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The development of mobile communication technologies beyond 5G tries to achieve strict requirements, such as ultra-high data rates, reduced latency, high reliability, and extensive coverage. A rising technology is Reconfigurable Intelligent Surfaces (RIS), which can ma- nipulate electromagnetic waves to improve signal quality without the need for additional Base Stations (BSs). This thesis studies the feedback control signaling required for op- timal RIS configuration, focusing on minimizing the signaling overhead while maximizing Signal-to-Noise Ratio (SNR). Two methods are explored: Phase-Shift Quantization and Codebook-Based methods. The Phase-Shift Quantization method studies the impact of discrete phase shifts on each of the elements of the RIS on SNR, demonstrating that with increased resolution, the SNR performance improves. On the other hand, the Codebook-Based method predefines a set of configurations of the RIS, known as the codebook, to reduce the feedback overhead. An optimized version of the Codebook method is also developed, which takes into account a typical position of BS and User Equipment (UE) in mobile networks, reducing the codebook size and improving efficiency. The Rician fading model is also studied in the thesis, to simulate more realistic urban com- munication environments, comparing its impact on SNR with the traditional Line-of-Sight (LoS) scenario. While the presence of multipath components in Rician fading scenarios increases the dispersion in the SNR values, the RIS still performs sufficiently well. The results show the importance of making control signaling as efficient as possible to achieve an optimal balance between maximizing SNR and minimizing resource usage, which is the trade-off studied in this thesis., Outgoing

The purpose of this project is to conduct a detailed evaluation of the coverage and quality of service of a 5G network in an urban environment at various heights, using raytracing-based simulations. The realistic deployment of 5G technology has been carried out in the city of Barcelona, particularly in the Eixample district. MATLAB will be used to create a model that simulates the network's performance for both ground-level and elevated users, which will serve as a reference for other studies. The simulations will be highly detailed, incorporating propagation calculations that account for reflections and diffractions, and will require code optimization to speed up the process. The project proposes incorporating beamforming technology to overcome the limitations of current deployments. This advanced technique adjusts and directs the power of signals precisely, providing the three-dimensional coverage necessary to keep both ground and aerial users connected. The study will develop in two distinct phases. The first phase will use a trisectorization strategy, to use this scenario to provide comparative data for the second phase, which will introduce beamforming technology. This second phase will include simulations using three different antenna geometries. Defining this first scenario requires careful network planning. This planning will include determining the positions of the base stations to adequately cover the study area. Planning of the radiation diagrams for the different antenna geometries will also be conducted, taking into account the \acs{ssb} (Synchronization Signal Block) and \acs{pdsch} (Physical Downlink Shared Channel) channels. Key metrics such as received power and \acs{sinr} (Signal to Interference plus Noise Ratio) will be measured. Results will show an improvement in performance with beamforming scenarios compared to trisectorization. On the other hand, for the different geometries, very similar results are observed, making it difficult to highlight

The aim of this investigation is to determine the feasibility of implementing ultra-lightweight base stations on a drone using LTE (Long Term Evolution) technology. To achieve this, different hardware configurations and drone models with varying payload capacities have been considered. The project analyses the feasibility and limitations of implementing open-source protocol stacks such as srsRAN on llight computers (Asus PN50) and integrated systems (Raspberry Pi 4 and Rock 5) combined with software-defined radio devices. Various implementation proposals have been designed to address the trade-off between flight time, coverage time, and performance. This project places particular emphasis on optimizing power consumption, given its importance in this context, always keeping in mind that better performance implies higher consumption and shorter flight and coverage times. The impact of possible LTE base station configurations in terms of bandwidth and Modulation and Coding Scheme (MCS) has been studied. This task has been automated and required coordination among multiple devices, as well as modification of part of the srsRAN code to obtain important metrics. Characterizing these power consumptions has been essential to determine the necessary batteries, the weight of which affects the drone's flight time. Based on the previous studies, a proposal has been made for implementation on a model drone from the leading brand DJI. Different drones for carrying out the implementation are also proposed. Limitations of using drones have been studied, such as available space for integrating the base station and electromagnetic compatibility between the new LTE signal emission and the communications for drone control and telemetry transmission. LTE frequencies that can cause problems in drone communication and vice versa have been analysed as well. Additionally, to improve flight time, the weight of the equipment has been reduced by 3D printing plastic cases to replace metal ones.

This paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decision-making tools to optimize cost reduction versus coverage-QoS tradeoff. Especially, the distribution of active Remote Radio Heads or Units (RRHs) needed, according to traffic demands, is improved. The proposed optimization platform is based on a multi-objective optimization model, which is designed to reduce the network cost while maintaining the coverage-QoS. Capacity constraints, User Equipments (UEs), and different slices are considered to test the results under realistic conditions. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Results show cost reductions from 30% to 70% depending on the scenario. Moreover, the proposed algorithm aggregates the possibility to consider the coordination between cells in order to improve the cost reduction. The results considering cooperation has been presented at both frequency bands with a fully centralized C-RAN (split option 8)., This work has been done under COST20120- INTERACT (Intelligence-Enabling Radio Communications for Seamless Inclusive Interactions) EU project. It was supported by the Grant RTI2018-099880-B-C32 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and the Grant FPIUPC, Peer Reviewed, Postprint (published version)

The fifth-generation (5G) cellular network is in an era of rapid development. 5G not only provide ultra low latency data transmissions, it also offers enhanced security based on 5G security specifications developed by the 3rd Generation Partnership Project (3GPP). However, with billions of people using mobile networks every single day, it is important to know which are the present security enhancements implemented by the network operators providing 5G services. The main goal of this thesis is to research the capabilities and features of the security in 5G New Radio networks and perform a reality check of these new security enhancements in commercial 5G networks. For this purpose, we use a commercial security analyser tool to capture the messages exchanged between a mobile equipment and two 5G commercial network within different areas of Spain. Analysing the different messages captured, we measured the compliance of these 5G networks with the major 5G security enhancements coming from the 3GPP standardisation group. Further, we designed and tested in a simulated 5G network environment the performance and reliability of our 5G protocol analyser tool which is capable of capture and decode some of the information coming from the physical downlink channels. The results show that a major discrepancy exists between 5G security standards and real world deployment. Therefore, it has been shown which new attacks have emerged and which ones, some of them coming from the previous 4G generation, can still be executed within the commercial networks analysed such as IMSI catching, user data leakage and location tracking.

Two popular Crest Factor Reduction techniques applicable to OFDM signals have been studied and implemented within this project. To provide adequate background, a brief description of OFDM communication systems is included in the first sections, and the problem of high Peak to Average Power Ratio in multi-carrier transmission schemes is presented. An overview of the most relevant Crest Factor Reduction techniques is performed, and the Clipping and Filtering and Peak Windowing algorithms are selected as implementation candidates due to their protocol agnostic nature. Several implementation architectures have been discussed, and the most computationally efficient one has been used to implement both algorithms within srsRAN, an open source, full software radio implementation of an LTE network with all of its components. In order to provide a comprehensive evaluation of the developed algorithms, an extensive measurement campaign has been designed and carried out, comprised of synthetic testing, as well as experimental physical signal measurements in a laboratory setting, and an outdoor measurement campaign. To speed up the measurement process, an automated measurement system has been designed in MATLAB, which remotely operates a signal analyser to perform spectrum and statistical power measurements, as well as IQ sample collection of the generated LTE signals. This measurement system has been used to carry out unwanted emissions tests following the LTE conformance testing procedures, which are also discussed within this document. For the outdoor measurements, an LTE single cell network has been deployed using a software radio transceiver and an LTE phone, with the aim of assessing the end to end quality of service improvements provided by the developed algorithms. The gathered results illustrate that the implemented Crest Factor Reduction techniques can provide significant network performance gains in situations with limited available transmission power. Additional gains, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

Private networks based on the 5th generation (5G) of mobile telephony are gaining a lot of importance in the Industry due to the use cases they support. Until now, private networks were set up using Wireless Fidelity (WiFi) technology, a technology that was easy to configure and use. However, due to technological progress in recent years, private networks are forced to use more powerful technology such as 5G. 5G technology is more complex due to its architecture and the number of parameters to configure. On the other hand, it is a very flexible technology, since most of the components are pieces of software that can be installed in different parts of the network, depending on the interests of the customer. This comes with the disadvantage that the number of possible architectures increases, and with it the complexity of managing this type of network. In addition, in the case of private networks, there are many providers that offer 5G networks, or different parts of it, which, on the one hand, increases flexibility when choosing the providers that will be used to set up the private network, but on the other hand, it makes it necessary to study how each of them works to configure the different components. This requires some study time to see how to deploy and configure the components of each provider that will be used in the private network. This project is developed in the context of the Neutroon company, whose main objective is to create a platform that solves this type of problem. It allows, in a very simple way, to manage private networks that use WiFi, Long Term Evolution (LTE) and 5G technologies. Neutroon not only reduces the complexity of deploying and configuring private networks, but also centralizes in a single platform the management of the different applications that can be running in this same network. In addition, Neutroon has the flexibility to manage the network regardless of the provider or providers chosen to set up the private network, since it has

The Bluetooth Low Energy (BLE) mesh profile, standardized by the Bluetooth Special Interest Group (SIG), has an increasing interest in IoT solutions. However, the standard assumes that relay and friend nodes should be continuously scanning the channel awaiting any incoming transmissions. This could be very inefficient in terms of energy consumption, particularly in application scenarios where the backbone of the mesh network cannot be powered and traffic is infrequent. Hence, we present a novel strategy, named PSM-DMO, that minimizes the scan periods and thus, significantly reduces the overall energy consumption of the mesh network. PSM-DMO is defined as a new and optional feature for the currently published BLE mesh specifications, coexists with the standard operation, and is implemented without modifying the core of the specification. The proposal, that ensures the reliability of the mesh operation, can be used in BLE sensor networks that can tolerate a certain transmission delay. PSM-DMO replaces the continuous scan by a periodic but asynchronous polling process whereby the relay and sink nodes interrogate their neighbors about the existence of data to receive or to retransmit through the network. Nodes only go into scan mode during the period of time the mesh network will be involved in the transmission and dissemination. This period is estimated by the node which is the source of data, it is announced to its neighbors and it is propagated consecutively by all the relay nodes until the destination. PSM-DMO allows a theoretical reduction in the energy consumption of relay nodes up to 99.24 %., This work has been supported in part by the Spanish Ministry of Science through the projects RTI2018-099880-B-C32. RTI2018-095684-B-I00 and RTI2018-099063-B-I00 with ERFD funds, and by the Government of Aragon (Reference Group T31_20R)., Peer Reviewed, Postprint (published version)

The methods usually employed to measure channel occupancy show limitations in the context of Bluetooth Low Energy (BLE) advertisements. We propose and analyze the use of BLE sniffers as light and portable low-cost spectrum occupancy meters to be used in scenarios where real time signal analyzers are not adequate. For the measurement technique to be successful, several low-level effects must be considered. The paper argues about on-air time, receiving blind times due to processing and intra system interference, buffer saturation and frequency anchoring. Hence, a compensation procedure based on collision rate estimation is proposed. Results with the refined method show that occupancies of 40% can be measured with an overestimation error whose percentile 95% is 5 percentage points. This is reduced to 1.9 points when the occupancy is 15%. The sniffers perform in real time and are shown to correctly track short term load variations. The strategy has been successfully used to characterize occupancy in highly variable and loaded scenarios such as subway platforms and a shopping mall. Values up to 25% have been observed, which implies a relevant packet error rate. Hence, the tool can be used to make agile audits and configure the parameters that control communication redundancy in new or existing networks., The work by UPC has been funded by MCIN/ AEI /10.13039/501100011033 and by ERDF A way of making Europe, with the grant RTI2018-099880-B-C32 and PID2021-125799OA-I00. The work by I3A-UZ has been funded by MCIN/ AEI /10.13039/501100011033 and by ERDF A way of making Europe, with the grants RTI2018-095684-B-I00 and RTI2018-099063-B-I00, and by the Government of Aragon (Reference Group T31 20R)., Peer Reviewed, Postprint (published version)

This paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decision-making tools to optimize cost reduction versus coverage-QoS trade-off, in other words, the number of active Remote Radio Heads or Units (RRHs) needed according to traffic demands. The cost and energy optimization are analytically expressed by modeling the complex relationships between input and output system parameters using realistic scenarios and traffic profiles for low, medium, and high traffic environments. The optimization tool is based on a multi-objective integer linear programming model, designed to reduce the network cost while maintaining a good coverage-QoS and accounting for capacity constraints, User Equipments (UEs), and different slices. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Cost reductions ranging from 30 % to 70 % have been obtained depending on the scenario. This proposal allows mobile network opera, This work was supported in part by the COST2020-INTERACT (Intelligence-Enabling Radio Communications for Seamless Inclusive Interactions) EU Project, in part by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe'' under Grant RTI2018-099880-B-C32, and in part by Grant FPI-UPC., Peer Reviewed, Postprint (published version)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The Bluetooth low-energy (BLE) mesh profile, currently standardized by the Bluetooth Special Interest Group (SIG), assumes that friend and relay nodes should be continuously scanning the channel ready to receive any incoming transmissions (TXs). This is very inefficient in terms of energy consumption. Hence, we present BMADS, an asynchronous dynamic scanning mechanism designed to reduce the overall energy consumption of the mesh network. BMADS is implemented as a new feature for the currently published BLE mesh specifications without modifying its core. This feature reduces the scanning cycles of the nodes, but still ensuring the reliability. To make this possible, BMADS sends a new control message sequence before transmitting the data. This sequence puts the nodes into continuous scan mode, preparing the network for data TX. The proposal can be used in BLE sensor networks that can tolerate a certain TX delay and require especially low-energy consumption. BMADS allows a theoretical reduction in the energy consumption of the relay nodes up to 98%., This work has been funded by the Spanish Ministry of Science through the projects RTI2018-099880-B-C32, RTI2018-095684-B-I00, RTI2018-099063-B-I00 with ERFD funds and by the Government of Aragon (Reference Group T31 20R), Peer Reviewed, Postprint (author's final draft)

Bluetooth has constantly evolved from its cradle in 1997 to the last 5.2 version in 2020. With each update and amendment, it has gained in speed, range, and versatility. One of the latest introductions was the Bluetooth Mesh Profile (BMP) making it a technology suitable for a wide variety of applications. Nevertheless, BMP was designed to maintain the compatibility with Bluetooth version 4 devices already deployed in the market. This imposes some restrictions that place Bluetooth Mesh under other competing technologies like Zigbee or Thread in terms of throughput performance. In this paper we propose two mechanisms to overcome these limitations and take advantage of the new extended advertising capabilities introduced with Bluetooth 5. These mechanisms are presented as modifications to the current protocol stack to allow the transmission of larger data structures. Thus, it is possible to boost the throughput of Bluetooth Mesh making it suitable to more demanding applications like, for example, image transmission. The first proposal is designed as an adaptation layer to avoid modifying the standard in its current form. The second makes minimal changes to the frame structure at the different layers enabling the user to accommodate possible encapsulations (i.e., tunneling) without incurring IPv6-layer fragmentation. We have analyzed both solutions and compared them with the current BMP in terms of throughput, delay, and energy consumption for different channel conditions and network size. The results show that except for very small messages or poor channel conditions the proposals improve the throughput and delay of the current BMP., This work was supported in part by the Spanish Ministry of Science through the European Regional Development Funds (ERDF) under Project RTI2018-099880-B-C32, Project RTI2018-095684-B-I00, and Project RTI2018-099063-B-I00; and in part by the Government of Aragon (Reference Group) under Grant T31_20R., Peer Reviewed, Postprint (published version)

The assumption of a fixed computational capacityat the Baseband Unit (BBU) pools in a Cloud Radio Access Network (C-RAN) deployment results in underutilized resourcesor unsatisfied users depending on traffic requirements. In thispaper a new strategy to predict the required resources based on Machine Learning techniques is proposed and analysed. SupportVector Machine (SVM), Time-Delay Neural Network (TDNN),and Long Short-Term Memory (LSTM) have been tested andcompared to select the best predicting approach. Instead of usinga regular synthetic scenario a realistic dense cell deployment overVienna city is used to validate the results. Authors show that theproposed solution reduces the unused resources average by 96 %, This work has been done under COST CA15104 IRACONEU project. It was supported in part by the Spanish ministryof science through the project RTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by the UPC., Peer Reviewed, Postprint (published version)

We propose an unsupervised learning based anomaly detection framework for identifying cells experiencing performance degradation due to mobility problems, in LTE networks. Handover failure rate is used as a performance metric, whereas the mobility problems considered include too-early and too-late handovers. In order to enable unsupervised learning, the framework leverages existing datasets in commercial LTE networks (e.g. performance management counters, configuration management data, geographical locations, and inventory data etc). To this end, the first step is data pre-processing, followed by feature extraction based on principal component analysis and clustering. For implementation, we use real data from an operational commercial LTE network. Results show that clustering is highly effective in understanding and identifying mobility related anomalous behaviour, and provides actionable insights for automation and self-optimization, paving the way for efficient mobility robustness optimization, which is an important self-optimization use-case for contemporary 4G/5G networks., This work was supported by the European Commission under TECSPR18-1-0081 project. The work by Mario García-Lozano has beenfunded by the Spanish ministry of science through the project CRIN-5G (RTI2018-099880-B-C32) and with ERFD funds., Peer Reviewed, Postprint (published version)

The massive amount of data available in operational mobile networks offers an invaluable opportunity for operators to detect and analyze possible anomalies and predict network performance. In particular, application of advanced machine learning (ML) techniques on data aggregated from multiple sources can lead to important insights, not only for the detection of anomalous behavior but also for performance forecasting, thereby complementing classic network operation and maintenance solutions with intelligent monitoring tools. In this paper, we propose a novel framework that aggregates diverse data sets (e.g. configuration, performance, inventory, locations, user speeds) from an operational LTE network and applies ML algorithms to diagnose network issues and analyze their impact on key performance indicators. To this end, pattern identification and time-series forecasting algorithms are used on the ingested data. Results show that proposed framework can indeed be leveraged to automate the identification of anomalous behaviors associated with the spatial-temporal characteristics, and predict customer impact in an accurate manner., This project has received funding from the European Union’s Horizon2020 research and innovation programme under the Marie Skłodowska-Curiegrant agreement No 712949 (TECNIOspring PLUS) and from the Agency forBusiness Competitiveness of the Government of Catalonia. The work by Mario García-Lozano has been funded by the Spanish ministry of science throughthe project CRIN-5G (RTI2018-099880-B-C32) and with ERFD funds., Peer Reviewed, Postprint (published version)

Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN)environments is a challenging problem because it has to account simultaneously for throughput, latency,power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at thebaseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overallQuality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instan-tiated according to the required computational capacity (RCC). In this paper, a new strategy for DynamicResource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML)techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: supportvector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higherthan the predicted computational capacity (PCC). To further improve, two new strategies are proposed andtested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting(DRM-AC-ES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectively, This work was supported in part by the Spanish ministry of science through the project CRIN-5G (RTI2018-099880-B-C32) withERDF (European Regional Development Fund) and in part by the UPC through COST CA15104 IRACON EU Project and theFPI-UPC-2018 Grant., Peer Reviewed, Postprint (published version)

BLE is a widely used short-range technology which has gained a relevant position inside the Internet-of-Things (IoT) paradigm development thanks to its simplicity, low-power consumption, low-cost and robustness. New enhancements over BLE have focused on supporting mesh network topology. Compared to other mesh networks, BLE mesh has only considered a managed flooding protocol in its first version. Managed flooding may generally seem inefficient in many contexts, but it is a high desirable option when data transmission is urgent, the network is small or its configuration changes in a very dynamic way. Knowing the interest to many application contexts, this paper analyses the impact of tweaking several features over the reliability and efficiency of the mesh network. These features are configured and controlled in different layers: message repetition schemes, the transmission randomization, the election of a scheme based on an acknowledged or unacknowledged transmission, etc. In order to estimate the real performance of a mesh network deployment, this paper evaluates the effects of the interaction of the chosen parameters, their appropriate adjustment in relation with the characteristics of real implementations and the true overhead related to the whole protocol stack. The paper identifies configuration challenges, proposes network tuning criteria and outlines possible standard improvements. For this purpose, a detailed assessment on the implementation and execution of real devices has been performed with their chipset limitations., This work was supported in part by the Spanish Ministry of Science with ERFD funds under Project RTI2018-099880-B-C32, Project RTI2018-095684-B-I00, and Project RTI2018-099063-B-I00, and in part by the Government of Aragon (reference group T31_17R)., Postprint (published version)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Mobility management in 5G networks is a very challenging issue. It requires novel ideas and improved management so that signaling is kept minimized and far from congesting the network. Mobile networks have become massive generators of data and in the forthcoming years this data is expected to increase drastically. The use of intelligence and analytics based on big data is a good ally for operators to enhance operational efficiency and provide individualized services. This work proposes to exploit User Equipment (UE) patterns and hidden relationships from geo-spatial time series to minimize signaling due to idle mode mobility. We propose a holistic methodology to generate optimized Tracking Area Lists (TALs) in a per UE manner, considering its learned individual behavior. The k -means algorithm is proposed to find the allocation of cells into tracking areas. This is used as a basis for the TALs optimization itself, which follows a combined multi-objective and single-objective approach depending on the UE behavior. The last stage identifies UE profiles and performs the allocation of the TAL by using a neural network. The goodness of each technique has been evaluated individually and jointly under very realistic conditions and different situations. Results demonstrate important signaling reductions and good sensitivity to changing conditions., This work was supported by the Spanish National Science Council and ERFD funds under projects TEC2014-60258-C2-2-R and RTI2018-099880-B-C32., Peer Reviewed, Postprint (author's final draft)

Examen E1

We study the feasibility of the dense 4G and 5G cellular networks at sub-6 GHz and millimeter wave carriers for vehicle-to-network applications. For this purpose, road-side network coverage, signal-to-interference-plus-noise (SINR) and handover rate are used as key performance indicators (KPIs). The KPIs are calculated over realistic vehicular user routes which are created by Google Maps APIs. The channel pathloss is simulated using a ray tracing software and it is shown that even for a dense 4G small cell deployment the coverage at 28 GHz carrier frequency is very fragmented and thus, service continuity depends on the availability of sub-mmWave carriers., This work was supported by Academy of Finland (grants 311752 and 287249). Work by Mario Garcia-Lozano was funded by the Spanish ministry of science through the project RTI2018-099880-B-C32, with ERFD funds., Peer Reviewed, Postprint (published version)

Remote driving (RD) is a critical backup option for an autonomous vehicle when it faces an unexpected situation on the road. Fifth generation (5G) mobile communication is a key enabler of RD providing link between the remote operator and the vehicle. There are stringent requirements in terms of coverage, data rate and latency for RD application. In this paper, a feasibility study at the radio access network level for the radio coverage conditions that are required for RD application is carried out. The study considers RD over three carriers including 2.6 GHz, 5 GHz and 28GHz. A ray tracing software is used for the channel pathloss computation. The RD provision in terms of coverage and rate statistics is analysed for these frequency carriers. The role of interference is also investigated. The statistics is aggregated using a large number of realistic vehicular routes created using Google Maps APIs. It is shown that the provision of RD can not be guaranteed over a wide network area especially in high-load/high-interference conditions and it is more feasible to define the RD application for specific roads., This work was supported by Academy of Finland (under grants 311752 and 287249). Work by Mario Garcia-Lozano was funded by the Spanish ministry of science through the project RTI2018-099880-B-C32, with ERFD funds., Peer Reviewed, Postprint (published version)

This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with QoS constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modelling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. Results show that even after the optimization, there are some time intervals where the allocated resources are underutilized, which opens the door to the definition of new Machine Learning algorithms able to predict the required capacity., This work has been funded by the Spanish ministry of science through the project RTI2018-099880-B-C32 and with ERFD funds. This work has been done under COST CA15104 IRACON EU project., Peer Reviewed, Postprint (published version)

The project’s objective is to improve the 3G and 4G indoor coverage of EETAC (Escola de Enginyeria de Telecomunicació i Aeroespacial de Castelldefels) through the installation of microcells.This memory explains all the necessary points to implement the indoor telecommunications system. To deploy the network, we have made an agreement with Movistar that it has allowed us to access and use his software (Tems Pocket, Tems Investigation e IBWAVE). This report has been divided in different chapters that its follows the next process: First of all, it is necessary to know the situation of the place where it will work, so in the first point,it has been made the measurements of the external signals with the Tems Pocket app and after that, the measurements have been analyzed using the software Tems Investigation. Once the measures have been analyzed and the number of channels has been defined, it has been designed the network and finally it has been done the prediction of coverage with the IBAWE program. Finally, it has been done the installation memory, where it has been dimensioned and it has beenindicated the real models of the network’s elements and their installation by plants. And the justification for compliance with the Royal Decree on the emission of electromagnetic fields and the budget of the entire project has been carried out.

The Unmanned Aircraft Vehicles (UAV) industry has grown at such an amazing rate, that it is almost impossible to count or describe all the applications that have become normal practice, not to mention the number of companies involved in either making drone platforms or supplying drone integrators with sensors: GNSS receivers, inertial sensors, LiDAR, photographic, thermal and infrared cameras, video and more. In particular, positioning systems have become crucial for almost all industrial drone’s applications, such as photogrammetry, 3D mapping, search and rescue operations, surveillance… Beyond the use of computed positions of the drone for professional matters, the need to “getting the right position to fly safely” is of the utmost importance. Yet, most of the GNSS receivers on-board of drones provide low-quality data, with no expected changes in the long-term, principally because of their cost and weight. Then, new questions are arising: Is the low-quality position data good enough for current or new applications? Are there other technologies, systems or methods able to improve performance without changing the drone’s GNSS receivers? The answer is positive: SBAS augmentation system is a complement to GPS built initially for civil aviation, but available to GNSS receivers, even those from drones. It is a method reduce more than a half the position error (accuracy) and also gives additional information in order to quantify how bad the measurements are (integrity). This document will explain how a software able to calculate GPS-based positioning augmented with SBAS has been developed and then, the results of it are assessed in order to analyse if the system would be useful in drone applications or operations

Cellular vehicle-to-network (V2N) communication will be the backbone of the connected vehicles of future. One of the key requirements of the connected vehicles is a near universal coverage on the streets. Traditional radio network planning for cellular coverage is done with raster format whereby all pixels in a network area have equal weighting. Whereas, for V2N communication the target is to primarily ensure continuous network coverage on the streets. In this work, a route-based methodology which is pertinent for V2N coverage analysis is presented. This method adds another key parameter into consideration, namely, the base stations (BS) deployment schema. Existing cellular networks, whereby, small base stations (BSs) are deployed at the macro BS cell-edge, at traffic hotspots or to compensate for coverage holes, may not be sufficient for V2N coverage especially at millimeter wave (mmWave) carrier frequencies. Herein, we perform the coverage analysis for an existing small BS deployment as well as for an ultra-dense deployment at 2 GHz, 5GHz and 28 GHz carriers. The statistics for signal-to-noise-plus-interference ratio (SINR) and achieved rate are aggregated by a large number of realistic vehicular routes from Google Directions application programming interface (API)., This work was supported by Academy of Finland (under grants 311752 and 287249). It was also partially funded by the 5G-MOBIX project which has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 825496. The work by Mario Garcia-Lozano has been funded by the Spanish ministry of science through the project RTI2018-099880-B-C32, with ERFD funds., Peer Reviewed, Postprint (published version)

This project aims at analyzing the NB-IoT technology both in a theoretical and practical manner. Its performance has been assessed from three different and complementary viewpoints: theoretical analysis, creation of a simulator with the correspondingsimulationsandmeasurements with a NB-IoT device in a real network deployment.The theoretical calculations have beenperformed under different NB-IoT configurations, with different levels of data protection / redundancy. The worst case allows very deep coverage conditions but it does negatively impact on transmission times. For short data packets throughput levelsare acceptable. On the other hand, large packetsof 1 Mbyteshow unacceptable transmission times ofmore than 12 days for the worst casescenario.A simulator of NB-IoT has been designed to compare the simulations with the theoretical cases and to allow the user to simulate any downlink or uplink scenario configuring all the possible parameters allowed by NB-IoT. Results revealthat the initial pointof the downlink transmissionin the system frameaffect the total transmission time, which means that average theoretical calculations may show an approximation error. In the uplink, resource allocation algorithms should smartly consider NPRACH opportunities occupation to minimize transmission delays, which can be very large for certain configurations.Finally, using Vodafone’s NB-IoT network, some uplink data transmissions have been performed and analyzed with different data packet length for anintermediate coverageand a bad coverage case.The results have revealeda high variability in the transmission times and the way of transmitting the packet even with similar coverage levels.The real scenario has been reproduced in the simulator and the results have beencompared with the real measurements.

©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Self-organizing network (SON) is a well-known term used to describe an autonomous cellular network. SON functionalities aim at improving network operational tasks through the capability to configure, optimize and heal itself. However, as the deployment of independent SON functions increases, the number of dependencies between them also grows. This work proposes a tool for efficient conflict resolution based on network performance predictions. Unlike other state-of-theart solutions, the proposed self-coordination framework guarantees the right selection of network operation even if conflicting SON functions are running in parallel. This self-coordination is based on the history of network measurements, which helps to optimize conflicting objectives with low computational complexity. To do this, machine learning (ML) is used to build a predictive model, and then we solve the SON conflict by optimizing more than one objective function simultaneously. Without loss of generality, we present an analysis of how the proposed scheme provides a solution to deal with the potential conflicts between two of the most important SON functions in the context of mobility, namely mobility load balancing (MLB) and mobility robustness optimization (MRO), which require the updating of the same set of handover parameters. The proposed scheme allows fast performance evaluations when the optimization is running. This is done by shifting the complexity to the creation of a prediction model that uses historical data and that allows to anticipate the network performance. The simulation results demonstrate the ability of the proposed scheme to find a compromise among conflicting actions, and show it is possible to improve the overall system throughput., Peer Reviewed, Postprint (author's final draft)

This paper provides new insights into the performance of Uplink and Downlink Splitting (UDS) in highly loaded wireless communication systems, in terms of both serving nodes and the number of users with high traffic activity. The study puts special focus on the gains that UDS could bring in terms of SINR and throughput when compared with systems with cell range expansion (CRE) in the classic downlink based cell association. CRE not only helps to offload users from macro- to pico-eNBs, but also improves UL service. Instead of an aggregated throughput analysis, a detailed classification of users is performed to figure out the causes of users’ gain or loss after applying each strategy at the system level. Results show marginal gains of a pure path loss based UDS when compared with the intrinsic UL gains of CRE. Given the extra flexibility in radio resource management that splitting both links could bring, using an individual UL adjustable cell offset appears to be an interesting strategy to allow for a finer control of UL interference. The dependency of UDS performance with small cell density has also been a matter of study. Results show that the gains of UDS do decrease after a certain density of pico-cells is surpassed., Peer Reviewed, Postprint (published version)

During the historical evolution of aerial photogrammetry all the production processes involved in its workflow have, in one way or another, been improved or optimized by the contemporaneous technological breakthroughs. There is however one area that, until today, continues unaltered since its inception, the Ground Control Point survey process which is still manually performed by a human. The motivation of this master thesis is twofold, in one hand to study a method to automate the Ground Control Point collection process by using Remotely Piloted Air Systems as both, visual targets as well as Global Navigation Satellite System receivers and, on the other hand, study if the technological concept may become a viable business or not.