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Non-terrestrial networks based on unmanned aerial vehicles (UAVs) are considered for integration with 6G mobile networks to provide ubiquitous communication through heterogeneous networks. UAVs offer benefits such as flexible deployment, mobility control, and strong line-of-sight links. In this paper, orthogonal multiple access (OMA) and nonorthogonal multiple access (NOMA) are combined to form a hybrid OMA-NOMA cognitive radio (CR) assisted cooperative UAV system. The selection of either the OMA or NOMA mode is carried out such that the best link quality is offered to the user equipments (UEs) compared to the individual modes. In this system, the base station uses the NOMA mode for the transmission to the UAVs which act as decode-and-forward relays each serving a cluster of UEs. The CR paradigm is used for the NOMA mode of the hybrid system to reduce the interference caused by the UAVs to adjacent clusters of UEs. Analytical expressions of the outage probability and sum rate of the OMA, NOMA, and hybrid OMA-NOMA systems are derived. Numerical results are provided to reveal the performance advantages of the proposed hybrid OMA-NOMA CR-assisted cooperative UAV system over Nakagami-m fading. Further, design options and tradeoffs between the OMA, NOMA, and hybrid OMA-NOMA systems are illustrated. © 2024 IEEE.

The research presented focuses on cryptographic-agility for End-to-End Encryption systems (E2EE) that could be implemented for telemetry data encryption. The recent report 1 by Microsoft after a security incident, describes the consequences that lead to encryption key leakage from telemetry data of highly protected production system. Internet of Things (IoT) and mobile devices constantly produce telemetry data which contains sensitive information. The data partially belongs to vendor, but IoT consumer’s consent is needed to access such data for troubleshooting or forensic analysis. Goyal et al [2] proposed Attribute Based Encryption (ABE) as a solution for legitimately access the audit log contents by engineering team. © 2024 Copyright held by the owner/author(s).

The sub-THz and THz frequency ranges, that have been designated for astronomy and military, are now considered for the next generation of cellular networks. Radar systems operating in these frequency ranges are popular. Sharing the same radio frequency (RF) resources of cellular networks and radar systems opens the great potential to integrate radar system on mobile equipment. With this integration, the radar applications based on detection and ranging are available for mobile equipment. Realizing synthetic aperture radar (SAR) is also possible due to the movement of the mobile equipment that helps to synthesize an aperture larger than the physical aperture of the mobile equipment. Precise active localization in indoor environment is therefore feasible. The paper presents a discussion about active localization by realizing monostatic, bistatic, multistatic and passive SAR on mobile equipment with an integrated radar system. The simulation and experiment results shows the feasibility of the proposal. © 2023 IEEE.

In this paper, we consider a downlink scenario in which a base station (BS) communicates with user equipment (UE) that move with high speed. Orthogonal time frequency space (OTFS) modulation is used to overcome the effects of high Doppler shifts inherent with high-mobility scenarios. To increase spectral efficiency, power-domain non-orthogonal multiple access (NOMA) is combined with OTFS to form an OTFS-NOMA system. The K-means clustering algorithm is deployed which partitions the coverage area of the BS into clusters such that the UEs within a cluster have a shorter distance to the respective cluster center than to the centers of the other clusters. The cluster centers act as simultaneous multiple users on which basis power-domain NOMA selects the power allocation coefficients to reduce the mutual interference among UEs. OTFS can control the resolution of the delay-Doppler domain to more accurately compensate for propagation delays and Doppler shifts. The performance of this OTFS-NOMA system with K-means clustering of the UEs is assessed in terms of sum rate. The numerical results illustrate that the successive interference cancellation of power-domain NOMA significantly improves sum rate. Additional improvements are obtained by choosing a sufficiently large size of the delay-Doppler grid of OTFS which effectively copes with the high Doppler shifts of the considered high-mobility scenario. © 2023 IEEE.

Precise mobile equipment localization in indoor environment is possible for mobile equipment with an integrated radar system. Deploying an omni-directional antenna at a base station allows localizing a single mobile unit at a time slot and a frequency resource block. With cell sectoring, an approach to cope with increasing capacity in a cell of a mobile network, helps to localize multiple mobile units at a time slot and a frequency resource block. Most importantly, cell sectoring helps to avoid localization ambiguity caused by the backprojection process. The paper presents the precise multiple mobile equipment localization approach in indoor environment based on cell sectoring. The simulation illustrates the benefit of the approach. The practicality of the approach is also addressed in the paper. © 2023, © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Mobile operators collect and store the network generated traffic data for analysis. Time Series Prediction (TSP) has been used in mobile network traffic data analysis to produce predictive results for network planning and resource allocation. We propose a novel method of predicting mobile network traffic using neural networks based on conditional probability modeling between adjacent data windows. Firstly, we develop a pre-processing method to aggregate the raw traffic log data and sample the aggregated time series to adjacent data windows, as training samples. Secondly, we use neural networks to parameterize the conditional probability between adjacent data windows and estimate the probability by training the neural networks with sampled data. The estimated conditional probability is then used to ensemble the prediction. Thirdly, we show theoretically that the prediction based on all historical data is equivalent to the prediction based on just previous data window, given the estimation of conditional probability between adjacent data windows. We also analyze computation complexity and show that seasonality will reduce the computational complexity. In the experiment, we compare the prediction performance among the models with different seasonality, sample size and number of hidden layers, and show that the proposed schemes achieve better prediction accuracy than state-of-the-art. IEEE, QC 20220921

There is a lack of research on the analysis of peruser traffic in cellular networks, for deriving and following traffic-aware network management. In fact, the legacy design approach, in which resource provisioning and operation control are performed based on the cell-aggregated traffic scenarios, are not so energy- and cost-efficient and need to be substituted with user-centric predictive analysis of mobile network traffic and proactive network resource management. Here, we shed light on this problem by designing traffic prediction tools that utilize standard machine learning (ML) tools, including long shortterm memory (LSTM) and autoregressive integrated moving average (ARIMA) on top of per-user data. We present an expansive empirical evaluation of the designed solutions over a real network traffic dataset. Within this analysis, the impact of different parameters, such as the time granularity, the length of future predictions, and feature selection are investigated. As a potential application of these solutions, we present an ML-powered Discontinuous reception (DRX) scheme for energy saving. Towards this end, we leverage the derived ML models for dynamic DRX parameter adaptation to user traffic. The performance evaluation results demonstrate the superiority of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. Furthermore, the results show that adaptation of DRX parameters by online prediction of future traffic provides much more energy-saving at low latency cost in comparison with the legacy cell-wide DRX parameter adaptation., QC 20220603

Controlling antenna tilts in cellular networks is imperative to reach an efficient trade-off between network coverage and capacity. In this paper, we devise algorithms learning optimal tilt control policies from existing data (in the so-called passive learning setting) or from data actively generated by the algorithms (the active learning setting). We formalize the design of such algorithms as a Best Policy Identification (BPI) problem in Contextual Linear Multi-Arm Bandits (CL-MAB). An arm represents an antenna tilt update; the context captures current network conditions; the reward corresponds to an improvement of performance, mixing coverage and capacity; and the objective is to identify, with a given level of confidence, an approximately optimal policy (a function mapping the context to an arm with maximal reward). For CL-MAB in both active and passive learning settings, we derive information-theoretical lower bounds on the number of samples required by any algorithm returning an approximately optimal policy with a given level of certainty, and devise algorithms achieving these fundamental limits. We apply our algorithms to the Remote Electrical Tilt (RET) optimization problem in cellular networks, and show that they can produce optimal tilt update policy using much fewer data samples than naive or existing rule-based learning algorithms., QC 20230426

29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536, In this paper, precoding methods used in downlink multi-user, multi-input multi-output systems (downlink MU-MIMO) are investigated. Precoding methods provide inter-user interference management and are designed to maximize the achievable sum rate. Downlink MU-MIMO systems can be underloaded, critically loaded or overloaded depending on the number of antennas at the base station and users and the number of users. This study provides a comparison between the generalized power iteration precoding (GPIP) (which is newly proposed in the literature) with the minimum mean square error precoding (MMSE). It is observed that the GPIP method results in similar or worse sum rate performance than the MMSE method in all load cases. Moreover, the MMSE method has lower complexity. © 2021 IEEE.

29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536, In this paper, precoding methods used in downlink multi-user, multi-input multi-output systems (downlink MU-MIMO) are investigated. Precoding methods provide inter-user interference management and are designed to maximize the achievable sum rate. Downlink MU-MIMO systems can be underloaded, critically loaded or overloaded depending on the number of antennas at the base station and users and the number of users. This study provides a comparison between the generalized power iteration precoding (GPIP) (which is newly proposed in the literature) with the minimum mean square error precoding (MMSE). It is observed that the GPIP method results in similar or worse sum rate performance than the MMSE method in all load cases. Moreover, the MMSE method has lower complexity. © 2021 IEEE.

Networked systems have recently aimed to use multiple access networks in parallel to increase resiliency, availability and capacity. However, different paths may have different latency characteristics, which may lead to out-of-order packet delivery. This may severely impact both the end-to-end application performance and the capacity utilisation of multiaccess systems. In this paper, we show that in-network support for packet reordering for multiaccess systems that are based on multiple transport layer tunnels is beneficial for several application types. Our findings are applicable to TCP and QUIC traffic in the 3GPP ATSSS context, where we use the MP-DCCP tunneling framework with a buffer-based packet reordering approach that uses a dynamic timing threshold to cope with variation of path delays over time. We demonstrate achievable performance gains for a wide range of path latency differences and end-to-end round trip times when using different in-network reordering algorithms.

A new model of global virtual Mobile Network Operator (MNO) - the Mobile Network Aggregator (MNA) - has recently been gaining significant traction. MNAs provide mobile communications services to their customers by leveraging multiple MNOs, and connecting through the one that best match their customers’ needs at any point in time (and space). MNAs naturally provide optimized global coverage by connecting through local MNOs across the different geographic regions they provide service. In this paper, we dissect the operations of three MNAs, namely, Google Fi, Twilio and Truphone. We perform measurements using the three selected MNAs to assess their performance for three major applications, namely, DNS, web browsing and video streaming. We benchmark their performance comparing it to the one of a traditional MNO. We find that even MNAs provide some delay penalty compared to the service accessed through the local MNOs in the geographic area where the user is roaming, they can significantly improve performance compared to traditional roaming model of the MNOs (e.g. home routed roaming). Finally, in order to fully quantify the potential benefits that can be realized using the MNA model, we perform a set of emulations by deploying both control and user plane functions of open-source 5G implementations in different locations of AWS, and measure the potential gains.

With globally increased migration and mobility across countries, it has become critical for many people to acquire a language of the host country. This study focuses on the case of adult migrant workers who are i) enrolled in second language education and ii) faced to a challenge of balancing between their professional and private lives and their studies of the target second language, needed for their successful integration into the host society. This explorative case study aims to support migrant learners in their second language acquisition across learning settings. It introduces and evaluates the use of specifically designed tool, TimeTracker, aimed at helping learners to keep track on how much time they spend on studying the target language influences their engagement and motivation to continue to study the host language. Eight participants from Sweden and Norway used the tool during the period of one to two weeks. Overall, the results show that using TimeTracker to log and keep track of the time spent on studying a host language inspired the learners to i) allocate more time on their second language learning, and ii) become more engaged in it through raising awareness and monitoring of their learning activities.., QC 20220413

Global Navigation Satellite Systems (GNSSs) are ubiquitously relied upon for positioning and timing. Detection and prevention of attacks against GNSS have been researched over the last decades, but many of these attacks and countermeasures were evaluated based on simulation. This work contributes to the experimental investigation of GNSS vulnerabilities, implementing a relay/replay attack with off-the-shelf hardware. Operating at the signal level, this attack type is not hindered by cryptographically protected transmissions, such as Galileo's Open Service Navigation Message Authentication (OS-NMA). The attack we investigate involves two colluding adversaries, relaying signals over large distances, to effectively spoof a GNSS receiver. We demonstrate the attack using off-the-shelf hardware, we investigate the requirements for such successful colluding attacks, and how they can be enhanced, e.g., allowing for finer adversarial control over the victim receiver., Part of proceedings: ISBN 978-1-4503-8349-3QC 20220404

To mitigate spoofing attacks targeting global navigation satellite systems (GNSS) receivers, one promising method is to rely on alternative time sources, such as network-based synchronization, in order to detect clock offset discrepancies caused by GNSS attacks. However, in case of no network connectivity, such validation references would not be available. A viable option is to rely on a local time reference; in particular, precision hardware clock ensembles of chip-scale thermally stable oscillators with extended holdover capabilities. We present a preliminary design and results towards a custom device capable of providing a stable reference, with smaller footprint and cost compared to traditional precision clocks. The system is fully compatible with existing receiver architecture, making this solution feasible for most industrial scenarios. Further integration with network-based synchronization can provide a complete time assurance system, with high short- and long-term stability., Part of proceedings ISBN 9781450383493QC 20220404

Mobile ad hoc network (MANET) is a miscellany of versatile nodes that communicate without any fixed physical framework. MANETs gained popularity due to various notable features like dynamic topology, rapid setup, multihop data transmission, and so on. These prominent features make MANETs suitable for many real-time applications like environmental monitoring, disaster management, and covert and combat operations. Moreover, MANETs can also be integrated with emerging technologies like cloud computing, IoT, and machine learning algorithms to achieve the vision of Industry 4.0. All MANET-based sensitive real-time applications require secure and reliable data transmission that must meet the required QoS. In MANET, achieving secure and energy-efficient data transmission is a challenging task. To accomplish such challenging objectives, it is necessary to design a secure routing protocol that enhances the MANET's QoS. In this paper, we proposed a trust-based multipath routing protocol called TBSMR to enhance the MANET's overall performance. The main strength of the proposed protocol is that it considers multiple factors like congestion control, packet loss reduction, malicious node detection, and secure data transmission to intensify the MANET's QoS. The performance of the proposed protocol is analyzed through the simulation in NS2. Our simulation results justify that the proposed routing protocol exhibits superior performance than the existing approaches.

The performance prediction of user equipment (UE) metrics has many applications in the 5G era and beyond. For instance, throughput prediction can improve carrier selection, adaptive video streaming's quality of experience (QoE), and traffic latency. Many studies suggest distributed learning algorithms (e.g., federated learning (FL)) for this purpose. However, in a multi-tier design, features are measured in different tiers, e.g., UE tier, and gNodeB (gNB) tier. On one hand, neglecting the measurements in one tier results in inaccurate predictions. On the other hand, transmitting the data from one tier to another improves the prediction performance at the expense of increasing network overhead and privacy risks. In this paper, we propose cascaded FL to enhance UE throughput prediction with minimum network footprint and privacy ramifications (if any). The idea is to introduce feedback to conventional FL, in multi-tier architectures. Although we use cascaded FL for UE prediction tasks, the idea is rather general and can be used for many prediction problems in multi-tier architectures, such as cellular networks. We evaluate the performance of cascaded FL by detailed and 3GPP compliant simulations of London's city center. Our simulations show that the proposed cascaded FL can achieve up to 54% improvement over conventional FL in the normalized gain, at the cost of 1.8 MB (without quantization) and no cost with quantization., Part of proceedings: ISBN 978-1-7281-9441-7QC 20220518

The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises. However, conventional terrestrial cellular networks have unfortunately neglected the huge geographical digital divide, since high-bandwidth wireless coverage is concentrated in urban areas. To meet the goal of 'connecting the unconnected,' integrating low Earth orbit (LEO) satellites with the terrestrial cellular networks has been widely considered as a promising solution. In this article, we first introduce the development roadmap of LEO satellite constellations (SatCons), including early attempts in LEO satellites with the emerging LEO constellations. Further, we discuss the unique opportunities of employing LEO SatCons for the delivery of integrating 5G networks. Specifically, we present their key performance indicators, which offer important guidelines for the design of associated enabling techniques, and then discuss the potential impact of integrating LEO SatCons with typical 5G use cases, where we engrave our vision of various vertical domains reshaped by LEO SatCons. Technical challenges are finally provided to specify future research directions., QC 20220531

The 5G wireless networks support diverse use-cases particularly ultra-reliable low-latency communications (URLLC). One of the key challenges in supporting URLLC services is to enhance the performance of the random access procedure to guarantee the stringent latency requirements. This is not only challenging for URLLC services but any delay sensitive services like voice over LTE (VoLTE) or voice over New Radio (VoNR). The access and mobility procedures rely on the random access procedure. Enhancing this procedure using artificial intelligence can thus support even more stringent latency requirements. In this paper, we present an experimental study aiming at performance evaluation of the access and mobility procedures based on an experimentation and data collection from the Monroe platform. We study the main causes of the delay induced to the access and mobility procedures, and evaluate machine learning based techniques to classify different procedures in terms of experienced delay and failure. Such results take step towards enabling the User Equipment (UE) to take appropriate actions for coping with predicted sub-optimal access or mobility procedures.

Bundling multiple access technologies increases capacity, resiliency and robustness of network connections. Multi-access is currently being standardized in the ATSSS framework in 3GPP, supporting different access bundling strategies. Within ATSSS, a multipath scheduler needs to decide which path to use for each user packet based on path characteristics. The Cheapest Path First (CPF) scheduler aims to utilize the cheapest path (e.g. WiFi) before sending packets over other paths (e.g. cellular). In this paper, we demonstrate that using CPF with an MP-DCCP tunnel may lead to sub-optimal performance. This is due to adverse interactions between the scheduler and end-to-end and tunnel congestion control. Hence, we design the Adaptive Cheapest Path First (ACPF) scheduler that limits queue buildup in the primary bottleneck and moves traffic to the secondary path earlier. We implement ACPF over both TCP and DCCP congestion controlled tunnels. Our evaluation shows that ACPF improves the average throughput over CPF between 24% to 86%.

"Roam like Home" is the initiative of the European Commission to end the levy of extra charges when roaming within the European region. As a result, people can use data services more freely across Europe. However, the implications of roaming solutions on network performance have not been carefully examined yet. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in 6 countries across Europe. Using this platform, we measure different aspects of international roaming in 4G networks in Europe, including mobile network configuration, performance characteristics, and quality of experience. We find that operators adopt a common approach to implement roaming called Home-routed roaming. This results in additional latency penalties of 60 ms or more, depending on geographical distance. This leads to worse browsing performance, with an increase in the metrics related to Quality of Experience (QoE) of users (Page Load time and Speed Index) in the order of 15-20%. We further analyze the impact of latency on QoE metrics in isolation and find that the penalty imposed by Home Routing leads to degradation on QoE metrics up to 150% in case of intercontinental roaming. We make our dataset public to allow reproducing the results.

Cloud-Radio Access Network (C-RAN) is a promising network architecture to reduce energy consumption and the increasing number of base station deployment costs in mobile networks. However, the necessity of enormous fronthaul bandwidth between a remote radio head and a baseband unit (BBU) calls for novel solutions. One of the solutions introduces the edge-cloud layer in addition to the centralized cloud (CC) to keep resources closer to the radio units (RUs). Then, split the BBU functions between the center cloud (CC) and edge clouds (ECs) to reduce the fronthaul bandwidth requirement and to relax the stringent end-to-end delay requirements. This paper expands this architecture by combining it with renewable energy sources in CC and ECs. We explain this novel system and formulate a mixed-integer linear programming (MILP) problem, which aims to reduce the operational expenditure of this system. Due to the NP-Hard property of this problem, we solve the smaller instances by using a MILP Solver and provide the results in this paper. Moreover, we propose a faster online heuristic to find solutions for high user densities. The results show that make splitting decisions by considering renewable energy provides more cost-effective solutions to mobile network operators (MNOs). Lastly, we provide an economic feasibility study for renewable energy sources in a CRAN architecture, which will encourage the MNOs to use these sources in this architecture., QC 20201224

A novel multi-band, dual-polarized, 28-port multiple-input multiple-output (MIMO) cube is presented. The cube structure enables dense placement of the antennas, whilst still allowing for low correlation between the individual antenna ports. The total size of the cube is roughly 2×2×7 λ30, where λ0 is the wavelength at the lowest used frequency. The novelty of the design lies in the use of a combination of orthogonally polarized endfire and broadside radiating antenna elements. Full use of all facets of the cube is achieved without the need to sacrifice bottom or top facets for mounting. Therefore, full azimuthal coverage for communication in two orthogonal polarizations is readily achieved. The system operates in both cellular and Wi-Fi 2:4, 5:5 GHz bands, with a targeted |S11| below –10 dB. The MIMO performance of the system is evaluated in two environments: the Rich Isotropic Multipath environment and the Random Line-of-Sight environment. These two scenarios represent the edge environments of any real-life propagation scenario in terms of the directional distribution of outgoing or incoming waves at a MIMO transceiver. The intended application of the system is micro base stations and repeaters, with possible extension to massive MIMO., QC 20200420

Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks. When it comes to supporting seamless connectivity in mobile scenarios, resource and handover management are two of the main challenges in mmWave networks. In this paper, we address these two problems jointly and propose a learning-based load balancing handover in multi-user mobile mmWave networks. Our handover algorithm selects a backup base station and allocates the resource to maximize the sum rate of all the users while ensuring a target rate threshold and preventing excessive handovers. We model the user association as a non-convex optimization problem. Then, by applying a deep deterministic policy gradient (DDPG) method, we approximate the solution of the optimization problem. Through simulations, we show that our proposed algorithm minimizes the number of the events where a user's rate is less than its minimum rate requirement and minimizes the number of handovers while increasing the sum rate of all users. © 2020 IEEE., QC 20210907

Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP5G Network Data Analytics (NWDA) architecture, and add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and find that it has much lower communication overhead than previous work, without affecting ML performance., QC 20210907

We address the problem of Remote Electrical Tilt (RET) optimization using off-policy Contextual Multi-Armed-Bandit (CMAB) techniques. The goal in RET optimization is to control the orientation of the vertical tilt angle of the antenna to optimize Key Performance Indicators (KPIs) representing the Quality of Service (QoS) perceived by the users in cellular networks. Learning an improved tilt update policy is hard. On the one hand, coming up with a new policy in an online manner in a real network requires exploring tilt updates that have never been used before, and is operationally too risky. On the other hand, devising this policy via simulations suffers from the simulation-to-reality gap. In this paper, we circumvent these issues by learning an improved policy in an offline manner using existing data collected on real networks. We formulate the problem of devising such a policy using the off-policy CMAB framework. We propose CMAB learning algorithms to extract optimal tilt update policies from the data. We train and evaluate these policies on real-world 4G Long Term Evolution (LTE) cellular network data. Our policies show consistent improvements over the rule-based logging policy used to collect the data., QC 20210505

Providing cellular voice onboard trains has proven to be a challenging task. The metallic chassis and the energysaving coating on the windows act as a radio shield. In combination with sparsely spaced cellular base stations on rural routes, the coverage onboard inter-city trains is under dimensioned. Since the late 1990s, analog 'amplify and forward' radio repeaters have been used extensively to provide coverage of cellular voice onboard trains. However, repeater systems have proven to be both expensive and technically problematic for both trains as well as mobile network operators. In this study, three alternative technologies for providing cellular voice onboard trains are investigated and benchmarked. The technologies under investigation are: circuit-switched voice on GSM and 3G; Voice over IP on LTE (VoLTE); Voice over the onboard Wi-Fi network (VoWiFi). To benchmark these systems against each other, measurements were conducted onboard Swedish X2000 trains, en-route between Stockholm and Gothenburg. In particular we measure: the Availability; the Retainability: Geographical Coverage and Voice quality. Our results show that VoWiFi over the onboard Wi-Fi network outperforms GSM, UMTS as well as VoLTE provided over the onboard repeaters, with equal voice quality as VoLTE but nearly 100% availability and nearly 100% retainability., QC 20210915

The performance of Global Navigation Satellite System (GNSS)-based positioning techniques degrades in tunnels, urban canyons and other areas, in which GNSS coverage is poor. Recent advances indicate that radio-based positioning techniques have the potential of complementing GNSS-based positioning services in such problematic areas. In this work, we propose to combine range and angle measurements routinely exercised by fifth generation of mobile communication (5G) base stations with acceleration measurements by vehicles to generate position estimates. Specifically, we propose to utilize an extended Kalman filter to combine the range, angle and acceleration measurements. The accuracy of this positioning system is studied in millimeter-wave 5G cellular networks. Specifically, the proposed positioning system using 5G and sensor fusion is tested in a highway scenario in which 5G base stations provide cellular coverage. In this setting, we study the impact of certain parameters of the 5G network such as the available bandwidth and the propagation environment on the positioning accuracy. We find that the positioning accuracy is largely affected by the number of antennas in the base station and that the proposed scheme outperforms GNSS based schemes in the problematic areas., Part of proceedings ISBN 9781728150895QC 20210917

Cyber-physical control applications impose strict requirements on the reliability and latency of the underlying communication system. Hence, they have been mostly implemented using wired channels where the communication service is highly predictable. Nevertheless, fulfilling such stringent demands is envisioned with the fifth generation of mobile networks (5G). The requirements of such applications are often defined on the application layer. However, cyber-physical control applications can usually tolerate sparse packet loss, and therefore it is not at all obvious what configurations and settings these application level requirements impose on the underlying wireless network. In this paper, we apply the fundamental metrics from reliability literature to wireless communications and derive a mapping function between application level requirements and network level parameters for those metrics under deterministic arrivals. Our mapping function enables network designers to realize the end-to-end performance (as the target application observes it). It provides insights to the network controller to either enable more reliability enhancement features (e.g., repetition), if the metrics are below requirements, or to enable features increasing network utilization, otherwise. We evaluate our theoretical results by realistic and detailed simulations of a factory automation scenario. Our simulation results confirm the viability of the theoretical framework under various burst error tolerance and load conditions., QC 20210427ISBN:nr 978-172814490-0