Existing registration, discovery, and authorization mechanisms in the 5G core control plane present scalability and efficiency challenges. As cellular deployments scale to accommodate diverse user demands, the 5G core control plane suffers from increased inter-Virtual Network Function (VNF) communication latency, thus deteriorating the reliability of critical procedures. To address this problem, we propose 5G-STREAM (Service mesh Tailored for Reliable, Efficient, and Authorized Microservices) to optimize control plane traffic in distributed cloud environments by establishing a topology awareness of service chains across cloud hierarchies. Leveraging this awareness, 5G-STREAM dynamically configures communication pathways to reduce discovery and authorization signaling overhead, thus increasing the reliability of inter-VNF communication. We develop a prototype of 5G-STREAM and evaluate its performance. Our evaluation results show that 5G-STREAM significantly reduces the process completion time in core service chains by up to 2× inter VNF-Network Repository Function (NRF) latency per transaction, with more pronounced benefits in larger service chains. Furthermore, we show that the cost required to deploy 5G-STREAM is an additional 0.1 USD/hr on AWS for a VNF handling a sustained rate of 50,000 requests/minute.

The Fifth Generation (5G) core network is designed as a set of Virtual Network Functions (VNFs) hosted on Commercial-Off-the-Shelf (COTS) hardware. This creates a growing demand for general-purpose computing resources. Given their elastic infrastructure, cloud services like Amazon Web Services (AWS) are attractive platforms to address this need. Therefore, it is crucial to understand the Quality of Service (QoS) requirements associated with deploying the 5G core in the cloud. We developed the 5G-MAP (5G Measurement and Assessment Platform) to understand the trade-offs between different deployment strategies. Our framework facilitates detailed control and user plane performance assessments in varied deployment scenarios. We integrated 5G-MAP with the OpenAirInterface (OAI) 5G core and utilized it in a series of deployments across seven countries, leveraging eight AWS regions and eighteen edge zones. Our evaluations cover from HTTP transactions to user plane throughput and packet loss. We identify topologies that can considerably lower the 5G core service chain latencies due to a significant reduction in the number of inter-site hops. Such actionable performance improvements illustrate how operators can leverage 5G-MAP to optimize their cloud-based 5G deployments.

In the evolving landscape of next-generation wireless networks, ensuring secure communications in covert military operations is paramount. This paper proposes an advanced localization-based security system utilizing passive receivers and Time Difference of Arrival (TDOA) techniques to continuously authenticate the signals of a commander in dynamic operational scenarios. Our system effectively counters physical layer spoofing attacks by distinguishing between the precise locations of a legitimate entity and potential adversaries. To that end, we derive the positioning error bound (PEB) specific to TDOA systems, emphasizing the critical impact of receiver arrangement on localization accuracy. Furthermore, we introduce a novel application of deep Q-learning for the NP-hard problem of optimal placement of receivers, addressing the challenge of spatial geometry, which significantly influences localization accuracy. Through extensive testing, we demonstrate that our proposed approach notably outperforms traditional placement methods in mitigating geometry-induced errors and enhancing overall localization precision. Ultimately, this facilitates realizing and maintaining a secure zone where users can authenticate each other through localization.

Precisely measuring the location of moving objects has been a long-standing research challenge. Here, we present a highly accurate 3-D positioning system named LIO: localization using isochrons in oscillators. LIO precisely measures the Time of Arrival (ToA) of incoming radio frequency (RF) signals employing a novel timing protocol. The proposed protocol measures ToA leveraging the phase shifts of limit cycle oscillators based on their isochrons’ structure. These ToA measurements are then translated into distances and are employed in LIO for high-accuracy 3-D positioning. Moreover, LIO utilizes a passive-round-trip-time (passive-RTT) protocol leveraging retro-reflective tags to address and enhance the synchronization challenge. We derive LIO’s positioning error bound (PEB) and attribute the localization error to ranging- and geometry-induced errors. While our primary objective in this work is to address the former source of error, we also provide a novel optimization framework to mitigate the geometry-induced errors by proposing optimal anchor placements. Lastly, we assess the performance of LIO by designing comprehensive simulations using real-world operational parameters for commercially available semiconductor laser oscillators. Our numerical results indicate that LIO achieves distance estimation with the accuracy of subtenth of the millimeter (mm) and overall 3-D localization with sub-1 mm accuracy. This is at least an order of magnitude better compared to the existing technologies.

In this paper, we present a comparative study of the performance of multiple terrestrial 5G networks and Starlink’s satellite service. To that end, we conduct comprehensive mobile measurements over an 860+ km route, incorporating a diverse range of terrains, speeds, and cell tower coverage. Our study focuses on two metrics: throughput and latency both locally and as perceived by global servers. This approach provides novel insights into the operational performance of terrestrial 5G and non-terrestrial networks (NTN), in particular satellite networks, in real-world driving scenarios. Our aim is to present the trade-offs between network types, geography, and infrastructure in high-mobility scenarios. Our findings suggest that terrestrial 5G networks, particularly Verizon, exhibit high peak downlink throughput. However, the satellite network (Starlink) offers a compelling case for consistent performance, particularly lower latency, across various regions and driving speeds. Moreover, Starlink performs seamlessly in rural areas, where 5G network providers offer little to no coverage. Our analysis shows that combining terrestrial 5G and satellite network service offers suitable throughput and latency for next-generation applications.

Mobile networks are swiftly advancing to accommodate the burgeoning spectrum of applications. The architecture of 5G networks integrates the principle of network slices, logically isolated end-to-end segments tailored to offer specific services. In this architectural schema, drones have emerged as a significant service category. Achieving the successful deployment of drone networks is heavily contingent upon the ability to accurately localize them in a three-dimensional (3D) setting, beyond the critical requirement for tight latency control. Transitioning from 4G to 5G, these networks are characterized by their operation at elevated frequency spectrums and more densely packed deployment configurations. Within such environments, the task of ensuring precise indoor localization poses a significant challenge, primarily due to the distinctive signal behavior at higher frequencies. To achieve this goal, we propose the RAPID framework, utilizing foundational principles from the third-generation partnership project (3GPP) to design a radio access network (RAN) that includes 5G femtocells. This architecture aims to shift positioning responsibilities from outdoor base stations (BSs) to improve indoor localization performance. Our study’s principal contribution is the demonstration of how the spatial distribution of 5G femtocells significantly influences the accuracy of drone positioning. To address the challenges inherent in femtocell deployment, we develop an innovative optimization framework coupled with a deep reinforcement learning (DRL) strategy, aimed at solving the NP-hard problem. Our findings reveal that adopting our DRL-based placement strategy significantly improves positioning accuracy compared to regular arbitrary deployment approaches.

Geofencing technologies have emerged as crucial tools in establishing virtual boundaries within both physical and digital spaces, providing a secure method to manage and supervise specified zones. They are now recognized as vital instruments for delineating and managing boundaries in a range of applications, from ensuring aviation safety in drone operations to regulating access in mixed reality environments such as the metaverse. Successful geofencing depends significantly on accurate tracking, which is essential for preserving the integrity and effectiveness of these systems. Utilizing the benefits of 5G technology, such as its broad bandwidth and widespread availability, offers a promising approach to improve geofencing performance. In this paper, we present DEFENCE: Deep Reinforcement Learning for Geofencing Enhancement, an innovative method for precise geofencing that utilizes "5G Points" within indoor 5G small cell networks, optimally placed using a deep Q-learning framework. Through the computation of the Cramér-Rao Lower Bound (CRLB), we evaluate tracking errors arising from spatial configurations and ranging inaccuracies. Our proposed deep Q-learning model tackles the NP-hard challenge of identifying the optimal placement of 5G Points to reduce errors caused by spatial geometry. We implemented an extensive testing campaign to assess the efficacy of DEFENCE. Our findings reveal that this strategic deployment enhances tracking accuracy by a factor of 100 over conventional placement methods. This breakthrough considerably bolsters geofencing systems, enhancing their defense against potential threats such as unauthorized drone incursions and security breaches within metaverse environments.

The deployment of commercial 5G network is gaining momentum while research is already moving towards more advanced features. Currently, the lack of an easy-to-construct, open-source testbed that can support commercial off-the-shelf (COTS) devices has hindered academic research. In this paper, we build an open-source over-the-air testbed leveraging advanced features of 5G radio access and core networks developed by the OpenAirInterface (OAI) project. We evaluate the quality of service (QoS) achievable using this testbed and provide visibility into the compute consumption of individual components. Additionally, we present a method to utilize WiFi devices for experimenting with 5G QoS. We collect resource consumption analytics from the 5G user plane in correlation to raw traffic patterns. Our results show that the OAI testbed sustains sub-20ms latency with up to 80Mbps throughput over a 25m range using COTS devices. Device connection remains stable while supporting different use cases such as AR/VR, online gaming, video streaming and voice over IP (VoIP). Finally, we illustrate how these popular use cases affect the CPU utilization in the user plane. This provides insight into the capabilities of existing 5G solutions by demystifying the resource needs of specific use cases. All our results can be recreated using COTS equipment.

Recent developments in the autonomous vehicle industries have increased the significance of accurate positioning. Popular techniques for localization include the Global Positioning System (GPS). However, owing to the presence of obstructions, GPS signals are unavailable in dense urban environments. Moreover, in indoor environments (such as a parking garage below the ground), GPS signals are inaccessible to users. In this article, we introduce a novel technique for accurate indoor vehicular positioning. The first step in our proposed system is localization based on received signal strength (RSS) fingerprints of 5G New Radio (NR) downlink signals. Furthermore, to compensate for the high susceptibility of RSS fingerprinting techniques in varying environments, we propose a real-time collaborative localization scheme based on 5G sidelink device-to-device (D2D) communication. We develop extensive test campaigns to assess the efficacy of our proposed two-step scheme. According to test results, our proposed algorithm outperforms scenarios that rely solely on 5G RSS fingerprints.

In the near future, unmanned aerial vehicles (UAVs) will be used to automate the logistics between organizations and their customers by relying on high accuracy localization. In this paper, we propose a framework that leverages the multi radio access technology (RAT) 5G network to solve the cellular positioning problem for such high mobility targets. For indoors, we utilize wireless fidelity (Wi-Fi) routers, denoted as Wi-Five dots, to improve positioning accuracy where the 5G signal is weaker compared with outdoor environments. These anchor points will use the 5G backhaul to report to the same location management function (LMF) as the cellular 5G access network. The primary contribution of this work is showing how the geometry of these indoor Wi-Five dots significantly affects positioning accuracy. Through a novel optimization algorithm based on the Evolutionary Algorithm (EA) class, we solve the NP-Hard problem of finding the optimal placement of Wi-Five dots for three-dimensional high-accuracy positioning of a mobile target. Our results show that the final positioning accuracy is the product of both the ranging errors and the geometric dilution of precision (GDOP). We experimentally verify that for the latter, the error stems primarily from the Z-axis estimations rather than the errors in the X − Y plane. Finally, we evaluate the results of our optimal placement to show it drastically improves positioning estimations in a three-dimensional space compared with arbitrary beacon placement.

Drones in many applications need the ability to fly fully or partially autonomously to accomplish their mission. To allow these fully/partially autonomous flights, first, the drone needs to be able to locate itself constantly. Then, the navigation command signal would be generated and passed on to the controller unit of the drone. In this article, we propose a localization scheme for drones called robust localization for indoor navigation of drones with optimized beacon placement (iDROP) that is specifically devised for GPS-denied environments (e.g., indoor spaces). Instead of GPS signals, iDROP relies on speaker-generated ultrasonic acoustic signals to enable a drone to estimate its location. In general, localization error is caused by two factors: the ranging error and the error induced by relative geometry between the transmitters and the receiver. iDROP mitigates these two types of errors and provides a high-precision 3-D localization scheme for drones. iDROP employs a waveform that is robust against multipath fading. Moreover, placing beacons in optimal locations reduces the localization error induced by the relative geometry between the transmitters and the receiver.

The pursuit of high-accuracy localization without relying on the global positioning system (GPS) has gained significant interest in recent years. The deployment of autonomous vehicles (AVs) in diverse indoor applications exemplifies a prominent domain where the demand for a robust positioning system is evident. With the advancements in 5G and beyond radio access networks (RAN), the availability of new positioning signals presents an opportunity to deliver accurate location estimates for these applications. Nevertheless, these signals encounter substantial path losses in indoor environments. Additionally, the precise localization within existing frameworks requires stringent synchronization, which is challenging to meet. In this paper, we propose OFDRA: Optimal Femtocell Deployment for Accurate Indoor Positioning of RIS-Mounted AVs, a novel positioning framework that is robust against multipath and does not require strict synchronization between anchor-anchor or anchor-target entities. Specifically, OFDRA is designed to operate in scenarios where the line of sight (LOS) exists. The first design objective of OFDRA is the mitigation of ranging errors by leveraging a compact reconfigurable intelligent surface (RIS) mounted on top of AVs acting as a programmable mirror in a 5G network. The second design objective is to achieve optimal anchor placement in three-dimensional indoor spaces, thereby reducing the geometric dilution of precision (GDOP) and mitigating geometric-induced errors in the final position estimation. Our experimental verification reveals that the localization error is influenced by GDOP, encompassing both the$X-Y$plane and$Z$-axis estimations. Through optimized anchor placement, OFDRA demonstrates a seven-fold enhancement in$Z$-axis accuracy compared to the state-of-the-art, achieving a sub-1 m three-dimensional accuracy for more than 95% of cases.

The next generation of mobile networks will support a wide range of service requirements over a shared virtual infrastructure. Network functions virtualization (NFV) enables the deployment of Radio Access Network (RAN) and core network functions as virtual network functions (VNFs) on commodity hardware instead of proprietary servers. The deployment of the 5G core will be orchestrated between mobile virtual network operators (MVNOs) and cloud infrastructure providers by middle-men Network-slice-as-a-service (NSaaS) providers that will consume Infrastructure-as-a-service (IaaS) from the latter and offer network slices to the former. In this paper, we seek to leverage an end-to-end emulated 5G deployment to offer insight into the cost implications surrounding large-scale core network deployments. Our deployment features real-life traffic patterns corresponding to practical use cases which are fitted with network slicing models. These models are implemented in a 5G testbed to gather compute resource consumption. This data is used to formulate infrastructure procurement costs for popular cloud providers. Our results show steady patterns in compute consumption across all use cases, which we use to make high scale cost projections. In the end, we are able to observe the trade-off between cost and throughput achieved by decentralizing the network slices and offloading the user plane.

Navigating in environments where the GPS signal is unavailable, weak, purposefully blocked, or spoofed has become crucial for a wide range of applications. A prime example is autonomous navigation for drones in indoor environments: to fly fully or partially autonomously, drones demand accurate and frequent updates of their locations. This paper proposes a Robust Acoustic Indoor Localization (RAIL) scheme for drones designed explicitly for GPS-denied environments. Instead of depending on GPS, RAIL leverages ultrasonic acoustic signals to achieve precise localization using a novel hybrid Frequency Hopping Code Division Multiple Access (FH-CDMA) technique. Contrary to previous approaches, RAIL is able to both overcome the multipath fading effect and provide precise signal separation in the receiver. Comprehensive simulations and experiments using a prototype implementation demonstrate that RAIL provides high-accuracy three-dimensional localization with an average error of less than 1.5 cm.

In many drone applications, drones need the ability to fly fully or partially autonomously to carry out their mission. To enable such fully/partially autonomous flights, the ground control station that is supporting the drone's operation needs to constantly localize and track the drone, and send this information to the drone's navigation controller to enable autonomous/semiautonomous navigation. In outdoor environments, localization and tracking can be readily carried out using GPS and the drone's Inertial Measurement Units (IMUs). However, in indoor areas or GPS-denied environments, such an approach is not feasible. In this paper, we propose a localization and tracking scheme for drones called ROLATIN (Robust Localization and Tracking for Indoor Navigation of drones) that was specifically devised for GPS-denied environments. Instead of GPS signals, ROLATIN relies on speakergenerated ultrasonic acoustic signals to estimate the target drone's location and track its movement. Compared to vision and RF signal-based methods, our scheme offers a number of advantages in terms of performance and cost.