1,721,016 research outputs found
Blind wireless network topology inference
Full text linkThis work proposes a framework to discover the topology of a non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing topology sensing of a network whose key features (i.e., number of nodes, physical layer signals, and medium access control (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to separate traffic patterns. Then, to infer the topology, we detect directed data flows among nodes by identifying causal relationships between the separated transmitted patterns. We propose causal inference methods such as Granger causality (GC), transfer entropy (TE), and conditional transfer entropy (CTE) that use the times series of traffic profiles, and a solution based on a neural network (NN) that exploits distilled time-based features. The framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. Numerical results reveal that the proposed approach reaches a high probability of link detection and a moderate false alarm rate in mild shadowing regimes and low to moderate network nodes mobility
Access Point Cooperation Strategies for Coded Random Access in Cell-Free Massive MIMO
Full text linkIn this article, grant-free uplink communication from a large number of machine-type devices in cell-free massive MIMO networks is explored. A novel approach that leverages coded random access (CRA), on the device side, with combining of signals received at properly selected access points (APs) and cooperative successive interference cancelation (SIC), on the network side, is presented. Initially, an analytical framework based on stochastic geometry is developed to investigate performance of AP cooperation through signal combining under diverse AP cluster compositions. The potential gain from AP signal combining is then assessed by evaluating a genie-aided scheme, guiding the network in cluster selection for each active device. Subsequently, two practical AP selection algorithms that operate in grant-free conditions (i.e., do not require prior information regarding the active users) are proposed. Numerical results show how AP cooperation through signal combining and distributed interference cancelation can bring tangible benefits even without prior information about active users, under different signal-to-noise ratio regimes, closing in some cases the gap to the genie-aided approach. Additionally, the results prove that AP cooperation can be used to reduce the devices' energy consumption and the number of APs that have to be deployed by the service providers to achieve specific performance levels
Detection of Jamming Attacks via Source Separation and Causal Inference
Full text linkJamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing
Using Outcome Harvesting to evaluate socio-economic development and social innovation generated by Social Enterprises in complex areas. The case of BADAEL project in Lebanon
No full textCoded Random Access in Cell-Free Massive MIMO Networks with Access Point Signal Combining
No full textGrant-free uplink from a massive number of machine-type devices in cell-free massive MIMO networks is considered. A new approach is introduced that relies on coded random access, on the device side, with combining of signals received at properly selected access points (APs) and successive interference cancellation, on the network side. The gain potentially achievable via AP signal combining is evaluated using a genie-aided scheme, where the network is assisted by a genie in the selection of the cluster of APs for each active device. Then, two practical AP selection algorithms are proposed, working in grant free conditions. These algorithms can substantially enhance the system performance despite absence of any prior information. Under specific conditions, one of them even performs very close to the genie-aided scheme
Dimensionality reduction in modal analysis for structural health monitoring
No full textAutonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%
Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks
No full textCell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation massive machine-type communications. In this paper, a fully grant-free deep learning (DL)-based method for user activity detection in CF-mMIMO networks is proposed. Initially, the known non-orthogonal pilot sequences are used to estimate the channel coefficients between each user and the access points. Then, a deep convolutional neural network is used to estimate the activity status of the users. The proposed method is 'blind', i.e., it is fully data-driven and does not require prior large-scale fading coefficients estimation. Numerical results show how the proposed DL-based algorithm is able to merge the information gathered by the distributed antennas to estimate the user activity status, yet outperforming a state-of-the-art covariance-based method
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