System Development for Geolocation in Harsh Environments

Wireless sensor networks (WSN) consist of a set of distributed devices equipped with multiple sensors, which can be employed in different environments of varying characteristics. Nowadays, node localization has become one of their most basic and important requirements. Due to the nature of certain environments, typical positioning systems, such as Global Navigation Satellite System (GNSS), cannot be employed. Therefore, in recent years several alternative positioning mechanisms have risen. ROMOVI is a project which has as its main goal the development of low cost autonomous robots capable of monitoring and perform logistic tasks on the steep slopes of the Douro river vineyards. Integrated in this project, this dissertation proposes the development of a full-custom wireless communication system for geolocation purposes in harsh environments. Using a Symmetric Double Sided Two Way Ranging (SDS-TWR) algorithm, it is possible to achieve ranging measures between nodes, thus providing accurate relative positioning. This work focuses mainly on the study of the SDS-TWR algorithm and its major error sources, such as those due to digital clock drift, among others. A preamble based on Frank-Zadoff-Chu sequence was developed and, due to its good periodic autocorrelation properties, a system employing the transmission and reception of this preamble was implemented in hardware, through a field programmable gate array (FPGA). By employing an embedded logic processor, the Altera Nios II, control over the complete procedure of the aforementioned algorithm is possible, to perform and analyze the main advantages of the SDS-TWR algorithm. Finally, a medium access control (MAC) layer frame format was defined, in order to enable future development of communication among multiple nodes, to enhance the original algorithm and, as such, provide the capability of trilateration

Using Sequence-to-Sequence Models for Carrier Frequency Offset Estimation of Short Messages and Chaotic Maps

Deep Learning methods have produced good carrier frequency offset estimations for short message sequences in comparison with methods based on the Fast Fourier Transform. However, these performance gains were observed for short ranges of frequency offsets, sequences with predefined pilot symbols and periodic modulation schemes. Chaotic modulation has an advantage over periodic signals in offering security through the continuous changes produced by parameterising the chaotic map function. However, synchronisation of chaotic map parameters in coherent receivers is dependent on the carrier recovery of phase and frequency which dramatically reduces the demodulation performance under high noise levels. This article presents a stacked sequence-to-sequence neural network architecture for blind carrier frequency offset estimation of both periodic and chaotic modulation schemes. The results obtained demonstrate better performance than conventional methods in low SNR for the Additive White Gaussian Noise channel. While this technique operates without feature engineering, the results demonstrate that data augmentation produces a higher degree of accuracy for such models, indicating the benefit of integration With conventional signal pre-processing steps as part of the deep learning pipeline. The proposed neural network architecture is shown to perform carrier frequency offset estimation, not only for the selected periodic modulations, but also in the case of highly non-linear chaotic maps. This suggests the applicability of deep learning methods for synchronisation in waveforms that employ chaotic modulation schemes for secure communication and for applications where short and sporadic messaging are required (e.g., Internet of Things)

DJI drone IDs are not encrypted

Drones are widely used in the energy, construction, agriculture, transportation, warehousing, real estate and movie industries. Key applications include surveys, inspections, deliveries and cinematography. With approximately 70-80% of the global market share of commercial off-the-shelf drones, Da-Jiang Innovations (DJI), headquartered in Shenzhen, China, essentially monopolizes the drone market. As commercial-off-the-shelf drone sales steadily rise, the Federal Aviation Administration has instituted regulations to protect the federal airspace. DJI has become a pioneer in developing remote identification technology in the form of drone ID (also known as AeroScope signals). Despite claims from the company touting its implementation of drone ID technology as "encrypted" yet later being proved incorrect for the claim, it remains a mystery on how one can grab and decode drone IDs over the air with low-cost radio frequency hardware in real-time. This research paper discusses a methodology using radio software and hardware to detect both Enhanced Wi-Fi and OcuSync drone IDs, the three types of drone ID packet structures and a functioning prototype of a DJI OcuSync detection system equipped with two HackRF Ones.Comment: 13 pages, 15 figures, 5 tables, 10 algorithm

AN OFDM platform for wireless systems testing: alamouti 2x1 MIMO example

In this paper, we present a real-time implementation of an OFDM hardware platform. The platform is based on HW blocks that can be put together to configure a wireless system based on OFDM modulation. The platform can be easily upgraded to test pre-coding cooperation algorithms. We evaluate the platform to implement a diversity Alamouti 2×1 MIMO scheme wireless system. The testbed is implemented using Field- Programmable Gate Array (FPGAs) through Xilinx System Generator for DSP. Blocks for time-domain synchronization and channel estimation are key components necessary in transmission system that require good time synchronization and channel estimation for efficient demodulation

Uplink data measurement and analysis for 5G eCPRI radio unit

Abstract. The new 5G mobile network generation aims to enhance the performance of the cellular network in almost every possible aspect, offering higher data rates, lower latencies, and massive number of network connections. Arguably the most important change from LTE are the new RU-BBU split options for 5G promoted by 3GPP and other organizations. Another big conceptual shift introduced with 5G is the open RAN concept, pushed forward by organizations such as the O-RAN alliance. O-RAN aims to standardize the interfaces between different RAN elements in a way that promotes vendor interoperability and lowers the entry barrier for new equipment suppliers. Moreover, the 7-2x split option standardized by O-RAN has risen as the most important option within the different low layer split options. As the fronthaul interface, O-RAN has selected the packet-based eCPRI protocol, which has been designed to be more flexible and dynamic in terms of transport network and data-rates compared to its predecessor CPRI. Due to being a new interface, tools to analyse data from this interface are lacking. In this thesis, a new, Python-based data analysis tool for UL eCPRI data was created for data quality validation purposes from any O-RAN 7-2x functional split based 5G eCPRI radio unit. The main goal for this was to provide concrete KPIs from captured data, including timing offset, signal power level and error vector magnitude. The tool produces visual and text-based outputs that can be used in both manual and automated testing. The tool has enhanced eCPRI UL datapath testing in radio unit integration teams by providing actual quality metrics and enabling test automation.Uplink datamittaukset ja -analyysi 5G eCPRI radiolla. Tiivistelmä. Uusi 5G mobiiliverkkogeneraatio tuo mukanaan parannuksia lähes kaikkiin mobiiliverkon ominaisuuksiin, tarjoten nopeamman datasiirron, pienemmät viiveet ja valtavat laiteverkostot. Luultavasti tärkein muutos LTE teknologiasta ovat 3GPP:n ja muiden organisaatioiden ehdottamat uudet radion ja systeemimoduulin väliset funktionaaliset jakovaihtoehdot. Toinen huomattava muutos 5G:ssä on O-RAN:in ajama avoimen RAN:in konsepti, jonka tarkoituksena on standardisoida verkkolaitteiden väliset rajapinnat niin, että RAN voidaan rakentaa eri valmistajien laitteista, laskien uusien laitevalmistajien kynnystä astua verkkolaitemarkkinoille. O-RAN:n standardisoima 7-2x funktionaalinen jako on noussut tärkeimmäksi alemman tason jakovaihtoehdoista. Fronthaul rajapinnan protokollaksi O-RAN on valinnut pakettitiedonsiirtoon perustuvan eCPRI:n, joka on suunniteltu dynaamisemmaksi ja joustavammaksi datanopeuksien ja lähetysverkon suhteen kuin edeltävä CPRI protokolla. Uutena protokollana, eCPRI rajapinnalle soveltuvia data-analyysityökaluja ei ole juurikaan saatavilla. Tässä työssä luotiin uusi pythonpohjainen data-analyysityökalu UL suunnan eCPRI datalle, jotta datan laatu voidaan määrittää millä tahansa O-RAN 7-2x funktionaaliseen jakoon perustuvalla 5G eCPRI radiolla. Työkalun päätarkoitus on analysoida ja kuvata datan laatua laskemalla datan ajoitusoffsettia, tehotasoa, sekä EVM:ää. Työkalu tuottaa tulokset visuaalisena ja tekstipohjaisena, jotta analyysia voidaan tehdä niin manuaalisessa kuin automaattisessa testauksessa. Työkalun käyttöönotto on tehostanut UL suunnan dataputken testausta radio-integrointitiimeissä, tarjoten datan laatua kuvaavaa metriikkaa sekä mahdollistaen testauksen automatisoinnin

Auto configuration dans LTE : procédés de mesure de l’occupation du canal radio pour une utilisation optimisée du spectre

Projecte final de carrera realitzat en col.laboració amb el centre INP Grenoble - ENSIMAG. École Nationale Supérieure d’Informatique et de Mathématiques Appliquées de Grenoble i Alcatel-Lucent Bell LabsLong Term Evolution (LTE) est la quatrième génération de technologies radio qui est conçue afin de fournir des débits de données élevés aux mobiles, offrir une faible latence et permettre une flexibilité accrue dans l'attribution du spectre de fréquence. Les techniques de réutilisation du spectre permettent ainsi de faire face à la demande croissante en bande passante des utilisateurs. Nous nous concentrons sur le cas où toutes les cellules partagent la même bande de fréquence (frequency reuse-1). Ces cellules ainsi déployées peuvent générer des niveaux d’interférence intra-canal importants, ce qui affecte considérablement les performances du réseau. Le but de ce stage est de développer des méthodes de sensing du spectre permettant de caractériser les cellules qui partagent les mêmes ressources radio. En utilisant des informations telles que nombre de cellules en compétition notamment, les mécanismes d’allocation des ressources radio peuvent être optimisés, améliorent ainsi la performance du réseau. Les méthodes ainsi étudiées exploitent les propriétés d’orthogonalité des canaux de contrôle tels que signaux de synchronisation diffusés par chaque station de base. Une première étape du stage a ainsi consisté à mettre en place des méthodes de synchronisation fiables en ‘frequency reuse-1’ et d’en étudier les performances. Au cours de la deuxième partie du stage, une méthode d’identification du nombre de cellules en compétition sur un même canal est proposée. Cette méthode repose sur l’utilisation des canaux de synchronisation. Le stage a lieu sur le site de Villarceaux d’ Alcatel-Lucent Bell Labs et s’est intégré aux projets de recherche sur l'auto-configuration des cellules dans un réseau LTE. Ce rapport présente les travaux réalisés pendant le stage. Celui-ci s’est concentré sur la procédure réalisée par les mobiles afin de se synchroniser au réseau. Dans cette optique,nous avons proposé une méthode pour trouver le nombre des cellules en compétition, afin de caractériser l'occupation du spectre