Logistics is a key economic sector where any optimization that reduces costs or improves service has a great impact on society at large. In this paper, the role of two 5G Network Slicing (NS) strategies in Smart Logistics is studied: the use of a static slice with a balance division of network resources and the use of a dynamic slice. To validate the potential gains of these strategies, a Distribution Center with 5G connectivity is simulated, recreating the activity that takes place in a real Smart Logistics scenario. Results show that a dynamic slice makes a more efficient usage of the network resources, improving the quality of service for the different traffic profiles, even when there is a traffic peak. This improvement ranges from 6.48\% to 95.65\%, depending on the specific traffic profile and the evaluated metric.

The integration of Digital Twin (DT) technology into wireless communication networks, particularly in Wi-Fi systems, is emerging as a transformative approach to optimizing network resources and performance. As the demand for next-generation wireless services increases, particularly with the advancements in Wi-Fi 7 and the anticipated developments in Wi-Fi 8, addressing the complexities of resource allocation, interference management, and user behavior prediction becomes crucial. This paper explores the role of DTs in optimizing resource management for WiFi networks, focusing on design aspects, high-level architecture, and the challenges associated with their implementation. A detailed overview of current DT frameworks and technologies is provided, emphasizing their potential to improve network efficiency in dynamic and dense environments. Furthermore, key open research challenges are discussed, including the modeling of interfering access points, user mobility, and the application of data-driven machine learning approaches for proactive network management. By addressing these challenges, DTs can enable advanced use cases such as ultra-low latency communication, extended reality applications, and Internet of Everything (IoE) services. This paper offers valuable insights for future research in the development of DT-based solutions for resource optimization in Wi-Fi networks, with a focus on Wi-Fi 7 and Wi-Fi 8 as the next frontiers in wireless technology.

5G New Radio (NR) network deployment in Non-Stand Alone (NSA) mode means that 5G networks rely on the control plane of existing Long Term Evolution (LTE) modules for control functions, while 5G modules are only dedicated to the user plane tasks, which could also be carried out by LTE modules simultaneously. The first deployments of 5G networks are essentially using this technology. These deployments enable what is known as E-UTRAN NR Dual Connectivity (ENDC), where a user establish a 5G connection simultaneously with a pre-existing LTE connection to boost their data rate. In this paper, a single Federated Deep Reinforcement Learning (FDRL) agent for the optimization of the event that triggers the dual connectivity between LTE and 5G is proposed. First, single Deep Reinforcement Learning (DRL) agents are trained in isolated cells. Later, these agents are merged into a unique global agent capable of optimizing the whole network with Federated Learning (FL). This scheme of training single agents and merging them also makes feasible the use of dynamic simulators for this type of learning algorithm and parameters related to mobility, by drastically reducing the number of possible combinations resulting in fewer simulations. The simulation results show that the final agent is capable of achieving a tradeoff between dropped calls and the user throughput to achieve global optimum without the need for interacting with all the cells for training.

Information and communications technologies are increasingly present in sports’ execution, being implemented in a huge variety of tools used to improve player performances and/or the decision-making of referees. However, the position and movement of the players, being an essential information variable, is still not widely measured by current systems, especially at indoors and outside the high-competition. To address this, the present work focuses on the application of positioning techniques for indoor sports. In this way, a system employing Ultra-Wideband based radio-positioning is designed and implemented to locate players in real time. The performance of such system is then evaluated in a real environment, considering several trajectories with direction changes and followed in different conditions.

The gaming industry has proposed the concept of Cloud Gaming (CG), a paradigm that enhances the gaming experience on reduced hardware devices. However, this paradigm puts a lot of pressure on the communication links that connect the user to the cloud. As a result, the service experience becomes highly dependent on network connectivity. In this context, the present work proposes a framework for measuring and estimating the most important E2E (end-to-end) metrics of the CG service, namely Key Quality Indicators (KQIs). Therefore, different machine learning (ML) techniques are evaluated to predict KQIs related to the CG user experience. For this purpose, the most important KQIs of the service, such as input lag, freezes or perceived video frame rate, are collected in a real network deployment. The results show that ML techniques can be used to estimate these indicators solely from network-related metrics. This is seen as a valuable asset for the delivery of CG services over cellular networks, even without access to the user’s device, as it is expected for telecom operators.

Traditionally, mobile network management has been based on reactive systems, where corrective actions are taken when a failure or a suboptimal network performance is detected. However, the requirements of low latency and high throughput associated with 5G new services, make these reactive systems insufficient to manage the new needs of users. Thus, the focus of mobile network management has changed to a proactive approach, where preventive actions are taken to avoid failures and suboptimal network performance. Moreover, in 5G, management algorithms are expected to make use of large amounts of information sources. The use of this large amount of information and the need to use historical data to generate forecasting models, may cause a loss of accuracy. To address these challenges, a forecasting framework is proposed in this work. The framework is based on the use of automatic feature selection techniques in both spatial and temporal dimensions as a pre-prediction stage. In this way, the proposed framework can improve the accuracy of other prediction schemes when using large amounts of data as input. The proposed scheme has been tested using different forecasting techniques. In addition, the impact of the proposed framework on predictions of different future lags has been analyzed.

The generalization in the use of virtualization in the upcoming generation of cellular networks involves new paradigms and approaches for their management. The correct sharing of the underlying resources between multiple virtualized network functions as well as any other processes sharing the same computational platform implies a complex architecture that makes virtualization challenging. As a result, numerous variables (e.g., computational capacity) were mostly ignored by previous management systems, but that can now lead to relevant impacts on the service performance. In this virtualized scenario with multiple coexistent processes over the same hardware, a “Noisy Neighbour” (NN) is identified as an entity that uses most of the underlying resources while other virtual units suffer a lack of them. While difficult to identify, such situations can affect the network service. In this context, the present work analyzes and assesses the NN problem for 5G Core scenarios. A complete emulated 5G network and analysis framework is defined and developed to evaluate the impact of a noisy entity. In this way, the degradation that Key Performance Indicators suffer in the network and by the end-users when a NN appears is assessed. Thus, the present work proposes a baseline for handling NN through a novel lifecycle management flow. For this purpose, it is evaluated the effectiveness of multiple Machine Learning (ML) models for the identification of NN, based on the metrics gathered from the proposed framework, achieving 99% of accuracy. Moreover, ML is applied to develop a method for network performance inference, along with a prediction model to forecast the number of CPU resources the network may demand at any given time supporting the proposed management flow.

Extended Reality (XR) arises as one of the current cutting-edge educational and entertainment emergent technologies. This service differs from traditional video streaming approaches due to its immersive experience, which allows the user to enjoy omnidirectional multimedia. However, the service experience must be guaranteed to avoid side effects such as cybersickness or disorientation. This work presents a framework to assess 360-video service streaming performance over mobile networks through Key Quality Indicators (KQIs) using VR (Virtual Reality) HMD (Head Mounted Device). The testbed is composed of a 360-video client for DASH (Dynamic Adaptive Streaming over HTTP), which playbacks multimedia content from a video server located in the cloud while KQI measuring tasks are performed in the user as on the network sides. Various metrics are collected such as resolution, frame rate, initial playback time, throughput, stall events, and round trip time (RTT), among various others. Finally, a performance comparison between LTE and 5G technologies is provided. Results from the KQI measurement highlight the potential of the new generation of mobile networks in the provision of service with high-quality levels of experience.

This paper proposes a distributed deep reinforcement learning (DRL) methodology for autonomous mobile robots (AMRs) to manage radio resources in an indoor factory with no network infrastructure. Hence, deep neural networks (DNN) are used to optimize the decision policy of the robots, which will make decisions in a distributed manner without signalling exchange. To speed up the learning phase, a centralized training is adopted in which a single DNN is trained using the experience from all robots. Once completed, the pre-trained DNN is deployed at all robots for distributed selection of resources. The performance of this approach is evaluated and compared to 5G NR sidelink mode 2 via simulations. The results show that the proposed method achieves up to 5% higher probability of successful reception when the density of robots in the scenario is high.

5G technology is called to support the next generation of wireless communications and realize the “Internet of Everything” through its mMTC (massive Machine-Type-Communications) service. The recently standardized 5G-AKA protocol is intended to deal with security and privacy issues detected in earlier generations. Nevertheless, several 5G-AKA shortcomings have been reported, including a possibly excessive computational complexity for many IoT devices. To address these, a promising lightweight 2-pass authentication and key agreement (AKA) protocol for 5G mobile communications has recently been proposed by Braeken. Compared to the 5G-AKA protocol, this does not require the use of public key encryption. This paper analyzes the security claims of Braeken’s protocol and shows that it does not provide full unlinkability, but only session unlinkability, and is (still) subject to Linkability of AKA Failure Messages (LFM) attacks. We propose solutions to such problems and prove that symmetric-key based protocols cannot offer higher privacy protection levels without compromising availability. We then describe an enhanced version of this protocol that addresses these vulnerabilities and supports forward secrecy, which is a desirable feature for low-cost IoT devices.

In this paper a novel, efficient, and fast-performing vision-based system for traffic flow monitoring is presented. Using standard traffic surveillance cameras and effectively applying simple techniques, the proposed method can produce accurate results on vehicle counting in different challenging situations, such as low-resolution videos, rainy scenes, and situations of stop-and-go traffic. Due to the simplicity of the proposed algorithm, the system is able to manage multiple video streams simultaneously in real time. The method follows a robust adaptive background segmentation strategy based on the Approximated Median Filter technique, which detects pixels corresponding to moving objects. Experimental results show that the proposed method can achieve sufficient accuracy and reliability while showing high performance rates, outperforming other state-of-the-art methods. Tests have proved that the system is able to work with up to 50 standard-resolution cameras at the same time in a standard computer, producing satisfactory results.

The current trend in the management of mobile communication networks is to increase the level of automation in order to enhance network performance while reducing Operational Expenditure (OPEX). In this context, the 3rd Generation Partnership Project (3GPP) has presented different solutions. On the one hand, Self-Organizing Networks (SON) include self-healing capabilities, which allow operators to automate their troubleshooting tasks in order to identify and solve the problems of the network. On the other hand, the use of mobile traces or Minimization of Drive Tests (MDT) are proposed to automate the collection of user's measurements and signalling messages. This paper proposes to combine both solutions, SON and traces, with the purpose of quickly detecting and solving issues related to the radio interface. That is, the user information gathered by the cell traces function is used to perform an automatic diagnosis of the RF condition of each cell. In addition, the proposed approach allows to precisely locate RF problems based on the assessment of the RF condition. Mobile traces constitute large sets of data, whose analysis requires the application of big-data analytics techniques. The proposed system has been evaluated in two different live LTE networks, demonstrating its validity and utility.

The Self-Organizing Networks (SON) paradigm proposes a set of functions to automate network management in mobile communication networks. Within SON, the purpose of Self-Healing is to detect cells with service degradation, diagnose the fault cause that affects them, rapidly compensate the problem with the support of neighboring cells and repair the network by performing some recovery actions. The diagnosis phase can be designed as a classifier. In this context, hybrid ensembles of classifiers enhance the diagnosis performance of expert systems of different kinds by combining their outputs. In this paper, a novel scheme of hybrid ensemble of classifiers is proposed as a two-step procedure: a modeling stage of the baseline classifiers and an application stage, when the combination of partial diagnoses is actually performed. The use of statistical models of the baseline classifiers allows an immediate ensemble diagnosis without running and querying them individually, thus resulting in a very low computational cost in the execution stage. Results show that the performance of the proposed method compared to its standalone components is significantly better in terms of diagnosis error rate, using both simulated data and cases from a live LTE network. Furthermore, this method relies on concepts which are not linked to a particular mobile communication technology, allowing it to be applied either on well established cellular networks, like UMTS, or on recent and forthcoming technologies, like LTE-A and 5G.

Accurate indoor positioning systems are still expensive in terms of hardware price or computing complexity. Cost reduction of wireless devices, reuse of already deployed infrastructures besides the non-intrusive features (compared to vision-based systems) of radiofrequency (RF) solutions, make these systems good candidates for cost effective indoor positioning systems. Here, the use of multi-antenna devices could help to improve location accuracy and to reduce the infrastructure costs. This paper presents a signal strength-based localization system using active UHF RFID. Different indoor positioning techniques are defined and analyzed, particularizing them for the multi-antenna case. The system performance is assessed for these methods and comparing one and two-antenna readers based on the field trial measurements. Finally, a trade-off of the number of tags and number of antennas is carried out in order to evaluate the capability of the presented system to reduce fixed infrastructure costs.

Nowadays, the size and complexity of mobile networks are growing ceaselessly. Therefore, the management of mobile networks is a significant, expensive and demanding task to perform. In order to simplify this task, Self-Organizing Networks (SON) appear as a unified solution to autonomously manage a mobile network. One of the fundamental functions of SON is self-healing. Within self- healing, the objective of fault diagnosis or root cause analysis is the identification of problem causes in faulty cells. With that aim, in this paper, an unsupervised diagnosis system for LTE (Long Term Evolution) based on Bayesian networks is presented. In particular, the system is divided in two separate steps. First of all, the discretization of the input data is done. Then, the system provides an identification of the cell status. Depending on the discretization method, the performance of the system is different, so, in this paper, different methods have been evaluated. Results have proven the high success rate achieved with the proposed system, particularly when the Expectation-Maximization (EM) algorithm is used for the discretization.

The evolution of cellular technologies has been followed by a continuous increase in their capacity and complexity. As a consequence, network management has become a challenge for network operators, especially at indoor environments. In these scenarios the percentage of calls and traffic is much higher than outdoors. Hence, intelligent and automatic mechanisms for network operation and maintenance are deemed necessary, leading to the so-called Self-Organizing Networks (SON). SON mechanisms analyze network indicators like counters, alarms, etc. in order to improve the network performance. Furthermore, in indoor scenarios, the recent advances in indoor positioning systems allow the integration of terminal position in the definition of novel SON techniques. The availability of this additional information provides knowledge about terminal distributions, mobility patterns, etc., which is useful data to enhance the efficiency and performance of SON techniques. In this sense, this paper proposes and develops location-aware SON techniques for indoor femtocell networks. In particular, novel self-optimization and self-healing algorithms are defined, which are supported by an indoor cellular-positioning system. Such mechanisms are evaluated in a real testbed environment.

This paper presents a novel approach for self-healing in cellular networks based on the application of mobile terminals context information: time, service, activity, identity and, especially, location. Context information is therefore used to support root cause analysis, providing improved network fault diagnosis compared to classical non-context-aware approaches. The integration of context information is implemented by means of the newly defined contextualized indicators. These are used in order to integrate user equipment context information in pre-existent failure management schemes. The presented techniques are especially suitable for indoor small cell scenarios, whose particular conditions of dynamic user distribution, overlapping coverage, dynamic radio and service provisioning environment, etc., make previous diagnosis schemes especially unreliable. The algorithms and methodology for the proposed context-aware system are defined and its performance is assessed by means of an LTE system-level simulator.

The recent developments in cellular networks, along with the increase in services, users and the demand of high quality have raised the Operational Expenditure (OPEX). Self-Organizing Networks (SON) are the solution to reduce these costs. Within SON, self-healing is the functionality that aims to automatically solve problems in the radio access network, at the same time reducing the downtime and the impact on the user experience. Self-healing comprises four main functions: fault detection, root cause analysis, fault compensation and recovery. To perform the root cause analysis (also known as diagnosis), Knowledge-Based Systems (KBS) are commonly used, such as fuzzy logic. In this paper, a novel method for extracting the Knowledge Base for a KBS from solved troubleshooting cases is proposed. This method is based on data mining techniques as opposed to the manual techniques currently used. The data mining problem of extracting knowledge out of LTE troubleshooting information can be considered a Big Data problem. Therefore, the proposed method has been designed so it can be easily scaled up to process a large volume of data with relatively low resources, as opposed to other existing algorithms. Tests show the feasibility and good results obtained by the diagnosis system created by the proposed methodology in LTE networks.

In Self-Organizing Networks (SON), load balancing has been recognized as an effective means to increase network performance. In cellular networks, cell load balancing can be achieved by tuning handover parameters, for which a Fuzzy Logic Controller (FLC) usually provides good performance and usability. Operator experience can be used to define the behavior of the FLCs. However, such a knowledge is not always available and hence optimization techniques must be applied in the controller design. In this work, a fuzzy $Q$-Learning algorithm is proposed to find the optimal set of fuzzy rules in an FLC for traffic balancing in GSM-EDGE Radio Access Network (GERAN). Load balancing is performed by modifying handover margins. Simulation results show that the optimized FLC provides a significant reduction in call blocking.

In this paper the behavior and the self-optimization of an LTE network under realistic conditions are investigated. To enhance network performance in a urban environment a controller to auto-tune parameters has been proposed. An urban mobility model has also been defined in order to test the proposed method under realistic conditions. This model allows to investigate some performance features, wich are not visible with a simple mobility model. In this paper, we propose to use a Fuzzy Logic Controller (FLC) for the optimization of handover parameters for adaptive load balancing. Results show that under an agglomeration of vehicles in a main road of the scenario, the proposed method achieves an improvement in the global Call Blocking Ratio (CBR).

In the near future, several radio access technologies will coexist in Beyond 3G mobile networks (B3G) and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e. those that self-configure, self-protect, self-heal and self-optimize) are the solution to tackle the high complexity inherent to these networks. In this context, this paper proposes a system for auto-diagnosis in the Radio Access Network (RAN) of wireless systems. The malfunction of the RAN may be due not only to a hardware fault, but also (and more difficult to identify) to a bad configuration. The proposed system is based on the analysis of Key Performance Indicators (KPIs) in order to isolate the cause of the network malfunction. In this paper, two alternative probabilistic systems are compared, which differ on how KPIs are modelled (continuous or discrete variables). Experimental results are examined in order to support the theoretical concepts, based on data from a live network. The drawbacks and benefits of both systems are studied and some conclusions on the scenarios under which each model should be used are presented.

Troubleshooting (TS) in UMTS networks are basic management tasks required to guarantee efficient usage of the network infrastructure. This paper presents a methodology for automating TS tasks based on Bayesian networks (BN). In a first learning phase, data relating symptoms and alarms to faults in the network is extracted and used to create the TS model. In a second phase, symptoms and alarms are used to identify faults with the highest probabilities. A TS case study using a dynamic system simulator illustrates the effectiveness of the proposed approach

In the forthcoming years, different radio access technologies (GSM, GPRS, UMTS, etc.) will have to coexist within the same cellular network. In this scenario of increasingly complex networks, automated management is becoming a crucial issue to provide high-quality services. In this paper, a system for automatic fault diagnosis of the radio access part of a mobile communication system is presented. For this purpose, a probabilistic diagnosis model based on discrete Bayesian Networks (BNs) is proposed. There is always a trade-off between accuracy and complexity of the model. Hence, two alternative structures to code the dependencies among elements in the model are compared with regard to their simplicity and performance. Empirical results are examined, based on data from a live GSMIGPRS network. Taking into account the experiments, a BN structure is selected for diagnosis in cellular networks.

Frequency plans (FPs) based on mobile measurements present several advantages compared to those based on propagation predictions. These FPs use interference matrices (IMs) created from measurement reports (MMRs). MMRs, as described in the GSM specifications, present some limitations: quantization and truncation of measured values, only six strongest neighbours reported and BSIC (base station identity code) decoding procedure. In addition, different types of IMs, depending on the processing of the raw data, can be considered. In this paper, MMRs limitations and their impact on different types of IMs are analyzed. Simulations for macrocell and microcell scenarios have been carried out to reinforce the conclusions.