Deterministic communications are essential to meet the stringent delay and jitter requirements of Industrial Internet of Things (IIoT) services. IIoT increasingly demands wide-area wireless mobility to support Autonomous Mobile Robots (AMR) and dynamic workflows. Integrating Time-Sensitive Networking (TSN) with 5G private networks is emerging as a promising approach to fulfill these requirements. In this architecture, 5G provides wireless access for industrial devices, which connect to a TSN backbone that interfaces with the enterprise edge/cloud, where IIoT control and computing systems reside. TSN achieves bounded latency and low jitter using IEEE 802.1Qbv Time-Aware Shaper (TAS), which schedules the network traffic in precise time slots. However, the stochastic delay and jitter inherent in 5G disrupt TSN scheduling, requiring careful tuning of TAS parameters to maintain end-to-end determinism. This paper presents an empirical study evaluating the impact of 5G downlink delay and jitter on TAS scheduling using a testbed with TSN switches and a commercial 5G network. Results show that guaranteeing bounded latency and jitter requires careful setting of TAS transmission window offset between TSN switches based on the measured 5G delay bounded by a high order p-th percentile. Otherwise, excessive offset may cause additional delay or even a complete loss of determinism.

Time synchronization is essential for industrial IoT and Industry 4.0/5.0 applications, but achieving high synchronization accuracy in Time-Sensitive Networking (TSN)-5G networks is challenging due to jitter and asymmetric delays. 3GPP TS 23.501 defines three 5G synchronization modes: time-aware system, boundary clock (BC), and transparent clock (TC), where TC offers a promising solution. However, to the best of our knowledge, there is no empirical evaluation of TC in a TSN5G network. This paper empirically evaluates an 5G end-to-end TC in a TSN-5G network, implemented on commercial TSN switches with a single clock. For TC development, we compute the residence time in 5G and recover the clock domain at the slave node. We deploy a TSN-5G testbed with commercial equipment for synchronization evaluation by modifying the Precision Timing Protocol (PTP) message transmission rates. Experimental results show a peak-to-peak synchronization of 500 ns, meeting the industrial requirement of ≤1 µs, with minimal synchronization offsets for specific PTP message transmission rates.

Deterministic communications are essential for industrial automation, ensuring strict latency requirements and minimal jitter in packet transmission. Modern production lines, specializing in robotics, require higher flexibility and mobility, which drives the integration of Time-Sensitive Networking (TSN) and 5 G networks in Industry 4.0. TSN achieves deterministic communications by using mechanisms such as the IEEE 802.1Qbv Time-Aware Shaper (TAS), which schedules packet transmissions within precise cycles, thereby reducing latency, jitter, and congestion. 5G networks complement TSN by providing wireless mobility and supporting ultra-Reliable LowLatency Communications. However, 5 G channel effects such as fast fading, interference, and network-induced latency and jitter can disrupt TSN traffic, potentially compromising deterministic scheduling and performance. This paper presents an empirical analysis of 5G network latency and jitter on IEEE 802.1Qbv performance in a $\mathbf{5 G}$-TSN network. We evaluate the impact of $\mathbf{5 G}$ integration on TSN’s deterministic scheduling through a testbed combining IEEE 802.1Qbv-enabled switches, TSN translators, and a commercial 5 G system. Our results show that, with proper TAS configuration in the TSN switch aligned with the 5G system, jitter can be mitigated, maintaining deterministic performance.

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.

Multi-Wireless Access Technology (WAT) Radio Access Networks (RANs) are becoming a key enabler in 5G and beyond networks due to the public spectrum scarcity, the level of signal confinement and security offered by some wireless technologies (e.g., Light Fidelity (Li-Fi)), and the reduction of the deployment and operational costs. For instance, Wireless Fidelity (Wi-Fi) technology is cheaper and easier to manage than 5G, and leveraging their already deployed infrastructures contributes to capital expenditures saving. Developing autonomous radio resource provisioning (RRP) solutions is fundamental to cost-effectively achieve the zero-touch management in private 5G networks while fulfilling the service requirements. However, modelling the Key Performance Indicators of the radio interface in 5G and beyond is a complex task that requires high-domain knowledge. Furthermore, the resulting models, as well as solving the respective RRP optimization problem using exact methods usually offer a high computational complexity, especially in multi-WAT scenarios. In order to cope with these issues, in this work, we propose an initial design of a Deep Reinforcement Learning-assisted solution for the RRP in a multi-WAT private 5G network. Furthermore, we contex-tualize the solution in the Open RAN architecture framework. A simulation-based proof-of-concept validates the proposal’s proper design and operation considering a realistic private 5G network scenario.

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.

Network slicing allows different applications and network services to be deployed on virtualized resources running on a common underlying physical infrastructure. Developing a scalable system for the orchestration of end-to-end (E2E) mobile network slices requires careful planning and very reliable algorithms. In this paper, we propose a novel E2E Network Slicing Orchestration System (NSOS) and a Dynamic Auto-Scaling Algorithm (DASA) for it. Our NSOS relies strongly on the foundation of a hierarchical architecture that incorporates dedicated entities per domain to manage every segment of the mobile network from the access, to the transport and core network part for a scalable orchestration of federated network slices. The DASA enables the NSOS to autonomously adapt its resources to changes in the demand for slice orchestration requests (SORs) while enforcing a given mean overall time taken by the NSOS to process any SOR. The proposed DASA includes both proactive and reactive resource provisioning techniques. The proposed resource dimensioning heuristic algorithm of the DASA is based on a queuing model for the NSOS, which consists of an open network of G/G/m queues. Finally, we validate the proper operation and evaluate the performance of our DASA solution for the NSOS by means of system-level simulations.

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.

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.

The present paper highlights the performance of the transport control protocol over WCDMA networks. The TCP flow control adjusts the transmission rate to the network and receiver's capacity. Due to this protocol self-adaptation to the transmission conditions of the links involved in the TCP connection, the protocol performance can not be disconnected from the underlying network. In WCDMA networks, the RRM assigns the radio resources to the UE in the cell, which directly impacts the TCP throughput performance. The results show that the allocation of high bit rate dedicated channels to small file downloads (such as Web pages) produce poor channelization code efficiency compared to allocating low bit rate channels. Under certain conditions, for large file downloads (such as ftp) the code efficiency and throughput performance improve significantly due to the protocol nature to fully utilize the bottleneck capacity. This protocol performance suggests a radio resource allocation strategy for DCH channels, which increases the bit rate stepwise as the file transmission evolves starting from a low bit rate.