Search

The Open Radio Access Network (O-RAN) architecture is reshaping the telecommunications landscape by enhancing network flexibility, openness, and intelligence. This paper establishes the requirements, evaluates the design tradeoffs, and introduces a scalable architecture and prototype of an open-source O-RAN experimentation platform within the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW), an at scale testbed that integrates unmanned aerial vehicles (UAVs) with advanced wireless network technologies, offering experimentation in both outdoor testbed and emulation via a custom digital twin (DT). Through a series of aerial experiments, we evaluate FlexRIC, an open-source RAN Intelligent Controller, within the AERPAW hardware-software platform for network data monitoring, providing valuable insights into the proposed integration and revealing opportunities for leveraging O-RAN to create custom service based optimizations for cellular connected UAVs. We discuss the challenges and potential use cases of this integration and demonstrate the use of a generative artificial intelligence model for generating realistic data based on collected real-world data to support AERPAW's DT., Comment: This article has been accepted for publication in the IEEE Communications Magazine

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 autoencoders, 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. Keywords: self-attention, real-time intrusion detection, RNN autoencoder, Transformer architecture, LSTM, time series anomaly detection, 5G Security, spectrum access security., Comment: This article has been accepted for publication in WiOpt 2024

Vehicle-to-everything (V2X) networks support a variety of safety, entertainment, and commercial applications. This is realized by applying the principles of the Internet of Vehicles (IoV) to facilitate connectivity among vehicles and between vehicles and roadside units (RSUs). Network congestion management is essential for IoVs and it represents a significant concern due to its impact on improving the efficiency of transportation systems and providing reliable communication among vehicles for the timely delivery of safety-critical packets. This paper introduces a framework for proactive congestion management for IoV networks. We generate congestion scenarios and a data set to predict the congestion using LSTM. We present the framework and the packet congestion dataset. Simulation results using SUMO with NS3 demonstrate the effectiveness of the framework for forecasting IoV network congestion and clustering/prioritizing packets employing recurrent neural networks., Comment: This article has been accepted for publication in the IEEE VTC Fall 2024

The rapid evolution of 5G and beyond has advanced space-air-terrestrial networks, with unmanned aerial vehicles (UAVs) offering enhanced coverage, flexible configurations, and cost efficiency. However, deploying UAV-based systems presents challenges including varying propagation conditions and hardware limitations. While simulators and theoretical models have been developed, real-world experimentation is critically important to validate the research. Digital twins, virtual replicas of physical systems, enable emulation that bridge theory and practice. This paper presents our experimental results from AERPAW's digital twin, showcasing its ability to simulate UAV communication scenarios and providing insights into system performance and reliability., Comment: This article has been accepted for publication in the IEEE VTC Fall 2024--UAV Communication and Experimentation Workshop

With the rise of 5G and open radio access networks (O-RAN), there is a growing demand for customizable experimental platforms dedicated to security testing, as existing testbeds do not prioritize this area. Traditional, hardware-dependent testing methods pose challenges for smaller companies and research institutions. The growing wireless threat landscape highlights the critical need for proactive security testing, as 5G and O-RAN deployments are appealing targets for cybercriminals. To address these challenges, this article introduces the Soft Tester UE (soft T-UE), a software-defined test equipment designed to evaluate the security of 5G and O-RAN deployments via the Uu air interface between the user equipment (UE) and the network. The outcome is to deliver a free, open-source, and expandable test instrument to address the need for both standardized and customizable automated security testing. By extending beyond traditional security metrics, the soft T-UE promotes the development of new security measures and enhances the capability to anticipate and mitigate potential security breaches. The tool's automated testing capabilities are demonstrated through a scenario where the Radio Access Network (RAN) under test is evaluated when it receives fuzzed data when initiating a connection with an UE., Comment: This article has been accepted for publication in the IEEE VTC Fall 2024--RitiRAN Workshop

The open radio access network (O-RAN) offers new degrees of freedom for building and operating advanced cellular networks. Emphasizing on RAN disaggregation, open interfaces, multi-vendor support, and RAN intelligent controllers (RICs), O-RAN facilitates adaptation to new applications and technology trends. Yet, this architecture introduces new security challenges. This paper proposes leveraging zero trust principles for O-RAN security. We introduce zero trust RAN (ZTRAN), which embeds service authentication, intrusion detection, and secure slicing subsystems that are encapsulated as xApps. We implement ZTRAN on the open artificial intelligence cellular (OAIC) research platform and demonstrate its feasibility and effectiveness in terms of legitimate user throughput and latency figures. Our experimental analysis illustrates how ZTRAN's intrusion detection and secure slicing microservices operate effectively and in concert as part of O-RAN Alliance's containerized near-real time RIC. Research directions include exploring machine learning and additional threat intelligence feeds for improving the performance and extending the scope of ZTRAN., Comment: This article has been accepted for publication in the IEEE Wireless Communications Magazine

The coexistence between active wireless communications and passive RF spectrum use becomes an increasingly important requirement for coordinated spectrum access supporting critical services. The ongoing research and technological progress are focused on effective spectrum utilization including large-scale MIMO and energy efficient and low-power communications, innovative spectrum use and management, and resilient spectrum sharing, just to name a few. This paper introduces a new tool for real time spectrum sharing among emerging cellular networks and passive RF sensing systems used for remote sensing and radio astronomy, among others. Specifically we propose leveraging wireless channel virtualization and propose a virtual-to-physical resource mapping framework, mapping types, and control signaling that extends the current 5G New Radio (NR) specifications. Our technology introduces minimal changes to the protocol and is meant to be transparent to the end user application. We validate the proposed technology by extending a 3GPP compliant 5G NR downlink simulator and identify further research directions where work is needed on designing effective ways to explicitly signal the need for spectrum or spectrum use predictions.

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., Comment: This paper has been accepted for publication in the IEEE Transactions on Vehicular Technology

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.

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., Comment: This paper has been accepted for presentation in 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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., Comment: This manuscript has been submitted to IEEE Vehicular Technology Magazine for possible publication

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., Comment: This article has been accepted for publication in the IEEE Wireless Communications and Networking Conference

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., Comment: This article has been accepted for publication in the IEEE Communications Magazine

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., Comment: This article has been accepted for publication in the IEEE Network Magazine

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.

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.

A reconfigurable intelligent surface (RIS) is a metamaterial that can be integrated into walls and influence the propagation of electromagnetic waves. This, typically passive radio frequency (RF) technology is emerging for indoor and outdoor use with the potential of making wireless communications more reliable in increasingly challenging radio environments. This paper goes one step further and introduces mobile RIS, specifically, RIS carried by unmanned aerial vehicles (UAVs) to support cellular communications networks and services of the future. We elaborate on several use cases, challenges, and future research opportunities for designing and optimizing wireless systems at low cost and with low energy footprint.

An unmanned aircraft system (UAS) consists of an unmanned aerial vehicle (UAV) and its controller which use radios to communicate. While the remote controller (RC) is traditionally operated by a person who is maintaining visual line of sight with the UAV it controls, the trend is moving towards long-range control and autonomous operation. To enable this, reliable and widely available wireless connectivity is needed because it is the only way to manually control a UAV or take control of an autonomous UAV flight. This article surveys the ongoing Third Generation Partnership Project (3GPP) standardization activities for enabling networked UASs. In particular, we present the requirements, envisaged architecture and services to be offered to/by UAVs and RCs, which will communicate with one another, with the UAS Traffic Management (UTM), and with other users through cellular networks. Critical research directions relate to security and spectrum coexistence, among others. We identify major R\&D platforms that will drive the standardization of cellular communications networks and applications., Comment: Accepted for publication in the IEEE Commun. Standards Mag

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.

Unmanned aerial vehicles (UAVs) are emerging as enablers for supporting many applications and services, such as precision agriculture, search and rescue, temporary network deployment or coverage extension, and security. UAVs are being considered for integration into emerging 5G networks as aerial users or network support nodes. We propose to leverage UAVs in 5G to assist in the prevention, detection, and recovery of attacks on 5G networks. Specifically, we consider jamming, spoofing, eavesdropping and the corresponding mitigation mechanisms that are enabled by the versatility of UAVs. We introduce the hot zone, safe zone and UAV-based secondary authorization entity, among others, to increase the resilience and confidentiality of 5G radio access networks and services. We present simulation results and discuss open issues and research directions, including the need for experimental evaluation and a research platform for prototyping and testing the proposed technologies.

Unmanned aerial vehicles (UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. The unmanned aircraft system (UAS) traffic management (UTM) provides a framework for safe UAV operation integrating UAV controllers and central data bases via a communications network. This paper discusses the challenges and opportunities for machine learning (ML) for effectively providing critical UTM services. We introduce the four pillars of UTM---operation planning, situational awareness, status and advisors and security---and discuss the main services, specific opportunities for ML and the ongoing research. We conclude that the multi-faceted operating environment and operational parameters will benefit from collected data and data-driven algorithms, as well as online learning to face new UAV operation situations.

The Internet of Things (IoT) will soon be omnipresent and billions of sensors and actuators will support our industries and well-being. IoT devices are embedded systems that are connected using wireless technology for most of the cases. The availability of the wireless network serving the IoT, the privacy, integrity, and trustworthiness of the data are of critical importance, since IoT will drive businesses and personal decisions. This paper proposes a new approach in the wireless security domain that leverages advanced wireless technology and the emergence of the unmanned aerial system or vehicle (UAS or UAV). We consider the problem of eavesdropping and analyze how UAVs can aid in reducing, or overcoming this threat in the mobile IoT context. The results show that huge improvements in terms of channel secrecy rate can be achieved when UAVs assist base stations for relaying the information to the desired IoT nodes. Our approach is technology agnostic and can be expanded to address other communications security aspects.