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Laura Cottatellucci

Publications and source records attributed to Laura Cottatellucci.

At least 19 recordsLinked to original sources
arXiv2026

Statistical Channel Model for FSO Systems Assisted by a UAV-Mounted IRS

Integrating optical intelligent reflecting surfaces (IRSs) into aerial platforms, such as unmanned aerial vehicles (UAVs), has been proposed to relax the line-of-sight (LoS) constraint, extend coverage, and enhance deployment flexibility of free space optical (FSO) systems. However, misalignment errors induced by the UAV hovering, in both position and orientation, may degrade connectivity and impair the end-to-end channel quality. In this paper, we develop novel expressions for the electric fields incident on and reflected by an optical IRS, based on the Huygens-Fresnel principle. The resulting expressions are applicable for any combination of incident and reflected propagation directions. Building on this framework, we derive a closed-form statistical channel model that captures the geometric and misalignment losses of an FSO link assisted by a UAV-mounted IRS in the presence of random UAV fluctuations. In particular, we develop a statistical model for the beam misalignment at the receiver lens, assuming Gaussian fluctuations in both the UAV position and orientation. The proposed analytical model is validated through Monte Carlo (MC) simulations and is further used to provide practical design guidelines regarding the optimal placement of the UAV-mounted IRS for the minimization of the outage probability.

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arXiv2026

On the Identifiability of Semi-Blind Estimation in Cell-Free Massive MIMO Networks

Semi-blind joint channel estimation and data detection (JCD) is a promising approach to mitigate pilot contamination in cell-free massive multiple-input multiple-output (CF-MaMIMO) networks. The effectiveness of such methods fundamentally depends on identifiability, i.e., the ability to unambiguously recover the unknown channel coefficients and transmitted data signals from the received uplink observations. In this work, we investigate the identifiability of semi-blind JCD from a large-scale system design perspective. We consider a CF-MaMIMO network in which access points (APs) and user equipments (UEs) are spatially distributed according to Poisson point processes (PPPs). The resulting network topology is modeled as bipartite random geometric graph (BRGG) that captures local connectivity induced by wireless propagation. To enable a tractable analysis, the spatially dependent graph model is approximated by a surrogate independent-edge random graph with matched degree distributions. Building on this model, we develop a recursive probabilistic analysis that characterizes the conditions under which semi-blind recovery succeeds with high probability. The proposed analysis reveals an identifiability region as a function of key system parameters, including AP and UE densities and the connectivity radius beyond which channel coefficients are assumed negligible. Monte Carlo simulations validate the predicted identifiability region and assess the accuracy of the proposed graph approximation. The proposed framework provides system level insights into how network density and connectivity affect identifiability in large-scale CF-MaMIMO systems and offers guidelines for selecting deployment parameters and pilot sequence lengths that enable reliable semi-blind recovery.

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arXiv2026

Expectation Propagation for Distributed Inference in Grant-Free Cell-Free Massive MIMO

Grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) systems are anticipated to be a key enabling technology for next-generation Internet-of-Things (IoT) networks, as they support massive connectivity without explicit scheduling. However, the large amount of connected devices prevents the use of orthogonal pilot sequences, resulting in severe pilot contamination (PC) that degrades channel estimation and data detection performance. Furthermore, scalable GF-CF-MaMIMO networks inherently rely on distributed signal processing. In this work, we consider the uplink of a GF-CF-MaMIMO system and propose two novel distributed algorithms for joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP). The first algorithm, denoted as JACD-EP, uses Gaussian approximations for the channel variables, whereas the second, referred to as JACD-EP-BG, models them as Bernoulli-Gaussian (BG) random variables. To integrate the BG distribution into the EP framework, we derive its exponential family representation and develop the two algorithms as efficient message passing over a factor graph constructed from the a posteriori probability (APP) distribution. The proposed framework is inherently scalable with respect to both the number of access points (APs) and user equipments (UEs). Simulation results show the efficient mitigation of PC by the proposed distributed algorithms and their superior detection accuracy compared to (genie-aided) centralized linear detectors.

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arXiv2025

Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO

Pilot contamination (PC) arises when the pilot sequences assigned to user equipments (UEs) are not mutually orthogonal, eventually due to their reuse. In this work, we propose a novel expectation propagation (EP)-based joint channel estimation and data detection (JCD) algorithm specifically designed to mitigate the effects of PC in the uplink of cell-free massive multiple-input multiple-output (CF-MaMIMO) systems. This modified bilinear-EP algorithm is distributed, scalable, demonstrates strong robustness to PC, and outperforms state-of-the-art Bayesian learning algorithms. Through a comprehensive performance evaluation, we assess the performance of Bayesian learning algorithms for different pilot sequences and observe that the use of non-orthogonal pilots can lead to better performance compared to shared orthogonal sequences. Motivated by this analysis, we introduce a new metric to quantify PC at the UE level. We show that the performance of the considered algorithms degrades monotonically with respect to this metric, providing a valuable theoretical and practical tool for understanding and managing PC via iterative JCD algorithms.

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arXiv2025

Distributed Iterative ML and Message Passing for Grant-Free Cell-Free Massive MIMO Systems

Cell-Free (CF) Massive Multiple-Input Multiple-Output (MaMIMO) is considered one of the leading candidates for enabling next-generation wireless communication. With the growing interest in the Internet of Things (IoT), the Grant-Free (GF) access scheme has emerged as a promising solution to support massive device connectivity. The integration of GF and CF-MaMIMO introduces significant challenges, particularly in designing distributed algorithms for activity detection and pilot contamination mitigation. In this paper, we propose a distributed algorithm that addresses these challenges. Our method first employs a component-wise iterative distributed Maximum Likelihood (ML) approach for activity detection, which considers both the pilot and data portions of the received signal. This is followed by a Pseudo-Prior Hybrid Variational Bayes and Expectation Propagation (PP-VB-EP) algorithm for joint data detection and channel estimation. Compared to conventional VB-EP, the proposed PP-VB-EP demonstrates improved convergence behavior and reduced sensitivity to initialization, especially when data symbols are drawn from a finite alphabet. The pseudo prior used in PP-VB-EP acts as an approximated posterior and serves as a regularization term that prevents the Message Passing (MP) algorithm from diverging. To compute the pseudo prior in a distributed fashion, we further develop a distributed version of the Variable-Level Expectation Propagation (VL-EP) algorithm.

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arXiv2024

Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO

We consider the uplink of a grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) system. We propose an algorithm for distributed joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP) called JACD-EP. We develop the algorithm by factorizing the a posteriori probability (APP) of activities, channels, and transmitted data, then, mapping functions and variables onto a factor graph, and finally, performing a message passing on the resulting factor graph. If users with the same pilot sequence are sufficiently distant from each other, the JACD-EP algorithm is able to mitigate the effects of pilot contamination which naturally occurs in grant-free systems due to the large number of potential users and limited signaling resources. Furthermore, it outperforms state-of-the-art algorithms for JACD in GF-CF-MaMIMO systems.

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arXiv2024

Wireless Information and Energy Transfer in the Era of 6G Communications

Wireless information and energy transfer (WIET) represents an emerging paradigm which employs controllable transmission of radio-frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned 6G use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective co-design of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions such as resource allocation and waveform design need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical but also practical aspects. The comprehensive overview of the state of the art of WIET presented in this paper highlights the potentials of WIET systems as well as their overall benefits in 6G networks.

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arXiv2023

Bilinear Expectation Propagation for Distributed Semi-Blind Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO

We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink transmission and propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD). We formulate the problem in the framework of bilinear inference and develop a solution based on the expectation propagation (EP) method for both channel estimation and data detection. We propose a new approximation of the joint a posteriori distribution of the channel and data whose representation as a factor graph enables the application of the EP approach using the message-passing technique, local low-complexity computations at the nodes, and an effective modeling of channel-data interplay. The derived algorithm, called bilinear-EP JCD, allows for a distributed implementation among access points (APs) and the central processing unit (CPU) and has polynomial complexity. Our simulation results show that it outperforms other EP-based state-of-the-art polynomial time algorithms.

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arXiv2023

Joint Transmit Signal and Beamforming Design for Integrated Sensing and Power Transfer Systems

Integrating different functionalities, conventionally implemented as dedicated systems, into a single platform allows utilising the available resources more efficiently. We consider an integrated sensing and power transfer (ISAPT) system and propose the joint optimisation of the rectangular pulse-shaped transmit signal and the beamforming vector to combine sensing and wireless power transfer (WPT) functionalities efficiently. In contrast to prior works, we adopt an accurate non-linear circuit-based energy harvesting (EH) model. We formulate and solve a non-convex optimisation problem for a general number of EH receivers to maximise a weighted sum of the average harvested powers at the EH receivers while ensuring the received echo signal reflected by a sensing target (ST) has sufficient power for estimating the range to the ST with a prescribed accuracy within the considered coverage region. The average harvested power is shown to monotonically increase with the pulse duration when the average transmit power budget is sufficiently large. We discuss the trade-off between sensing performance and power transfer for the considered ISAPT system. The proposed approach significantly outperforms a heuristic baseline scheme based on a linear EH model, which linearly combines energy beamforming with the beamsteering vector in the direction to the ST as its transmit strategy.

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arXiv2023

Accurate EH Modelling and Achievable Information Rate for SLIPT Systems with Multi-Junction Photovoltaic Receivers

In this paper, we study optical simultaneous lightwave information and power transfer (SLIPT) systems employing photovoltaic optical receivers (RXs). To be able to efficiently harvest energy from broadband light, we propose to employ multi-junction photovoltaic cells at the optical RX. We consider the case, where the optical RX is illuminated by ambient light, an intensity-modulated information free space optical (FSO) signal, and since the ambient light may not be always present, a dedicated energy-providing broadband optical signal. Based on the analysis of the equivalent electrical circuit of the multi-junction photovoltaic RX, we model the current flow through the photovoltaic cell and derive a novel accurate and two novel approximate energy harvesting (EH) models for the two cases, where the optical RX is equipped with a single and multiple p-n junctions, respectively. We derive the optimal distribution of the transmit information signal that maximizes the achievable information rate. We validate the proposed EH models by circuit simulations and show that the photovoltaic RXs saturate for high received signal powers. For single-junction RXs, we compare the proposed EH model with two well-known baseline models and demonstrate that, in contrast to the proposed EH model, they are not able to fully capture the RX non-linearity. Since multi-junction RXs allow a more efficient allocation of the optical power, they are more robust against saturation, and thus, are able to harvest significantly more power and achieve higher data rates than RXs employing a single p-n junction. Finally, we highlight a tradeoff between the information rate and harvested power in SLIPT systems and demonstrate that the proposed optimal distribution yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs.

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arXiv2023

Resonant Tunneling Diode-Based THz SWIPT for Microscopic 6G IoT Devices

In this paper, we study terahertz (THz) simultaneous wireless information and power transfer (SWIPT) for future micro-scale 6G Internet-of-Things (IoT) networks. Since Schottky diodes are not efficient for THz energy harvesting (EH), we propose resonant tunneling diodes (RTDs) for EH at the IoT receiver (RX). As the electrical properties of RTDs are different from those of Schottky diodes, we develop a novel closed-form EH model for RTD-based RXs. In particular, we model the dependency of the instantaneous RX output power on the instantaneous received power by a non-linear piecewise function, whose parameters are adjusted to fit circuit simulation results. Furthermore, since coherent information detection is challenging at THz frequencies, we employ unipolar amplitude shift keying (ASK) modulation at the transmitter (TX) and utilize the RTD-based EH circuit at the RX to extract both information and energy from the received signal. We formulate an optimization problem to maximize the mutual information between the TX and RX signals subject to constraints on the peak amplitude of the transmitted signal and the required average harvested power at the RX. Moreover, we determine a feasibility condition for the formulated problem and, for high and low required average harvested powers, we derive the achievable information rate numerically and in closed form, respectively. Our simulation results highlight a tradeoff between the information rate and the average harvested power. Finally, we show that this tradeoff is determined by the peak amplitude of the transmitted signal and the maximum instantaneous harvested power for low and high received signal powers, respectively.

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arXiv2023

Information Rate-Harvested Power Tradeoff in THz SWIPT Systems Employing Resonant Tunnelling Diode-based EH Circuits

In this paper, we study THz simultaneous wireless information and power transfer (SWIPT) systems. Since coherent information detection is challenging at THz frequencies and Schottky diodes may not be efficient for THz energy harvesting (EH) and information detection, we employ unipolar amplitude shift keying (ASK) modulation at the transmitter (TX) and a resonant tunnelling diode (RTD)-based EH circuit at the receiver (RX) to extract both information and power from the RX signal. We model the dependence of the instantaneous output power at the RX on the instantaneous received power by a non-linear piecewise function, whose parameters are adjusted to fit circuit simulation results. To determine the rate-power tradeoff in THz SWIPT systems, we derive the distribution of the TX signal that maximizes the mutual information between the TX and RX signals subject to constraints on the required average harvested power at the RX and the peak signal amplitude at the TX. Since the computational complexity of maximizing the mutual information may be too high for real-time THz SWIPT systems, for high and low required average harvested powers, we also obtain the suboptimal input signal distribution that maximizes the achievable information rate numerically and in closed form, respectively. Furthermore, based on the obtained results, we propose a suboptimal closed-form TX distribution which also achieves a desired harvested power at the RX. Our simulation results show that a lower reverse current flow and a higher breakdown voltage of the employed RTD are preferable when the input signal power at the RX is low and high, respectively. Finally, we demonstrate that for low and high received signal powers, the rate-power tradeoff of THz SWIPT systems is determined by the peak amplitude of the TX signal and the maximum instantaneous harvested power, respectively.

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arXiv2023

Optimal Transmit Antenna Deployment and Power Allocation for Wireless Power Supply in an Indoor Space

As Internet of Things (IoT) devices proliferate, sustainable methods for powering them are becoming indispensable. The wireless provision of power enables battery-free operation and is crucial for complying with weight and size restrictions. For the energy harvesting (EH) components of these devices to be small, a high operating frequency is necessary. In conjunction with a large transmit antenna, the receivers may be located in the radiating near-field (Fresnel) region, e.g., in indoor scenarios. In this paper, we propose a wireless power transfer (WPT) system ensuring reliable supply of power to an arbitrary number of mobile, low-power, and single-antenna receivers, whose locations in a three-dimensional cuboid room are unknown. A max-min optimisation problem is formulated to determine the optimal transmit power distribution. We rigorously prove that the optimal transmit power distribution's support has a lower dimensionality than its domain and thus, the employment of a continuous aperture antenna, utilised in Holographic MIMO (HMIMO), is unnecessary in the context of the considered WPT problem. Indeed, deploying a discrete transmit antenna architecture, i.e., a transmit antenna array, is sufficient and our proposed solution provides the optimal transmit antenna deployment and power allocation. Moreover, for a one-dimensional transmit antenna architecture, a finite number of transmit antennas is proven to be optimal. The proposed optimal solution is validated through computer simulations. Our simulation results indicate that the optimal transmit antenna architecture requires a finite number of transmit antennas and depends on the geometry of the environment and the dimensionality of the transmit antenna array.

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arXiv2023

EH Modelling and Achievable Rate for FSO SWIPT Systems with Non-linear Photovoltaic Receivers

In this paper, we study optical simultaneous wireless information and power transfer (SWIPT) systems, where a photovoltaic optical receiver (RX) is illuminated by ambient light and an intensity-modulated free space optical (FSO) signal. To facilitate simultaneous information reception and energy harvesting (EH) at the RX, the received optical signal is first converted to an electrical signal, and then, its alternating current (AC) and direct current (DC) components are separated and utilized for information decoding and EH, respectively. By accurately analysing the equivalent electrical circuit of the photovoltaic RX, we model the current flow through the photovoltaic p-n junction in both the low and high input power regimes using a two-diode model of the p-n junction and we derive a closed-form non-linear EH model that characterizes the harvested power at the RX. Furthermore, taking into account the non-linear behaviour of the photovoltaic RX on information reception, we derive the optimal distribution of the transmit information signal that maximizes the achievable information rate. The proposed EH model is validated by circuit simulation results. Furthermore, we compare with two baseline models based on maximum power point (MPP) tracking at the RX and a single-diode p-n junction model, respectively, and demonstrate that in contrast to the proposed EH model, they are not able to fully capture the non-linearity of photovoltaic optical RXs. Finally, our numerical results highlight that the proposed optimal distribution of the transmit signal yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs.

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arXiv2022

Optimal Antenna Placement for Two-Antenna Near-Field Wireless Power Transfer

Current trends in communication system design precipitate a change in the operating regime from the traditional far-field to the radiating near-field (Fresnel) region. We investigate the optimal transmit antenna placement for a multiple-input single-output (MISO) wireless power transfer (WPT) system designed for a three-dimensional cuboid room under line-of-sight (LoS) conditions in the Fresnel region. We formulate an optimisation problem for maximising the received power at the worst possible receiver location by considering the spherical nature of the electromagnetic (EM) wavefronts in the Fresnel region while assuming perfect knowledge of the channel at the transmitter. For the case of two transmit antennas, we derive a closed-form expression for the optimal positioning of the antennas which is purely determined by the geometry of the environment. If the room contains locations where the far-field approximation holds, the proposed positioning is shown to reduce to the far-field solution. The analytical solution is validated through simulation. Furthermore, the maximum received power at the locations yielding the worst performance is quantified and the power gain over the optimal far-field solution is presented. For the considered cuboid environment, we show that a distributed antenna system is optimal in the Fresnel region, whereas a co-located antenna architecture is ideal for the far-field.

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arXiv2022

Optimal Energy Signal Design for Multi-user MISO WPCNs With Non-linear Energy Harvesting Circuits

In this work, we study a multi-user wireless powered communication network (WPCN), where a multi-antenna base station (BS) sends an energy signal to multiple single-antenna users equipped with non-linear energy harvesting (EH) circuits. The users, in turn, harvest energy from the received signal and utilize it for information transmission in the uplink. Furthermore, to jointly optimize the energy signal waveform and downlink beamforming, we assume that the BS broadcasts a pulse-modulated signal employing multiple energy signal vectors. We formulate an optimization problem for the joint design of the downlink transmit energy signal vectors, their number, the durations of the transmit pulses, and the time allocation policy for minimization of the average transmit power at the BS. We show that for single-user WPCNs, a single energy signal vector, which is collinear with the maximum ratio transmission (MRT) vector and drives the EH circuit at the user device into saturation, is optimal. Next, for the general multi-user case, we show that the optimal signal design requires a maximum number of energy signal vectors that exceeds the number of users by one and propose an algorithm to obtain the optimal energy signal vectors. Since the complexity of the optimal design is high, we also propose two suboptimal schemes for WPCN design. First, for asymptotic massive WPCNs, where the ratio of the number of users to the number of BS antennas, i.e., the system load, tends to zero, we show that the optimal downlink transmit signal can be obtained in closed-form and comprises a sequence of weighted sums of MRT vectors. Next, based on this result, for general WPCNs with finite system loads, we propose a suboptimal closed-form MRT-based design and a suboptimal semidefinite relaxation (SDR)-based scheme.

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arXiv2022

Cache-Aided Massive MIMO with Linear Precoding in Multi-cell Systems

In this paper, we propose a novel joint caching and massive multiple-input multiple-output (MIMO) transmission scheme, referred to as \emph{cache-aided massive MIMO}, for multi-cell downlink transmission to multiple cache-enabled receivers. With the proposed scheme, users who have cached (a portion of) the files that they request are offloaded and, hence, (partially) inactive during downlink transmission. The other users either benefit from the cache-enabled offloading for mitigating pilot contamination or exploit the cached but unrequested files to cancel interference during uplink channel estimation and downlink file reception. Moreover, by redesigning the transmit precoders based on the cache status of the users and channel state information, we gain additional degrees of freedom for massive MIMO transmission. For a given cache status, we analyze the equivalent content delivery rates (ECDRs), i.e., the average rates of delivering a requested file via both caching and massive MIMO transmission to the requesting user, for cache-aided massive MIMO employing re-designed maximum ratio transmission (MRT), zero-forcing (ZF) precoding, and regularized zero-forcing (RZF) precoding. Based on the derived results, the impact of (random) uncoded caching and coded caching on the performance of the re-designed precoding schemes is investigated. Simulation results validate our derivations and show that caching is beneficial for precoded downlink transmission as it enhances the transmit power allocation, mitigates intra- and inter-cell interference, and reduces the impairment caused by pilot contamination. Compared with conventional massive MIMO without caching and with cache-oblivious precoding, the proposed cache-aided massive MIMO scheme achieves a significantly higher ECDR even when the number of users approaches the number of transmit antennas.

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arXiv2022

Enforcing Statistical Orthogonality in Massive MIMO Systems via Covariance Shaping

This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipments (UEs) exhibit high spatial correlation and channel estimation is limited by strong pilot contamination. Signal subspace separation among UEs is, in fact, rarely realized in practice and is generally beyond the control of the network designer (as it is dictated by the physical scattering environment). In this context, we propose a novel statistical beamforming technique, referred to as MIMO covariance shaping, that exploits multiple antennas at the UEs and leverages the realistic non-Kronecker structure of massive MIMO channels to target a suitable shaping of the channel statistics performed at the UE-side. To optimize the covariance shaping strategies, we propose a low-complexity block coordinate descent algorithm that is proved to converge to a limit point of the original nonconvex problem. For the two-UE case, this is shown to converge to a stationary point of the original problem. Numerical results illustrate the sum-rate performance gains of the proposed method with respect to spatial multiplexing in scenarios where the spatial selectivity of the base station is not sufficient to separate closely spaced UEs.

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