arXiv subjects

Luc Vandendorpe

Publications and source records attributed to Luc Vandendorpe.

At least 37 records · Page 2Linked to original sources

arXiv2023

Joint data rate and EMF exposure analysis in Manhattan environments: stochastic geometry and ray tracing approaches

The objective of this study is to jointly analyze the data rate and electromagnetic field (EMF) exposure in urban environments. Capitalizing on stochastic geometry (SG), a network level analysis is performed by modelling these environments via Manhattan Poisson line processes (MPLP). Using this framework, a number of performance metrics are derived: first moments, marginal distributions and joint distributions of the data rate and exposure. In addition, the original Manhattan model is generalized to include advanced features: corner diffraction, presence of potential blockages in streets, and users positioned at crossroads. As a second approach, deterministic ray tracing (RT) is utilized to compute the same metrics. The two methods are shown to provide close results on the condition that the model parameters are coherently selected. Furthermore, the numerical results enable to gain insight into several aspects: the role of the propagation mechanisms in the performance metrics, existing trade-offs between the rate and exposure requirements, as well as the impact of the user location (at a crossroad or in a single street). (This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible)

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arXiv2023

Joint data rate and EMF exposure analysis in user-centric cell-free massive MIMO networks

The objective of this study is to analyze the statistics of the data rate and of the incident power density (IPD) in user-centric cell-free networks (UCCFNs). To this purpose, our analysis proposes a number of performance metrics derived using stochastic geometry (SG). On the one hand, the first moments and the marginal distribution of the IPD are calculated. On the other hand, bounds on the joint distributions of rate and IPD are provided for two scenarios: when it is relevant to obtain IPD values above a given threshold (for energy harvesting purposes), and when these values should instead remain below the threshold (for public health reasons). In addition to deriving these metrics, this work incorporates features related to UCCFNs which are new in SG models: a power allocation based on collective channel statistics, as well as the presence of potential overlaps between adjacent clusters. Our numerical results illustrate the achievable trade-offs between the rate and IPD performance. For the considered system, these results also highlight the existence of an optimal node density maximizing the joint distributions. (This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)

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arXiv2022

Joint Performance Metrics for Integrated Sensing and Communication Systems in Automotive Scenarios

In this paper, multiple metrics are presented in order to jointly evaluate the performance of the radar and communication functions in scenarios involving Dual Function Radar Communication (DFRC) systems using stochastic geometry. These metrics are applied in an automotive scenario involving a two-lane road with vehicles and smart traffic lights, both equipped with DFRC systems. First, the performance achieved with these metrics are validated using Monte-Carlo (MC) simulations. Additionally, optimisation w.r.t. the power of the vehicles and smart traffic lights is performed based on the metrics. Then, the model is extended to include interference cancellation for the radar and/or communication function in all the metrics. Either full interference cancellation is applied, or a new model is proposed for imperfect interference cancellation.

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arXiv2022

Real-Time CRLB based Antenna Selection in Planar Antenna Arrays

Estimation of User Terminals' (UTs') Angle of Arrival (AoA) plays a significant role in the next generation of wireless systems. Due to high demands, energy efficiency concerns, and scarcity of available resources, it is pivotal how these resources are used. Installed antennas and their corresponding hardware at the Base Station (BS) are of these resources. In this paper, we address the problem of antenna selection to minimize Cramer-Rao Lower Bound (CRLB) of a planar antenna array when fewer antennas than total available antennas have to be used for a UT. First, the optimal antenna selection strategy to minimize the expected CRLB is proposed. Then, using this strategy as a preliminary step, we present a two-stage greedy antenna selection method whose goal is to minimize the instantaneous CRLB. The optimal start point of the greedy algorithm is presented alongside some methods to reduce the algorithm's computational complexity. Numerical results confirm the accuracy of proposed solutions. They demonstrate that the proposed antenna selection method only requires a small proportion of the total available antennas to accomplish a significant amount of the total performance, enhancing hardware utilization efficiency. Also, it is shown that the presented algorithm has a high error tolerance.

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arXiv2021

Stochastic Geometry-based Modelling of Mobile UAV Relay Networks under Realistic Fading

We consider a relay network based on Unmanned Aerial Vehicles (UAV). Terrestrial Base Stations (TBS) and UAV Relay Nodes (RN) are modelled using two Homogeneous Poisson Point Processes (HPPP). UAVs can hover at a fixed position or move following specific displacement schemes. The Coverage Probability (CP) of a typical user equipment (UE) is derived, either when it communicates via a direct link (from the TBS to the UE) or via a relay link (from the TBS to the UE through a UAV RN). Every link can be in Line-of-Sight (LoS) or Non Line-of-Sight (NLoS), and suffers from Rician fading with distance-dependent parameters. This coverage is calculated by means of both stochastic geometry (SG) and Monte-Carlo (MC) simulations. The benefits of the use of UAV as RNs are analysed depending on their altitude, density, and mobility scheme.

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arXiv2021

Sharing is Caring: A Mobile Edge Computing Perspective

In this paper, we consider a system model in conjunction with two major technologies in 5G communications, i.e., mobile edge computing and spectrum sharing. An IoT network, which does not have access to any licensed spectrum, carries its computational task offloading activities with help of spectrum sharing. The IoT network cooperates with a licensed spectrum holding network by relaying its data and in return gets access to the licensed spectrum. The licensed spectrum holding network focuses on throughput maximization, whereas, the IoT network tries to maximize task computation rate. We formulate an optimization problem giving importance to both networks' interests. Typical IoT nodes may be energy-constrained, which is considered along with task computation time constraints. Parallel computation is considered while computing IoT nodes' computational tasks at the mobile edge computing server, where the processor is allocated based on IoT nodes' offloading capabilities. The advantage of such processor allocation is shown in the result section. Moreover, we also show how both IoT and licensed spectrum holding networks benefit from the spectrum sharing by caring for each other requirements, which echoes the fact, i.e., Sharing is Caring.

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arXiv2021

Energy Efficient Competitive Resource Allocation in MIMO networks

This paper considers the competitive resource allocation problem in Multiple-Input Multiple-Output (MIMO) interfering channels, when users maximize their energy efficiency. Considering each transmitter-receiver pair as a selfish player, conditions on the existence and uniqueness of the Nash equilibrium of the underlying noncooperative game are obtained. A decentralized asynchronous algorithm is proven to converge towards this equilibrium under the same conditions. Two frameworks are considered for the analysis of this game. On the one hand, the game is rephrased as a Quasi-Variational Inequality (QVI). On the other hand, the best response of the players is analyzed in light of the contraction mappings. For this problem, the contraction approach is shown to lead to tighter results than the QVI one. When specializing the obtained results to OFDM networks, the obtained conditions appear to significantly outperform state-of-the-art works, and to lead to much simpler decentralized algorithms. Numerical results finally assess the obtained conditions in different settings.

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arXiv2021

Stochastic geometry analysis of a distance-based JT scheme in C-RAN

This paper considers a joint transmission scheme (JT) developed for cloud radio access networks (C-RANs). This proposed scheme features cooperative sets of remote radio heads (RRH) defined in a disk around each user location. The nodes belonging to each of these sets perform a weighted maximum ratio transmission to jointly serve the user. The powers allocated to the beamformers are computed at the network baseband unit, taking into account channel gains, as well an equity criterion between the users. In comparison with the existing literature, our model includes a saturation assumption, with all transmissions taking place over the same resource block. A RRH belonging to multiple sets can hence transmit to several users simultaneously. The distributions of the network coverage and spectral efficiency are calculated by means of stochastic geometry (SG), and compared with Monte Carlo simulations. The derived expressions take into account the power allocation, the user and RRH densities, as well as the statistical correlation resulting from the set overlaps.

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arXiv2021

Localization Efficiency in Massive MIMO Systems

In the next generation of wireless systems, Massive MIMO offers high angular resolution for localization. By virtue of large number of antennas, the Angle of Arrival (AoA) of User Terminals (UTs) can be estimated with high accuracy. According to Dense Multipath Component (DMC) channel model, local scatters around UTs can create different multipath signals for each antenna at the Base Station (BS). We obtain a deterministic form for the Cramer-Rao Lower Bound (CRLB) in a multi-user scenario when the contribution of the multipath signals is considered. We do this when the multipath signals are independent and identically distributed (i.i.d) with arbitrary distribution. Then, we redefine a localization efficiency function for a multi-user scenario and numerically optimize it with respect to (w.r.t) the number of antennas. We prove when only a subset of the available antennas is used, CRLB can be minimized w.r.t which set of antennas is used. Then, an antenna selection strategy that minimizes CRLB is proposed. As a benchmark, we apply the proposed antenna selection scheme to the MUltiple SIgnal Classification (MUSIC) algorithm and study its efficiency. Numerical results validate the accuracy of our analysis and show significant improvement in efficiency when the proposed antenna selection strategy is employed.

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arXiv2021

Sparse Factorization-based Detection of Off-the-Grid Moving targets using FMCW radars

In this paper, we investigate the application of continuous sparse signal reconstruction algorithms for the estimation of the ranges and speeds of multiple moving targets using an FMCW radar. Conventionally, to be reconstructed, continuous sparse signals are approximated by a discrete representation. This discretization of the signal's parameter domain leads to mismatches with the actual signal. While increasing the grid density mitigates these errors, it dramatically increases the algorithmic complexity of the reconstruction. To overcome this issue, we propose a fast greedy algorithm for off-the-grid detection of multiple moving targets. This algorithm extends existing continuous greedy algorithms to the framework of factorized sparse representations of the signals. This factorized representation is obtained from simplifications of the radar signal model which, up to a model mismatch, strongly reduces the dimensionality of the problem. Monte-Carlo simulations of a K-band radar system validate the ability of our method to produce more accurate estimations with less computation time than the on-the-grid methods and than methods based on non-factorized representations.

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arXiv2020

Factorization over interpolation: A fast continuous orthogonal matching pursuit

We propose a fast greedy algorithm to compute sparse representations of signals from continuous dictionaries that are factorizable, i.e., with atoms that can be separated as a product of sub-atoms. Existing algorithms strongly reduce the computational complexity of the sparse decomposition of signals in discrete factorizable dictionaries. On another flavour, existing greedy algorithms use interpolation strategies from a discretization of continuous dictionaries to perform off-the-grid decomposition. Our algorithm aims to combine the factorization and the interpolation concepts to enable low complexity computation of continuous sparse representation of signals. The efficiency of our algorithm is highlighted by simulations of its application to a radar system.

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arXiv2020

Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements

In this paper we propose to bridge the gap between using extremely low resolution 1-bit measurements and estimating targets' parameters, such as their velocities, that exist in a continuum, i.e., by performing Off-the-Grid estimation. To that end, a Continuous version of Orthogonal Matching Pursuit (COMP) is modified in order to leverage the 1-bit measurements coming from a simple Doppler Radar. Using Monte-Carlo simulations, we show that although the resolution of the acquisition is dramatically reduced, velocity estimation of multiple targets can still be achieved and reaches performances beyond classic On-the-Grid methods. Furthermore, we show empirically that adding a random and uniform dithering before the quantization is necessary when estimating more than one target.

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arXiv2020

Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Collaborative Fog Computing

Motivated by applications such as on-device collaborative neural network inference, this work investigates edge-facilitated collaborative fog computing - in which edge-devices collaborate with each other and with the edge of the network to complete a processing task - to augment the computing capabilities of individual edge-devices while optimizing the collaboration for energy-efficiency. Collaborative computing is modeled using the Map-Reduce distributed computing framework, consisting in two rounds of computations separated by a communication phase. The computing load is optimally distributed among the edge-devices, taking into account their diversity in term of computing and communications capabilities. In addition, edge-devices local parameters such as CPU clock frequency and RF transmit power are also optimized for energy-efficiency. The corresponding optimization problem can be shown to be convex and optimality conditions can be obtained through Lagrange duality theory. A waterfilling-like interpretation for the size of the computing load assigned to each edge-device is given. Numerical experiments demonstrate the benefits of the proposed optimal collaborative-computing scheme over various other schemes in several respects. Most notably, the proposed scheme exhibits increased probability of successfully dealing with heavier computations and/or smaller latency along with energy-efficiency gains of up to two orders of magnitude. Both improvements come from the scheme ability to optimally leverage edge-devices diversity.

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arXiv2020

One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing

This work focuses on the reconstruction of sparse signals from their 1-bit measurements. The context is the one of 1-bit compressive sensing where the measurements amount to quantizing (dithered) random projections. Our main contribution shows that, in addition to the measurement process, we can additionally reconstruct the signal with a binarization of the sensing matrix. This binary representation of both the measurements and sensing matrix can dramatically simplify the hardware architecture on embedded systems, enabling cheaper and more power efficient alternatives. Within this framework, given a sensing matrix respecting the restricted isometry property (RIP), we prove that for any sparse signal the quantized projected back-projection (QPBP) algorithm achieves a reconstruction error decaying like O(m-1/2)when the number of measurements m increases. Simulations highlight the practicality of the developed scheme for different sensing scenarios, including random partial Fourier sensing.

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arXiv2020

Clustering and Power Optimization for NOMA Multi-Objective Problems

This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multi-objective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques.

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arXiv2020

Performance Analysis of Uplink Adaptive NOMA Depending on Channel Knowledge

Non Orthogonal Multiple Access (NOMA) is a key technique to satisfy large users densities in future wireless networks. However, NOMA may provide poor performance compared to Orthogonal Multiple Access (OMA) due to inter-user interference. In this paper, we obtain closed-form expressions of the uplink NOMA and OMA throughputs when no Channel State Information at Transmitter (CSIT) is available, and of the average data rates assuming that instantaneous rates should be larger than a minimum threshold when full CSIT is available. Analytical comparisons of OMA and NOMA prove that there is no global dominant strategy valid in all situations. Based on this conclusion, we propose a new multiple-access (MA) strategy called NOMA-Adaptive (NOMA-A) that selects the best MA technique between OMA and NOMA. NOMA-A aims at maximizing the sum throughput in the no CSIT case, and the probability that both users are active in the full CSIT case. NOMA-A is shown to outperform the other strategies in terms of sum throughput and rate.

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arXiv2020

Sparsity-Driven Moving Target Detection in Distributed Multistatic FMCW Radars

We investigate the problem of sparse target detection from widely distributed multistatic \textit{Frequency Modulated Continuous Wave} (FMCW) radar systems (using chirp modulation). Unlike previous strategies (\emph{e.g.}, developed for FMCW or distributed multistatic radars), we propose a generic framework that scales well in terms of computational complexity for high-resolution space-velocity grid. Our approach assumes that \emph{(i)} the target signal is sparse in a discrete space-velocity domain, hence allowing for non-static target detection, and \emph{(ii)} the resulting multiple baseband radar signals share a common support. By simplifying the representation of the FMCW radar signals, we propose a versatile scheme balancing complexity and detection accuracy. In particular, we design a low-complexity, factorized alternative for the Matching Pursuit algorithm leveraging this simplified model, as well as an iterative methodology to compensate for the errors caused by the model simplifications. Extensive Monte-Carlo simulations of a K-band radar system show that our method achieves a fast estimation of moving target's parameters on dense grids, with controllable accuracy, and reaching state-of-the-art performances compared to previous sparsity-driven approaches.

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arXiv2020

An Ultra-wideband Battery-less Positioning System for Space Applications

An ultra-wide bandwidth (UWB) remote-powered positioning system for potential use in tracking floating objects inside space stations is presented. It makes use of battery-less tags that are powered-up and addressed through wireless power transfer in the UHF band and embed an energy efficient pulse generator in the 3-5 GHz UWB band. The system has been mounted on the ESA Mars Rover prototype to demonstrate its functionality and performance. Experimental results show the feasibility of centimeter-level localization accuracy at distances larger than 10 meters, with the capability of determining the position of multiple tags using a 2W-ERP power source in the UHF RFID frequency band.

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