Simone Barbera’s research while affiliated with Telenor and other places

This article presents an overview of current Industry 4.0 applied research topics, addressed from both the industrial production and wireless communication points of view. A roadmap toward achieving the more advanced industrial manufacturing visions and concepts, such as “swarm production” (nonlinear and fully decentralized production) is defined, highlighting relevant industrial use cases, their associated communication requirements, as well as the integrated technological wireless solutions applicable to each of them. Further, the article introduces the Aalborg University 5G Smart Production Lab, an industrial lab test environment specifically designed to prototype and demonstrate different Industrial IoT use cases enabled by the integration of robotics, edge-cloud platforms, and autonomous systems operated over wireless technologies such as 4G, 5G, and Wi-Fi. Wireless performance results from various operational trials are also presented for two use cases: wireless control of industrial production and wireless control of autonomous mobile robots.

Some of the most recent LTE features require synchronous base stations, and time-synchronized base stations also offer opportunities for improved handover mechanisms by introducing a new synchronized RACH-less handover scheme. The synchronized RACH-less handover solution offers significant reductions in the data connectivity interruption time at each handover, no need for random access in the target cell, and reduced overall handover execution time. Laboratory handover measurement results, using commercial LTE equipment, are presented and analyzed to justify the latency benefits of the proposed handover solution. Secondly, extensive system level simulation results are presented to further quantify the network level benefits. The results of these performance investigations reveal reduction of the interruption time at every handover from 55 ms (mean) to 5 ms and improvements in the radio link failure probability as a result of faster handover execution.

DC is one of the most important features introduced in Release 12 of the 3GPP specifications. DC aims at increasing the per-user throughput by improving the utilization of radio resources across two base stations connected via non-ideal backhaul (X2) and operating on different carrier frequencies. By making it possible to maintain the connection to the primary cell located in the macro base station while accessing the extra capacity provided by the small cell layer, DC can also improve the mobility performance in small cell deployments. This article gives an overview of the DC feature as standardized in Release 12. We summarize the supported scenarios and the DC functionality, and also demonstrate by means of detailed system-level simulations how DC can improve end-user throughput and mobility performance.

Simulations play a key role in validating new concepts in cellular networks, since most of the features proposed and introduced into the standards are typically studied by means of simulations. In order to increase the trustworthiness of the simulation results, proper models and settings must be provided as inputs to the simulators. It is therefore crucial to perform a thorough validation of the models used for generating results. The objective of this paper is to compare measured and simulated mobility performance results with the purpose of understanding whether simulation models are close to reality. The presented study is based on drive tests measurements and explicit simulations of an operator network in the city of Aalborg (Denmark) - modelling a real 3D environment and using a commonly accepted dynamic system level simulation methodology. In short, the presented results show that the simulated handover rate, location of handovers, radio link failures, and signal/interference level statistics match well with measurements, giving confidence that the simulations produce realistic performance results.

A mobility performance sensitivity analysis is presented for Dual Connectivity cases where the users can be served simultaneously by a macro and a small cell. The performance is assessed for the 3GPP generic simulation scenario and for two site-specific cases, with the aim of comparing the results coming from these different scenarios. The site-specific scenarios are based on detailed three-dimensional topography map data and advanced ray-tracing techniques. It is generally found that the first order statistics and overall conclusions are in good alignment for the considered environments. However, there are also a number of differences in the obtained performance results for the different cases, e.g. in terms of small cell time-of-stay statistics. The explicit modeling of streets and building topology, propagation in street canyons, etc., is found to have an important impact on the mobility performance. Especially the time-of-stay statistics and the location of handover failures differ between generic 3GPP case and the site-specific modeling scenarios. So in conclusion, trends and observations from simulations based on the 3GPP scenario are valuable, but should be supplemented by results from site-specific scenarios, as additional performance-determining effects are more accurately represented for such cases.

In this paper we analyze the mobility performance for an LTE Heterogeneous Network with macro and pico cells deployed on different carriers. Cases with/without downlink inter-site carrier aggregation are investigated. Extensive system level simulations are exploited to quantify the performance. It is shown that not only does the use of inter-site carrier aggregation offer higher end-user throughput, but also several benefits in terms of improved mobility performance. Among others, it is shown that the performance becomes less sensitive to the setting of mobility parameters when inter-site carrier aggregation is used, while still keeping the handover signaling burden at an acceptable level. The probabilities of radio link failures, handover failures, and unnecessary handovers (also known as ping-pongs) are kept at a minimum for proposed the solution.

In this article we first summarize some of the most recent HetNet mobility studies for LTEAdvanced, and use these to highlight the challenges that should be further addressed in the research community. A state-of-the-art HetNet scenario with macros and small cells deployed on different carriers, while using inter-site carrier aggregation, is hereafter studied. Hybrid Het- Net mobility solutions for such cases are derived, where macrocell mobility is network controlled, while small-cell mobility is made UE-autonomous. The proposed scheme is characterized by having the UE devices autonomously decide small cell addition, removal, and change without any explicit signaling of measurement events to the network or any signaling of hand - over commands from the network. Hence, the proposed solution effectively offloads the network from having to perform frequent small cell handoff decisions, and reduces the signaling overhead compared to known network controlled mobility solutions. Results from extensive dynamic system-level simulations are presented to demonstrate the advantages of the proposed technique, showing significant savings in signaling overhead.

This paper analyzes the mobility performance of LTE (Long Term Evolution) co-channel heterogeneous networks (HetNet) with macro and pico cells. Improved methods for differentiating offload and mobility robustness as a function of the UE (User Equipment) mobility are proposed. The suggested solution comprises two key elements, namely enhanced UE MSE (Mobility State Estimation), as well as optimized methods such that high speed users are primarily kept at the macro layer, while the offload to pico cells for low speed users is maximized. The proposed methods are designed as UE autonomous solutions, requiring minimum assistance and signaling from the network. Extensive system level simulations are used to quantify the benefits. Results confirm that the proposed solutions offer improvements in several mobility key performance indicators such as radio link failure, number of handovers, offload to pico layer.