Architectures and Key Technical Challenges for 5G Systems Incorporating Satellites

Satellite Communication systems are a promising solution to extend and complement terrestrial networks in unserved or under-served areas. This aspect is reflected by recent commercial and standardisation endeavours. In particular, 3GPP recently initiated a Study Item for New Radio-based, i.e., 5G, Non-Terrestrial Networks aimed at deploying satellite systems either as a stand-alone solution or as an integration to terrestrial networks in mobile broadband and machine-type communication scenarios. However, typical satellite channel impairments, as large path losses, delays, and Doppler shifts, pose severe challenges to the realisation of a satellite-based NR network. In this paper, based on the architecture options currently being discussed in the standardisation fora, we discuss and assess the impact of the satellite channel characteristics on the physical and Medium Access Control layers, both in terms of transmitted waveforms and procedures for enhanced Mobile BroadBand (eMBB) and NarrowBand-Internet of Things (NB-IoT) applications. The proposed analysis shows that the main technical challenges are related to the PHY/MAC procedures, in particular Random Access (RA), Timing Advance (TA), and Hybrid Automatic Repeat reQuest (HARQ) and, depending on the considered service and architecture, different solutions are proposed.Comment: Submitted to Transactions on Vehicular Technologies, April 201

Telling faults from cyber-attacks in a multi-modal logistic system with complex network analysis

We investigate the properties of systems of systems in a cybersecurity context by using complex network methodologies. We are interested in resilience and attribution. The first relates to the system's behavior in case of faults/attacks, namely to its capacity to recover full or partial functionality after a fault/attack. The second corresponds to the capability to tell faults from attacks, namely to trace the cause of an observed malfunction back to its originating cause(s). We present experiments to witness the effectiveness of our methodology considering a discrete event simulation of a multimodal logistic network featuring 40 nodes distributed across Italy and daily traffic roughly corresponding to the number of containers shipped through in Italian ports yearly averaged daily

Semantic Memory Activation in Individuals at Risk for Developing Alzheimer Disease

Objective: To determine whether whole-brain, event-related fMRI can distinguish healthy older adults with known Alzheimer disease (AD) risk factors (family history, APOE ε4) from controls using a semantic memory task involving discrimination of famous from unfamiliar names. Methods: Sixty-nine cognitively asymptomatic adults were divided into 3 groups (n = 23 each) based on AD risk: 1) no family history, no ε4 allele (control [CON]); 2) family history, no ε4 allele (FH); and 3) family history and ε4 allele (FH+ε4). Separate hemodynamic response functions were extracted for famous and unfamiliar names using deconvolution analysis (correct trials only). Results: Cognitively intact older adults with AD risk factors (FH and FH+ε4) exhibited greater activation in recognizing famous relative to unfamiliar names than a group without risk factors (CON), especially in the bilateral posterior cingulate/precuneus, bilateral temporoparietal junction, and bilateral prefrontal cortex. The increased activation was more apparent in the FH+ε4 than in the FH group. Unlike the 2 at-risk groups, the control group demonstrated greater activation for unfamiliar than familiar names, predominately in the supplementary motor area, bilateral precentral, left inferior frontal, right insula, precuneus, and angular gyrus. These results could not be attributed to differences in demographic variables, cerebral atrophy, episodic memory performance, global cognitive functioning, activities of daily living, or depression. Conclusions: Results demonstrate that a low-effort, high-accuracy semantic memory activation task is sensitive to Alzheimer disease risk factors in a dose-related manner. This increased activation in at-risk individuals may reflect a compensatory brain response to support task performance in otherwise asymptomatic older adults

A Fast Monte Carlo algorithm for evaluating matrix functions with application in complex networks

We propose a novel stochastic algorithm that randomly samples entire rows and columns of the matrix as a way to approximate an arbitrary matrix function. This contrasts with the "classical" Monte Carlo method which only works with one entry at a time, resulting in a significant better convergence rate than the "classical" approach. To assess the applicability of our method, we compute the subgraph centrality and total communicability of several large networks. In all benchmarks analyzed so far, the performance of our method was significantly superior to the competition, being able to scale up to 64 CPU cores with a remarkable efficiency.Comment: Submitted to the Journal of Scientific Computin

XAI.it 2021 - Preface to the Second Italian Workshop on Explainable Artificial Intelligence

Artificial Intelligence systems are increasingly playing an increasingly important role in our daily lives. As their importance in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the recent General Data Protection Regulation (GDPR) emphasized the users' right to explanation when people face artificial intelligencebased technologies. Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the models (e.g., recommendation accuracy) at the expense of the explainability and the transparency. The main research questions which arise from this scenario is straightforward: how can we deal with such a dichotomy between the need for effective adaptive systems and the right to transparency and interpretability? Several research lines are triggered by this question: building transparent intelligent systems, analyzing the impact of opaque algorithms on final users, studying the role of explanation strategies, investigating how to provide users with more control in the behavior of intelligent systems. XAI.it, the Italian workshop on Explainable AI, tries to address these research lines and aims to provide a forum for the Italian community to discuss problems, challenges and innovative approaches in the various sub-fields of XAI

Semantic Knowledge for Famous Names in Mild Cognitive Impairment

Person identification represents a unique category of semantic knowledge that is commonly impaired in Alzheimer\u27s disease (AD), but has received relatively little investigation in patients with mild cognitive impairment (MCI). The current study examined the retrieval of semantic knowledge for famous names from three time epochs (recent, remote, and enduring) in two participant groups: 23 amnestic MCI (aMCI) patients and 23 healthy elderly controls. The aMCI group was less accurate and produced less semantic knowledge than controls for famous names. Names from the enduring period were recognized faster than both recent and remote names in both groups, and remote names were recognized more quickly than recent names. Episodic memory performance was correlated with greater semantic knowledge particularly for recent names. We suggest that the anterograde memory deficits in the aMCI group interferes with learning of recent famous names and as a result produces difficulties with updating and integrating new semantic information with previously stored information. The implications of these findings for characterizing semantic memory deficits in MCI are discussed. (JINS, 2009, 15, 9-18.

Joint Graph-based User Scheduling and Beamforming in LEO-MIMO Satellite Communication Systems

In this paper, a Low earth orbit (LEO) High-Throughput Satellite (HTS) Multi-User multiple-input multiple-output (MIMO) system is considered. With the objective of minimizing inter-beam interference among users, we propose a joint graph-based user scheduling and feed space beamforming framework for the downlink. First, we construct a graph where the vertices are the users and edges are based on a dissimilarity measure of their channels. Secondly, we design a low complexity greedy user clustering strategy, in which we iteratively search for the maximum clique in the graph. Finally, a Minimum Mean Square Error (MMSE) beamforming matrix is applied on a cluster basis with different power normalization schemes. A heuristic optimization of the graph density, i.e., optimal cluster size, is performed in order to maximize the system capacity. The proposed scheduling algorithm is compared with a position-based scheduler, in which a beam lattice is generated on ground and one user per beam is randomly selected to form a cluster. Results are presented in terms of achievable per-user capacity and show the superiority in performance of the proposed scheduler w.r.t. to the position-based approach

Improved Graph-Based User Scheduling For Sum-Rate Maximization in LEO-NTN Systems

In this paper, we study the problem of user scheduling for Low Earth Orbit (LEO) Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) Non-Terrestrial Network (NTN) systems with the objective of maximizing the sum-rate capacity while minimizing the total number of clusters. We propose an iterative graph-based maximum clique scheduling approach with constant graph density. Users are grouped together based on the channel coefficient of correlation (CoC) as dissimilarity metric and served by the satellite via Space Division Multiple Access (SDMA) by means of Minimum Mean Square Error (MMSE) digital beamforming on a cluster basis. Clusters are then served in different time slots via Time Division Multiple Access (TDMA). The results, presented in terms of per-cluster sum-rate capacity and per-user throughput, show that the presented approach can significantly improve the system performance

Graph-Based User Scheduling Algorithms for LEO-MIMO Non-Terrestrial Networks

In this paper, we study the user scheduling prob-lem in a Low Earth Orbit (LEO) Multi-User Multiple-Input-Multiple-Output (MIMO) system. We propose an iterative graph-based maximum clique scheduling approach, in which users are grouped together based on a dissimilarity measure and served by the satellite via space-division multiple access (SDMA) by means of Minimum Mean Square Error (MMSE) digital beamforming on a cluster basis. User groups are then served in different time slots via time-division multiple access (TDMA). As dissimilarity measure, we consider both the channel coefficient of correlation and the users' great circle distance. A heuristic optimization of the optimal cluster size is performed in order to maximize the system capacity. To further validate our analysis, we compare our proposed graph-based schedulers with the well-established algorithm known as Multiple Antenna Downlink Orthogonal clustering (MADOC). Results are presented in terms of achievable per-user capacity and show the superiority in performance of the proposed schedulers w.r.t. MADOC