Multi-User MIMO (MU-MIMO) systems in consumer devices suffer from imperfect or delayed Channel State Information (CSI), limiting the effectiveness of conventional combiners. This paper introduces a two-stage learning framework that combines a Convolutional Neural Network-Gated Recurrent Unit (CNN–GRU) encoder for spatio-temporal CSI feature extraction with a Proximal Policy Optimization agent for adaptive, power-constrained combining. Simulations using 3GPP channel models with one-step CSI delay show that the proposed method enhances spectral efficiency by over 35% compared to Matched Filtering and by more than 10% over Minimum Mean Square Error, offering robust performance for interference-limited and dynamic environments.

Millimeter-wave (mmWave) communication systems face increasing susceptibility to advanced beam-stealing attacks, posing a significant physical layer security threat. This paper introduces a novel framework employing an advanced Deep Reinforcement Learning (DRL) agent for proactive and adaptive defense against these sophisticated attacks. A key innovation is leveraging Integrated Sensing and Communications (ISAC) capabilities for active, intelligent threat assessment. The DRL agent, built on a Proximal Policy Optimization (PPO) algorithm, dynamically controls ISAC probing actions to investigate suspicious activities. We introduce an intensive curriculum learning strategy that guarantees the agent experiences successful detection during training to overcome the complex exploration challenges inherent to such a security-critical task. Consequently, the agent learns a robust and adaptive policy that intelligently balances security and communication performance. Numerical results demonstrate that our framework achieves a mean attacker detection rate of 92.8% while maintaining an average user SINR of over 13 dB.

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.

This research proposes a dynamic resource allocation method for vehicle-to-everything (V2X) communications in the sixth generation (6G) cellular networks. Cellular V2X (C-V2X) communications empower advanced applications but at the same time bring unprecedented challenges in how to fully utilize the limited physical-layer resources, given the fact that most of the applications require both ultra low latency, high-data rate and high reliability. Resource allocation plays a pivotal role to satisfy such requirements as well as guarantee Quality of Service (QoS). Based on this observation, a novel fuzzy-logic-assisted$Q$learning (FAQ) model is proposed to intelligently and dynamically allocate resources by taking advantage of the centralized allocation mode. The proposed FAQ model reuses the resources to maximize the network throughput while minimizing the interference caused by concurrent transmissions. The fuzzy-logic module expedites the learning and improves the performance of the$Q$-learning. A mathematical model is developed to analyze the network throughput considering the interference. To evaluate the performance, a system model for V2X communications is built for urban areas, where various V2X services are deployed in the network. Simulation results show that the proposed FAQ algorithm can significantly outperform deep reinforcement learning,$Q$-learning and other advanced allocation strategies regarding the convergence speed and the network throughput.

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 introduce the aerial radio unit (A-RU) that dynamically serves an underserved area and connects to the distributed unit (ODU) via a wireless fronthaul between the UAV and the closest fixed network infrastructure tower. In this paper we employ artificial intelligence (AI) for determining the UAV trajectory for serving User Equipment (UEs) while maintaining the fronthaul connectivity to the O-DU at the same time in a multiple-input multiple-output (MIMO) fading channel. We first formulate the trajectory time and throughput rate; however, owing to the nonconvexity of the problem of maximizing the network throughput based on UAV location, we put our effort to achieve these goals by RL approach. Three different approaches have been presented. We first divide the area into a grid and 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 pathloss as the reward. With the intention of maximizing the average payoff, the trajectory in the first scenario is described as a Markov decision process (MDP). According to simulations, MDP produces better results in a smaller area and in less time. In contrast, SARSA performs better in larger environments at the expense of a longer flight duration.

Passive remote sensing through microwave radiometry has been utilized in Earth observation by estimating several geophysical parameters. Because of the low noise floor associated with the instrument (i.e., radiometer), the received geophysical emission is sampled in a protected band dedicated to remote sensing. This protected L-band occupying 1400-1427 MHz is also exciting and ideal for science because of lower attenuation from the atmosphere. This reason has also made this microwave region ideal for next-generation (xG) wireless communication. 5G cellular systems support two frequency ranges FR1 (0.45 GHz–6 GHz) and FR2 (24.45 GHz-52.6 GHz). Although operating bands are prohibited from conducting any up-link or down-link operations in the protected portion of the L-band, out-of-band (OOB) emissions can still have a significant impact on passive sensors because of the high sensitivity requirements related to science. This study will demonstrate a unique physical testbed that has the capability to observe in-band and OOB emissions in a protected anechoic chamber. Flexibility on transmitted waveforms and the potential to analyze raw measurements (IQ samples) of radiometers will help in designing onboard radio frequency interference (RFI) processing along with the coexistence of communication and passive sensing technologies.

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.

The main resource for providing wireless services is radio frequency (RF) spectrum. In order to explore new uses of spectrum shared among radio systems and services, field data needs to be collected. In this paper we design a testbed that can generate different 5G New Radio (NR) downlink transmission frames using the MATLAB 5G Toolbox, software-defined radio (SDR) hardware and GNU Radio Companion. This system will be used as a part of a testbed to study the RF interference caused by 5G transmissions to remote sensing receivers and evaluate different mechanisms for co-channel coexistence.

Research conducted in LTE and 5G wireless communications systems uses common off-the-shelf hardware components and commercial software defined radio (SDR) hardware. One of the more popular SDR platforms is the Ettus USRP product line which uses the UHD driver and transport protocol framework. System performance can be increased using kernel bypass frameworks along with UHD. This paper investigates UHD with DPDK in an SDR environment using srslTE as the SDR application. We present measurement results using the iperl3 network performance application that show performance improvements when employing a kernel bypass framework to facilitate data transfer over the network interface between the SDR application and the radio hardware.

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.

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.

Receiver nonlinearity gives rise to intermodulation products that are caused by two strong adjacent channel signals called blockers. The nonlinear distortion effects are significantly higher for multiple antenna wideband systems in dispersive environments because third order intermodulation products decreases the signal-to-noise ratio (SNR) at the output of the equalization process. This complicates the demodulation process and increases the bit error rate. This paper considers such nonlinear distortion in the context of space-time shift keying (STSK)-enabled wideband single-carrier systems and proposes an iterative space-time block equalization (ISTBE) framework for frequency domain equalization. We present our design of a practical ISTBE receiver based on the turbo principle and numerically demonstrate that it effectively removes the residual inter-symbol interference while suppressing high-power blockers and the in-band intermodulation distortion that they cause. The proposed system is thus suitable for simple wideband radio frequency front ends operating in the weak nonlinear region and enables adjacent channel spectrum coexistence with heterogeneous transmitters and receivers of different qualities.

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 passive 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. We therefore define the best and worst-case scenarios and provide empirical data collected at six operating crossings in the U.S. with numerous system configurations, including 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 communications latency is less than 1 ms on average and around 5 m worst case. We use the radio communication coverage and introduce the safeness level metric to evaluate the suitability of DSRC for collision avoidance. 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 to avoid a collision for 25–65 mph vehicle speeds. Higher train speeds are expected to be safe, but additional data over extended distances are needed for a definite conclusion.

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.

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.

With the advances in wireless communications towards beyond 5G (B5G) and 6G networks, new signal processing and resource management methods need to be explored to overcome the channel impairments and other radio and computing obstacles. In contrast to the conventional methods which are based on classic digital communications structures, B5G and 6G will leverage artificial intelligence (AI) to configure or adapt the radios and networks to the operational context. This requires the ability to reformulate legacy transceiver structures and drive research, development and standardization that can leverage the amount of data that is available and that can be processed with the available computing technology. This paper describes this vision and discusses successful research that justifies it as well as the remaining challenges. We numerically analyze some of the tradeoffs when replacing the physical layer receiver processing with an artificial neural network (ANN).

This paper presents the design of a hovering, tethered unmanned aerial vehicle (UAV) capable of lifting a high gain antenna which can be used as a relay node for Control and Non-Payload Communications (CNPC) from a ground control station to a distant UAV. Ground based remote controllers are constrained by the terrain and the curvature of the Earth. The proposed design overcomes these limitations and provides a tower alternative, which is inexpensive, easily deployable, and moveable in all three dimensions. We show that a typical battery-powered UAV flight time is limited to just above one hour, or under half an hour with its maximum liftable payload, and therefore propose a power tether that connects the UAV to a ground power source. This is important to be able to serve rover UAVs that can be asynchronously launched. The hovering UAV will turn to steer the high gain antenna based on position reports from the roving UAV. The prototype design leverages commercial off the shelf equipment as much as possible and uses the L-band unlicensed frequency range of 902-928 MHz which is close to the aviation protected frequency band.

Spectrum sharing alleviates the severe shortage of spectrum in sub-6 GHz frequency bands through the harmonious coexistence of two or more wireless technologies on the same frequency resources. 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.). We also provide useful system design insights using radar receiver operating characteristic (ROC) curves by applying our analytical results to design the minimum exclusion zone radius in current and future radar-cellular spectrum sharing scenarios.

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.

This paper presents our methodology and software toolbox that allows analyzing the radio access network security of laboratory and commercial 4G and future 5G cellular networks. We leverage a free open-source software suite that implements the LTE UE and eNB enabling real-time signaling using software radio peripherals. We modify the UE software processing stack to act as an LTE packet collection and examination tool. This is possible because of the openness of the 3GPP specifications. Hence, we are able to receive and decode LTE downlink messages for the purpose of analyzing potential security problems of the standard. This paper shows how to rapidly prototype LTE tools and build a software-defined radio access network (RAN) analysis instrument for research and education. Using the Cellular Security Analysis Instrument (CSAI), a researcher can analyze broadcast and paging messages of cellular networks. CSAI is also able to test networks to aid in the identification of vulnerabilities and verify functionality post-remediation. Additionally, we found that it can crash a software eNB which motivates equivalent analyses of commercial network equipment and its robustness against denial of service attacks.

Nonlinearity of radio frequency components can lead to undesirable effects such as desensitization, cross-modulation and intermodulation. It is especially of concern in wideband receivers in emerging shared spectrum spaces where adjacent channel signals, or blockers, can enter the receiver circuitry and cause third order intermodulation distortion. This paper analyzes the fundamental aspects of communication system performance with receiver nonlinearity. We derive symbol error rate expressions as a function of blocker power levels and receiver nonlinearity for modulated signals with modulated blockers. These expressions enable analyzing dynamic spectrum access performance and devising receiver-cognizant spectrum access systems. Our numerical results show that SNR losses of more than 2 dB can incur in heterogeneous radio environments.

In this paper, we study a three-dimensional (3D) spectrum sharing between device-to-device (D2D) and unmanned aerial vehicles (UAVs) communications. We consider that UAVs perform spatial spectrum sensing to opportunistically access the licensed channels that are occupied by the D2D communications of ground users. The objective of the considered 3D spectrum sharing networks is to maximize the area spectral efficiency (ASE) of UAV networks while guaranteeing the required minimum ASE of D2D networks. Using the tools from machine learning, we obtain the probability of spatial false alarm and the probability of spatial missed detection at the UAV, which helps us to characterize the density of active UAVs. Then, based on the Neyman-Pearson criterion, we further derive the coverage probability of D2D and UAV communications by leveraging the tools from stochastic geometry. In addition, the ASE of the D2D and UAV networks are also obtained. Simulation results show that a decrease in the spatial spectrum sensing radius of UAVs reduces the coverage probability of UAV communications but improves the ASE of UAV networks. Furthermore, the proposed tools allow obtaining the optimal spatial spectrum sensing radius of UAVs given certain network parameters.

This Innovative Practice Full Paper presents our methodology and tools for introducing competition in the electrical engineering curriculum. Our focus is on the education of advanced wireless communications technologies and systems. We leverage software radio technologies and open-source software frameworks to design hands-on laboratory exercises and a grading system based on competition. The motivation behind this is to potentiate creativity while unleashing the boundaries of traditional hands-on laboratories. The students form teams and use the open-source software radio framework ALOE and a partially defined protocol that implements a simplified version of the long-term evolution (LTE) standard in software. The proposed student challenge follows the Spanish soccer league format LaLiga, which consists of weekly matches and accumulative scores. The students are tasked to build an optimized transmitter and receiver following the LTE standard guidelines. The evaluation metric is link performance. The students learn how to (1) assess the feasibility, key challenges and costs for meeting the requirements of wireless communications systems, (2) analyze alternative radio design options and argue about their benefits and drawbacks, and (3) develop strategies and solutions to software radio engineering problems. We discuss our experiences and lessons learned with focus on the suitability of the proposed teaching methodology and evaluation process. Whereas the methodology is well perceived by the students, our analysis shows that the competitive scoring mechanism needs further adjustments.

In this paper, we consider an underlay radar- massive MIMO spectrum sharing scenario in which massive MIMO base stations (BSs) with elevation beamforming capabilities are allowed to operate outside a circular exclusion zone centered at the radar. Modeling the locations of the massive MIMO BSs as a homogeneous Poisson point process (PPP), we derive an analytical expression for a tight upper bound on the average interference at the radar due to cellular transmissions. The challenge lies in bounding the worst-case elevation angle for each massive MIMO BS, for which we devise a novel construction based on the circumradius distribution of a typical Poisson-Voronoi (PV) cell. While these worst-case elevation angles are correlated for neighboring BSs due to the structure of the PV tessellation, it does not explicitly appear in our analysis because of our focus on the average interference. We also provide an estimate of the nominal average interference by approximating each cell as a circle with area equal to the average area of the typical cell. Using these results, we demonstrate that the gap between the two results remains approximately constant with respect to the exclusion zone radius. Our analysis reveals useful trends in average interference power, as a function of key deployment parameters such as radar/BS antenna heights, number of antenna elements per radar/BS, BS density, and exclusion zone radius.

The 5.9 GHz spectrum band is proposed for vehicular communications using Dedicated Short Range Communications (DSRC), but this band may need to be shared with unlicensed Wi-Fi devices. A recent Federal Communications Commission (FCC)'s Notice of Proposed Rulemaking (NPRM) outlines two interference mitigation techniques for spectrum coexistence: Detect and Vacate and Re-channelization. A major technical challenge of Re-channelization is that DSRC may experience harmful or a reduced functionality because of adjacent channel interference from Wi-Fi transmitters. We therefore conducted Wi-Fi/DSRC adjacent channel interference experiments to evaluate the significance of Wi-Fi signals adjacent to DSRC transmissions. Our measurements show a significant degradation of DSRC performance from 802.11ac adjacent channel interference only for certain scenarios where the distances between the Wi-Fi transmitter to DSRC receiver is 15 m or below and the distances between the DSRC transmitter to receiver is 300m or higher. However, there is no severe effect when the DSRC transmit power is at the standard 33 dBm level, even considering the recommended fading margin of 5-10 dB.

Spectrum regulators and stakeholders from the wireless industry and Intelligent Transportation System (ITS) communities are exploring the use of the 5.9 GHz band for the dissemination of basic safety messages. Dedicated Short Range Communications (DSRC) sends out these messages at a constant rate of 10 Hz and packet collisions occur in dense vehicular environments. In this paper, we propose a priority-based dynamic beaconing scheme. The scheme determines a higher beacon transmission rate for vehicles that are at a higher risk of collision. Two risk based beacon rate protocols are evaluated in our ns-3 simulator, one that adapts the beacon rate between 1 and 10 Hz, and another between 1 and 20 Hz. This improves the packet delivery ratio (PDR) performance by up to 45% in congested environments using the 1-10 Hz adaptive beacon rate protocol and by 38% using the 1-20 Hz adaptive scheme. The simulation results also show that the likelihood of a vehicle collision due to missed packets decreases by up to 77% in a three lane dense highway scenario with 160 vehicles operating at different speeds.

New use cases for advanced wireless technologies are emerging in the unmanned aerial systems (UAS) spaces. These put pressure on technology and regulation. The way to overcome this is to gain experience and collect data while operating UAS in production environments. To this end, we introduce AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless, and present an architecture for designing a large-scale community testbed in a production-like environment to enable controllable experiments with latest wireless technologies and systems. Using advanced networking and virtualization technology to manage the platform resources, users will be able to configure the testbed for running a variety of at-scale experiments for UAS localization and tracking, networking, trajectory optimization, spectrum management, and aerial-terrestrial cellular network design and optimization based on 5G and software radio technology, among others.

Modern wireless standards such as Long-Term Evolution (LTE) and 5G New Radio (5G NR) use pilot-aided SINR estimates to adapt the modulation and coding scheme (MCS) and transmission mode of data blocks, to fully utilize the channel capacity. However, when interference is localized exclusively on non-pilot resources, pilot-aided SINR estimates become inaccurate. We show that this leads to congestion due to retransmissions, and in the worst case, outage due to very high block error rate (BLER). We demonstrate this behavior through numerical as well as experimental results with the 4G LTE downlink, which show high BLER and significant throughput detriment in the presence of non-pilot interference (NPI). To provide useful insights on the impact of NPI on low-latency communications, we derive an approximate relation between the retransmission- induced latency and BLER. Our results show that NPI can severely compromise low-latency applications in vehicle-to-vehicle (V2V) communications and 5G NR. We identify robust link adaptation schemes as the key to reliable communications.

In this workshop, communications systems and wireless communications educators will experience and provide feedback on remote laboratory exercises and tutorials that employ an Internet-accessible, software-defined radio (SDR) based testbed. The tutorials introduce and demonstrate concepts relevant to spectrum sharing, cognitive radio, and other radio / wireless communications applications that involve spectrum agility. Students run, modify, and / or configure code for cognitive engines or adaptive controllers that make real-time modifications to radio waveform parameters such as operating frequency, transmitting power, signal bandwidth, modulation, and error correction. The controllers make adaptations to optimize over-the-air operation in challenging signal environments. The resulting radio link performance is measured using an experiment management framework and can be visualized using a web interface that displays performance metrics as well as three-dimensional and two-dimensional waterfall plots. In the process of working through the exercises, students learn to use the above tools, which also enable them to design and perform original experiments. This workshop will be valuable for anyone teaching or studying wireless communications or using interactive remote laboratories and tutorials to teach technical concepts.

This Innovative Practice Full Paper introduces a novel tool for educating electrical engineering students about hardware impairments in wireless communications. A radio frequency (RF) front end is an essential part of a wireless transmitter or receiver. It features analog processing components and data converters which are driven by today's digital communication systems. Advancements in computing and software-defined radio (SDR) technology have enabled shaping waveforms in software and using experimental and easily accessible plug-and-play RF front ends for education, research and development. We use this same technology to teach nonlinear effects of RF front ends and their implications. It uses widely available RF instruments and components and SDR technology-well-established affordable hardware and free open source software-to teach students how to characterize the nonlinearity of RF receivers while providing hands-on experience with SDR tools. We present the hardware, software and procedures of our laboratory session that enable easy reproducibility in other classrooms. We discuss different forms of evaluating the suitability of the new class modules and conclude that it provides a valuable learning experience that bolsters the theory that is typically provided in lectures only.

This Innovative Practice Full Paper presents our methodology and tools for introducing competition in the electrical engineering curriculum to accelerate education and innovation in wireless communications. Software radio or software-defined radio (SDR) enables wireless technology, systems and standards education where the student acts as the radio developer or engineer. This is still a huge endeavor because of the complexity of current wireless systems and the diverse student backgrounds. We suggest creating a competition among student teams to potentiate creativity while leveraging the SDR development methodology and open-source tools to facilitate cooperation. The proposed student challenge follows the European UEFA Champions League format, which includes a qualification phase followed by the elimination round or playoffs. The students are tasked to build an SDR transmitter and receiver following the guidelines of the long-term evolution standard. The metric is system performance. After completing this course, the students will be able to (1) analyze alternative radio design options and argue about their benefits and drawbacks and (2) contribute to the evolution of wireless standards. We discuss our experiences and lessons learned with particular focus on the suitability of the proposed teaching and evaluation methodology and conclude that competition in the electrical engineering classroom can spur innovation.

The primary source of nonlinear distortion in wireless transmitters is the power amplifier (PA). Conventional digital predistortion (DPD) schemes use high- order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is not practical for scaling to tens or hundreds of PAs in massive multiple-input multiple-output (MIMO) systems. There is more than one candidate precoding matrix in a massive MIMO system because of the excess degrees-of- freedom (DoFs), and each precoding matrix requires a different DPD polynomial order to compensate for the PA nonlinearity. This paper proposes a low-order DPD method achieved by exploiting massive DoFs of next-generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to achieve the same performance as the conventional DPD using an 11th order polynomial for a 100×10 massive MIMO configuration. Experimental results show a 70% reduction in computational complexity, enabling ultra-low latency communications.

Multi-way and device-to-device (D2D) communications are currently considered for the design of future communication systems. Unmanned aerial vehicles (UAVs) can be effectively deployed to extend the communication range of D2D networks. To model the UAV-D2D interaction, we study a multi-antenna multi-way channel with two D2D users and an intermittently available UAV node. The performance in terms of sum-rate of various transmission schemes is compared. Numerical results show that for different ground environments, the scheme based on a combination of interference alignment, zero-forcing and erasure-channel treatment outperforms other schemes at low, medium and high SNRs and thus represents a viable transmission strategy for UAV-aided multi-way D2D networks.

Despite the evolution of wireless technology, enabling safety-critical applications, only a few systems have been developed for improving the safety at railroad crossings. We present a Vehicle-to-Train (V2T) communications architecture for an early warning application carried over Dedicated Short-Range Communications (DSRC) radios. We conduct DSRC performance measurements at railroad crossings in suburban environments in the US to evaluate its feasibility. Two types of measurement setups are proposed: direct warning and indirect warning. Our results show that DSRC can be deployed and configured for effectively providing a V2T communications system to warn drivers of an approaching train.

The 3rdGeneration Partnership Project released the cellular vehicular-to-everything (C-V2X) specifications as part of the LTE framework in Release 14. C-V2X is the alternative to dedicated short range communications and both are specifically designed for V2X control signaling. C-V2X extends the device-to-de-vice specifications by adding two more modes of operation targeting the vehicular environment in coverage and out of coverage of LTE base stations. Vehicle-to-vehicle communications (V2V) is established with Mode 4, where the devices schedule their transmissions in a distributed way employing sensing-based semi-persis-tent scheduling (SPS). Research is needed to assess the performance of SPS, especially in congested radio environments. This paper presents the first open-source C-V2X simulator that enables such research. The simulator is implemented in ns-3. We analyze the effect of the Mode 4 resource pool configuration and some of the key SPS parameters on the scheduling performance and find that the resource reservation interval significantly influences packet data rate performance, whereas resource reselection probability has little effect in dense vehicular highway scenarios. Our results show that proper configuration of scheduling parameters can significantly improve performance. We conclude that research on congestion control mechanisms is needed to further enhance the SPS performance for many practical use cases.

In this paper, we propose and evaluate rate-maximizing pilot configurations for Unmanned Aerial Vehicle (UAV) communications employing OFDM waveforms. OFDM relies on pilot symbols for effective communications. We formulate a rate-maximization problem in which the pilot spacing (in the time-frequency resource grid) and power is varied as a function of the time-varying channel statistics. The receiver solves this rate-maximization problem, and the optimal pilot spacing and power are explicitly fed back to the transmitter to adapt to the time-varying channel statistics in an air-to-ground (A2G) environment. We show the enhanced throughput performance of this scheme for UAV communications in sub-6 GHz bands. These performance gains are achieved at the cost of very low computational complexity and feedback requirements, making it attractive for A2G UAV communications in 5G.

The application domains of civilian unmanned aerial systems (UASs) include agriculture, exploration, transportation, and entertainment. The expected growth of the UAS industry brings along new challenges: Unmanned aerial vehicle (UAV) flight control signaling requires low throughput, but extremely high reliability, whereas the data rate for payload data can be significant. This paper develops UAV number projections and concludes that small and micro UAVs will dominate the US airspace with accelerated growth between 2028 and 2032. We analyze the orthogonal frequency division multiplexing (OFDM) waveform because it can provide the much needed flexibility, spectral efficiency, and, potentially, reliability and derive suitable OFDM waveform parameters as a function of UAV flight characteristics. OFDM also lends itself to agile spectrum access. Based on our UAV growth predictions, we conclude that dynamic spectrum access is needed and discuss the applicability of spectrum sharing techniques for future UAS communications.

Mission-critical wireless networks are being upgraded to 4G long-term evolution (LTE). These networks require very high reliability and security as well as easy deployment and operation in the field. Wireless communications systems have been vulnerable to jamming, spoofing and other radio frequency (RF) attacks since the early days of analog systems. Although wireless systems have evolved, important security and reliability concerns still exist. This paper presents our methodology for testing 4G LTE operating in harsh signaling environments. We use software-defined radio technology and open-source software to develop a fully configurable protocol-aware interference waveform. We define several test cases that target the entire LTE signal or part of it and evaluate the performance of a mission- critical production LTE system. Our RF experiments show that LTE synchronization signal interference causes significant throughput degradation at low interference power. By dynamically evaluating the performance measurement counters, the k-nearest neighbor classification method can detect the specific RF signaling attack to aid in effective mitigation.

The long-term evolution (LTE) has spread around the globe for deploying 4G cellular networks for commercial use. These days, it is gaining interest for new applications where mobile broadband services can be of benefit to society. Whereas the basic concepts of LTE are well understood, its long-term evolution has just started. New areas of Ramp;amp;D look into operation in unlicensed and shared bands, where new versions of LTE need to coexist with other communication systems and radars. Virginia Tech has developed an LTE testbed with unique features to spur LTE research and education. This pa-per introduces Virginia Tech's LTE testbed, its main features and components, access and configuration mechanisms, and some of the research thrusts that it enables. It is unique in several aspects, including the extensive use of software-defined radio technology, the combination of industry-grade hardware and software-based systems, and the remote access feature for user- defined configurations of experiments and radio frequency paths.

Dynamic spectrum access (DSA) has emerged as an enabling technology to allow more intensive sharing of the radio spectrum. A requirement for most proposed DSA techniques is prior knowledge of the primary user's access pattern or the ability to predict primary user activities. Therefore, spectrum surveys are taking place on an even wider scale to provide data on spectrum usage and occupancy for developing new prediction models and for spectrum planning by regulators. This paper investigates the potential of mining spectrum data for correlation between human activities in a neighborhood and the resulting spectrum occupancy across different bands. We propose a systematic approach based on two clustering techniques: Gaussian mixture models (GMMs) and self-organizing map neural networks (SOMNNs). We mine spectrum measurements gathered by our network of spectrum observatories in Virginia and Illinois. The results confirm the existence of observable correlation and show that our proposed techniques detect correlation across various land mobile radio (LMR) and cellular bands under a wide range of scenarios with a high detection ratio. These results inspire us to develop more efficient prediction models for applications in opportunistic spectrum access (OSA) or self-organized networks.

Radio communications terminals and infrastructure tend to support an increasing range of algorithms and radio access technologies. Flexible processing platforms are therefore needed for supporting multi-standard or heterogeneous radios. Channel decoding is one of the most computing demanding digital signal processing blocks of a radio transceiver. At the same time, it provides a high degree of implementation flexibility as well as facilitates dynamic parameter adjustments. This paper presents a flexible LDPC decoder implemented on an FPGA device following the ALOE middleware design paradigm. We analyse the middleware efficiency in terms of flexibility versus resource requirements. The results show a relative middleware area overhead of 32 %.

It is widely known that the SDR industry campaigns component-based radio applications, which will enable fast prototyping and deployment of new radio devices and may increase manufacturing profits. Through the JTRS program, the US Dept. of Defense proposed the SCA specification as the standard for military communications. The SDR Forum is now reviewing these specifications and trying to adapt them to the commercial market. The significant differences between military and commercial communications' requirements make this migration a hazardous task. On the other hand, the SCA specification does not consider any method or procedure that enables cognitive functionalities, which would be necessary for future cognitive radio implementations. This paper therefore presents an alternative approach to SCA, introducing a low-profile operating environment for next generation cognitive radios. We demonstrate its suitability for present and future commercial radios.

Cognitive radio characterizes an ambient aware and software-defined radio system that is capable to autonomously and intelligently adjust the system parameters. It has the potential to optimize the usage of all relevant resources for service- driven wireless communications. We identify four types of resources; these are the radio, the computing, and the radio and user application resources. This paper presents their modeling, including several models for each resource type. The modeling serves as the basis for a joint resource management in a cognitive radio system that may eventually trade-off one resource (type) against the other(s). Two approaches for such a management are presented: a distributed-cooperative and a centralized- integrated. We finally discuss the applicability and utility of the proposed models, and the potentials of a joint resource management in cognitive radio in terms of user satisfaction and revenues.

Software-defined radio (SDR) is an emerging concept that leverages the design of software-defined and hardware-independent signal processing chains for radio communication. It introduces flexibility to wireless systems, facilitating the dynamic switch from one radio access technology to another or, in other words, the de- and reallocation of computing resources from one SDR application to another. This paper introduces an SDR computing resource management framework. It accounts for several SDR system characteristics, including real-time computing requirements, limited computing resources, and the use of heterogeneous multiprocessor plat-forms, including multiprocessor systems-on-chip. The framework features the tw-mapping, a dy-namic mapping algorithm that is apt for many cost functions and, thus, adaptable to any radio scenario. The cost function proposal dynamically manages the available computing resources to satisfy the given SDR computing constraints. Two relevant SDR scenarios and the corresponding simulations, based on representative SDR platforms and processing chains, demonstrate the framework's importance and suitability for software-defined radios.

Cognitive radio is generally understood as an intelligent wireless communication system aiming at the efficient utilization of radio resources. We argue for the extension of its scope to also address the management of computing resources, including processing and bandwidth capacities of SDR platforms, and present a cooperative resource management framework: The joint radio resource management (JRRM) and the computing resources management (CRM) entities interchange information to cooperatively decide if and what kind of terminal reconfiguration would be the most appropriate in each situation. Therefore, the cognitive radio system continuously observes the radio and the computing environments. We discuss a realistic case study and present a simple CRM algorithm. Simulation results show that such a cooperative resource management approach can achieve important improvements over the JRRM by itself. In particular, our proposal considerably reduces the number of lost user sessions due to inappropriate reconfiguration decisions and, thus, serves more wireless users.

Due to the wireless industry tendencies, different radio access technologies will coexist in a heterogeneous environment. Software defined radio, as a concept that tries to provide flexibility, might ease the integration of that environment. The goal of this work is to introduce the concept of computing resource management, that working in cooperation with the radio resource management strategies leading to minimize computing costs assuring the same or better QoS in such future heterogeneous systems. Furthermore, the action of computing management might help to bring balance over the communication load.

The division in several layers of the implementation of systems is a solution adopted to avoid complexity, provide flexibility and improve portability and code reusability through different hardware. Middleware (intermediate layer between two other layers) implementations are based on the use of increasingly high-level languages and application programming interfaces (API). The field programmable gate arrays (FPGA) world can also apply this approach to produce building blocks independent from hardware platforms and devices. This paper presents details of the implementation of a middleware, called platform and hardware abstraction layer (P-HAL) when applied to FPGA devices. It was specially designed for radio applications and allows designing specific functions independently of the hardware context where they are applied, thus providing flexibility to the so-called software radios employing FPGA devices.

Software Defined Radio (SDR) is an emerging technology that is based on the software implementation of the signal processing blocks found in a radio transceiver. The switch between radio access technologies may then be as easy as changing the software running on a future SDR terminal. SDR terminals refer to mobile equipment and base stations. These terminals will comprise general purpose processors, digital signal processors and/or reconfigurable logic devices. As a result, typical heterogeneous computing problems may appear in the SDR context. This article focuses on the mapping issue, discusses its relevance in software defined radio, and introduces an adequate mapping algorithm. The algorithm efficiently tackles the problem of mapping SDR function chains, i.e. signal processing blocks of a SDR transceiver, to heterogeneous processing platforms. We expose our approach, discuss its performance, present extensive simulation results and derive conclusions.

Software radio applications require a framework to develop and deploy applications, especially those related to radio infrastructure. It is interesting that a given application may be executed on any software radio. But, since hardware platforms used in this context will have multiple architectures and devices, a software layer to make applications independent from hardware is mandatory. Ad-hoc software for a given hardware platform may produce the best software performance. Conversely, when software is not targeted to any concrete platform the lost of performance may be excessive and the overhead introduced by any platform-dependent library could become intolerable. In this paper the resource utilization of a software radio application using a simple hardware abstraction layer is studied and compared to an ad-hoc implementation to make an assessment of the introduced overhead. The particularity of the hardware abstraction layer is that it runs on a platform which only processors are FPGA devices.