Marina Barbiroli

Bio: Marina Barbiroli is an academic researcher from University of Bologna. The author has contributed to research in topics: Wireless & Optical wireless. The author has an hindex of 12, co-authored 82 publications receiving 630 citations.

Ray-Tracing-Based mm-Wave Beamforming Assessment

Abstract: The use of large-size antenna arrays to implement pencil-beam forming techniques is becoming a key asset to cope with the very high throughput density requirements and high path-loss of future millimeter-wave (mm-wave) gigabit-wireless applications. Suboptimal beamforming (BF) strategies based on search over discrete set of beams (steering vectors) are proposed and implemented in present standards and applications. The potential of fully adaptive advanced BF strategies that will become possible in the future, thanks to the availability of accurate localization and powerful distributed computing, is evaluated in this paper through system simulation. After validation and calibration against mm-wave directional indoor channel measurements, a 3-D ray tracing model is used as a propagation-prediction engine to evaluate performance in a number of simple, reference cases. Ray tracing itself, however, is proposed and evaluated as a real-time prediction tool to assist future BF techniques.

Ray tracing propagation modeling for future small-cell and indoor applications: a review of current techniques

Abstract: Applied for the first time to mobile radio propagation modeling at the beginning of the nineties, ray tracing is now living a second youth. It is probably the best model to assist in the design and planning of future short-range, millimeter-wave wireless systems, where the more limited propagation environment with respect to UHF frequencies allows to overcome traditional high-CPU time limitations while the higher operating frequency makes ray-optics approximations less drastic and allows to achieve an unprecedented level of accuracy. An overview of ray tracing propagation modeling is given in this paper, with a special attention to future prospects and applications. In particular, frontiers of ray-based propagation modeling such as extension to diffuse scattering, multidimensional channel characterization, multiple-input multiple-output (MIMO) capacity assessments, and future applications such as real-time ray tracing are addressed in the paper with reference to the work recently carried out at the University of Bologna.

HABITAT: An IoT Solution for Independent Elderly

Abstract: In this work, a flexible and extensive digital platform for Smart Homes is presented, exploiting the most advanced technologies of the Internet of Things, such as Radio Frequency Identification, wearable electronics, Wireless Sensor Networks, and Artificial Intelligence. Thus, the main novelty of the paper is the system-level description of the platform flexibility allowing the interoperability of different smart devices. This research was developed within the framework of the operative project HABITAT (Home Assistance Based on the Internet of Things for the Autonomy of Everybody), aiming at developing smart devices to support elderly people both in their own houses and in retirement homes, and embedding them in everyday life objects, thus reducing the expenses for healthcare due to the lower need for personal assistance, and providing a better life quality to the elderly users.

Analysis of In-Room mm-Wave Propagation: Directional Channel Measurements and Ray Tracing Simulations

Abstract: Frequency bands above 6 GHz are being considered for future 5G wireless systems because of the larger bandwidth availability and of the smaller wavelength, which can ease the implementation of high-throughput massive MIMO schemes. However, great challenges are around the corner at each implementation level, including the achievement of a thorough multi-dimensional characterization of the mm-wave radio channel, which represents the base for the realization of reliable and high-performance radio interfaces and system architectures. The main properties of the indoor radio channel at 70 GHz, including angular and temporal dispersion as well as an assessment of the major interaction mechanisms, are investigated in this study by means of UWB directional measurements and ray tracing simulations in a reference, small-indoor office environment.

A Discrete Environment-Driven GPU-Based Ray Launching Algorithm

Abstract: We present here a novel, fully discrete ray launching field prediction algorithm that takes advantage of environment preprocessing to efficiently trace rays undergoing both specular and diffuse interactions. The algorithm is “environment driven” because rays are traced from the ray source according to the presence and distribution of obstacles in the surrounding space, therefore adapting ray density to the environment’s characteristics. The environment is discretized into simple regular shapes to facilitate faster geometric computations, to allow for visibility preprocessing and for the algorithm to be parallelized in a straightforward way. These innovative features combined together and implemented on a NVIDIA graphical processing unit (GPU) are shown to speed-up computation by several orders of magnitude compared to more conventional algorithms, while retaining a similar accuracy level. The speed-up and prediction accuracy achieved in reference cases is presented in comparison with a pre-existing ray-based model and RF-coverage measurements.

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

Abstract: Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: (1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and (2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual “more data needs more power and emission of radio waves” status quo, and motivate that future wireless networks necessitate a smart radio environment: a transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as reconfigurable intelligent meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. We will discuss, in particular, two major types of reconfigurable intelligent meta-surfaces applied to wireless networks. The first type of meta-surfaces will be embedded into, e.g., walls, and will be directly controlled by the wireless network operators via a software controller in order to shape the radio waves for, e.g., improving the network coverage. The second type of meta-surfaces will be embedded into objects, e.g., smart t-shirts with sensors for health monitoring, and will backscatter the radio waves generated by cellular base stations in order to report their sensed data to mobile phones. These functionalities will enable wireless network operators to offer new services without the emission of additional radio waves, but by recycling those already existing for other purposes. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of reconfigurable intelligent meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.

Proposal on Millimeter-Wave Channel Modeling for 5G Cellular System

Abstract: This paper presents 28 GHz wideband propagation channel characteristics for millimeter wave (mmWave) urban cellular communication systems. The mmWave spectrum is considered as a key-enabling feature of 5G cellular communication systems to provide an enormous capacity increment; however, mmWave channel models are lacking today. The paper compares measurements conducted with a spherical scanning 28 GHz channel sounder system in the urban street-canyon environments of Daejeon, Korea and NYU campus, Manhattan, with ray-tracing simulations made for the same areas. Since such scanning measurements are very costly and time-intensive, only a relatively small number of channel samples can be obtained. The measurements are thus used to quantify the accuracy of a ray-tracer; the ray-tracer is subsequently used to obtain a large number of channel samples to fill gaps in the measurements. A set of mmWave radio propagation parameters is presented based on both the measurement results and ray-tracing, and the corresponding channel models following the 3GPP spatial channel model (SCM) methodology are also described.

The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial

Abstract: The application scenarios and requirements are more diverse in the fifth-generation (5G) era than before. In order to successfully support the system design and deployment, accurate channel modeling is important. Ray-tracing (RT) based deterministic modeling approach is accurate with detailed angular information and is a suitable candidate for predicting time-varying channel and multiple-input multiple-output (MIMO) channel for various frequency bands. However, the computational complexity and the utility of RT are the main concerns of users. Aiming at 5G and beyond wireless communications, this paper presents a comprehensive tutorial on the design of RT and the applications. The role of RT and the state-of-the-art RT techniques are reviewed. The features of academic and commercial RT based simulators are summarized and compared. The requirements, challenges, and developing trends of RT to enable the visions are discussed. The practices of the design of high-performance RT simulation platform for 5G and beyond communications are introduced, with the publicly available high-performance cloud-based RT simulation platform as the main reference. The hardware structure, networking, workflow, data flow and fundamental functions of a flexible high-performance RT platform are discussed. The applications of high-performance RT are presented based on two 5G scenarios, i.e., a 3.5 GHz Beijing vehicle-to-infrastructure scenario and a 28 GHz Manhattan outdoor scenario. The questions on how to calibrate and validate RT based on measurements, how to apply RT for mobile communications in moving scenarios, and how to evaluate MIMO beamforming technologies are answered. This tutorial will be especially useful for researchers who work on RT algorithms development and channel modeling to meet the evaluation requirements of 5G and beyond technologies.

5G MIMO Data for Machine Learning: Application to Beam-Selection Using Deep Learning

Abstract: The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a ray-tracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beam-selection techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology.

Energy-Aware UMTS Access Networks

Abstract: The sensitiveness toward energy consumption problems is driving Telecommunications operators to optimize network equipment utilization. Since cellular systems are often dimensioned for peak hour traffic, during low traffic periods, such as night, many devices are underutilized but still, by being active, consume power. In this paper, we show that dynamic planning, consisting in reducing the number of active access devices when traffic is low, can achieve significant power saving. In our study, we consider three different UMTS scenarios with a simplified traffic model describing three classes of services, quality of service guarantees, link-budget, propagation and electromagnetic exposure constraints.