# Alessio ZapponeCentraleSupélec | ECP · Large Networks and Systems Group

Alessio Zappone

Ph.D.

## About

166

Publications

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Introduction

Alessio Zappone currently works with the Large Networks and Systems Group (LANEAS) at CentraleSupelec, Paris, France.
Alessio's current work is on green communications and on applications of artificial intelligence and neural networks to wireless communications.
Alessio's research is supported by the Marie Curie grant for experienced researchers H2020-MSCA-IF-BESMART, funded by the European Commission.

Additional affiliations

September 2012 - present

January 2011 - August 2012

## Publications

Publications (166)

Reconfigurable intelligent surface (RIS) is an emerging technology that is under investigation for different applications in wireless communications. RISs are often analyzed and optimized by considering simplified electromagnetic reradiation models. In this chapter, we aim to study the impact of realistic reradiation models for RISs as a function o...

We study channel-aware binary-decision fusion over a shared flat-fading channel with multiple antennas at the Fusion Center (FC). This paper considers the aid of a Reconfigurable Intelligent Surface (RIS) to effectively convey the information of the phenomenon of interest to the FC and foster energy-efficient data analytics supporting the Internet...

The problem of simultaneously optimizing the information rate and the harvested power in a reconfigurable intelligent surface (RIS)-aided multiple-input single-output downlink wireless network with simultaneous wireless information and power transfer (SWIPT) is addressed. The beamforming vectors, RIS reflection coefficients, and power split ratios...

In this paper, we study the uplink transmission in an intelligent reflecting surface (IRS) assisted cell-free multiple-input multiple-output (MIMO) system where the central processing unit (CPU) only has statistical channel state information (CSI) to detect symbols, and to design the receiver filter coefficients, the power allocations, and the IRS...

In this paper, we concentrate on the employment of a user-centric (UC) cell-free massive MIMO (CFmMIMO) network for providing ultra reliable low latency communication (URLLC) when traditional ground users (GUs) coexists with unmanned aerial vehicles (UAVs). We study power control in both the downlink and the uplink of such a scenario when partial z...

In this paper, a novel optimization model for joint beamforming and power control in the downlink (DL) of a cell-free massive MIMO (CFmMIMO) system is presented. The objective of the proposed optimization model is to minimize the maximum user interference while satisfying quality of service (QoS) constraints and power consumption limits. The propos...

The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that...

The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, increases both, signa...

This work tackles the problem of maximizing the achievable rate in a reconfigurable intelligent surface (RIS)-assisted communication link, by enforcing conventional maximum power constraints and additional constraints on the maximum exposure to electromagnetic radiations of the end-users. The RIS phase shift matrix, the transmit beamforming filter,...

Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide interest due to their capability of tuning wireless propagation environments in order to increase the system performance of wireless networks. In this paper, a multiuser wireless network assisted by a RIS is studied and resource allocation algorithms are presented for several...

The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which th...

Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide interest due to their capability of tuning wireless propagation environments in order to increase the system performance of wireless networks. In this paper, a multiuser wireless network assisted by a RIS is studied and resource allocation algorithms are presented for several...

The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which th...

Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sen...

This work considers a point-to-point link where a reconfigurable intelligent surface assists the communication between a transmitter and a receiver. The system rate, energy efficiency, and their trade-off are optimized by tuning the number of reflecting elements to be activated and the phase shifts that they apply. Unlike most previous works, the c...

Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to repor...

This work considers a point-to-point link where a reconfigurable intelligent surface assists the communication between transmitter and receiver. The system rate, energy efficiency, and their trade-off are optimized with respect to the number of individually tunable elements of the intelligent surface. The resource allocation accounts for the commun...

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scatt...

Future wireless networks are expected to evolve toward an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices. They will also be capable of sensing, controlling, and optimizing the wireless environment to fulfill the visions of low-power, high-throughput, massively-connected, and low...

During the last decade, stochastic geometry has been widely employed for system-level analysis in cellular networks. The resulting analytical frameworks are, however, not always amenable for system-level optimization. This is due to three main reasons: (i) the performance metric of interest may not be formulated in closed-form; (ii) under some anal...

This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of the most common energy-efficien...

What is a reconfigurable intelligent surface? What is a smart radio environment? What is a metasurface? How do metasurfaces work and how to model them? How to reconcile the mathematical theories of communication and electromagnetism? What are the most suitable uses and applications of reconfigurable intelligent surfaces in wireless networks? What a...

Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide interest due to their capability of tuning wireless propagation environments in order to increase the system performance of wireless networks. In this paper, a multiuser single-cell wireless network assisted by a RIS is studied. First of all, for the special case of a single-...

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where...

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where...

Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used, and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to repo...

Future wireless networks are expected to evolve towards an intelligent and software reconfigurable functionality paradigm enabling ubiquitous communication between humans and mobile devices, but also being capable of sensing, controlling, and optimizing the wireless environment to fulfill the visions of low powered, high throughput, massive connect...

In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a
large
energy harvesting (EH) multiple access channel, when only causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power control problem as a discrete-...

This work addresses the problem of energy-efficient radio resource allocation in large-scale multiple-input multiple-output (MIMO) systems using hybrid beamforming and spatial modulation, communicating using mmWaves. The problem is formulated as the maximization of the bit-per-Joule energy efficiency with respect to the transmit power and number of...

This paper focuses on the use of a deep learning approach to perform sum-rate-max and max-min power allocation in the uplink of a cell-free massive MIMO network. In particular, we train a deep neural network in order to learn the mapping between a set of input data and the optimal solution of the power allocation strategy. Numerical results show th...

Deep learning based on artificial neural networks (ANNs) is a powerful machine-learning method that, in recent years, has been successfully used to realize tasks such as image classification, speech recognition, and language translation, among others, that are usually simple for human beings but extremely difficult for machines. This is one reason...

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will b...

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mob...

Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such...

In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only the causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power control problem as a discret...

In a cell-free massive MIMO architecture a very large number of distributed access points simultaneously and jointly serves a much smaller number of mobile stations; a variant of the cell-free technique is the user-centric approach, wherein each access point just serves a reduced set of mobile stations. This paper introduces and analyzes the cell-f...

Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such...

In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems. In the proposed approach, for a given EH network, the optimal online power control rule is learned by training a deep neural network (DNN), using the solution of offline pol...

One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transcei...

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will b...

This work develops a beamforming framework for energy efficiency optimization in MIMO multiuser systems with confidentiality constraints. Two channel models are considered, namely a broadcast channel with confidential messages (corresponding to single-cell downlink) and an interference channel with confidential messages (corresponding to multi-cell...

This work investigates the use of deep learning to perform user cell association for sum-rate maximization in Massive MIMO networks. It is shown how a deep neural network can be trained to approach the optimal association rule with a much more limited computational complexity, thus enabling to update the association rule in real-time, on the basis...

This work develops a deep learning power control framework for energy efficiency maximization in wireless interference networks. Rather than relying on suboptimal power allocation policies, the training of the deep neural network is based on the globally optimal power allocation rule, leveraging a newly proposed branch-and-bound procedure with a co...

This work advocates the use of deep learning to perform max-min and max-prod power allocation in the downlink of Massive MIMO networks. More precisely, a deep neural network is trained to learn the map between the positions of user equipments (UEs) and the optimal power allocation policies, and then used to predict the power allocation profiles for...

The adoption of Large Intelligent Surfaces (LIS) in assisting downlink multi-user communication from a multi-antenna base station is investigated in this paper. We present efficient designs for both the transmit power allocation and the coefficients of the surface reflecting elements that target at maximizing either the energy or the spectral effic...

We compute the spectral-energy efficiency Pareto front in Poisson cellular networks, by formulating a spectral-energy efficiency bi-objective optimization problem as a function of either the transmit power or the density of the base stations. Capitalizing on fundamental theoretical results on weighted Tchebycheff optimization problems applied to st...

We consider a multi-user Multiple-Input Single-Output (MISO) communication system comprising of a multi-antenna base station communicating in the downlink simultaneously with multiple single-antenna mobile users. This communication is assumed to be assisted by a Large Intelligent Surface (LIS) that consists of many nearly passive antenna elements,...

The coverage probability of cellular networks is usually defined as the probability that the signal-to-interference+noise-ratio (SINR) is greater than a reliability threshold. Based on this definition, the coverage probability cannot, in general, be formulated in a tractable closed-form expression. Di Renzo
et al.
have introduced a new definition...

A novel optimization framework for resource allocation in wireless networks and radar systems is proposed, which merges the methods of Maximum Block Improvement (MBI) and of sequential optimization. A detailed convergence proof is provided, showing that the proposed algorithm is able to monotonically increase the objective value while ensuring that...

This work aims to introduce the framework of polynomial optimization theory to solve fractional polynomial problems (FPPs). Unlike other widely used optimization frameworks, the proposed one applies to a larger class of FPPs, not necessarily defined by concave and convex functions. An iterative algorithm that is provably convergent and enjoys asymp...

Deep learning based on artificial neural networks is a powerful machine learning method that, in the last few years, has been successfully used to realize tasks, e.g., image classification, speech recognition, translation of languages, etc., that are usually simple to execute by human beings but extremely difficult to perform by machines. This is o...

We consider a multi-user, multi-relay network where each source communicates with its destination via an assigned relay. To ensure fairness among the users, the minimum of the users’ energy efficiencies (in bit-per-Joule) is jointly maximized with respect to the relay assignment and transmit power of both source and relay nodes. The resulting algor...

This paper investigates the use of a Passive Intelligent Mirrors (PIM) to operate a multi-user MISO downlink communication. The transmit powers and the mirror reflection coefficients are designed for sum-rate maximization subject to individual QoS guarantees to the mobile users. The resulting problem is non-convex, and is tackled by combining alter...

This work aims to introduce the framework of polynomial optimization theory to solve fractional polynomial problems (FPPs). Unlike other widely used optimization frameworks, the proposed one applies to a larger class of FPPs, not necessarily defined by concave and convex functions. An iterative algorithm that is provably convergent and enjoys asymp...

The problem of radio resource allocation for global energy efficiency (GEE) maximization in mmWaves large-scale multiple-input multiple-output (MIMO) systems using hybrid-beamforming with spatial modulation is addressed. The theoretical properties of the optimization problem at hand are analyzed and two provably convergent optimization algorithms w...

This paper considers cell-free and user-centric approaches for coverage improvement in wireless cellular systems operating at millimeter wave frequencies, and proposes downlink power control algorithms aimed at maximizing the global energy efficiency. To tackle the non-convexity of the problems, an interaction between sequential and alternating opt...

The problem of radio resource allocation for global energy efficiency (GEE) maximization in mmWaves large-scale multiple-input multiple-output (MIMO) systems using hybrid-beamforming with spatial modulation is addressed. The theoretical properties of the optimization problem at hand are analyzed and two provably convergent optimization algorithms w...