This paper studies the problem of securing task offloading transmissions from ground users against ground eavesdropping threats. Our study introduces a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV)mobile edge computing (MEC) scheme to enhance the secure task offloading while minimizing the energy consumption of the UAV subject to task completion constraints. Leveraging a data-driven approach, we propose a comprehensive optimization strategy that jointly optimizes the aerial MEC (AMEC)'s trajectory, task offloading partitioning, UE transmission scheduling, and RIS phase shifts. Our objective centers on optimizing the secrecy energy efficiency (SEE) of UE task offloading transmissions while preserving the AMEC's energy resources and meeting the task completion time requirements. Numerical results show that the proposed solution can effectively safeguard legitimate task offloading transmissions while preserving AMEC energy.

Cellular networks require strict security procedures and measures across various network components, from core to radio access network (RAN) and end-user devices. As networks become increasingly complex and interconnected, as in O-RAN deployments, they are exposed to a numerous security threats. Therefore, ensuring robust security is critical for O-RAN to protect network integrity and safeguard user data. This requires rigorous testing methodologies to mitigate threats. This paper introduces an automated, adaptive, and scalable user equipment (UE) based RAN security testing framework designed to address the shortcomings of existing RAN testing solutions. Experimental results on a 5G software radio testbed built with commercial off-the-shelf hardware and open source software validate the efficiency and reproducibility of sample security test procedures developed on the RAN Tester UE framework.

In response to the evolving landscape of wireless communication networks and the escalating demand for unprecedented wireless connectivity performance in the forthcoming 6G era, this paper proposes a new 6G architecture to enhance the wireless network's sum rate performance. Therefore, we introduce an aerial base station (ABS) network with reconfig-urable intelligent surfaces (RISs) while leveraging the multi-users multiple-input single-output (MU-MISO) antenna technology. The motivation behind our proposal stems from the imperative to address critical challenges in contemporary wireless networks and harness emerging technologies for substantial performance gains. We employ deep reinforcement learning (DRL) to jointly optimize the ABS trajectories, the active beamforming weights, and the RIS phase shifts. Simulation results show that this joint optimization effectively improves the system's sum rate while meeting minimum quality of service (Qos) requirements for diverse mobile users.

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.

This paper explores an innovative approach to enhance the resilience and security of beyond 5G (B5G) networks through the implementation of cross-bandwidth part (C-BWP) frequency hopping at mini-slot granularity. Utilizing dynamic channel estimation, the proposed system assigns resource blocks (RBs) to user equipment (UEs) of varying priorities, mitigating the impact of jamming in hostile radio environments. We introduce strategic C-BWP frequency hopping for high-priority UEs, optimizing the use of unaffected RBs. This method is shown to effectively counter various types of jamming, ensuring robust and secure communication in both current and future cellular networks. Through rigorous simulation, we demonstrate that intra-slot frequency hopping offers superior resilience by adapting quickly to dynamic channel conditions, significantly enhancing the performance and security of the communications system.

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.

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.

As we progress from fifth generation (5G) to emerging 6G wireless, the spectrum of cellular communication services is set to broaden significantly, encompassing real-time remote healthcare applications and sophisticated smart infrastructure solutions, among others. This expansion brings to the forefront a diverse set of service requirements, underscoring the challenges and complexities inherent in next-generation networks. In the realm of 5G, enhanced mobile broadband (eMBB) and ultrareliable low-latency communications (URLLCs) have been pivotal service categories. As we venture into the 6G era, these foundational use cases will evolve and embody additional performance criteria, further diversifying the network service portfolio. This evolution amplifies the necessity for dynamic and efficient resource allocation strategies capable of balancing the diverse service demands. In response to this need, we introduce the intelligent dynamic resource allocation and puncturing (IDRAP) framework. leveraging deep reinforcement learning (DRL), IDRAP is designed to balance between the bandwidth-intensive requirements of eMBB services and the latency and reliability needs of URLLC users. The performance of IDRAP is evaluated and compared against other resource management solutions, including intelligent dynamic resource slicing (IDRS), policy gradient actor-critic learning (PGACL), system-wide tradeoff scheduling (SWTS), sum-log, and sum-rate. The results show an improved service satisfaction level (SSL) for eMBB users while maintaining the essential SSL threshold for URLLC services.

The terrestrial wireless network deployment challenges and the high associated costs encourage the exploration of aerial base stations (ABSs). An ABS carried by an unmanned aerial vehicle (UAV) can be dispatched at a relatively low cost to provide coverage on demand, such as in emergency situations and during temporary hot-spot events. While relatively inexpensive, battery-powered UAVs have a limited flight time and can only provide temporary service in practice. This article, therefore, considers and monitors the available energy of UAVs as a constraint for the proposed communication architecture consisting of dynamically dispatched ABSs that are managed by a high-altitude platform station (HAPS) performing network optimization. We consider a fleet of UAVs for providing secure wireless service to sparsely distributed users in urban areas and propose an efficient coverage strategy to satisfy the users’ data rate demands while meeting their secrecy rate requirements. Because of the complexity, dynamics, and distributed nature of the problem, we employ multiple ABSs as the agents and design a deep deterministic policy gradient (DDPG) algorithm to optimize their positions in the ABS network with time-constrained nodes. Numerical results illustrate how the DDPG-empowered HAPS is able to coordinate and leverage the ABSs fleet for wide-spread secure coverage and adjust the network deployment topology when nodes become unavailable. While the DDPG has a higher training complexity, it provides better performance over state-of-the-art solutions in terms of the number of securely served users. We discuss the practical implications of the training process and identify opportunities for research and development.

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.

The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAVs can also enhance the 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 two baseline approaches.

An unmanned aerial vehicle (UAV) is both an enabler and user of future wireless networks. It experiences different radio propagation conditions than a radio node on the ground. Therefore, it is important to experimentally investigate the performance of cellular communications and networking innovations while serving aerial radios. In this paper, we examine the performance of low-altitude aerial nodes that are served by an open-source software-defined radio (SDR) network. We provide a detailed description of the open-source hardware and software components needed for establishing an SDR-based broadband wireless link, and present radio performance measurements. Our results with a standard compliant software-defined 4G system show that an advanced wireless testbed for innovation in UAV communications and networking is feasible with commercial off-the shelf hardware, open-source software, and low-power signaling.

Unmanned aerial vehicles (UAVs) are emerging in commercial spaces and will support many applications, such as smart agriculture, dynamic network deployment, network coverage extension, surveillance and security. The unmanned aircraft system (UAS) traffic management (UTM) provides a framework for safe UAV operation by integrating UAV controllers and central data bases through 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, failure detection and recovery, and remote identification—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 support new UAV operation situations.

Unmanned aerial systems/vehicles (UAS/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. Emerging 5G terrestrial cellular communications networks will support UAS communications. This paper describes the communications security implications of integrating UAVs into cellular networks. We consider two roles for UAVs in a terrestrial cellular system—guardians and attackers—and analyze solutions against eavesdropping. Our approach leverages the mobility of UAV guardians that act as relays or jammers. The numerical analysis using common air-to-ground and air-to-air channel models demonstrates how the use of ground and aerial relay nodes can improve the secrecy rate in light of ground and UAV-based attacks. Specifically, the dependency on height and elevation angle between the ground and aerial communicating nodes is analyzed. The results show that the strategic use of single and multi-hop aerial relays can significantly increase the secrecy rate of ground cellular network users.