arXiv subjects

Carlo Fischione

Publications and source records attributed to Carlo Fischione.

At least 73 records · Page 4Linked to original sources

arXiv2022

Wireless for Machine Learning

As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. In this survey, we give an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.

eess.SP↗

arXiv2022

EVM Mitigation with PAPR and ACLR Constraints in Large-Scale MIMO-OFDM Using TOP-ADMM

Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems. Moreover, these systems must constrain the adjacent channel leakage ratio (ACLR) to comply with regulatory requirements. Several recent works have investigated the mitigation of the EVM seen at the receivers by capitalizing on the excess spatial dimensions inherent in the large-scale MIMO that assume the availability of perfect channel state information (CSI) with spatially uncorrelated wireless channels. Unfortunately, practical systems operate with erroneous CSI and spatially correlated channels. Additionally, most standards support user-specific/CSI-aware beamformed and cell-specific/non-CSI-aware broadcasting channels. Hence, we formulate a robust EVM mitigation problem under channel uncertainty with nonconvex PAPR and ACLR constraints catering to beamforming/broadcasting. To solve this formidable problem, we develop an efficient scheme using our recently proposed three-operator alternating direction method of multipliers (TOP-ADMM) algorithm and benchmark it against two three-operator algorithms previously presented for machine learning purposes. Numerical results show the efficacy of the proposed algorithm under imperfect CSI and spatially correlated channels.

eess.SP↗

arXiv2021

Over-the-Air Federated Learning with Retransmissions (Extended Version)

Motivated by increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL convergence over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Finally, we propose a heuristic for selecting the optimal number of retransmissions, which can be calculated before training the ML model. Numerical results demonstrate that the introduction of retransmissions can lead to improved ML performance, without incurring extra costs in terms of communication or computation. Additionally, we provide simulation results on our heuristic which indicate that it can correctly identify the optimal number of retransmissions for different wireless network setups and machine learning problems.

eess.SP↗

arXiv2021

Distributed control of DC grids: integrating prosumers motives

In this paper, a novel distributed control strategy addressing a (feasible) psycho-social-physical welfare problem in islanded Direct Current (DC) smart grids is proposed. Firstly, we formulate a (convex) optimization problem that allows prosumers to share current with each other, taking into account the technical and physical aspects and constraints of the grid (e.g., stability, safety), as well as psycho-social factors (i.e., prosumers' personal values). Secondly, we design a controller whose (unforced) dynamics represent the continuous time primal-dual dynamics of the considered optimization problem. Thirdly, a passive interconnection between the physical grid and the controller is presented. Global asymptotic convergence of the closed-loop system to the desired steady-state is proved and simulations based on collected data on psycho-social aspects illustrate and confirm the theoretical results.

eess.SY↗

arXiv2021

Simultaneous Wireless Information and Power Transfer for Federated Learning

In the Internet of Things, learning is one of most prominent tasks. In this paper, we consider an Internet of Things scenario where federated learning is used with simultaneous transmission of model data and wireless power. We investigate the trade-off between the number of communication rounds and communication round time while harvesting energy to compensate the energy expenditure. We formulate and solve an optimization problem by considering the number of local iterations on devices, the time to transmit-receive the model updates, and to harvest sufficient energy. Numerical results indicate that maximum ratio transmission and zero-forcing beamforming for the optimization of the local iterations on devices substantially boost the test accuracy of the learning task. Moreover, maximum ratio transmission instead of zero-forcing provides the best test accuracy and communication round time trade-off for various energy harvesting percentages. Thus, it is possible to learn a model quickly with few communication rounds without depleting the battery.

eess.SP↗

arXiv2021

Wireless for Control: Over-the-Air Controller

In closed-loop wireless control systems, the state-of-the-art approach prescribes that a controller receives by wireless communications the individual sensor measurements, and then sends the computed control signal to the actuators. We propose an over-the-air controller scheme where all sensors attached to the plant simultaneously transmit scaled sensing signals directly to the actuator; then the feedback control signal is computed partially over the air and partially by a scaling operation at the actuator. Such over-the-air controller essentially adopts the over-the-air computation concept to compute the control signal for closed-loop wireless control systems. In contrast to the state-of-the-art sensor-to-controller and controller-to-actuator communication approach, the over-the-air controller exploits the superposition properties of multiple-access wireless channels to complete the communication and computation of a large number of sensing signals in a single communication resource unit. Therefore, the proposed scheme can obtain significant benefits in terms of low actuation delay and low wireless resource utilization by a simple network architecture that does not require a dedicated controller. Numerical results show that our proposed over-the-air controller achieves a huge widening of the stability region in terms of sampling time and delay, and a significant reduction of the computation error of the control signal.

eess.SY↗

arXiv2020

Dynamic Clustering in Federated Learning

In the resource management of wireless networks, Federated Learning has been used to predict handovers. However, non-independent and identically distributed data degrade the accuracy performance of such predictions. To overcome the problem, Federated Learning can leverage data clustering algorithms and build a machine learning model for each cluster. However, traditional data clustering algorithms, when applied to the handover prediction, exhibit three main limitations: the risk of data privacy breach, the fixed shape of clusters, and the non-adaptive number of clusters. To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division. We show that the generative adversarial network-based clustering preserves privacy. The cluster calibration deals with dynamic environments by modifying clusters. Moreover, the divisive clustering explores the different number of clusters by repeatedly selecting and dividing a cluster into multiple clusters. A baseline algorithm and our algorithm are tested on a time series forecasting task. We show that our algorithm improves the performance of forecasting models, including cellular network handover, by 43%.

cs.LG↗

arXiv2020

Full-Duplex and Dynamic-TDD: Pushing the Limits of Spectrum Reuse in Multi-Cell Communications

Although in cellular networks full-duplex and dynamic time-division duplexing promise increased spectrum efficiency, their potential is so far challenged by increased interference. While previous studies have shown that self-interference can be suppressed to a sufficient level, we show that the cross-link interference for both duplexing modes, especially from base station to base station, is the remaining challenge in multi-cell networks, restricting the uplink performance. Using beamforming techniques of low-complexity, we show that this interference can be mitigated, and that full-duplex and dynamic time-division duplexing can substantially increase the capacity of multi-cell networks. Our results suggest that if we can control the cross link interference in full-duplex, then we can almost double the multi cell network capacity as well as user throughput. Therefore, the techniques in this paper have the potentiality to enable a smooth introduction of full-duplex into cellular systems.

cs.IT↗

arXiv2020

EVM-Constrained and Mask-Compliant MIMO-OFDM Spectral Precoding

Spectral precoding is a promising technique to suppress out-of-band emissions and comply with leakage constraints over adjacent frequency channels and with mask requirements on the unwanted emissions. However, spectral precoding may distort the original data vector, which is formally expressed as the error vector magnitude (EVM) between the precoded and original data vectors. Notably, EVM has a deleterious impact on the performance of multiple-input multiple-output orthogonal frequency division multiplexing-based systems. In this paper we propose a novel spectral precoding approach which constrains the EVM while complying with the mask requirements. We first formulate and solve the EVM-unconstrained mask-compliant spectral precoding problem, which serves as a springboard to the design of two EVM-constrained spectral precoding schemes. The first scheme takes into account a wideband EVM-constraint which limits the average in-band distortion. The second scheme takes into account frequency-selective EVM-constraints, and consequently, limits the signal distortion at the subcarrier level. Numerical examples illustrate that both proposed schemes outperform previously developed schemes in terms of important performance indicators such as block error rate and system-wide throughput while complying with spectral mask and EVM constraints.

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arXiv2020

Wireless Avionics Intra-Communications: A Survey of Benefits, Challenges, and Solutions

In the aeronautics industry, wireless avionics intra-communications have a tremendous potential to improve efficiency and flexibility while reducing the weight, fuel consumption, and maintenance costs over traditional wired avionics systems. This survey starts with an overview of the major benefits and opportunities in the deployment of wireless technologies for critical applications of an aircraft. The current state-of-art is presented in terms of system classifications based on data rate demands and transceiver installation locations. We then discuss major technical challenges in the design and realization of the envisioned aircraft applications. Although wireless avionics intra-communication has aspects and requirements similar to mission-critical applications of industrial automation, it also has specific issues such as complex structures, operations, and safety of the aircraft that make this area of research self-standing and challenging. To support the critical operations of an aircraft, existing wireless standards for mission-critical industrial applications are briefly discussed to investigate the applicability of the current solutions. Specifically, IEEE 802.15.4-based protocols and Bluetooth are discussed for low data rate applications, whereas IEEE 802.11- based standards are considered for high data rate applications. Eventually, we propose fundamental schemes in terms of network architecture, protocol, and resource management to support the critical avionics applications and discuss the research directions in this emerging area.

cs.NI↗

arXiv2020

The Internet of Things as a Deep Neural Network

An important task in the Internet of Things (IoT) is field monitoring, where multiple IoT nodes take measurements and communicate them to the base station or the cloud for processing, inference, and analysis. This communication becomes costly when the measurements are high-dimensional (e.g., videos or time-series data). The IoT networks with limited bandwidth and low power devices may not be able to support such frequent transmissions with high data rates. To ensure communication efficiency, this article proposes to model the measurement compression at IoT nodes and the inference at the base station or cloud as a deep neural network (DNN). We propose a new framework where the data to be transmitted from nodes are the intermediate outputs of a layer of the DNN. We show how to learn the model parameters of the DNN and study the trade-off between the communication rate and the inference accuracy. The experimental results show that we can save approximately 96% transmissions with only a degradation of 2.5% in inference accuracy. Our findings have the potentiality to enable many new IoT data analysis applications generating large amount of measurements.

cs.LG↗

arXiv2020

A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks

Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference. Unfortunately, traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms in terms of latency and protocol overhead, while being sensitive to missing channel state information. In this paper, we propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms, which incorporate model-based beamforming and user association complemented by data-driven model refinements. Our solution has the same computational complexity as a model-based approach but has the major advantage of having substantially less signaling overhead. We discuss how limited channel state information and quantized codebook-based beamforming affect the learning and the spectrum sharing performance. We show that the proposed hybrid sharing scheme significantly improves spectrum utilization under realistic assumptions on inter-operator coordination and channel state information acquisition.

cs.IT↗

arXiv2020

Wirelessly-powered Sensor Networks Power Allocation for Channel Estimation and Energy Beamforming

Wirelessly-powered sensor networks (WPSNs) are becoming increasingly important in different monitoring applications. We consider a WPSN where a multiple-antenna base station, which is dedicated for energy transmission, sends pilot signals to estimate the channel state information and consequently shapes the energy beams toward the sensor nodes. Given a fixed energy budget at the base station, in this paper, we investigate the novel problem of optimally allocating the power for the channel estimation and for the energy transmission. We formulate this non-convex optimization problem for general channel estimation and beamforming schemes that satisfy some qualification conditions. We provide a new solution approach and a performance analysis in terms of optimality and complexity. We also present a closed-form solution for the case where the channels are estimated based on a least square channel estimation and a maximum ratio transmit beamforming scheme. The analysis and simulations indicate a significant gain in terms of the network sensing rate, compared to the fixed power allocation, and the importance of improving the channel estimation efficiency.

cs.NI↗

arXiv2019

Bayesian Model Selection for Change Point Detection and Clustering

We address the new problem of estimating a piece-wise constant signal with the purpose of detecting its change points and the levels of clusters. Our approach is to model it as a nonparametric penalized least square model selection on a family of models indexed over the collection of partitions of the design points and propose a computationally efficient algorithm to approximately solve it. Statistically, minimizing such a penalized criterion yields an approximation to the maximum a posteriori probability (MAP) estimator. The criterion is then analyzed and an oracle inequality is derived using a Gaussian concentration inequality. The oracle inequality is used to derive on one hand conditions for consistency and on the other hand an adaptive upper bound on the expected square risk of the estimator, which statistically motivates our approximation. Finally, we apply our algorithm to simulated data to experimentally validate the statistical guarantees and illustrate its behavior.

stat.ML↗

arXiv2019

Low-Complexity OFDM Spectral Precoding

This paper proposes a new large-scale mask-compliant spectral precoder (LS-MSP) for orthogonal frequency division multiplexing systems. In this paper, we first consider a previously proposed mask-compliant spectral precoding scheme that utilizes a generic convex optimization solver which suffers from high computational complexity, notably in large-scale systems. To mitigate the complexity of computing the LS-MSP, we propose a divide-and-conquer approach that breaks the original problem into smaller rank 1 quadratic-constraint problems and each small problem yields closed-form solution. Based on these solutions, we develop three specialized first-order low-complexity algorithms, based on 1) projection on convex sets and 2) the alternating direction method of multipliers. We also develop an algorithm that capitalizes on the closed-form solutions for the rank 1 quadratic constraints, which is referred to as 3) semi-analytical spectral precoding. Numerical results show that the proposed LS-MSP techniques outperform previously proposed techniques in terms of the computational burden while complying with the spectrum mask. The results also indicate that 3) typically needs 3 iterations to achieve similar results as 1) and 2) at the expense of a slightly increased computational complexity.

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arXiv2019

Voltage Control Using Limited Communication

In electricity distribution networks, the increasing penetration of renewable energy generation necessitates faster and more sophisticated voltage controls. Unfortunately, recent research shows that local voltage control fails in achieving the desired regulation, unless there is communication between the controllers. However, the communication infrastructure for distribution systems is less reliable and less ubiquitous as compared to that for the bulk transmission system. In this paper, we design distributed voltage control that uses limited communication. That is, only neighboring buses need to communicate a few bits between each other for each control step. We investigate how these controllers can achieve the desired asymptotic behavior of the voltage regulation and we provide upper bounds on the number of bits that are needed to ensure a predefined accuracy of the regulation. Finally, we illustrate the results by numerical simulations.

math.OC↗

arXiv2018

Fast and Reliable Initial Access with Random Beamforming for mmWave Networks

Millimeter-wave (mmWave) communications rely on directional transmissions to overcome severe path loss. Nevertheless, the use of narrow beams complicates the initial access procedure and increase the latency as the transmitter and receiver beams should be aligned for a proper link establishment. In this paper, we investigate the feasibility of random beamforming for the cell-search phase of initial access. We develop a stochastic geometry framework to analyze the performance in terms of detection failure probability and expected latency of initial access as well as total data transmission. Meanwhile, we compare our scheme with the widely used exhaustive search and iterative search schemes, in both control plane and data plane. Our numerical results show that, compared to the other two schemes, random beamforming can substantially reduce the latency of initial access with comparable failure probability in dense networks. We show that the gain of the random beamforming is more prominent in light traffics and low-latency services. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave network design.

cs.IT↗

arXiv2018

On Musical Onset Detection via the S-Transform

Musical onset detection is a key component in any beat tracking system. Existing onset detection methods are based on temporal/spectral analysis, or methods that integrate temporal and spectral information together with statistical estimation and machine learning models. In this paper, we propose a method to localize onset components in music by using the S-transform, and thus, the method is purely based on temporal/spectral data. Unlike the other methods based on temporal/spectral data, which usually rely short time Fourier transform (STFT), our method enables effective isolation of crucial frequency subbands due to the frequency dependent resolution of S-transform. Moreover, numerical results show, even with less computationally intensive steps, the proposed method can closely resemble the performance of more resource intensive statistical estimation based approaches.

eess.AS↗