Other than pure performance and cybersecurity, a value that is becoming increasingly important for a mobile network is its power consumption. In fact, the transition from legacy network deployments tightly coupled with the underlying hardware towards fully virtualized ones yields distinct options based on the adopted virtualization technology, each of which deserve appropriate evaluation in terms of energy efficiency. In this paper, we aim at providing a preliminary assessment of the realistic power consumption of a fifth-generation core network deployed in a network edge environment leveraging bare metal, containers, and virtual machines. The results are based on a testbed consisting of commercial off-the-shelf hardware and open-source software, and show that the deployment based on virtual machines is the first one that saturates the power consumption, thus reducing the maximum achievable throughput. These preliminary insights show the feasibility of a real-time power monitoring system that can condition the dynamic policies applied by the 5G network orchestrator.

The management of a 5G system comprises Operation and Management aspects defined by 3GPP, including Network Slicing, and the Management and Orchestration aspects specified in ETSI’s Network Function Virtualization framework. Our Proof-of-Concept demonstrates the implementation of an on-demand provisioning procedure of a Network Slice Subnet composed of Virtual Network Functions from potentially different vendors. The demonstration includes a Network Management System conforming to the 3GPP Service-Based Management Architecture, an ETSI MANO orchestrator, and a Network Function Virtualization Infrastructure.

Besides developing new Cooperative, Connected and Automated Mobility (CCAM) services for the improvement of road safety and travel experience, researchers are considering protection mechanisms to ensure the security of these services and the safety of involved users (drivers but also, e.g., cyclists and pedestrians). In particular, several Identity Management (IDM) protocols have been designed as the first line of defence against external attackers. Among these protocols, a promising trend in research consists in the use of a Smart Card (SC) as a technical enabler for strong authentication. Indeed, many SC-based IDM protocols for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications in real-time CCAM services have been proposed in the literature which present interesting features and promising usability experimental results. However, this research line is far from being exhausted, especially considering the recent spread of SC technologies and use cases. For this reason, in this paper we propose a systematic literature review on SC-based IDM protocols for real-time CCAM services. In particular, we identify characterising assumptions of CCAM scenarios and extrapolate a unified high-level view of the steps composing a SC-based IDM protocol. Then, we present a detailed survey of several SC-based IDM protocols. Finally, we identify trends in research and formulate guidelines and useful recommendations to provide a solid base which researchers can use as a starting point for the design of new and improved SC-based IDM protocols for CCAM scenarios.

Dual connectivity for reliability is considered one of the most important enablers of ultra-reliable low-latency communication in fifth-generation cellular systems. In this paper, we focus on downlink dual connectivity, proposing two methods that prevent unnecessary duplicates transmissions to enhance spectral efficiency when using data duplication. In the first method, the duplicates of packets already successfully delivered to the terminal are promptly dropped from the transmission queues of the involved base stations thanks to an uplink indication provided by terminal itself. In the second method, the transmission of duplicates from the secondary node is enforced only upon a failure of the master node. The performance of the two proposed schemes is evaluated in terms of i) achievable packet reliability within the 1-ms latency target of ultra-reliable low-latency communication, and ii) resource-block utilization via system-level simulation campaigns. The results show that, in the investigated scenario, the second proposal can support a four-times higher offered load with respect to the baseline approach.

In this paper, we propose a novel channel quality reporting approach for cellular communication systems. The proposed approach features a differential coding scheme in stationary propagation conditions and a detector of non-stationary propagation conditions, which further triggers a channel-quality predictor for the non-stationary environment based on a machine learning method. In particular, the machine learning engine learns about the specific large variations of the channel quality by collecting signaling information from mobile terminals in a given region. Our simulations in a controlled urban environment with vehicular users show that the proposed solution can effectively replace the 4-bit channel-quality reporting scheme of LTE and NR standards with a 2-bit one, providing correct channel-quality indication in non-stationary conditions with high probability.

Uplink ultra-reliable low-latency communication (URLLC) entails dependable and almost instantaneous packet transmissions from industrial Internet of Things terminals to the serving base station. While most of the literature addresses terminals which are always connected to the network, here we focus on terminals which would benefit from a connection-less radio access, because they are employed for low-duty-cycle applications related to industrial safety, while still requiring a URLLC quality of service. Our aim is to investigate the performance of two standard-compliant radio access protocols, i.e., the one-stage approach and the two-stage approach, in supporting uplink URLLC in connection-less mode. We derive a mathematical model of these protocols under the assumption that the base station has multi-user detection capabilities. The model is used to cross-validate our simulator and calibrate the protocols together with the validated simulator, providing well-grounded guidelines for the selection of the parameter values. The simulator is finally exploited to assess the URLLC performance of the proposed protocols. The key outcome of this study is that, when combined with multi-user detection at the base station and after an appropriate system parameters selection, the one-stage approach satisfies the desired URLLC service requirements, thus representing a viable solution to support safety-related industrial applications.

In-region location verification (IRLV) in wireless networks is the problem of deciding if user equipment (UE) is transmitting from inside or outside a specific physical region (e.g., a safe room). The decision process exploits the features of the channel between the UE and a set of network access points (APs). We propose a solution based on machine learning (ML) implemented by a neural network (NN) trained with the channel features (in particular, noisy attenuation values) collected by the APs for various positions both inside and outside the specific region. The output is a decision on the UE position (inside or outside the region). By seeing IRLV as an hypothesis testing problem, we address the optimal positioning of the APs for minimizing either the area under the curve (AUC) of the receiver operating characteristic (ROC) or the cross entropy (CE) between the NN output and ground truth (available during the training). In order to solve the minimization problem we propose a two-stage particle swarm optimization (PSO) algorithm. We show that for a long training and a NN with enough neurons the proposed solution achieves the performance of the Neyman-Pearson (N-P) lemma.

This paper presents a spectrum-edge detection algorithm for initial cell-search in Long-Term Evolution (LTE) wireless networks. The algorithm consists of three steps. First, we roughly identify possible edges in the spectrum. Then, we confirm these “candidate” edges by means of a threshold on the local average spectrum. Finally, the center frequency is precisely determined by searching a pair of edges located at a distance approximately equal to a possible bandwidth of the LTE transmission. Based on tests with field signals, the proposed algorithm is much faster than the conventional sequential cell-search algorithm, where the signal is demodulated for a candidate center frequency and minimum bandwidth: if demodulation is not successful a new candidate center frequency is tried.

Recently, the role of Low-Power Wide Area Networks (LPWANs) as enablers of the Internet of Things (IoT) is emerging. Contrary to what is commonly believed, despite they operate in unlicensed frequency bands, these technologies are subject to stringent spectrum usage regulations, in terms of both channel occupancy and transmission power. In this work, we focus on the LPWAN technology based on the Long-Range™ (LoRa) modulation, proposing a radically new approach with respect to the default Medium Access Control (MAC) procedure in LoRaWAN™, i.e., the protocol stack most widely used on top of LoRa. In particular, we describe and evaluate a new MAC protocol that exploits a novel operation mode called time-power multiplexing, to boost the capacity of LoRa networks. The simulation results show that, with respect to the default LoRaWAN MAC protocol, the proposed solution effectively increases the number of users that the network can sustain.

Low-Power Wide Area Networks (LPWANs) are continuously gaining momentum as fundamental enablers of the Internet of Things (IoT) paradigm. These networks provide longrange coverage to end nodes, exploiting license-free frequency bands. The focus of this work is on one of the most prominent LPWAN technologies: LoRa™. We implemented a new ns-3 module to study the performance of a LoRa-based IoT network in a typical urban scenario. Simulation results show that a LoRa network can scale well, achieving packet success rates above 95% in presence of a number of end devices in the order of 104.

Recently, the role of Low-Power Wide Area Networks (LPWANs) as enablers of the Internet of Things (IoT) is emerging. However, many concerns arise on the performance of LPWANs if higher Quality of Service (QoS) requirements are needed by certain classes of IoT services. In this work, we will focus on one of the most prominent of these technologies, i.e., Long-RangeTM(LoRa). Our aim is to study to what extent a LoRa network is able to support uplink (UL) traffic that requires to be confirmed by the Network Server (NS). The simulation results show that the network performance is severely impacted by the downlink (DL) traffic generated by feedback packets.

The fifth-generation (5G) cellular networks will face the challenge of integrating the traditional broadband services with the Internet of Things (IoT), which is characterized by sporadic uplink transmissions of small data packets. Indeed, the access procedure of the previous generation cellular network (4G) is not able to support IoT traffic efficiently, because it requires a large amount of signaling for the connection setup before the actual data transmission. In this context, we introduce two innovative radio access protocols for sporadic transmissions of small data packets, which are suitable for 5G networks, because they provide a resource-efficient packet delivery exploiting a connectionless approach. The core of this paper resides in the derivation of an analytical framework to evaluate the performance of all the aforementioned protocols. The final goal is the comparison between 4G and 5G radio access solutions employing both our analytical framework and computer simulations. The performance evaluation results show the benefits of the protocols envisioned for 5G in terms of signaling overhead and access latency.

We consider a cellular Internet of things (CIoT) network where many source nodes aim at exchanging messages with a single concentrator node. To this end, they are assisted by anchor nodes that are trusted and securely connected with the concentrator node. In this context, we aim at providing a message authentication scheme based on the characteristics of the channel between the source nodes and the anchor nodes. According to this approach, the anchor nodes estimate the channel to source nodes in an initially externally authenticated fashion, while forthcoming messages are authenticated by comparing the current channel estimate with the initial estimate. Moreover, assuming that the anchor nodes have a limited energy availability, we derive suitable scheduling policies for the activation of the anchor nodes for authentication purposes. In particular, we aim at maximizing the anchors lifespan while guaranteeing given false alarm and missed detection probabilities of the authentication process. The performance of the proposed authentication protocols is evaluated in a typical CIoT scenario.

Several studies assert that the random access procedure of the Long Term Evolution (LTE) cellular standard may not be effective whenever a massive number of simultaneous connection attempts are performed by terminals, as may happen in a typical Internet of Things or Smart City scenario. Nevertheless, simulation studies in real deployment scenarios are missing because many system-level simulators do not implement the LTE random access procedure in detail. In this paper, we propose a patch for the LTE module of ns-3, one of the most prominent open-source network simulators, to improve the accuracy of the routine that simulates the LTE Random Access Channel (RACH). The patched version of the random access procedure is compared with the default one and the issues arising from massive simultaneous access from mobile terminals in LTE are assessed via a simulation campaign.

In this paper a Bayesian approach to increase the throughput and reduce the latency of the Hybrid-ARQ processes in the Long Term Evolution uplink is proposed. The performance of the enhanced system is evaluated and compared to the standard one using a theoretical framework based on the Accumulated Mutual Information and the theory of Markov chains. The proposed modification is simple to implement in real systems and it is shown to be effective, especially in low mobility scenarios.