arXiv subjects

Jimmy Jessen Nielsen

Publications and source records attributed to Jimmy Jessen Nielsen.

17 recordsLinked to original sources

arXiv2026

Statistical Characterization and Prediction of E2E Latency over LEO Satellite Networks

Low Earth Orbit (LEO) satellite networks are emerging as an essential communication infrastructure, with standardized 5G-based non-terrestrial networks and their integration with terrestrial systems envisioned as a key feature of 6G. However, current LEO systems still exhibit significant latency variations, limiting their suitability for latency-sensitive services. We present a detailed statistical analysis of end-to-end latency based on 500Hz experimental bidirectional one-way measurements and introduce a segmentation of the deterministic 15-second periodic behavior observed in Starlink. We characterize handover-induced boundary regions that produce latency spikes lasting approximately 140 ms at the beginning and 75 ms at the end of each cycle, followed by a stable intra-period regime, enabling accurate short-term prediction. This analysis shows that latency prediction based on long-term statistics leads to pessimistic estimates. In contrast, by exploiting the periodic structure, isolating boundary regions, and applying lightweight parametric and non-parametric models to intra-period latency distributions, we achieve 99th-percentile latency prediction errors below 50 ms. Furthermore, period-level latency prediction and classification enable adaptive transmission strategies by identifying upcoming periods where application latency requirements cannot be satisfied, necessitating the use of alternative systems.

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arXiv2026

Experimental Study of Low-Latency Video Streaming in an ORAN Setup with Generative AI

Current Adaptive Bit Rate (ABR) methods react to network congestion after it occurs, causing application layer buffering and latency spikes in live video streaming. We introduce a proactive semantic control channel that enables coordination between Open Radio Access Network (ORAN) xApp, Mobile Edge computing (MEC), and User Equipment (UE) components for seamless live video streaming between mobile devices. When the transmitting UE experiences poor Uplink (UL) conditions, the MEC proactively instructs downscaling based on low-level RAN metrics, including channel SNR updated every millisecond, preventing buffering before it occurs. A Generative AI (GAI) module at the MEC reconstructs high-quality frames from downscaled video before forwarding to the receiving UE via the typically more robust Downlink (DL). Experimental validation on a live ORAN testbed with 50 video streams shows that our approach reduces latency tail behavior while achieving up to 4 dB improvement in PSNR and 15 points in VMAF compared to reactive ABR methods. The proactive control eliminates latency spikes exceeding 600 ms, demonstrating effective cross-layer coordination for latency-critical live video streaming.

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arXiv2024

Near Real-Time Data-Driven Control of Virtual Reality Traffic in Open Radio Access Network

In mobile networks, Open Radio Access Network (ORAN) provides a framework for implementing network slicing that interacts with the resources at the lower layers. Both monitoring and Radio Access Network (RAN) control is feasible for both 4G and 5G systems. In this work, we consider how data-driven resource allocation in a 4G context can enable adaptive slice allocation to steer the experienced latency of Virtual Reality (VR) traffic towards a requested latency. We develop an xApp for the near real-time RAN Intelligent Controller (RIC) that embeds a heuristic algorithm for latency control, aiming to: (1) maintain latency of a VR stream around a requested value; and (2) improve the available RAN allocation to offer higher bit rate to another user. We have experimentally demonstrated the proposed approach in an ORAN testbed. Our results show that the data-driven approach can dynamically follow the variation of the traffic load while satisfying the required latency. This results in 15.8% more resources to secondary users than a latency-equivalent static allocation.

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arXiv2022

Sustainable Wireless Services with UAV Swarms Tailored to Renewable Energy Sources

Unmanned Aerial Vehicle (UAV) swarms are often required in off-grid scenarios, such as disaster-struck, war-torn or rural areas, where the UAVs have no access to the power grid and instead rely on renewable energy. Considering a main battery fed from two renewable sources, wind and solar, we scale such a system based on the financial budget, environmental characteristics, and seasonal variations. Interestingly, the source of energy is correlated with the energy expenditure of the UAVs, since strong winds cause UAV hovering to become increasingly energy-hungry. The aim is to maximize the cost efficiency of coverage at a particular location, which is a combinatorial optimization problem for dimensioning of the multivariate energy generation system under non-convex criteria. We have devised a customized algorithm by lowering the processing complexity and reducing the solution space through sampling. Evaluation is done with condensed real-world data on wind, solar energy, and traffic load per unit area, driven by vendor-provided prices. The implementation was tested in four locations, with varying wind or solar intensity. The best results were achieved in locations with mild wind presence and strong solar irradiation, while locations with strong winds and low solar intensity require higher Capital Expenditure (CAPEX) allocation.

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arXiv2022

Enabling On-Demand Cyber-Physical Control Applications with UAV Access Points

Achieving cyber-physical control over a wireless channel requires satisfying both the timeliness of a single packet and preserving the latency reliability across several consecutive packets. To satisfy those requirements as an ubiquitous service requires big infrastructural developments, or flexible on-demand equipment such as UAVs. To avoid the upfront cost in terms of finance and energy, this paper analyzes the capability of UAV access points (UAVAPs) to satisfy the requirements for cyber-physical traffic. To investigate this, we perform a Gilbert-Eliott burst-error analysis that is analytically derived as a combination of two separate latency measurement campaigns and provide an upper-bound analysis of the UAVAP system. The analysis is centered around a UAVAP that uses its LTE connection to reach the backhaul, while providing service to ground nodes (GNs) with a Wi-Fi access point (AP). Thus, we combine both measurement campaigns to analyze the plausibility of the described setup in casual, crowded or mixed network settings.

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arXiv2022

Fairness Based Energy-Efficient 3D Path Planning of a Portable Access Point: A Deep Reinforcement Learning Approach

In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for the battery of the UAV. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the energy efficiency of the PAP. The formulated problem takes the form of a non-convex problem with non-tractable constraints. To obtain a solution, we represent the problem as a Markov Decision Process (MDP) with continuous state and action spaces. Considering the complexity of the solution space, we use the twin delayed deep deterministic policy gradient (TD3) actor-critic deep reinforcement learning (DRL) framework to learn a policy that maximizes the FEE of the system. We perform two types of RL training to exhibit the effectiveness of our approach: the first (offline) approach keeps the positions of the GNs the same throughout the training phase; the second approach generalizes the learned policy to any arrangement of GNs by changing the positions of GNs after each training episode. Numerical evaluations show that neglecting the Peukert effect overestimates the air-time of the PAP and can be addressed by optimally selecting the PAP's flying speed. Moreover, the user fairness, energy efficiency, and hence the FEE value of the system can be improved by efficiently moving the PAP above the GNs. As such, we notice massive FEE improvements over baseline scenarios of up to 88.31%, 272.34%, and 318.13% for suburban, urban, and dense urban environments, respectively.

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arXiv2022

Modelling and Experimental Validation for Battery Lifetime Estimation in NB-IoT and LTE-M

Internet of Things (IoT) is one of the main features in 5G. Low-power wide-area networking (LPWAN) has attracted enormous research interests to enable large scale deployment of IoT, with the design objectives of low cost, wide coverage area, as well as low power consumption. In particular, long battery lifetime is essential since many of the IoT devices will be deployed in hard-to-access locations. Prediction of the battery lifetime depends on the accurate modelling of energy consumption. This paper presents a comprehensive power consumption model for battery lifetime estimation, which is based on User Equipment(UE) states and procedures, for two cellular IoT technologies: Narrowband Internet of Things (NB-IoT) and Long Term Evolution for Machines (LTE-M). A measurement testbed has been setup and the proposed model has been tested and validated via extensive measurements under various traffic patterns and network scenarios, achieving the modelling inaccuracy within5%. The measurement results show that the battery lifetime of an IoT device can reach up to 10 years as required by 3GPP, with proper configuration of the traffic profile, the coverage scenario, as well as the network configuration parameters.

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arXiv2021

Performance trade-offs in cyber-physical control applications with multi-connectivity

Modern communication devices are often equipped with multiple wireless communication interfaces with diverse characteristics. This enables exploiting a form of multi-connectivity known as interface diversity to provide path diversity with multiple communication interfaces. Interface diversity helps to combat the problems suffered by single-interface systems due to error bursts in the link, which are a consequence of temporal correlation in the wireless channel. The length of an error burst is an essential performance indicator for cyber-physical control applications with periodic traffic, as these define the period in which the control link is unavailable. However, the available interfaces must be correctly orchestrated to achieve an adequate trade-off between latency, reliability, and energy consumption. This work investigates how the packet error statistics from different interfaces impacts the overall latency-reliability characteristics and explores mechanisms to derive adequate interface diversity policies. For this, we model the optimization problem as a partially observable Markov Decision Process (POMDP), where the state of each interface is determined by a Gilbert-Elliott model whose parameters are estimated based on experimental measurement traces from LTE and Wi-Fi. Our results show that the POMDP approach provides an all-round adaptable solution, whose performance is only 0.1% below the absolute upper bound, dictated by the optimal policy under the impractical assumption of full observability.

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arXiv2021

On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator

In this paper we envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles, through a drone traffic monitor (DTM), that also acts as an orchestrator. Expecting non-IID data distribution, we focus on the issue of accelerating the learning of a particular class of critical object (CO), that may harm the nominal operation of an autonomous vehicle. This can be done through proper allocation of the wireless resources for addressing learner and data heterogeneity. Thus, we propose a reactive method for the allocation of wireless resources, that happens dynamically each FL round, and is based on each learner's contribution to the general model. In addition to this, we explore the use of static methods that remain constant across all rounds. Since we expect partial work from each learner, we use the FedProx FL algorithm, in the task of computer vision. For testing, we construct a non-IID data distribution of the MNIST and FMNIST datasets among four types of learners, in scenarios that represent the quickly changing environment. The results show that proactive measures are effective and versatile at improving system accuracy, and quickly learning the CO class when underrepresented in the network. Furthermore, the experiments show a tradeoff between FedProx intensity and resource allocation efforts. Nonetheless, a well adjusted FedProx local optimizer allows for an even better overall accuracy, particularly when using deeper neural network (NN) implementations.

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arXiv2021

An Experimental Analysis on Drone-Mounted Access Points for Improved Latency-Reliability

The anticipated densification of contemporary communications infrastructure expects the use of drone small cells (DSCs). Thus, we experimentally evaluate the capability of providing local and personalized coverage with a drone mounted Wi-Fi access point that uses the nearby LTE infrastructure as a backhaul in areas with mixed line of sight(LoS) and Non-LoS (NLoS) links to the local cellular infrastructure. To assess the potential of DSCs for reliable and low latency communication of outdoor users, we measure the channel quality and the total round trip latency of the system. For a drone following the ground user, the DSC-provided network extends the coverage for an extra 6.4% when compared to the classical LTE-direct link. Moreover, the DSC setup provides latencies that are consistently smaller than 50 msfor 95% of the experiment. Within the coverage of the LTE-direct connection, we observed a latency ceiling of 120ms for 95% reliability of the LTE-direct connection. The highest latency observed for the DSC system was 1200ms, while the LTE-direct link never exceeded 500 ms. As such, DSC setups are not only essential in NLoS situations, but consistently improve the latency of users in outdoor scenarios.

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arXiv2021

Standalone Deployment of a Dynamic Drone Cell for Wireless Connectivity of Two Services

We treat a setting in which two priority wireless service classes are offered in a given area by a drone small cell (DSC). Specifically, we consider broadband (BB) user with high priority and reliability requirements that coexists with random access machine-type-communications (MTC) devices. The drone serves both connectivity types with a combination of orthogonal slicing of the wireless resources and dynamic horizontal opportunistic positioning (D-HOP). We treat the D-HOP as a computational geometry function over stochastic BB user locations which requires careful adjustment in the deployment parameters to ensure MTC service at all times. Using an information theoretic approach, we optimize DSC deployment properties and radio resource allocation for the purpose of maximizing the average rate of BB users. While respecting the strict dual service requirements we analyze how system performance is affected by stochastic user positioning and density, topology, and reliability constraints combinations. The numerical results show that this approach outperforms static DSCs that fit the same coverage constraints, with outstanding performance in the urban setting.

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arXiv2020

Queuing with Deterministic Service Times and No Waiting Lines in Machine Type Communications

The growth of Machine-Type Communication (MTC) increases the relevance of queuing scenarios with deterministic service times. In this letter, we present a model for queues without waiting lines and with degenerate service time distributions and show how the framework is extendable to model general service time distributions. Simple bounds and a close approximation of the blocking probability are derived and the results are shown to hold for simulated queues with Markovian and degenerate arrival processes.

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arXiv2020

Dynamic Standalone Drone-Mounted Small Cells

This paper investigates the feasibility of Dynamic Horizontal Opportunistic Positioning (D-HOP) use in Drone Small Cells (DSCs), with a central analysis on the impact of antenna equipment efficiency onto the optimal DSC altitude that has been chosen in favor of maximizing coverage. We extend the common urban propagation model of an isotropic antenna to account for a directional antenna, making it dependent on the antenna's ability to fit the ideal propagation pattern. This leads us to define a closed-form expression for calculating the Rate improvement of D-HOP implementations that maintain constant coverage through antenna tilting. Assuming full knowledge of the uniformly distributed active users' locations, three D-HOP techniques were tested: in the center of the Smallest Bounding Circle (SBC); the point of Maximum Aggregated Rate (MAR); and the Center-Most Point (CMP) out of the two aforementioned. Through analytic study and simulation we infer that DSC D-HOP implementations are feasible when using electrically small and tiltable antennas. Nonetheless, it is possible to achieve average per user average rate increases of up to 20-35% in low user density scenarios, or 3-5% in user-dense scenarios, even when using efficient antennas in a DSC that has been designed for standalone coverage.

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arXiv2020

Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks

One of the central problems in massive Internet of Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermittency can be disguised as a link failure and vice versa. In this work we present a traffic model for IoT devices running quasi-periodic applications and we present both supervised and unsupervised machine learning methods for monitoring the network performance of IoT deployments with quasi-periodic reporting, such as smart-metering, environmental monitoring and agricultural monitoring. The unsupervised methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series.

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arXiv2019

Analysis of LoRaWAN Uplink with Multiple Demodulating Paths and Capture Effect

Low power wide area networks (LPWANs), such as the ones based on the LoRaWAN protocol, are seen as enablers of large number of IoT applications and services. In this work, we assess the scalability of LoRaWAN by analyzing the frame success probability (FSP) of a LoRa frame while taking into account the capture effect and the number of parallel demodulation paths of the receiving gateway. We have based our model on the commonly used {SX1301 gateway chipset}, which is capable of demodulating {up to} eight frames simultaneously; however, the results of the model can be generalized to architectures with arbitrary number of demodulation paths. We have also introduced and investigated {three} policies for Spreading Factor (SF) allocation. Each policy is evaluated in terms of coverage {probability}, {FSP}, and {throughput}. The overall conclusion is that the presence of multiple demodulation paths introduces a significant change in the analysis and performance of the LoRa random access schemes.

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arXiv2017

Network Slicing for Ultra-Reliable Low Latency Communication in Industry 4.0 Scenarios

An important novelty of 5G is its role in transforming the industrial production into Industry 4.0. Specifically, Ultra-Reliable Low Latency Communications (URLLC) will, in many cases, enable replacement of cables with wireless connections and bring freedom in designing and operating interconnected machines, robots, and devices. However, not all industrial links will be of URLLC type; e.g. some applications will require high data rates. Furthermore, these industrial networks will be highly heterogeneous, featuring various communication technologies. We consider network slicing as a mechanism to handle the diverse set of requirements to the network. We present methods for slicing deterministic and packet-switched industrial communication protocols at an abstraction level that is decoupled from the specific implementation of the underlying technologies. Finally, we show how network calculus can be used to assess the end-to-end properties of the network slices.

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arXiv2016

Estimation of Received Signal Strength Distribution for Smart Meters with Biased Measurement Data Set

This letter presents an experimental study and a novel modelling approach of the wireless channel of smart utility meters placed in basements or sculleries. The experimental data consist of signal strength measurements of consumption report packets. Since such packets are only registered if they can be decoded by the receiver, the part of the signal strength distribution that falls below the receiver sensitivity threshold is not observable. We combine a Rician fading model with a bias function that captures the cut-off in the observed signal strength measurements. Two sets of experimental data are analysed. It is shown that the proposed method offers an approximation of the distribution of the signal strength measurements that is better than a naïve Rician fitting.

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