arXiv subjects

Sofie Pollin

Publications and source records attributed to Sofie Pollin.

At least 127 records · Page 7Linked to original sources

arXiv2019

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Trade-Offs

Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this work, we introduce a novel received signal strength indicator (RSSI) based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPS-enabled sensors (GSNs) in the network to split the wide coverage into classes, enabling RSSI based fingerprinting of other sensors (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.

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arXiv2019

Towards Efficient BLE Mesh: Design of an Autonomous Network Joining Algorithm

The Internet of Things (IoT) opens the doors to a digital revolution, but requires a robust protocol to wirelessly interconnect a large number of devices. Bluetooth Low Energy (BLE) Mesh emerges as a suitable candidate, solving the range limitations of original BLE. Nevertheless, the main limitation is shifted to the scatternet architecture: how the considerable number of end systems are interconnected to ensure the network efficiency and scalability. As of today, some timid solutions have emerged, though the most relevant parameters for the best parent selection in BLE mesh network joining procedures have not been identified yet. In this work, we perform a thorough exploration of different device and environmental parameters in a BLE mesh testbed to analyze their impact in the overall network figures of merit, such as end-to-end delay and packet delivery ratio (PDR). According to the inferred relevance, the second part of the work implements and measures the proposed parent selection algorithm. The implementation is based on the open source Fruitymesh protocol and is used as a baseline. The results reflect an enhancement in the network scalability and fairness, accomplishing a delay and PDR improvement of 26% and 10% respectively, and the avoidance of saturated branches of 24%.

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arXiv2019

Cellular Coverage-Aware Path Planning for UAVs

Up until now, path planning for unmanned aerial vehicles (UAVs) has mainly been focused on the optimisation towards energy efficiency. However, to operate UAVs safely, wireless coverage is of utmost importance. Currently, deployed cellular networks often exhibit an inadequate performance for aerial users due to high amounts of intercell interference. Furthermore, taking the never-ending trend of densification into account, the level of interference experienced by UAVs will only increase in the future. For the purpose of UAV trajectory planning, wireless coverage should be taken into account to mitigate interference and to lower the risk of dangerous connectivity outages. In this paper, several path planning strategies are proposed and evaluated to optimise wireless coverage for UAVs. A simulator using a real-life 3D map is used to evaluate the proposed algorithms for both 4G and 5G scenarios. We show that the proposed Coverage-Aware A* algorithm, which alters the UAV's flying altitude, is able to improve the mean SINR by 3-4dB and lower the cellular outage probability by a factor of 10. Furthermore, the outages that still occur have a 60% shorter length, hence posing a lower risk to induce harmful accidents.

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arXiv2019

Cellular UAV-to-UAV Communications

Reliable and direct communication between unmanned aerial vehicles (UAVs) could facilitate autonomous flight, collision avoidance, and cooperation in UAV swarms. In this paper, we consider UAV-to-UAV (U2U) communications underlaying a cellular network, where UAV transmit-receive pairs share the same spectrum with the uplink (UL) of cellular ground users (GUEs). We evaluate the performance of this setup through an analytical framework that embraces realistic height-dependent channel models, antenna patterns, and practical power control mechanisms. Our results demonstrate that, although the presence of U2U communications may worsen the performance of the GUEs, such effect is limited as base stations receive UAV interference through their antenna sidelobes. Moreover, we illustrate that the quality of all links degrades as the UAV height increases---due to a larger number of line-of-sight interferers---, and how the performance of the U2U links can be traded off against that of the GUEs by varying the UAV power control policy.

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arXiv2019

Crowdsourced wireless spectrum anomaly detection

Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming impossible because of the large number of measurement locations and complexity of the spectrum use landscape. Detecting unexpected behaviors in the wireless spectrum from the collected data is a crucial part of this automated monitoring, and the control of detected anomalies is a key functionality to enable interaction between the automated system and the end user. In this paper we look into the wireless spectrum anomaly detection problem for crowdsourced sensors. We first analyze in detail the nature of these anomalies and design effective algorithms to bring the higher dimensional input data to a common feature space across sensors. Anomalies can then be detected as outliers in this feature space. In addition, we investigate the importance of user feedback in the anomaly detection process to improve the performance of unsupervised anomaly detection. Furthermore, schemes for generalizing user feedback across sensors are also developed to close the anomaly detection loop.

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arXiv2019

SDN On BLE: Controlling Resource Constrained Mesh Networks

Wireless connectivity for the Internet of Things (IoT) requires combining low latency and high reliability with power and cost constraints. On top of that, diverse applications and end-to-end services with very different performance requirements should ideally share the same network infrastructure. Software defined networks (SDN) were defined to allow this coexistence, but they have seen very limited adoption in IoT systems due to the inherent resource constrained nature of the wireless devices. Additionally, the low-power nature of IoT communication standards does not usually allow for physically separated control and data channels, which is a basic requirement for real-time network slicing. In this work, we propose a SDN implementation for Bluetooth Low Energy (BLE), which has become the de-facto technology for IoT applications. The proposed BLE mesh structure is able to use different protocols for different services, ensuring a clear separation between control and data channels while still being sent over the wireless medium. A proof-of-concept for the proposed SDN implementation is given in a real BLE mesh testbed, where measurements show how the system is able to automatically detect and recover from network congestion by identifying the nodes responsible of such situation and reconfiguring their parameters over the air.

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arXiv2019

Tutorial on UAV: A Blue Sky View on Wireless Communication

The growing use of Unmanned Aerial Vehicles (UAVs) for various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground terminals. Depending on the application, UAV-mounted wireless equipment can either be an aerial user equipment (AUE) that co-exists with the terrestrial users, or it can be a part of wireless infrastructure providing a range of services to the ground users. For instance, AUE can be used for real-time search and rescue and Aerial Base Station (ABS) can enhance coverage, capacity and energy efficiency of wireless networks. In both cases, UAV-based solutions are scalable, mobile, fast to deploy. However, several technical challenges have to be addressed. In this work, we present a tutorial on wireless communication with UAVs, taking into account a wide range of potential applications. The main goal of this work is to provide a complete overview of the main scenarios (AUE and ABS), channel and performance models, compare them, and discuss open research points. This work gives a comprehensive overview of the research done until now and depicts a comprehensive picture to foster new ideas and solutions while avoiding duplication of past work. We start by discussing the open challenges of wireless communication with UAVs. To give answers to the posed questions, we focus on the UAV communication basics, mainly providing the necessary channel modeling background and giving guidelines on how various channel models should be used. Next, theoretical, simulation- and measurement-based approaches, to address the key challenges for AUE usage, are presented. Moreover, in this work, we aim to provide a comprehensive overview on how UAV-mounted equipment can be used as a part of a communication network. Based on the theoretical analysis, we show how various network parameters can be optimized.

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arXiv2018

A multi-layered energy consumption model for smart wireless acoustic sensor networks

Smart sensing is expected to become a pervasive technology in smart cities and environments of the near future. These services are improving their capabilities due to integrated devices shrinking in size while maintaining their computational power, which can run diverse Machine Learning algorithms and achieve high performance in various data-processing tasks. One attractive sensor modality to be used for smart sensing are acoustic sensors, which can convey highly informative data while keeping a moderate energy consumption. Unfortunately, the energy budget of current wireless sensor networks is usually not enough to support the requirements of standard microphones. Therefore, energy efficiency needs to be increased at all layers --- sensing, signal processing and communication --- in order to bring wireless smart acoustic sensors into the market. To help to attain this goal, this paper introduces WASN-EM: an energy consumption model for wireless acoustic sensors networks (WASN), whose aim is to aid in the development of novel techniques to increase the energy-efficient of smart wireless acoustic sensors. This model provides a first step of exploration prior to custom design of a smart wireless acoustic sensor, and also can be used to compare the energy consumption of different protocols.

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arXiv2018

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features

Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data which achieves good anomaly detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semisupervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.

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arXiv2018

Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors

This paper looks into the technology classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for Automatic Modulation Classification (AMC) based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying SNR conditions ranging from 0dB to 20dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75% on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data, or online classification on the low cost sensors is studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.

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arXiv2018

Energy-Constrained UAV Trajectory Design for Ground Node Localization

The use of aerial anchors for localizing terrestrial nodes has recently been recognized as a cost-effective, swift and flexible solution for better localization accuracy, providing localization services when the GPS is jammed or satellite reception is not possible. In this paper, the localization of terrestrial nodes when using mobile unmanned aerial vehicles (UAVs) as aerial anchors is presented. We propose a novel framework to derive localization error in urban areas. In contrast to the existing works, our framework includes height-dependent UAV to ground channel characteristics and a highly detailed UAV energy consumption model. This enables us to explore different tradeoffs and optimize UAV trajectory for minimum localization error. In particular, we investigate the impact of UAV altitude, hovering time, number of waypoints and path length through formulating an energy-constrained optimization problem. Our results show that increasing the hovering time decreases the localization error considerably at the cost of a higher energy consumption. To keep the localization error below 100m, shorter hovering is only possible when the path altitude and radius are optimized. For a constant hovering time of 5 seconds, tuning both parameters to their optimal values brings the localization error from 150m down to 65m with a power saving around 25%

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arXiv2018

Electrosense: Open and Big Spectrum Data

While the radio spectrum allocation is well regulated, there is little knowledge about its actual utilization over time and space. This limitation hinders taking effective actions in various applications including cognitive radios, electrosmog monitoring, and law enforcement. We introduce Electrosense, an initiative that seeks a more efficient, safe and reliable monitoring of the electromagnetic space by improving the accessibility of spectrum data for the general public. A collaborative spectrum monitoring network is designed that monitors the spectrum at large scale with low-cost spectrum sensing nodes. The large set of data is stored and processed in a big data architecture and provided back to the community with an open spectrum data as a service model, that allows users to build diverse and novel applications with different requirements. We illustrate useful usage scenarios of the Electrosense data.

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arXiv2018

Cellular Connectivity for UAVs: Network Modeling, Performance Analysis and Design Guidelines

The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity, and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased co-channel interference received from neighboring ground BSs is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 meters can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase their coverage and throughput from 23% to 89% and from 3.5 b/s/Hz to 5.8 b/s/Hz, respectively, outperforming ground UEs. Furthermore, our findings reveal that depending on UAV altitude, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of micro cells limit UAV performance. While UAV usage has the potential to increase area spectral efficiency (ASE) of cellular networks with moderate number of cells, they might hamper the development of future ultra dense networks.

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arXiv2018

Multi-User Frequency-Selective Hybrid MIMO Demonstrated Using 60 GHz RF Modules

Given the high throughput requirement for 5G, merging millimeter wave technologies and multi-user MIMO seems a very promising strategy. As hardware limitations impede to realize a full digital architecture, hybrid MIMO architectures, using digital precoding and phased antenna arrays, are considered a feasible solution to implement multi-user MIMO at millimeter wave. However, real channel propagation and hardware non-idealities can significantly degrade the performance of such systems. Experimenting the new architecture is thus crucial to confirm and to support system design. Nevertheless, hybrid MIMO systems are not yet understood as the effects of the wide channel bandwidths at millimeter wave, the non-ideal RF front end as well as the imperfections of the analog beamforming are often neglected. In this paper, we present a 60 GHz MU-MIMO testbed using phased antenna arrays at both transmitter and receiver. The base station equipped with a 32 phased antenna array allocates simultaneously two users. We show that frequency selective hybrid precoding can efficiently suppress inter-user interference enabling spatial multiplexing in interference limited scenario doubling the throughput compared to a SISO scenario and compensating the frequency fluctuation of the channel. In addition, we report an EVM constellation improvement of 6 dB when comparing the hybrid MIMO architecture with a fully analog architecture.

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arXiv2018

Finite Large Antenna Arrays for Massive MIMO: Characterization and System Impact

Massive MIMO is considered a key technology for 5G. Various studies analyze the impact of the number of antennas, relying on channel properties only and assuming uniform antenna gains in very large arrays. In this paper, we investigate the impact of mutual coupling and edge effects on the gain pattern variation in the array. Our analysis focuses on the comparison of patch antennas versus dipoles, representative for the antennas typically used in massive MIMO experiments today. Through simulations and measurements, we show that the finite patch array has a lower gain pattern variation compared with a dipole array. The impact of a large gain pattern variation on the massive MIMO system is that not all antennas contribute equally for all users, and the effective number of antennas seen for a single user is reduced. We show that the effect of this at system level is a decreased rate for all users for the zero-forcing MIMO detector, up to 20% for the patch array and 35% for the dipole array. The maximum ratio combining on the other hand, introduces user unfairness.

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arXiv2018

Reshaping Cellular Networks for the Sky: Major Factors and Feasibility

This paper studies the feasibility of supporting drone operations using existent cellular infrastructure. We propose an analytical framework that includes the effects of base station (BS) height and antenna radiation pattern, drone antenna directivity and various propagation environments. With this framework, we derive an exact expression for the coverage probability of ground and drone users through a practical cell association strategy. Our results show that a carefully designed network can control the radiated interference that is received by the drones, and therefore guarantees a satisfactory quality of service. Moreover, as the network density grows the increasing level of interference can be partially managed by lowering the drone flying altitude. However, even at optimal conditions the drone coverage performance converges to zero considerably fast, suggesting that ultra-dense networks might be poor candidates for serving aerial users.

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arXiv2017

Aerial Anchors Positioning for Reliable RSS-Based Outdoor Localization in Urban Environments

In this letter, the localization of terrestrial nodes when unmanned aerial vehicles (UAVs) are used as base stations is investigated. Particularly, a novel localization scenario based on received signal strength (RSS) from terrestrial nodes is introduced. In contrast to the existing literature, our analysis includes height-dependent path loss exponent and shadowing which results in an optimum UAV altitude for minimum localization error. Furthermore, the Cramer-Rao lower bound is derived for the estimated distance which emphasizes, analytically, the existence of an optimal UAV altitude. Our simulation results show that the localization error is decreased from over 300m when using ground-based anchors to 80m when using UAVs flying at the optimal altitude in an urban scenario.

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arXiv2017

Key Technologies and System Trade-Offs for Detection and Localization of Amateur Drones

The use of amateur drones (ADrs) is expected to significantly increase over the upcoming years. However, regulations do not allow such drones to fly over all areas, in addition to typical altitude limitations. As a result, there is an urgent need for ADrs surveillance solutions. These solutions should include means of accurate detection, classification, and localization of the unwanted drones in a no-fly zone. In this paper, we give an overview of promising techniques for modulation classification and signal strength based localization of ADrs by using surveillance drones (SDrs). By introducing a generic altitude dependent propagation model, we show how detection and localization performance depend on the altitude of SDrs. Particularly, our simulation results show a 25 dB reduction in the minimum detectable power or 10 times coverage enhancement of an SDr by flying at the optimum altitude. Moreover, for a target no-fly zone, the location estimation error of an ADr can be remarkably reduced by optimizing the positions of the SDrs. Finally, we conclude the paper with a general discussion about the future work and possible challenges of the aerial surveillance systems.

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