SDR base stations connected to an open-source software core network, implementing the 4G long-term evolution protocol, and one low altitude UAV user equipment demonstrate the handover process.

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arXiv2021

Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach

The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAV will play a critical role in enhancing the physical layer security of wireless networks. This paper defines the problem of eavesdropping on the link between the ground user and the UAV, which serves as an aerial base station (ABS). The reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power to enhance the data rate of the ground user. This increases the secrecy capacity without the system knowing the location of the eavesdropper. Simulation results show fast convergence and the highest secrecy capacity of the proposed DQN compared to Q-learning and baseline approaches.

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arXiv2021

Open-Source Software Radio Platform for Research on Cellular Networked UAVs -- It Works!

Cellular network-connected unmanned aerial vehicles (UAVs) experience different radio propagation conditions than radio nodes on the ground. Therefore, it has become critical to investigate the performance of aerial radios, both theoretically and through field trials. In this paper, we consider low-altitude aerial nodes that are served by an experimental cellular network. We provide a detailed description of the hardware and software components needed for establishing a broadband wireless testbed for UAV communications research using software radios. Results show that a testbed for innovation in UAV communications and networking is feasible with commercial off-the-shelf hardware, open-source software, and low-power signaling.

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arXiv2021

Security Threats and Cellular Network Procedures for Unmanned Aircraft Systems

This paper discusses cellular network security for unmanned aircraft systems (UASs) and provides insights into the ongoing Third Generation Partnership Project (3GPP) standardization efforts with respect to authentication and authorization, location information privacy, and command and control signaling. We introduce the 3GPP reference architecture for network connected UAS and the new network functions as part of the 5G core network, discuss introduce the three security contexts, potential threats, and the 3GPP procedures. The paper identifies research opportunities for UAS communications security and recommends critical security features and processes to be considered for standardization.

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arXiv2021

Securing Mobile Multiuser Transmissions with UAVs in the Presence of Multiple Eavesdroppers

This paper discusses the problem of securing the transmissions of multiple ground users against eavesdropping attacks. We propose and optimize the deployment of a single unmanned aerial vehicle (UAV), which serves as an aerial relay between the user cluster and the base station. The focus is on maximizing the secrecy energy efficiency by jointly optimizing the uplink transmission powers of the ground users and the position of the UAV. The joint optimization problem is nonconvex; therefore we split it into two subproblems and solve them using an iterative algorithm.

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arXiv2021

Underlay Radar-Massive MIMO Spectrum Sharing: Modeling Fundamentals and Performance Analysis

In this work, we study underlay radar-massive MIMO cellular coexistence in LoS/near-LoS channels, where both systems have 3D beamforming capabilities. Using mathematical tools from stochastic geometry, we derive an upper bound on the average interference power at the radar due to the 3D massive MIMO cellular downlink under the worst-case `cell-edge beamforming' conditions. To overcome the technical challenges imposed by asymmetric and arbitrarily large cells, we devise a novel construction in which each Poisson Voronoi (PV) cell is bounded by its circumcircle to bound the effect of the random cell shapes on average interference. Since this model is intractable for further analysis due to the correlation between adjacent PV cells' shapes and sizes, we propose a tractable nominal interference model, where we model each PV cell as a circular disk with an area equal to the average area of the typical cell. We quantify the gap in the average interference power between these two models and show that the upper bound is tight for realistic deployment parameters. We also compare them with a more practical but intractable MU-MIMO scheduling model to show that our worst-case interference models show the same trends and do not deviate significantly from realistic scheduler models. Under the nominal interference model, we characterize the interference distribution using the dominant interferer approximation by deriving the equi-interference contour expression when the typical receiver uses 3D beamforming. Finally, we use tractable expressions for the interference distribution to characterize radar's spatial probability of false alarm/detection in a quasi-static target tracking scenario. Our results reveal useful trends in the average interference as a function of the deployment parameters (BS density, exclusion zone radius, antenna height, transmit power of each BS, etc.).

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arXiv2021

DSRC-Enabled Train Safety Communication System at Unmanned Crossings

Although wireless technology is available for safety-critical applications, few applications have been used to improve train crossing safety. To prevent potential collisions between trains and vehicles, we present a Dedicated Short-Range Communication (DSRC)-enabled train safety communication system targeting to implement at unmanned crossings. Since our application's purpose is preventing collisions between trains and vehicles, we present a method to calculate the minimum required warning time for head-to-head collision at the train crossing. Furthermore, we define the best- and worst-case scenarios and provide practical measurements at six operating crossings in the U.S. with numerous system configurations such as modulation scheme, transmission power, antenna type, train speed, and vehicle braking distances. From our measurements, we find that the warning application coverage range is independent of the train speed, that the omnidirectional antenna with high transmission power is the best configuration for our system, and that the latency values are mostly less than 5 ms. We use the radio communication coverage to evaluate the time to avoid collision and introduce the safeness level metric. From the measured data, we observe that the DSRC-enabled train safety communication system is feasible for up to 35 mph train speeds which is providing more than 25-30 s time to avoid the collision for 25-65 mph vehicle speeds. Higher train speeds are expected to be safe, but more measurements beyond the 200 m mark with respect to a crossing considered here are needed for a definite conclusion.

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