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One of the most crucial aspects of cellular networks is coverage, as it determines the areas where users can connect to the network and utilize its services. In the past, the use of planning tools was common practice for the establishment of coverage areas and network capacity prior to the deployment of a network. However, issues with coverage, such as interference or coverage gaps, may arise due to equipment malfunctions, suboptimal configurations, or alterations in the propagation environment. In particular, an inadequate antenna tilt configuration can result in overshoot or undershoot situations on the network, which in turn can give rise to the aforementioned problems. This paper proposes a methodology for the real-time detection of overshoot and undershoot situations. To achieve this goal, KPI (Key Performance Indicators) are analyzed using machine learning techniques. Given the difficulty of detecting coverage problems in mobile networks, the results obtained suggest that the methodology provides a consistent knowledge base for optimizing the antenna tilt, thereby improving network performance.

Fifth Generation (5G) Millimeter cellular networks are particularly interesting within the Industry 4.0 paradigm. mmWaves provide flexibility to the industrial use case, as well as enable specific application requirements, e.g. robots needing high data rates and low latency communication. Coverage at mmWaves, however, is challenged by additional attenuation and blocking effects, requiring the use of beamforming to overcome this by steering transmissions towards specific directions. Reliability is a key requirement as well, and requires, e.g, seamless beam switching during movement. Here, the resulting directivity of the antennas makes the beam switching procedures key to maintain the connectivity as the users move by. The present work aims to analyze the beam switching performance in a real-world indoor industrial scenario with high clutter to identify potential failures and propose optimization strategies. Firstly, this work has gathered insights into how beamforming operates under specific situations, identifying unused beams, relating the beam switching to the User Equipment (UE)’s movement, and collecting statistics from the beam switchings. Finally, a refined beam switching criteria is proposed based on radio metrics and location information, which outperforms the current criteria used by the network. This is evaluated based on the Reference Signal Received Power (RSRP) and the number of beam switchings under different criteria.

The growing complexity of cellular networks makes it harder for network operators to control and manage the system. To ease the management and automatically detect network problems, unsupervised techniques have been put to use. This work proposes a novel method that combines Multi-Resolution Analysis (MRA) by wavelet transforms and unsupervised clustering with pre-initialized Gaussian Mixture Models (GMMs) for the totally unsupervised grouping of cellular network behaviors using different metrics. The application of multi-resolution decomposition, allows the much simpler clustering technique to take into account temporal information that would require of a much complex method otherwise, being useful for cluster analysis by experts as different duration issues are now segregated and automatically labeled. The generated labels are indicative of the intensity and duration of the anomalies, such labeling can be linguistic or visual, providing faster issue identification. The proposed approach has been tested with real network data, successfully separating different behaviors analyzed in the evaluation section of the manuscript.

Under the Zero-touch network and Service Management (ZSM) paradigm, cellular networks are moving to fully automated schemes in order to minimize human intervention and maximize efficiency. Until now, such management has been based on network metrics and complemented with drive tests. However, this approach is clearly insufficient to achieve Zero Touch since the information available from the network side is limited, aggregated from all the users within a cell, and collected with a frequency of minutes. Also, the complexity and high cost of performing drive tests to collect user-side data are among the main drawbacks. Nevertheless, there is an option between the two aforementioned: Next-Generation Crowdsourcing Metrics (gCMs). In this regard, the present work describes a novel framework for ZSM cellular networks that exploits the advantages of integrating crowdsourcing User Equipment (UE) side metrics to advanced network management. We analyzed the suitability of the proposal by using data acquired from commercial cellular networks. The obtained results state that there is a wide range of applications for gCMs that can benefit network operation, including the detection of coverage holes, mobility issues, and interference problems. Finally, the identified open challenges are disclosed based on the achieved results.

Precise positioning will play a key role in future 5G/6G services. The upcoming location-based services drive the necessity of high-precision positioning to indoors. In fingerprinting, which is the most commonly used location algorithm indoors, comprehensive radio maps are essential for a precise localization service and highly influence on the result of the final position of the user. A Machine Learning (ML) algorithm that supports missing reference points information when maps are incomplete that are used during the training phase may improve the robustness and reliability of the localization service. In this work, we compare the performance of the classical fingerprinting technique and different Decision Tree Regressor (DTR)-based algorithms that are Decision Tree Adaboost (DTA), Linear Tree Adaboost (LTA) and Random Forest (RF). The experiments were carried out with real 5G and WiFi data in an indoor scenario to test the performance of the techniques. Additionally, we demonstrate the benefits of fusion of technologies when positioning with radio maps. Finally, an evaluation of the robustness from the different methods was carried out when missing information in radio maps during the training phase.

Industry 4.0 is being adopted by the manufacturing sector to improve the flexibility and reduce installation costs by the use of wireless connectivity. There is an open question of which wireless technology deployment should be used in the factory to fulfil the requirements for next-generation applications such as autonomous mobile robots. Wi-Fi technology is the most extended and easy to deploy, while the fifth generation of mobile networks (5G) has been designed to support these industrial needs. Therefore, it is important to compare both technologies from a performance point of view, especially under different load conditions and number of devices. The use of multi-connectivity between 5G and Wi-Fi can also be an option to fulfil the requirements for the most critical real-time applications. In this paper, we empirically measure the scalability of 5G, Wi-Fi and multi-connectivity in the “Aalborg University 5G Smart Production Lab” and compare them in terms of latency and packet loss with different packet sizes. We found that in general Wi-Fi obtains lower latencies but large tails in the distribution, with a higher packet loss compared to 5G. On the other hand, 5G latency is very consistent with bounded tails, and low packet loss is obtained. In terms of scalability, 5G scales better than Wi-Fi, the latter being very affected by the number of devices transmitting data. Finally, multi-connectivity showed an improved reliability and lower latencies in all evaluated cases.

The emergence of the novel approach of Industry 5.0 entails the utilization of immersive technologies such as XR (Extended Reality), which promise to transform the conceptualization of industry. The advent of new technology enablers, such as next-generation mobile networks, including 5G and B5G (Beyond 5G), plays a pivotal role in enhancing productivity, optimizing operators’ training programs, and minimizing unnecessary risks. This study presents a performance assessment of Cloud VR services over 5G and WiFi in industrial scenarios. This scenario involves several factors that can degrade radio links, including fading and interference. To assess the suitability of 5G for the Industry 5.0 hot topic of XR, a 5G SA/WiFi-6 Edge Cloud VR setup was developed in the Smart Production Lab at Aalborg University. The results demonstrate that 5G outperforms WiFi in terms of latency and throughput consistency during the experiments in DL (Downlink), which belongs to the streaming channel with UDP (User Datagram Protocol). In the uplink (UL), WiFi generally exhibits lower latency than 5G when employing Transmission Control Protocol (TCP) for the control channel. To provide a comprehensive End-to-End (E2E) analysis, an objective Quality of experience (QoE) metric has been estimated. The outcomes demonstrate that 5G achieves higher QoE values for mobility than WiFi, which shows a decline in performance. The results indicate that 5G performance is within theoretical limits for XR experiences and suggest that it is a promising candidate for enabling Industry 5.0 and its use cases.

The emergence of cutting-edge technologies and services such as Extended Reality (XR) promises to change how people approach everyday living. At the same time, the emergence of modern and decentralized architectural approaches has ushered in a new generation of mobile networks, such as 5G, as well as outlining the roadmap for B5G (Beyond-5G) and further advancements. These networks are expected to be the enablers for the realization of the metaverse and other futuristic services. In this context, quantifying the service performance is a key enabler for dynamic, environment-adaptive, and proactive network management. This work presents an ML-based (Machine Learning) framework that uses data from the network, such as radio measurements, statistics, and configuration parameters to infer the best ML models that fit diverse XR Key Quality Indicators (KQIs). The output models integrate feature engineering techniques that enhance model size and performance. The proposed framework comprises data preprocessing, model definition, training, tuning, and validation. Additionally, to select the best combination algorithm this work introduces a metric called PET_{score}, which evaluates algorithm candidates in terms of error performance and prediction time. These are considerations that are needed for time-sensitive services like XR’s. To validate our proposal, the 360-video service has been chosen to demonstrate the potential of this ML framework with a real XR use case. In addition, the dataset generated for the use case evaluation is publicly accessible and properly referenced. Furthermore, this work serves as a foundation for future research on end-to-end (E2E) quality of experience (QoE)-based network management in conjunction with other enabling technologies, including network slicing, virtualization, and multi-access edge computing (MEC).

In the last few years, location-aware services and network management have driven the demand for user location estimation in mobile networks. Nevertheless, the location obtained from user terminals is not usually accessible to mobile operators. In addition, available cell Key Performance Indicators (KPI) vary highly from network to network, and only a few of them are always enabled widely. Currently prevalent Machine Learning (ML) based solutions have achieved high precision, but they are bound to a trained scenario, restricting their application to new areas. We propose a method for creating scenario-agnostic prediction models which solves these problems through applying feature engineering, over a small set of easily obtainable KPIs, applicable for any ML method. Finally, the performance of the proposed method is demonstrated using real network datasets.

The integration of Non-Terrestrial Networks (NTN) with 5G represents a monumental leap in wireless network capabilities, significantly enhancing capacity, range, and reliability. This fusion fosters technological innovation and global connectivity. Crucial to this development is the softwarization of 5G networks through Virtual Network Functions (VNFs) within the OpenRAN paradigm, which disaggregates traditional radio access networks into Central and Distributed Units (CU and DU), accelerating deployment and facilitating upgrades. However, incorporating edge computing in Low Earth Orbit (LEO) satellites into this virtualized infrastructure presents arising management complexities, particularly in the interaction and resource sharing among VNFs, including satellite components. The absence of advanced emulators capable of simulating these environments represents a significant hurdle. As a response, this study introduces a groundbreaking emulation framework based on the Open Air Interface platform, tailored to reflect OpenRAN’s disaggregated network architecture. The contribution of this work encompasses the design and implementation of a 5G-NTN platform, laying on Kubernetes for VNF lifecycle management automation, enabling a boost in multi-domain networks operational efficiency. The emulation of the NTN environment is performed at network level, abstracting the setup from the radio link implementation. The platform’s capabilities in VNF deployment and resource optimization are validated by extensive performance metrics, with a full slice instantiation taking approximately 76.30 seconds, highlighting its contribution in emulating 5G-NTN orchestrated systems. The presented innovation lays the groundwork for future research in efficient resource management within complex multi-domain network environments, enabling the exploration of diverse VNF deployment scenarios and facilitating networks to adapt dynamically to advanced mobile communication demands.

The deployment of the Fifth Generation (5G) cellular networks has been addressed by the operators during the last years. However, it has been focused on 5G Non-Standalone (NSA) because they have leveraged the current Long Term Evolution (LTE) deployments. NSA mode uses LTE as anchor technology, allowing the operators to keep providing coverage in all the areas they covered with LTE, and to gradually introduce 5G in high-traffic or crowded areas. But NSA operation do not enable all the novelties introduced by 5G in order to achieve better performance. Moreover, novel features (e.g., beamforming) also introduce a lot of parameters whose configuration will become a work item towards network management. At the University of Malaga (UMA) MobileNet research group, a complete 5G Standalone (SA) infrastructure has been deployed for Research & Development (R&D) purposes. It is a private network composed of commercial equipment that allows controlling all the components, modifying all the parameters, and getting metrics from each part of the network. The aim of this work is to describe the infrastructure and potential opportunities regarding the different 5G-enabled UC. To achieve this, an in-depth study has been accomplished in order to unveil the different options to configure the network, interact with it, and collect its metrics. First, the indoor and outdoor cells’ baseline performance has been measured. Then, research experiments have been performed on the infrastructure, and the results are presented in this paper. The infrastructure capabilities are mainly disclosed, and additional beamforming-based analysis is conducted from the real field measurements. This analysis comprises beam switching, beam-based localization, and Key Performance Indicator (KPI) definition toward network management.

Location will have a central role in Research and Development (R&D) towards 6G networks, both as a service offered by the network (improving the current offering of 5G) and as an input to increasingly location-aware services and network functions. To integrate location into 6G standards, it will be very important to design validation systems such as testbeds, even when the actual technology is not yet commercially available. This paper performs a review of the use cases and their requirements, enabling technologies in 6G, and challenges; and proposes a flexible testbed architecture for performing network location related R&D. This architecture will allow to deploy an evolving infrastructure which will allow early validation of 6G technologies.

The new generation of mobile networks goes beyond classic radio-focus access networks by adopting the softwareisation of functions that typically relied on special-purpose hardware, now to be deployed in as general as possible purpose platforms. The resulting virtualised Radio Access Network (vRAN) follows the Network Function Virtualisation (NFV) paradigm, enabling a distributed and scalable network architecture. However, this approach increases the complexity of management tasks as computing resources now play an essential role in the network provisioning process. In this sense, this work aims to assess the impact of computational resources on the Quality of Experience (QoE) of video streaming services delivered over vRAN-based networks. This is done by performing multiple playback of 4K video while allocating varying numbers of Physical Resource Blocks (PRBs) and virtual CPU to vRAN. The obtained results prove that inadequate resource assignment to vRAN instances leads to degradation of the QoE, even if the allocation of radio resources is adequate for the service.

The purpose of this paper is to fill a gap in the existing literature about the general analysis of airport activities and detailed specifications for 5G implementation. The gap spans from overgeneralized studies to highly specific ones as will be mentioned in the paper. This research aims to bridge this gap by identifying the requirements of 5G implementation in various airport use cases, proposing detailed 5G solutions for these cases. As is well known, 5G is the Fifth Generation of cellular communications, aiming to provide high-throughput high-reliability connectivity to greatly diversified scenarios. With this objective, it shall act as a viable solution for environments as complex as an airport terminal, whose dynamic work cycle includes a wide range of diversified activities. As such, 5G capacities are assessed, identifying those airport processes that can benefit from its application as well as the general requirements of the network deployed for such scenarios. From the identified use cases, the streaming of high quality video for aircraft visual inspection as well as the monitoring of luggage trolleys via an IoT system are developed and evaluated, showing the capabilities of the approach.

Aircraft are composed of many electronic systems: sensors, displays, navigation equipment, and communication elements. These elements require a reliable interconnection, which is a major challenge for communication networks since high reliability and predictability requirements must be verified for safe operation. In addition, their verification via hardware deployments is limited because these are costly and it is difficult to try different architectures and configurations, thus delaying design and development in this area. Therefore, verification at early stages in the design process is of great importance and must be supported with simulation. In this context, this work presents an event-driven link-level framework and simulator for the validation of avionics networks. The tool presented supports communication protocols commonly used in avionics, such as Avionics Full-Duplex Switched Ethernet (AFDX), as well as Ethernet, which is used with static routing. Also, the simulator provides accurate results by employing realistic models for various devices. The proposed platform was evaluated in the Clean Sky’s Disruptive Cockpit for Large Passenger Aircraft architecture scenario, showing the capabilities of the simulator. Verification speed is a key factor in its application, so the computational cost was analyzed, proving that the execution time is linearly dependent on the number of messages sent and that the increase in the number of nodes has few quadratic components.

The fifth-generation technology is called to support the next generation of wireless services and realize the “Internet of Everything” through Machine-Type Communications and Cellular Internet of Things optimizations. As part of these optimizations, Release 15 introduced a new mechanism, known as Early Data Transmission (EDT), that allows the transmission of data during the Random Access procedure. This feature, intended particularly for infrequent and small data transmissions, aims to reduce the latency and the power consumption of user equipments. Nonetheless, despite the importance of this novelty and the general agreement about its effectiveness, there are few papers in the literature that provide insight into its implementation and analyze the advantages and disadvantages of its two different implementation options (Control and User Plane). Moreover, although security is recognized as a crucial aspect for the correct deployment of this technology, we have not found any paper that reviews the security issues and features of this mechanism. As a consequence of such a lack of papers and the complexity of mobile network protocols, there is a divide between security experts and EDT researchers, that prevents the easy development of security schemes. To overcome this important gap, this paper offers a tutorial of EDT and its security, analyzing its main vulnerabilities and concluding with a set of recommendations for researchers and manufacturers. In particular, due to the simplifications in the protocols done by EDT, vulnerabilities such as packet injection, replay attacks and injection of fake values to disable EDT have been found.

5G communication systems are one of the major enabling technologies to meet the needs of Industry 4.0. This paper focuses on the use case of automated guided vehicles (AGVs) in an outdoor industrial scenario. To meet the communication requirements in these type of use cases, dual connectivity (DC) with resource aggregation in the uplink (UL) is generally proposed. However, uncontrolled use of DC schemes may negatively affect the network causing effects such as reduced network capacity, increased signaling, and increased interference. To overcome these issues, this paper proposes and evaluates the use of a proactive DC activation algorithm based on the instantaneous quality of service (QoS) and network conditions. The proposed algorithm has two phases, a first phase in which the QoS prediction is performed, and a second phase in which the DC activation decision is made. The performance evaluation of the algorithm has been carried out in two different scenarios: a single-frequency (SF) network and a dual-frequency (DF) network; and compared to two baselines. Our results show that our predictive DC algorithm is sufficiently robust and can offer benefits in terms of reduced signaling and increased UL performance, especially in scenarios with low to medium traffic load.

In the field of rescue robotics, data collection about the environment and efficient communications are fundamental for the success of search and rescue missions. Digitalization provides new ways of detecting and localizing potential victims via the wireless devices carried by the users. Nowadays, the number of personal Bluetooth low energy wearables in use (smartbands, smartwatches, earbuds $\ldots$ ) increases constantly, being a yet-to-be-exploited personal radio frequency beacon in the case of an emergency, where the user may not be localized and unconscious. In this paper, the results of experimental tests of a Bluetooth low energy based detection system ported by terrestrial and aerial robots are provided, in order to test the feasibility of such system for the localization of the victims in unknown complex disaster areas. The results show that the tested devices can be reliably detected up to 15 meters away when using transmission power values typical of a smartphone, while being able to detect even lightly burdened devices. These results support the idea of developing an algorithm for the delimitation of areas of interest for the search and rescue groups, influencing the routes followed by the robot with the objective of exploring the detected devices area in the search of victims.

Aircrafts fully rely in a wide variety of electronic systems: sensors, navigation equipment, representation screens and communication elements. The interconnection of these avionics implies a huge challenge for the aircraft communication networks. Extremely stringent requirements in terms of reliability and predictability have to be provided for a safe operation. There are also important limitations to recreate those networks in a prototype form, due to the costs of the related equipment and the interest of testing different architectures and possible configurations dynamically. Moreover, performance estimation approaches based solely on network calculus provide limits for worst-case scenarios, without generating a detailed feedback under different circumstances and configurations of the network and different data sources. In this context, the present work proposes and develops an evaluation framework for avionics networks in order to support the verification and validation (V&V) procedures of the communication system before applying for certification. The proposed framework implements an event-based simulator in Simulink for Avionics Full-Duplex Switched Ethernet (AFDX), one of the most extended data protocols for avionics. This is evaluated in comparison with network calculus and bibliography performance estimation approaches, demonstrating its accuracy and capabilities.

The management of cellular networks, particularly within the environment rapidly advancing to 6G, presents considerable challenges due to the highly dynamic radio environment. Traditional tools such as Radio Environment Maps (REMs) have proven inadequate for real-time network changes, underlining the need for more sophisticated solutions. In response to these challenges, this work introduces a novel approach that harnesses the unprecedented power of state-of-the-art image classifiers for network management. This method involves the generation of Network Synthetic Images (NSIs), which are enriched heat maps that precisely reflect varying cellular network operating states. Created from user location traces linked with Key Performance Indicators (KPIs), NSIs are strategically designed to meet the intricate demands of 6G networks. This research delves deep into a comprehensive analysis of the diverse factors that could potentially impact the successful application of this methodology in the realm of 6G. The results from this investigation, coupled with a comparative assessment against traditional REM usage, emphasize the superior performance of this innovative method. Additionally, a case study involving an automatic network diagnosis scenario validates the effectiveness of this approach. The findings reveal that a generic Convolutional Neural Network (CNN), one of the most powerful tools in the arsenal of modern image classifiers, delivers enhanced performance, even with a reduced demand for positioning accuracy. This contributes significantly to the real-time, robust management of cellular networks as we transition into the era of 6G.

Over time, load balancing systems in cellular networks have been key to avoiding overload problems in the network and to maintaining a correct resource allocation and performance. However, the classical approaches were not designed for the dynamism generated by user behavior. The big crowds of users at certain social venues are one of the main concerns of mobile operators due to the load imbalance generated. Additionally, the mobility of users who attend social events (e.g., sports events, concerts, etc.) greatly impacts network performance due to its high correlation with network traffic. The availability to inform about events, particularly regarding venue location (e.g., stadiums, concert halls, convention centers) is exponentially growing thanks to its proliferation in social networks through geolocation databases and other functionalities. Therefore, the present work proposes a novel load balancing system integrating a fuzzy logic controller algorithm with social-awareness, which considers the relative position between cell sites and the social event venue in order to configure the network parameters. This approach is evaluated for different configurations of load balancing methods simulated on an urban macro scenario, mitigating the impact of the number of users per cell without degrading the signal quality. In this way, results show that social event data information plus soft or aggressive transmission power changes in cells can help to maintain the balance in the number of users per cell during mass events.

Abstract The use of unlicensed bands is one of the most promising features envisaged to increase capacity in 5G. However, this poses multiple challenges associated with the operation when coexisting networks are present, such as WiFi. Previous coexistence analyses have been focused on the user-plane data-related transmissions and mainly based on abstract models. Meanwhile, the effects of the shared channel signaling defined by the standards have been mostly disregarded, particularly for ultra-dense scenarios. This paper assesses how the shared data channel signaling mechanisms influence the performance of the coexisting technologies operating unlicensed bands in indoor environments. Based on this analysis, some DRS signaling modifications are envisaged to additionally enhance the service provision and fairness towards WiFi in these scenarios.

Due to the great complexity, heterogeneity, and variety of services, anomaly detection is becoming an increasingly important challenge in the operation of new generations of mobile communications. In many cases, the underlying relationships between the multiplicity of parameters and factors that can cause anomalous behavior are only determined by human expert knowledge. On the other hand, although automatic algorithms have a great capacity to process multiple sources of information, they are not always able to correctly signal such abnormalities. In this sense, this paper proposes the integration of both components in a framework based on Active Learning that enables enhanced performance in anomaly detection tasks. A series of tests have been conducted using an online anomaly detection algorithm comparing the proposed solution with a method based on the algorithm output alone. The obtained results demonstrate that a hybrid anomaly detection model that automates part of the process and includes the knowledge of an expert following the described methodology yields increased performance.

Abstract During the last few years, mobile communication networks have experienced a huge evolution. This evolution culminates with the arrival of the fifth generation (5G) of mobile communication networks. As a result, the complexity of network management tasks has been increasing and the need to use automatic management algorithms has been demonstrated. However, many mobile network operators (MNOs) are reluctant to evaluate these algorithms in their networks. To address this issue, in this paper, a modeling approach is proposed. In this sense, the behavior of a commercial network, as it is perceived by user equipments (UEs), has been replicated in a research testbed using a three-step modeling process. The first step consists on performing a measurement campaign in several external networks. The second step is composed of the measurement campaign result analysis and the classification of the results in different types of scenarios. Finally, the third step is related to the application of a modeling algorithm in a research testbed. In order to perform the last step, the use of a method based on a controller is proposed. The modeling process presented in this paper allows to replicate the network behavior from users located in different areas and with different conditions point of view. Moreover, the use of a testbed environment can help to avoid downtime in commercial networks caused by possible algorithm bugs.

Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios.

A high-precision location is becoming a necessity in the future Industry 4.0 applications that will come up in the near future. However, the construction sector remains particularly obsolete in the adoption of Industry 4.0 applications. In this work, we study the accuracy and penetration capacity of two technologies that are expected to deal with future high-precision location services, such as ultra-wide band (UWB) and WiFi fine time measurement (FTM). For this, a measurement campaign has been performed in a construction environment, where UWB and WiFi-FTM setups have been deployed. The performance of UWB and WiFi-FTM have been compared with a prior set of indoors measurements. UWB seems to provide better ranging estimation in LOS conditions but it seems cancelled by reinforcement concrete for propagation and WiFi is able to take advantage of holes in the structure to provide location services. Moreover, the impact of fusion of location technologies has been assessed to measure the potential improvements in the construction scenario.

Network roll-out is a process composed of several actions, which, if not completed correctly, may degrade the final system performance. A common error during the roll-out process is the case in which feeders from baseband units to radio units or feeders from radio units to antennas are interchanged. Consequently, in sectorized sites, the service areas of two or more co-sited cells become swapped, affecting the initial network design. This paper proposes to utilize the location of the user during mobility procedures to enhance the state-of-the-art methods to detect swapped sectors automatically. There are multiple solutions to obtain the location of subscribers in mobile communications networks. However, the most accurate ones are either costly or not possible in current networks. Therefore, this paper addresses the problem of applying time-of-arrival techniques based on multilateration in present-day mobile networks by proposing an alternative using standardized signaling. One of the main benefits of the proposed method is its ease of being implemented in real networks without adding additional costs to the operators. Finally, the results are presented to demonstrate that the proposed method improves the performance of state-of-the-art methods.

The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. To comply with critical messages, 5G offers the Ultra-Reliable and Low latency Communications (URLLC) service category with a millisecond end-to-end delay and reduced probability of failure. There are several approaches to achieve these requirements; however, these come at a cost in terms of redundancy, particularly the solutions based on multi-connectivity, such as Packet Duplication (PD). Specifically, this paper proposes a Machine Learning (ML) method to predict whether PD is required at a specific data transmission to successfully send a URLLC message. This paper is focused on reducing the resource usage with respect to pure static PD. The concept was evaluated on a 5G simulator, comparing between single connection, static PD and PD with the proposed prediction model. The evaluation results show that the prediction model reduced the number of packets sent with PD by 81% while maintaining the same level of latency as a static PD technique, which derives from a more efficient usage of the network resources.

The location of user equipments (UEs) allows application developers to customize the services for users to perceive an enhanced experience. In addition, this UE location enables network operators to develop location-aware solutions to optimize network resource management. Moreover, the combination of location-aware approaches and new network features introduced by 5G enables to further improve the network performance. In this sense, dual connectivity (DC) allows users to simultaneously communicate with two nodes. The basic strategy proposed by 3GPP to select these nodes is based only on the power received by the users. However, the network performance could be enhanced if an alternative methodology is proposed to make this decision. This paper proposes, instead of power-based selection, to choose the nodes that provide the highest quality of experience (QoE) to the user. With this purpose, the proposed system uses the UE location as well as multiple network metrics as inputs. A dense urban scenario is assumed to test the solution in a system-level simulation tool. The results show that the optimal selection varies depending on the UE location, as well as the increase in the QoE perceived by users of different services.

High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.

The mobile communication networks sector has experienced a great evolution during the last few years. The emergence of new services as well as the growth in the number of subscribers have motivated the search for new ways to optimize mobile networks. In this way, the objective pursued by optimization techniques has been evolving, shifting from the traditional optimization of radio parameters to the improvement of the quality perceived by users, known as quality of experience (QoE). In mobile networks, the radio link control (RLC) layer provides a reliable link between both ends of the communication and has a great impact on the QoE. In this paper, the optimization of the QoE for users based on the adjustment of the RLC layer is proposed. For this purpose, two typical services demanded by the users of mobile networks have been selected: the real-time video streaming service and file transfer service. For a broader view of the behavior of the QoE in relation to RLC, optimization tests have been carried out in scenarios with different system bandwidths. In this way, the relationship between the QoE and the optimal configuration of RLC in different network load situations has been analyzed. A proof of concept has been carried out to show the capability of this optimization. To that end, both a cellular network simulator and a live cellular network devised for research purposes have been used.

The arrival of the Fifth Generation (5G) entails a significant evolution in the context of mobile communication networks. This new technology will bring heterogeneous scenarios with new types of services and an increasingly high number of users and nodes. The efficient management of such complex networks has become an important challenge. To address this problem, automatic and efficient algorithms must be developed to facilitate operators’ management and optimization of their networks. These algorithms must be able to cope with a very high number of heterogeneous data and different types of scenarios. In this paper, a novel framework for a cellular network behavioral analysis and monitoring is presented. This framework is based on a combination of unsupervised and supervised machine learning techniques. The proposed system can analyze the behavior of cells and monitor them, searching for behavior changes over time. The information extracted by the framework can be used to improve subsequent management and optimization functions.

Although log processing of network equipment is a common technique in cellular network management, several factors make said task challenging, especially during mass attendance events. The present paper assesses classic methods for cellular network measurement and acquisition, showing how the use of on-the-field user probes can provide relevant capabilities to the analysis of cellular network performance. Therefore, a framework for the deployment of this kind of system is proposed here based on the development of a new hardware virtualization platform with radio frequency capabilities. Accordingly, an analysis of the characteristics and requirements for the use of virtual probes was performed. Moreover, the impact that social events (e.g., sports matches) may have on the service provision was evaluated through a real cellular scenario. For this purpose, a long-term measurement study during crowded events (i.e., football matches) in a stadium has been conducted, and the performances of different services and operators under realistic settings has been evaluated. As a result, several considerations are presented that can be used for better management of future networks.

The year 2020 was marked by the emergence of the COVID-19 pandemic. After months of uncontrolled spread worldwide, a clear conclusion is that controlling the mobility of the general population can slow down the propagation of the pandemic. Tracking the location of the population enables better use of mobility limitation policies and the prediction of potential hotspots, as well as improved alert services to individuals that may have been exposed to the virus. With mobility in their core functionality and a high degree of penetration of mobile devices within the general population, cellular networks are an invaluable asset for this purpose. This paper shows an overview of the possibilities offered by cellular networks for the massive tacking of the population at different levels. The major privacy concerns are also reviewed and a specific use case is shown, correlating mobility and number of cases in the province of Málaga (Spain).

The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. Ultra-Reliable and Low Latency Communications (URLLC) is the 5G service category used by critical mechanisms, with a millisecond end-to-end delay and reduced probability of failure. 5G defines new numerologies, together with mini-slots for a faster scheduling. The main challenge of this is to select the appropriate numerology according to radio conditions. This fact is very important in industrial scenarios, where the fundamental problems are interference and multipath propagation, due to the presence of concrete walls and large metallic machinery and structures. Therefore, this paper is focused on analyzing the impact of the numerology selection on the delay experienced at radio link level for a remote-control service. The study, which has been carried out in a simulated cellular factory environment, has been performed for different packet sizes and channel conditions, focusing on outliers. Evaluation results show that not always a higher numerology, with a shorter slot duration, is appropriate for this type of service, particularly under Non-Line-of-Sight (NLOS) conditions.

This work presents a method for estimating key quality indicators (KQIs) from measurements gathered at the nodes of a wireless network. The procedure employs multivariate adaptive filtering and a clustering algorithm to produce a KQI time-series suitable for post-processing by the network management system. The framework design, aimed to be applied to 5G and 6G systems, can cope with a nonstationary environment, allow fast and online training, and provide flexibility for its implementation. The concept’s feasibility was evaluated using measurements collected from a live heterogeneous network, and initial results were compared to other linear regression techniques. Suggestions for modifications in the algorithms are also described, as well as directions for future research.

Industry 4.0 is generalizing the use of wireless connectivity in manufacturing and logistics. Specifically, in Smart Logistics, novel Industry 4.0 technologies are used to enable agile supply chains, with reduced management, energy and storage costs. Cellular networks allow connectivity throughout all the scenarios where logistics processes take place, each having their own challenges. This paper explores such scenarios and challenges, and proposes 5G technology as a global unified connectivity solution. Moreover, this paper proposes a system for exploiting the application-specific optimization capabilities of 5G networks to better cater for the needs of Smart Logistics. An application traffic modeling process is proposed, along with a proactive approach to network optimization that can improve the Quality of Service and reduce connectivity costs.

Cloud Gaming is a cutting-edge paradigm in the video game provision where the graphics rendering and logic are computed in the cloud. This allows a user’s thin client systems with much more limited capabilities to offer a comparable experience with traditional local and online gaming but using reduced hardware requirements. In contrast, this approach stresses the communication networks between the client and the cloud. In this context, it is necessary to know how to configure the network in order to provide service with the best quality. To that end, the present work defines a novel framework for Cloud Gaming performance evaluation. This system is implemented in a real testbed and evaluates the Cloud Gaming approach for different transport networks (Ethernet, WiFi, and LTE (Long Term Evolution)) and scenarios, automating the acquisition of the gaming metrics. From this, the impact on the overall gaming experience is analyzed identifying the main parameters involved in its performance. Hence, the future lines for Cloud Gaming QoE-based (Quality of Experience) optimization are established, this way being of configuration, a trendy paradigm in the new-generation networks, such as 4G and 5G (Fourth and Fifth Generation of Mobile Networks).

Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

Next-generation mobile communications networks will have to cope with an extraordinary amount and variety of network performance indicators, causing an increase in the storage needs of the network databases and the degradation of the management functions due to the high-dimensionality of every network observation. In this paper, different techniques for feature extraction are described and proposed as a means for reducing this high dimensionality, to be integrated as an intermediate stage between the monitoring of the network performance indicators and their usage in mobile networks’ management functions. Results using a dataset gathered from a live cellular network show the benefits of this approach, in terms both of storage savings and subsequent management function improvements.

The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous monitoring of the performance indicators and the automatic detection of anomalies is especially important for network operators to prevent the network degradation and user complaints. Therefore, this paper proposes a methodology to detect and track anomalies in the mobile networks performance indicators online, i.e., in real time. The feasibility of this system was evaluated with several performance metrics and a real LTE Advanced dataset. In addition, it was also compared with the performances of other state-of-the-art anomaly detection systems.

Anomaly detection in the performance of the huge number of elements that are part of cellular networks (base stations, core entities, and user equipment) is one of the most time consuming and key activities for supporting failure management procedures and ensuring the required performance of the telecommunication services. This activity originally relied on direct human inspection of cellular metrics (counters, key performance indicators, etc.). Currently, degradation detection procedures have experienced an evolution towards the use of automatic mechanisms of statistical analysis and machine learning. However, pre-existent solutions typically rely on the manual definition of the values to be considered abnormal or on large sets of labeled data, highly reducing their performance in the presence of long-term trends in the metrics or previously unknown patterns of degradation. In this field, the present work proposes a novel application of transform-based analysis, using wavelet transform, for the detection and study of network degradations. The proposed system is tested using cell-level metrics obtained from a real-world LTE cellular network, showing its capabilities to detect and characterize anomalies of different patterns and in the presence of varied temporal trends. This is performed without the need for manually establishing normality thresholds and taking advantage of wavelet transform capabilities to separate the metrics in multiple time-frequency components. Our results show how direct statistical analysis of these components allows for a successful detection of anomalies beyond the capabilities of detection of previous methods.

The constant evolution in mobile communications networks have led operators to seek new techniques to optimize their mobile networks with the objective of satisfying the expectations of the users. In this way, traditional optimization techniques based on improving radio indicators, have given way to new techniques based on improving the quality of experience (QoE) perceived by users. This paper is focused on analyzing the impact of the adjustment of radio link control (RLC) layer configuration parameters on the QoE perceived by the users of two different types of services. Firstly, an evaluation of the QoE experienced by the user of a real-time video streaming service with respect to the transmission buffer size of the RLC layer in unacknowledged mode (UM) has been carried out. Secondly, the QoE perceived by the user of a file transfer service in relation to the variation of the configuration parameters of the RLC layer in acknowledged mode (AM) has been evaluated. The study, which has been carried out in a simulated cellular environment, has been performed for different system bandwidth values, thus proving the relationship between the QoE perceived by the users, the optimal RLC configuration parameters values and the available bandwidth.

Measuring traffic in real time is one of the main functionalities of Smart Cities. To reduce the costs of deployment and operation, traffic measurement with mobile devices has been widely studied. In this paper, a traffic monitoring system using mobile devices is proposed. The proposed algorithm has the advantage of having a very low computational cost, allowing most of the pre-processing to be done in the mobile device and therefore making possible the centralized collection of a massive number of measurements. The proposed system is composed of three algorithms; a map-matching algorithm to correct minor location errors, a Virtual Inductive Loop that estimates the traffic and a traffic data collector that aggregates the information from many devices and combines it with other information sources. The system has been tested in a real scenario, comparing its accuracy with a traditional traffic sensor, showing its accuracy.

For the past few years, the concept of the Internet of Things (IoT) has been a recurrent view of the technological environment where nearly every object is expected to be connected to the network. This infrastructure will progressively allow one to monitor and efficiently manage the environment. Until recent years, the IoT applications have been constrained by the limited computational capacity and especially by efficient communications, but the emergence of new communication technologies allows us to overcome most of these issues. This situation paves the way for the fulfillment of the Smart-City concept, where the cities become a fully efficient, monitored, and managed environment able to sustain the increasing needs of its citizens and achieve environmental goals and challenges. However, many Smart-City approaches still require testing and study for their full development and adoption. To facilitate this, the university of Málaga made the commitment to investigate and innovate the concept of Smart-Campus. The goal is to transform university campuses into “small” smart cities able to support efficient management of their area as well as innovative educational and research activities, which would be key factors to the proper development of the smart-cities of the future. This paper presents the University of Málaga long-term commitment to the development of its Smart-Campus in the fields of its infrastructure, management, research support, and learning activities. In this way, the adopted IoT and telecommunication architecture is presented, detailing the schemes and initiatives defined for its use in learning activities. This approach is then assessed, establishing the principles for its general application.