Fast Cell Discovery in mm-wave 5G Networks with Context Information

The exploitation of mm-wave bands is one of the key-enabler for 5G mobile radio networks. However, the introduction of mm-wave technologies in cellular networks is not straightforward due to harsh propagation conditions that limit the mm-wave access availability. Mm-wave technologies require high-gain antenna systems to compensate for high path loss and limited power. As a consequence, directional transmissions must be used for cell discovery and synchronization processes: this can lead to a non-negligible access delay caused by the exploration of the cell area with multiple transmissions along different directions. The integration of mm-wave technologies and conventional wireless access networks with the objective of speeding up the cell search process requires new 5G network architectural solutions. Such architectures introduce a functional split between C-plane and U-plane, thereby guaranteeing the availability of a reliable signaling channel through conventional wireless technologies that provides the opportunity to collect useful context information from the network edge. In this article, we leverage the context information related to user positions to improve the directional cell discovery process. We investigate fundamental trade-offs of this process and the effects of the context information accuracy on the overall system performance. We also cope with obstacle obstructions in the cell area and propose an approach based on a geo-located context database where information gathered over time is stored to guide future searches. Analytic models and numerical results are provided to validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin

Facing the Millimeter-wave Cell Discovery Challenge in 5G Networks with Context-awareness

The introduction of mm-wave technologies in the future 5G networks poses a rich set of network access challenges. We need new ways of dealing with legacy network functionalities to fully unleash their great potential, among them the cell discovery procedure is one of the most critical. In this article, we propose novel cell discovery algorithms enhanced by the context information available through a C-/Uplane- split heterogeneous network architecture. They rely on a geo-located context database to overcome the severe effects of obstacle blockages. Moreover, we investigate the coordination problem of multiple mm-wave base stations that jointly process user access requests. We show that optimizing the resource allocated to the discovery has a great importance in defining perceived latency and supported user request rate. We have performed complete and accurate numerical simulations to provide a clear overview of the main challenging aspects. Results show that the proposed solutions have an outstanding performance with respect to basic discovery approaches and can fully enable mm-wave cell discovery in 5G networks

Passive and Privacy-preserving Human Localization via mmWave Access Points for Social Distancing

The pandemic outbreak has profoundly changed our life, especially our social habits and communication behaviors. While this dramatic shock has heavily impacted human interaction rules, novel localization techniques are emerging to help society in complying with new policies, such as social distancing. Wireless sensing and machine learning are well suited to alleviate viruses propagation in a privacy-preserving manner. However, its wide deployment requires cost-effective installation and operational solutions. In public environments, individual localization information-such as social distancing-needs to be monitored to avoid safety threats when not properly observed. To this end, the high penetration of wireless devices can be exploited to continuously analyze-and-learn the propagation environment, thereby passively detecting breaches and triggering alerts if required. In this paper, we describe a novel passive and privacy-preserving human localization solution that relies on the directive transmission properties of mmWave communications to monitor social distancing and notify people in the area in case of violations. Thus, addressing the social distancing challenge in a privacy-preserving and cost-efficient manner. Our solution provides an overall accuracy of about 99% in the tested scenarios

On the specialization of FDRL agents for scalable and distributed 6G RAN slicing orchestration

©2022 IEEE. Reprinted, with permission, from Rezazadeh, F., Zanzi, L., Devoti, F. et.al. On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration. IEEE Transactions on vehicular technology (Online) October 2022Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllersPeer ReviewedPreprin

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.Comment: 15 pages, 15 Figures, accepted for publication at IEEE TV

An Orchestration Framework for Open System Models of Reconfigurable Intelligent Surfaces

To obviate the control of reflective intelligent surfaces (RISs) and the related control overhead, recent works envisioned autonomous and self-configuring RISs that do not need explicit use of control channels. Instead, these devices, named hybrid RISs (HRISs), are equipped with receiving radio-frequency (RF) chains and can perform sensing operations to act independently and in parallel to the other network entities. A natural problem then emerges: as the HRIS operates concurrently with the communication protocols, how should its operation modes be scheduled in time such that it helps the network while minimizing any undesirable effects? In this paper, we propose an orchestration framework that answers this question revealing an engineering trade-off, called the self-configuring trade-off, that characterizes the applicability of self-configuring HRISs under the consideration of massive multiple-input multiple-output (mMIMO) networks. We evaluate our proposed framework considering two different HRIS hardware architectures, the power- and signal-based HRISs that differ in their hardware complexity. The numerical results show that the self-configuring HRIS can offer significant performance gains when adopting our framework.Comment: 31 pages, 7 figures, submitted to an IEEE journa

A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.Comment: 33 pages, 13 figure

Leonardo. Tecnica e territorio.

Il catalogo della mostra presso il castello del Valentino (15 aprile-14 luglio 2019), svolta in perfetto parallelismo con quella presso i Musei Reali di Torino, esplora, nel cinquecentenario della morte del Vinciano, il lascito di Leonardo nel contesto della cultura politecnica. Organizzata in tre stanze dell'appartamento dorato o meridionale del Palazzo (Gigli, Vallantino e Zodiaco o Pianeti), corrisponde ad altrettante sezioni, di cui il catalogo rende ragione. I temi delle edizioni critiche dei codici di Leonardo, delle costruzioni di macchine, dello studio dei minerali e delle pietre da costruzione, della formazione della sensibilità geografica si intrecciano con l'esposizione di esemplari, anche di pregio, appartenenti alle collezioni politecniche, in un dialogo serrato tra lascito del Vinciano e acquisizione di consapevolezza da parte di architetti e ingegneri. Il catalogo è anche l'occasione per approfondire, in ampie schede, la natura dei singoli manufatti presentati, e si presenta in versione bilingu

sustainability and resilience: socio-spatial perspective

Sustainability and resilience have become indispensable parts of the contemporary debate over the built environment. Although recognised as imperatives, the complexity and the variety of interpretations of sustainability and resilience have raised the necessity to again rethink their notion in the context of the built environment and to reframe the state-of-the-art body of knowledge. The book Sustainability and Resilience: Socio-Spatial Perspective so begins with the exploration of the broadest conceptual frame-of-reference of issues related to sustainability, and the re-establishment of the connection between the built environment and the conditions that are vital to its functioning, primarily in relation to energy, land use, climate, and economy. Subsequent discussion on resilience as a term, approach, and philosophy aims to conceptualise an interpretation of key resilience concepts, explain relationships and links among them, and propose the classification of resilience as applicable to the context of urban studies. By studying the processes of transition of the built environment, the book then reveals a coherent formula of ‘thinking sustainability + resilience’ aimed at improving the ability to respond to disruptions and hazards while enhancing human and environmental welfare. The necessity to integrate the two approaches is further accented as a result of a deliberative discourse on the notions of ‘social sustainability’, ‘sustainable community’, and ‘socio-cultural resilience’. The potential of measuring sustainable development and urban sustainability on the basis of defined social, human, and, additionally, natural and economic values is presented though an overview of different wellknown indicators and the identification of a currently relevant tangible framework of sustainable development. Correspondingly, the role of policies and governance is demonstrated on the case of climate-proof cities. In this way, the consideration of approaches to sustainability and resilience of the urban environment is rounded, and the focus of the book is shifted towards an urban/rural dichotomy and the sustainability prospects of identified forms-in-between, and, subsequently, towards the exploration of values, challenges, and the socio-cultural role in achieving sustainability for rural areas. In the final chapters, the book offers several peculiarised socio-spatial perspectives, from defining the path towards more resilient communities and sustainable spaces based on a shared wellbeing, to proposing the approach to define community resilience as an intentional action that aims to respond to, and influence, the course of social and economic change, to deliberating the notion of a ’healthy place’ and questioning its optimal scale in the built environment. The study of sustainability and resilience in this book is concluded by drawing a parallel between environmental, economic, and social determinants of the built environment and the determinants that are relevant to human health and well-being

GINGER

In this paper, we outline the scientific objectives, the experimental layout, and the collaborations envisaged for the GINGER (Gyroscopes IN GEneral Relativity) project. The GINGER project brings together different scientific disciplines aiming at building an array of Ring Laser Gyroscopes (RLGs), exploiting the Sagnac effect, to measure continuously, with sensitivity better than picorad/ s, large bandwidth (ca. 1 kHz), and high dynamic range, the absolute angular rotation rate of the Earth. In the paper, we address the feasibility of the apparatus with respect to the ambitious specifications above, as well as prove how such an apparatus, which will be able to detect strong Earthquakes, very weak geodetic signals, as well as general relativity effects like Lense-Thirring and De Sitter, will help scientific advancements in Theoretical Physics, Geophysics, and Geodesy, among other scientific fields.Comment: 21 pages, 9 figure