The rapid growth of the Internet of Things (IoT) has increased demand for Low Power Wide Area Networks (LPWANs) that support massive Machine-Type Communications (mMTC) in Fifth Generation (5G) ecosystems. However, integrating LPWAN technologies like Long Range Wide Area Network (LoRaWAN) with 5G infrastructure presents challenges. This paper introduces 6G-LoRaGRAN, a novel testbed framework designed to facilitate research on LoRaWAN and 5G integration. Developed at the University of Granada, this testbed connects a real LoRaWAN network to 5 G cores across multiple pilot sites as part of the European 6G-SANDBOX project. The testbed supports remote experimentation through an Experiment Manager, enabling researchers to explore diverse configurations, including customizable network slices, traffic patterns, and realtime resource management. With over 100 deployed LoRaWAN devices and automated connectivity monitoring, the platform serves as a powerful tool for experimentation within a realworld LoRaWAN-5G testbed. Two experiments are presented. The first compares empirical results to theoretical collision probabilities, demonstrating the testbed's ability to support research on LoRaWAN performance and scalability. The second uses two LoRaWAN slices to demonstrate inter-slice isolation.

Industry 4.0 arises to realize smart factories by the assistance and even the full automation of the different industrial manufacturing processes. Such industry digitization requires high-performance wireless connectivity to convey the traffic heterogeneity of the emerging wireless-enabled Industry 4.0 services such as mobile robots-assisted production. Albeit all eyes are on the Fifth Generation (5G) to meet the requirements imposed by this traffic, the use of alternative wireless access technologies is appealing to reduce the deployment costs of private 5G networks. More precisely, part of the network traffic generated by non-critical data-hungry services such as streaming-based X-Reality (XR) assisted workers could be served, for instance, by Wireless Fidelity (Wi-Fi). Recent 5G standards include capabilities for both facilitating the integration of non3GPP Wireless Access Technologies (WATs), such as Wi-Fi, and traffic steering among the available WATs. In this article, we assess the throughput gain brought by 5G multi-connectivity capabilities in an industrial multi-WAT Radio Access Network (RAN). The industrial scenario considered and setup resemble a real smart factory use case. Last, we translate this throughput gain into an increase of the attainable 5G aggregated capacity for Ultra-Reliable and Low-Latency Communications (URLLC) services as a function of their requirements and numerology.

Radio Access Network (RAN) slicing involves several challenges. In particular, the Mobile Network Operator (MNO) must ensure —before deploying each slice—that corresponding requirements can be met throughout its lifetime. For ultra-Reliable Low Latency Communication (uRLLC) slices, the MNO must guarantee the packet transmission delay within a delay budget with a certain probability. Most existing solutions focus on allocating dynamically radio resources to maximize the number of packets, whose transmission delay is within the delay budget. However, these solutions do not ensure the violation probability is below a target value in the long term. In this paper, we focus on slicing from a planning perspective. Specifically, we propose a Stochastic Network Calculus (SNC)-based model, which given the amount of radio resources allocated for a uRLLC slice, the target violation probability and the traffic demand distribution, provides the delay bound for such conditions. Additionally, we propose heuristics for planning uRLLC slices. Interestingly, such heuristics benefit from the proposed SNC-based model to compute the amount of radio resources to be assigned to each slice while its delay bound, given a target violation probability, is within the delay budget. We validate the SNC-based model and demonstrate the effectiveness of the heuristics.

When a network operator designs strategies for planning and operating Guaranteed Bit Rate (GBR) slices, there are inherent issues such as the under(over)-provisioning of radio resources. To avoid them, modeling the User Equipment (UE) blocking probability in each cell is key. This task is challenging due to the total required bandwidth depends on the channel quality of each UE and the spatio-temporal variations in the number of UE sessions. Under this context, we propose an analytical model to evaluate the UE blocking probability in an Orthogonal Frequency Division Multiple Access (OFDMA) cell. The main novelty of our model is the adoption of a multi-dimensional Erlang-B system which meets the reversibility property. This means our model is insensitive to the holding time distribution for the UE session. In addition, this property reduces the computational complexity of our model due to the solution for the state transition probabilities has product form. The provided results show that our model exhibits an estimation error for the UE blocking probability below 3.5%.

Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson's networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10 percent. Finally, we show the usefulness of the model for performing the dynamic resource provisioning of the vMME experimentally.

5G is the next telecommunications standards that will enable the sharing of physical infrastructures to provision ultra shortlatency applications, mobile broadband services, Internet of Things, etc. Network slicing is the virtualization technique that is expected to achieve that, as it can allow logical networks to run on top of a common physical infrastructure and ensure service level agreement requirements for different services and applications. In this vein, our paper proposes a novel and complete solution for planning network slices of the LTE EPC, tailored for the enhanced Mobile BroadBand use case. The solution defines a framework which consists of: i) an abstraction of the LTE workload generation process, ii) a compound traffic model, iii) performance models of the whole LTE network, and iv) an algorithm to jointly perform the resource dimensioning and network embedding. Our results show that the aggregated signaling generation is a Poisson process and the data traffic exhibits self-similarity and long-range-dependence features. The proposed performance models for the LTE network rely on these results. We formulate the joint optimization problem of resources dimensioning and embedding of a virtualized EPC and propose a heuristic to solve it. By using simulation tools, we validate the proper operation of our solution.

The recent standardization of 3GPP Narrowband Internet of Things (NB-IoT) paves the way to support low-power wide-area (LPWA) use cases in cellular networks. NB-IoT design goals are extended coverage, low power and low cost devices, and massive connections. As a new radio access technology, it is necessary to analyze the possibilities NB-IoT provides to support different traffic and coverage needs. In this paper, we propose and validate an NB-IoT energy consumption model. The analytical model is based on a Markov chain. For the validation, an experimental setup is used to measure the energy consumption of two commercial NB-IoT user equipments (UEs) connected to a base station emulator. The evaluation is done considering three test cases. The comparison of the model and measurements is done in terms of the estimated battery lifetime and the latency needed to finish the control plane procedure. The conducted evaluation shows the analytical model performs well, obtaining a maximum relative error of the battery lifetime estimation between the model and the measurements of 21% for an assumed interarrival time (IAT) of 6 min.

Narrowband Internet of Things (NB-IoT) technology emerged in Release 13 as one of the solutions to provide cellular IoT connectivity. NB-IoT is designed to achieve better indoor coverage, support of a massive number of low-throughput devices, with relaxed delay requirements, and lower energy consumption. Particularly, the extensive coverage of NB-IoT poses a great challenge. The goal is to cover devices in areas previously inaccessible by cellular networks due to penetration losses or remote locations. To solve this, NB-IoT utilizes bandwidth reduction and repetitions. However, for the targeted low range of signal to noise ratio (SNR), the coverage gain due to repetitions can be significantly limited by the performance of the channel estimator. In this paper, we provide an analytical evaluation framework to study the performance of NB-IoT. Our analysis includes the limitations due to realistic channel estimation (CE) and delves into the estimation of the SNR. Additionally, the conducted evaluation shows the impact of the coverage extension in the final performance of the NB-IoT user equipment (UE) in terms of uplink packet transmission latency and battery lifetime. Specifically, regarding UE's battery lifetime, for a maximum coupling loss (MCL) of 164 dB, realistic CE evaluations obtain a battery lifetime reduction of approximately 90% compared to ideal CE.

The Internet of Things is one of the hottest topics in communications today, with current revenues of $151B, around 7 billion connected devices, and an unprecedented growth expected for next years. A massive number of sensors and actuators are expected to emerge, requiring new wireless technologies that can extend their battery life and can cover large areas. LoRaWAN is one of the most outstanding technologies which fulfill these demands, attracting the attention of both academia and industry. In this paper, the design of a LoRaWAN testbed to support critical situations, such as emergency scenarios or natural disasters, is proposed. This self-healing LoRaWAN network architecture will provide resilience when part of the equipment in the core network may become faulty. This resilience is achieved by virtualizing and properly orchestrating the different network entities. Different options have been designed and implemented as real prototypes. Based on our performance evaluation, we claim that the usage of microservice orchestration with several replicas of the LoRaWAN network entities and a load balancer produces an almost seamless recovery which makes it a proper solution to recover after a system crash caused by any catastrophic event.

Requirements for 5G mobile networks includes a higher flexibility, scalability, cost effectiveness and energy efficiency. Towards these goals, Software Defined Networking (SDN) and Network Functions Virtualization have been adopted in recent proposals for future mobile networks architectures because they are considered critical technologies for 5G. In this paper, we propose an X2-based handover implementation in an SDN-based and partially virtualized LTE architecture. Moreover, the architecture considered operates at link level, which provides lower latency and higher scalability. In our implementation, we use MPLS tunnels for user plane instead of GTP-U protocol, which introduces a significant overhead. To verify the correct operation of our system, we developed a simulator. It implements the messages exchange and processing of the primary network entities. Using this tool we measured the handover preparation and completion times, whose estimated values were roughly 6.94 ms and 8.31 ms, respectively, according to our experimental setup. These latencies meet the expected requirements concerning control plane delay budgets for 5G networks.

In this paper, we study the performance of the YouTube service over 3G Long Term Evolution (LTE) by means of dynamic network simulations. We consider a typical configuration of an LTE network for TCP traffic and the traffic generation model for YouTube `Flash' videos downloaded onto a personal computer (PC). Furthermore, in order to achieve more reliable results in the simulations, the main configuration parameters are obtained for TCP Cubic congestion control algorithm used by YouTube media servers. The results obtained show that: the number of pauses experimented by users during video download are heavily influenced by the cell load, but the same is not true for pause duration; most of the packet losses occur during initial burst due to the TCP adaptation to the BDP of the link, and, unlike packet losses during throttling phase, these are not depend on radio link quality; and in most cases the user do not use the maximum data rate achievable in the LTE interface.

Cloud computing has become an essential technology not only for web provisioning, but also in mobile scenarios. Mobile devices are usually resource constrained due to processing and power limitations, so typical applications are not easy portable. Battery draining and application performance (resource shortage) have a big impact on the experienced quality, so shifting applications and services to the Cloud may improve mobile user's satisfaction. However, available Cloud solutions are mostly focused on scenarios with slowly changing provisioning, which are unable to support and promptly react to short-term provisioning requests. To address the new scenario, this paper proposes a novel Cloud monitoring and management architecture based on the data-centric publish-subscribe Data Distribution Service (DDS) standard. We present not only an architecture proposal, but also a real prototype that we have deployed in our experimental testbed. The experimental results show that our architecture is able to support the scheduling of highly dynamic tasks in the Cloud while maintaining low overheads.

This paper presents a new method for testing dialogue systems using a variety of real-world conditions simulated in lab. The method is based on the use of an additional dialogue system, called simulator, designed to behave as users interacting with the dialogue system to test. The behavior of the simulator is decided from diverse scenarios that represent user goals. The simulator tries to achieve the goals in the scenarios during the interaction with the dialogue system. We applied the method to test in noise conditions a dialogue system under development in our lab, considering white and babble noise. The method allowed to find out errors in the recognizer and in the strategy the system uses to handle user confirmations. Using the method, we tested the behavior of the system in terms of task completion, taking into account a VTS noise compensation technique. The experiments show that, if the VTS technique is not used, the average task completion is 31,43% for the white noise and 29,64% for the babble noise. If the VTS technique is used, the average task completion increases to 57,35% for the white noise and to 54,44% for the babble noise.

Combined source and channel coding is a technique to mitigate channel errors without increasing the bit error rate. Channel optimized vector quantizer (COVQ) performs these objectives in the context of vector quantization. This paper presents a study of channel optimized matrix quantizer (COMQ) applied to quantize the line spectral pair (LSP) parameters as an extension of COVQ technique. Gaussian and slow-fading Rayleigh channels are considered and GMSK (Gaussian minimum shift-keying) is used as modulation technique. Several channel signal to noise ratio (CSNR) are considered to measure the performance of this system. In addition, for comparison purposes, the performance of other schemes for quantizing the LSP parameters are computed.

A channel optimized vector quantizer (COVQ) is studied for the case of transmission over waveform channels. In this work, a number of modulation schemes with multidimensional signal constellations are considered, specifically, results on the binary signalling. M-ary phase-shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) performance using COVQ with hard-decision decoding, is optimized for additive white Gaussian noise (AWGN) and flat-fading Rayleigh channel. In addition, when a flat-fading Rayleigh channel is assumed, diversity techniques are used and evaluated to improve the performance of the system.

A new parameter for estimating the difficulty of a continuous speech recognition task, called speech decoding difficulty, is presented. It is obtained from the language model defined for the recognition task and the phonetic similarity between the transcriptions of the words that make up the vocabulary used. Two variants of the proposed task difficulty measure are introduced: ideal speech decoding difficulty (ISDD), which is not influenced by practical considerations on the recognition system implemented, and a second, more realistic variant, called practical speech decoding difficulty (PSDD) to study the performance of a specific recognition system when confronting a given task.

This work presents the STACC, Sistema Telef onico Autom atico de Consulta de Calificaciones (Automatic Telephone System for Consulting Marks). This system has been developed at our laboratory during 1996 and implements a service through telephone line that allows the students to consult by speech their marks after the exams by means of a simple phone call. This experience provided us an interesting point of view about the problems of real applications of speech technology. In this work we describe the system and some statistics about the use of STACC by the students are presented.

Proposes a transform trellis coded quantization (TTCQ) scheme suitable for low-delay speech coding. This scheme is based on a general transform domain formulation for small frame sizes and the trellis coded quantization technique previously proposed where Ungerboeck's amplitude modulation trellises and set partitioning ideas are used for source coding. Using the discrete cosine transform the authors apply this technique to the coding of 7 kHz wideband speech, a matter of interest due to ISDN based applications. A 32 kbps low-delay coder is developed, simulation results showing a very high speech quality comparable to that obtained by the G722 standard at 64 kbps.>