arXiv subjects

Nicola Marchetti

Publications and source records attributed to Nicola Marchetti.

At least 55 records · Page 3Linked to original sources

arXiv2023

Energy-Efficient Physical Layer Security for Wearable IoT Devices

This work proposes an energy-efficient Directional Modulation (DM) scheme for on-body Internet of Things (IoT) devices. DM performance is tested using a 5-port stacked-patch MIMO antenna under two scenarios: a free space case and using a four-layer human forearm phantom to simulate the user's wrist. It is demonstrated that the scheme achieves steerable secure transmissions across the entire horizontal plane. With a low Bit Error Rate (BER) of ${1.5\times10^{-5}}$ at the desired directions, eavesdroppers experience a high error rate of up to ${0.498}$. Furthermore, this work investigates the DM performance using a subset of the stacked patches in the MIMO antenna, revealing that some combinations achieve a low BER performance using a lower antenna profile, albeit high side-lobes of BER${<10^{-2}}$ seen outside the desired region. Overall, the solution is proposed as a good candidate to enable secure wireless communications in emerging wearable IoT devices that are subject to size and energy constraints.

eess.SP↗

arXiv2023

Flexible Multimode-Based Beamforming MIMO Antenna

This work proposes compact, flexible Multiple Input Multiple Output (MIMO) antennas. The design principle is based on the excitation of different orthogonal radiating modes within the same antenna volume. Via phase control of the excited modes, beamforming is demonstrated in azimuth and elevation planes using single-layered structures. For flexibility, the antennas are designed using Polydimethylsiloxane (PDMS) as the substrate. Numerical results demonstrate that isolation better than 23 dB is realized in all investigated antennas under different bend configurations. Moreover, the proposed technique demonstrates an antenna with unidirectional beamsteering across the entire elevation plane, and a second design realizes a bidirectional beamsteering in the horizontal plane. Overall, the results highlight the potential of multimode-based beamforming for flexible MIMO antennas in Internet of Things (IoT) systems.

eess.SP↗

arXiv2022

Space-Time- and Frequency- Spreading for Interference Minimization in Dense IoT

In this article, we propose a space spreading-assisted framework that leverages either time or frequency diversity or both to reduce interference and signal loss owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance.

cs.IT↗

arXiv2022

Analysis of Temporal Robustness in Massive Machine Type Communications

The evolution of fifth generation (5G) networks needs to support the latest use cases, which demand robust network connectivity for the collaborative performance of the network agents, like multi-robot systems and vehicle to anything (V2X) communication. Unfortunately, the user device's limited communication range and battery constraint confirm the unfitness of known robustness metrics suggested for fixed networks, when applied to time-switching communication graphs. Furthermore, the calculation of most of the existing robustness metrics involves non-deterministic polynomial-time complexity, and hence are best-fitted only for small networks. Despite a large volume of works, the complete analysis of a $\textit{low-complexity}$ temporal robustness metric for a communication network is absent in the literature, and the present work aims to fill this gap. More in detail, our work provides a stochastic analysis of network robustness for a massive machine type communication (mMTC) network. The numerical investigation corroborates the exactness of the proposed analytical framework for temporal robustness metric. Along with studying the impact on network robustness of various system parameters, such as cluster head (CH) probability, power threshold value, network size, and node failure probability, we justify the observed trend of numerical results probabilistically.

cs.NI↗

arXiv2022

Applying Intelligent Reflector Surfaces for Detecting Violent Expiratory Aerosol Cloud using Terahertz Signals

The recent COVID-19 pandemic has driven researchers from different spectrum to develop novel solutions that can improve detection and understanding of SARS-CoV-2 virus. In this article we propose the use of Intelligent Reflector Surface (IRS) emitting terahertz signals to detect airborne respiratory aerosol cloud that are secreted from people. Our proposed approach makes use of future IRS infrastructure to extend beyond communication functionality by adding environmental scanning for aerosol clouds. Simulations have also been conducted to analyze the accuracy of aerosol cloud detection based on a signal scanning and path optimization algorithm. Utilizing IRS for detecting respiratory aerosol cloud can lead to new added value of telecommunication infrastructures for sensor monitoring data that can be used for public health.

eess.SP↗

arXiv2022

Joint Modelling of Quantum and Classical Noise over Unity Quantum Channel

For a continuous-input-continuous-output arbitrarily distributed quantum channel carrying classical information, the channel capacity can be computed in terms of the distribution of the channel envelope, received signal strength over a quantum propagation field and the noise spectral density. If the channel envelope is considered to be unity with unit received signal strength, the factor controlling the capacity is the noise. Quantum channel carrying classical information will suffer from the combination of classical and quantum noise. Assuming additive Gaussian-distributed classical noise and Poisson-distributed quantum noise, we formulate a hybrid noise model by deriving a joint Gaussian- Poisson distribution in this letter. For the transmitted signal, we consider the mean of signal sample space instead of considering a particular distribution and study how the maximum mutual information varies over such mean value. Capacity is estimated by maximizing the mutual information over unity channel envelope.

cs.IT↗

arXiv2022

Minimizing the Signaling Overhead and Latency based on Users' Mobility Patterns

We demonstrate a distributed and a centralized 4G/5G compliant approach to minimize signaling and latency related to user mobility in cellular networks. This is crucial due to the densification of networks and the additional signaling introduced by the new 5G service based architecture. By exploiting standardized protocols, our solutions dynamically reorganize the association between nodes in Radio Access Network (RAN) and the core. We validated the proposed approaches using real user mobility datasets. Our results show that both our distributed and centralized solutions significantly reduce the signaling between core and RAN compared to the traditional approach based on geographical proximity. As a result, both approaches significantly reduce the average handover procedure processing time. Moreover, by relying on locally available information, the distributed approach can quickly adapt to changes in the user movement patterns as they happen.

cs.NI↗

arXiv2021

Artificial Intelligence for Dynamical Systems in Wireless Communications: Modeling for the Future

Dynamical systems are no strangers in wireless communications. Our story will necessarily involve chaos, but not in the terms secure chaotic communications have introduced it: we will look for the chaos, complexity and dynamics that already exist in everyday wireless communications. We present a short overview of dynamical systems and chaos before focusing on the applications of dynamical systems theory to wireless communications in the past 30 years, ranging from the modeling on the physical layer to different kinds of self-similar traffic encountered all the way up to the network layer. The examples of past research and its implications are grouped and mapped onto the media layers of ISO OSI model to show just how ubiquitous dynamical systems theory can be and to trace the paths that may be taken now. When considering the future paths, we argue that the time has come for us to revive the interest in dynamical systems for wireless communications. It did not happen already because of the big question: can we afford observing systems of our interest as dynamical systems and what are the trade-offs? The answers to these questions are dynamical systems of its own: they change not only with the modeling context, but also with time. In the current moment the available resources allow such approach and the current demands ask for it. Reservoir computing, the major player in dynamical systems-related learning originated in wireless communications, and to wireless communications it should return.

math.DS↗

arXiv2021

Performance of Predictive Indoor mmWave Networks with Dynamic Blockers

In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams' blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantee higher signal level stability and up to $82\%$ data rate improvement with respect detection-based methods when blockers move at speed of $2$ m/s.

cs.NI↗

arXiv2020

Spectral Domain Spline Graph Filter Bank

In this paper, we present a structure for two-channel spline graph filter bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our proposed structure has many desirable properties; namely, perfect reconstruction, critical sampling in spectral domain, flexibility in choice of shape and cut-off frequency of the filters, and low complexity implementation of the synthesis section, thanks to our closed-form derivation of the synthesis filter and its sparse structure. These properties play a pivotal role in multi-scale transforms of graph signals. Additionally, this framework can use both normalized and non-normalized Laplacian of any undirected graph. We evaluate the performance of our proposed SGFBSS structure in nonlinear approximation and denoising applications through simulations. We also compare our method with the existing graph filter bank structures and show its superior performance.

eess.SP↗

arXiv2020

Evolving Intelligent Reflector Surface towards 6G for Public Health: Application in Airborne Virus Detection

While metasurface based intelligent reflecting surfaces (IRS) are an important emerging technology for future generations of wireless connectivity in its own right, the plans for the mass deployment of these surfaces motivate the question of their integration with other new and emerging technologies that would require mass proliferation. This question of integration and the vision of future communication systems as an invaluable component for public health motivated our new concept of Intelligent Reflector-Viral Detectors (IR-VD). In this novel scheme, we propose deployment of intelligent reflectors with strips of receptor-based viral detectors placed between the reflective surface tiles. Our proposed approach encodes information of the virus by flicking the angle of the reflected beams, using time variations between the beam deviations to represent the messages. This information includes the presence of the virus, its location and load size. The paper presents simulation to demonstrate the encoding process based on varying quantity of virus that have bound onto the IR-VD.

eess.SP↗

arXiv2020

Performance Evaluation of Scheduling in 5G-mmWave Networks under Human Blockage

The millimetre-wave spectrum provisions enormous enhancement to the achievable data rate of 5G networks. However, human blockages affecting the millimetre-wave signal can severely degrade the performance if proper resource allocation is not considered. In this paper, we assess how conventional schedulers, such as the Proportional Fair scheduler, react to the presence of blockage. Our results show that the resource allocation may disfavour users suffering from blockage, leading to low data rate for those users. To circumvent this problem, we show that the data rate of those users can be improved by using a scheduler adapted to react to upcoming blockage events. The adapted scheduler aims at proactively allocating the resources before a blockage happens, mitigating losses. Such adaptation is motivated by recent progress in blockage prediction for millimetre-wave signals in a dynamic human blockage scenario. Our extensive simulations indicate gains in the 1st percentile rate and fairness with respect to Proportional Fair scheduler when blockage conditions are severe.

cs.IT↗

arXiv2020

Performance Analysis and Optimization of Cache-Assisted CoMP for Clustered D2D Networks

Caching at mobile devices and leveraging cooperative device-to-device (D2D) communications are two promising approaches to support massive content delivery over wireless networks while mitigating the effects of interference. To show the impact of cooperative communication on the performance of cache-enabled D2D networks, the notion of device clustering must be factored in to convey a realistic description of the network performance. In this regard, this paper develops a novel mathematical model, based on stochastic geometry and an optimization framework for cache-assisted coordinated multi-point (CoMP) transmissions with clustered devices. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory to cache files from a known library, following a random probabilistic caching scheme. Desired contents that are not self-cached can be obtained via D2D CoMP transmissions from neighboring devices or, as a last resort, from the network. For this model, we analytically characterize the offloading gain and rate coverage probability as functions of the system parameters. An optimal caching strategy is then defined as the content placement scheme that maximizes the offloading gain. For a tractable optimization framework, we pursue two separate approaches to obtain a lower bound and a provably accurate approximation of the offloading gain, which allows us to obtain optimized caching strategies.

cs.IT↗

arXiv2020

Indoor Millimeter-Wave Systems: Design and Performance Evaluation

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. While we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. The paper introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts, and then moves on to experimentally-validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

cs.NI↗

arXiv2020

Objective Multi-variable Classification and Inference of Biological Neuronal Networks

Classification of biological neuron types and networks poses challenges to the full understanding of the brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal types and networks based on the communication metrics of neurons. This presents advantages against the existing approaches since the mutual information or the delay between neurons obtained from spike trains are more abundant data compare to conventional morphological data. We firstly designed two open-access supporting computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then we investigate how the concept of network tomography could be achieved with cortical neuronal circuits for morphological, topological and electrical classification of neurons. We extract the simulated data to many different classifiers (including SVM, Decision Trees, Random Forest, and Artificial Neuron Networks) classifying the specific cell type (and sub-group types) achieving accuracies of up to 70\%. Inference of biological network structures using network tomography reached up to 65\% of accuracy. We also analysed recall, precision and F1score of the classification of five layers, 25 cell m-types, and 14 cell e-types. Our research not only contributes to existing classification efforts but sets the road-map for future usage of cellular-scaled brain-machine interfaces for in-vivo objective classification of neurons as a sensing mechanism of the brain's structure.

q-bio.NC↗

arXiv2020

Optimized Caching and Spectrum Partitioning for D2D enabled Cellular Systems with Clustered Devices

Caching at mobile devices and leveraging device- to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively.

cs.IT↗

arXiv2020

Optimizing Joint Probabilistic Caching and Channel Access for Clustered D2D Networks

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. Analysis of such D2D caching networks based on a physical interference model is usually carried out by assuming uniformly distributed devices. However, this approach does not capture the notion of device clustering. In this regard, this paper proposes a joint communication and caching optimization framework for clustered D2D networks. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory that is utilized to proactively cache files, following a random probabilistic caching scheme. The cache offloading gain is maximized by jointly optimizing channel access and caching scheme. A closed-form caching solution is obtained and bisection search method is adopted to heuristically obtain the optimal channel access probability. Results show significant improvement in the offloading gain reaching up to 10% compared to the Zipf caching baseline.

cs.IT↗

arXiv2020

Performance Analysis of Indoor mmWave Networks with Ceiling-Mounted Access Points

The objective of the Enhanced Mobile Broadband use case in 5G networks is to deliver high capacity access to densely populated areas, like city centres, transportation hubs or convention centres. Millimetre-wave communications are the go-to technology to realise that objective, yet due to weak outdoor-to-indoor penetration, outdoor deployments will not suffice and dedicated indoor deployments will be necessary. In this article, we study dense deployments of millimetre-wave access points mounted on the ceiling, with directional antennas pointing downwards to illuminate selected spots on the ground. In this setup, the signal propagation is primarily limited by human body blockages. Therefore, we develop a body blockage model and derive an expression for the probability of blockage. Using the developed expressions and our simulation framework, we assess the impact of densification and body blockage on the achievable performance. We find that both coverage and area spectral efficiency curves exhibit non-trivial behaviour with respect to the access point density and that there is an optimal beamwidth-density configuration that only maximises either coverage or area spectral efficiency. Such optimal configuration changes depending on the body blockage probability, leading to a necessity for network designers to carefully consider their intended application and scenario.

cs.IT↗