arXiv subjects

Jérôme Louveaux

Publications and source records attributed to Jérôme Louveaux.

At least 19 recordsLinked to original sources

arXiv2026

A LoRa-Compatible Receiver with Programmable Noise-Power Trade-Off and Integrated Wake-Up Mode

Programmable receivers allow an optimal performance in the variable conditions of the wireless channel. An extensive analysis of their noise-power trade-off enables the development of a main receiver with embedded wake-up circuitry. This makes the seamless integration of wake-up receivers (WuRXs) in RF receivers possible, avoiding power, area, complexity, and cost overheads in the system. In this paper, we propose the first LoRa-compatible receiver with an integrated wake-up mode and a programmable noise-power trade-off. In main-receiver mode, it consumes 4.57 mW for a sensitivity of -138 dBm, but it can save up to 62.6% of power consumption at the cost of an 8.3-dB sensitivity degradation. In wake-up mode, sensitivity down to -126 dBm is achieved for a 32-ms latency and 1.5 mW of power consumption. The power consumption remains relatively high due to a sub-optimal design of the the phase-locked loop (PLL). In the end, the wake-up mode consumes 3X less than the lowest-noise configuration in main-receiver mode.

cs.AR↗

arXiv2026

A Computationally Efficient Joint Maximum Likelihood Estimator for Passive Localization in OFDM Distributed Antenna Systems with Pilots and Unknown Data Payloads

Communication-centric Integrated Sensing and Communications (ISAC) is a promising paradigm for sixth-generation (6G) wireless systems, enabling new sensing services by leveraging the already-deployed communication infrastructure. Communication signals typically comprise both known deterministic pilot sequences and unknown random data payloads. For localization and sensing tasks, the prevailing approach in multistatic and distributed ISAC systems relies exclusively on pilot symbols, entirely overlooking the positioning information carried by data payloads, which constitute the majority of each transmitted frame. Alternatively, Decision-Directed (DD) approaches treat data estimates as additional pilots, inherently limiting localization performance to that of the underlying communication system, while Non-Data-Aided (NDA) methods from the literature require prior knowledge of the data symbol distribution and incur a computational cost that grows with constellation size. In this paper, we derive a Joint Maximum Likelihood (JML) estimator that jointly exploits pilot and data symbols for localization without requiring data decoding, in a passive scenario where a distributed sensing receiver localizes a User Equipment (UE) by exploiting its Orthogonal Frequency-Division Multiplexing (OFDM) communication signal as a signal of opportunity. The optimal solution is derived and shown to be computationally intractable for typical 6G parameters. Two tractable approximations are then proposed, achieving localization performance superior to DD baselines at comparable computational complexity, while remaining constellation-agnostic and yielding substantially lower computational requirements than existing NDA approaches. Furthermore, the proposed estimators are shown to admit a geometric interpretation, providing insight into their intrinsic localization behavior.

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arXiv2026

Orthogonal Least Squares with Integrated Information Theoretic Criteria for Joint Number of Targets and DoA Estimation

We address the joint estimation of the number of targets and their direction-of-arrivals (DoAs) using antenna arrays. Target-number estimation can be formulated as a model-order selection problem and solved with the information theoretic criteria (ITC). The ITC minimize an objective function that balances a likelihood term and a complexity penalty. However, direct application of the ITC requires maximum-likelihood DoA estimates for each candidate model order, which is computationally prohibitive because it entails a multidimensional search over all angle combinations. To reduce complexity, many radar processing exploit greedy methods such as orthogonal least squares (OLS). In this paper, we explore three distinct methods to integrate the ITC model-order selection into the OLS estimation procedure for joint target-number and DoA estimation. Specifically, we propose the disjoint rank-based, the joint selection-based, and the hybrid rank-and-selection-based ITC-OLS algorithms. Each algorithm is derived under both the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) frameworks. Numerical simulations show that the proposed hybrid ITC-OLS algorithm consistently outperforms both the other proposed variants and a baseline method from the literature.

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arXiv2026

G-iMUSIC: Greedy Iterative MUSIC Algorithms for Multi-Target DoA Estimation

This paper presents novel algorithms for multi-target direction-of-arrival (DoA) estimation in array signal processing. Although the maximum likelihood estimator (MLE) asymptotically attains the Cramér-Rao bound, its exponential complexity motivates practical alternatives, such as greedy or subspace-based methods. In this context, greedy methods such as orthogonal matching pursuit (OMP) and orthogonal least squares (OLS) are sensitive to early selection errors, especially for angularly proximate targets, whereas subspace-based methods such as multiple signal classification (MUSIC) present angular super-resolution capabilities but degrade under strong inter-target signal correlation. To overcome these limitations, we propose two greedy iterative MUSIC (G-iMUSIC) algorithms, namely OMP-iMUSIC and OLS-iMUSIC, derived from a unified framework that links subspace and greedy estimations. Unlike prior iMUSIC approaches, the proposed methods require only one initial eigen value decomposition (EVD) and avoid computing eigendecomposition at each iteration. They also admit Fast Fourier Transform (FFT)-accelerated implementations for uniform linear arrays (ULAs), enabling low-complexity operation. Monte Carlo simulations demonstrate improved detection and precision over conventional OMP, OLS, and MUSIC, as well as reduced processing time compared to greedy baselines. Finally, we introduce diagnostic metrics that interpret performance across signal correlation and angular proximity regimes, supporting generalization beyond the specific orthogonal frequency-division multiplexing (OFDM) radar scenario considered.

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arXiv2026

Aliasing in Near-Field Array Ambiguity Functions: a Spatial Frequency-Domain Framework

Next-generation communication and localization systems increasingly rely on extremely large-scale arrays (XL-arrays), which promise unprecedented spatial resolution and new functionalities. These gains arise from their inherent operation in the near field (NF) regime, where the spherical nature of the wavefront can no longer be ignored; consequently, characterizing the ambiguity function -- which amounts to the matched beam pattern -- is considerably more challenging. Implementing very wide apertures with half-wavelength element spacing is costly and complex. This motivates thinning the array (removing elements), which introduces intricate aliasing structures, i.e., grating lobes. Whereas prior work has addressed this challenge using approximations tailored to specific array geometries, this paper develops a general framework that reveals the fundamental origins and geometric behavior of grating lobes in near-field ambiguity functions. Using a local spatial-frequency analysis of steering signals, we derive a systematic methodology to model NF grating lobes as aliasing artifacts, quantifying their structure on the AF, and providing design guidelines for XL-arrays that operate within aliasing-safe regions. We further connect our framework to established far-field principles. Finally, we demonstrate the practical value of the approach by deriving closed-form expressions for aliasing-free regions in canonical uniform linear arrays and uniform circular arrays.

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arXiv2026

Localization in OFDM Passive Distributed Antenna Systems with Pilots and Unknown Data Payloads: A Marginal Maximum Likelihood Approach

Integrated Sensing and Communications (ISAC) is emerging as a key paradigm for future Sixth-Generation (6G) networks, with communication-centric designs favored for their compatibility with existing standards. Communication signals contain both known deterministic pilot symbols and unknown random data payloads. Most localization approaches rely solely on pilots, discarding the position information contained in the data symbols, which constitute the majority of each transmitted frame. Alternatively, Decision-Directed (DD) approaches exploit data decisions, thereby inherently limiting positioning performance to that of the communication system. In this paper, we derive a Marginal Maximum Likelihood (MML) estimator that jointly leverages pilot and data payloads without requiring data decoding, enabling operation with high-order constellations and under challenging noise conditions. We consider an opportunistic scenario in which an Orthogonal Frequency-Division Multiplexing (OFDM) signal transmitted by a User Equipment (UE) is captured by a distributed receiver array. Through numerical simulations, we demonstrate that the proposed method achieves superior localization performance compared to existing approaches and consistently converges to the genie bound (where data symbols are assumed perfectly known) at a lower Signal-to-Noise Ratio (SNR) than any DD method. Furthermore, the proposed method remains robust to constellation size, unlike DD approaches, whose performance degrades with increasing modulation order. Finally, we provide a computational complexity analysis of the proposed method and the considered baselines, highlighting the impact of system parameters on their respective computational costs.

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arXiv2026

Joint Pilot and Unknown Data-based Localization for OFDM Opportunistic Radar Systems

Integrating Sensing and Communications (ISAC) has emerged as a promising paradigm for Sixth Generation (6G) and Wi-Fi 7 networks, with the communication-centric approach being particularly attractive due to its compatibility with current standards. Typical communication signals comprise both deterministic known pilot signals and random unknown data payloads. Most existing approaches either rely solely on pilots for positioning, thereby ignoring the radar information present in the received data symbols that constitute the majority of each frame, or rely on data decisions, which bounds positioning performance to that of the communication system. To overcome these limitations, we propose a novel method that extracts positioning information from data payloads without decoding them. We consider an opportunistic scenario in which communication signals from a user are captured by a passive radar equipped with a uniform linear array of antennas. We show that, in this setting, the estimation can be efficiently implemented using Fast Fourier Transforms. Finally, we demonstrate superior localization performance compared to existing methods in the literature through numerical simulations.

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arXiv2026

Near-Field Beampointing with Low Exposure Regions: a Dominant Subspace Projection Approach

The spherical nature of the wavefronts exhibited in the near-field of antenna arrays enables advanced beamforming capabilities, such as beampointing and beamnulling. In this paper, we exploit these properties to design a near-field beam pattern under a low exposure region constraint. We address the continuous region constraint through spatial discretization, which results in a large number of constraints that lead to prohibitive computational complexity. We propose a novel low-complexity algorithm that enables a computationally tractable beam pattern design. It uses a low-dimensional subspace representation of the low exposure region based on a singular value decomposition. Our approach achieves low complexity while providing a power received at a target user close to the optimal achievable power, yet with uniform power mitigation over the low exposure region.

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arXiv2025

Chirp-Based Aliasing Analysis of Arrays in the Spherical Wavefront Regime

In antenna arrays, wave propagation modeling based on Euclidean principles is typically represented by steering vectors or signals. This paper provides a new, chirp-based, interpretation of steering vectors in the Spherical Wavefront Regime (SWR), establishing a relationship between the spatial spectrum of the received (resp. transmitted) signal and the geometry of the array and the source (resp. target). Leveraging the well-known sampling theorem, we analyze aliasing effects arising from spatial sampling with a finite number of antennas and understand how these effects degrade the Ambiguity Function (AF). Our framework provides geometric insight into these degradations, offering a deeper understanding of the non-space-invariant aliasing mechanisms in the SWR. The proposed approach is formulated for general antenna arrays and then instantiated to Circular Array and to Uniform Linear Array structures operating in Near Field conditions.

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arXiv2024

Maximum Likelihood Alternating Summation for Multistatic Angle-based Multitarget Localization

Recent advancements in Wi-Fi sensing have sparked interest in exploiting OFDM modulated communication signals for target detection and tracking. In this study, we address the angle-based localization of multiple targets using a multistatic OFDM radar. While the maximum likelihood approach optimally merges data from each radar pair comprised by the system, it entails a complex multi-dimensional search process. Leveraging pre-estimation of the targets' parameters obtained via the MUSIC algorithm, our method decouples this multi-dimensional search into a single two-dimensional estimator per target. The proposed alternating summation method allows the computation of a combined likelihood map aggregating contributions from each radar pair, enabling target detection via peak selection. Besides reducing computational complexity, the method effectively captures target interactions and accommodates varying radar pair localization abilities. Also, it requires transmitting only the estimated channel covariance matrices of each radar pair to the central processor. Numerical simulations demonstrate superior performance over existing approaches.

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arXiv2024

Cellular Load Dependent Sleep Control for Energy Efficient HetNets with Non-Uniform User Distributions

This study proposes a novel stochastic geometry framework analyzing power control strategies in spatially correlated network topologies. Heterogeneous networks are studied, with users modeled via the superposition of homogeneous and Poisson cluster processes. First, a new expression approaching the distribution of the number of users per base station is provided. This distribution defines the load associated with each Voronoï cell, capturing non-uniformities in user locations and correlation to BSs positions. The power allocation is adjusted based on this load, allowing BSs to enter sleep mode when their activity falls below a defined threshold. Furthermore, the propagation model features millimeter wave transmission characteristics and directional beamforming. Considering these aspects, revisited definitions of coverage probability, spectral efficiency, and energy efficiency are proposed. Tractable expressions for these metrics are derived and validated using Monte-Carlo simulations. Asymptotic expressions are also proposed, providing further understanding on the influence of the system parameters. Our numerical results finally analyze the impact of the sleep control on the performance and display the optimal strategies in terms of energy efficiency.

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arXiv2024

Multistatic OFDM Radar Fusion of MUSIC-based Angle Estimation

This study investigates the problem of angle-based localization of multiple targets using a multistatic OFDM radar. Although the maximum likelihood (ML) approach can be employed to merge data from different radar pairs, this method requires a high complexity multi-dimensional search process. The multiple signal classification (MUSIC) algorithm simplifies the complexity to a two-dimensional search, but no framework is derived for combining MUSIC pseudo-spectrums in a multistatic configuration. This paper exploits the relationship between MUSIC and ML estimators to approximate the multidimensional ML parameter estimation with a weighted combination of MUSIC pseudo-spectrum. This enables the computation of a likelihood map on which a peak selection is applied for target detection. In addition to reducing the computational complexity, the proposed method relies only on transmitting the estimated channel covariance matrices of each radar pair to the central processor. A numerical analysis is conducted to assess the benefits of the proposed fusion.

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arXiv2021

A Low-Complexity LoRa Synchronization Algorithm Robust to Sampling Time Offsets

LoRaWAN is nowadays one of the most popular protocols for low-power Internet-of-Things communications. Although its physical layer, namely LoRa, has been thoroughly studied in the literature, aspects related to the synchronization of LoRa receivers have received little attention so far. The estimation and correction of carrier frequency and sampling time offsets is however crucial to attain the low sensitivity levels offered by the LoRa spread-spectrum modulation. The goal of this paper is to build a low-complexity, yet efficient synchronization algorithm capable of correcting both offsets. To this end, a complete analytical model of a LoRa signal corrupted by these offsets is first derived. Using this model, we propose a new estimator for the sampling time offset. We also show that the estimations of the carrier frequency and the sampling time offsets cannot be performed independently. Therefore, to avoid a complex joint estimation of both offsets, an iterative low-complexity synchronization algorithm is proposed. To reach a packet error rate of 10^-3, performance evaluations show that the proposed receiver requires only 1 or 2 dB higher SNR than a theoretical perfectly synchronized receiver, while incurring a very low computational overhead.

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arXiv2021

Implementing a LoRa Software-Defined Radio on a General-Purpose ULP Microcontroller

Emerging Internet-of-Things sensing applications rely on ultra low-power (ULP) microcontroller units (MCUs) that wirelessly transmit data to the cloud. Typical MCUs nowadays consist of generic blocks, except for the protocol-specific radios implemented in hardware. Hardware radios however slow down the evolution of wireless protocols due to retrocompatiblity concerns. In this work, we explore a software-defined radio architecture by demonstrating a LoRa transceiver running on custom ULP MCU codenamed SleepRider with an ARM Cortex-M4 CPU. In SleepRider MCU, we offload the generic baseband operations (e.g., low-pass filtering) to a reconfigurable digital front-end block and use the Cortex-M4 CPU to perform the protocol-specific computations. Our software implementation of the LoRa physical layer only uses the native SIMD instructions of the Cortex-M4 to achieve real-time transmission and reception of LoRa packets. SleepRider MCU has been fabricated in a 28nm FDSOI CMOS technology and is used in a testbed to experimentally validate the software implementation. Experimental results show that the proposed software-defined radio requires only a CPU frequency of 20 MHz to correctly receive a LoRa packet, with an ultra-low power consumption of 0.42 mW on average.

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arXiv2020

CSI-based versus RSS-based Secret-Key Generation under Correlated Eavesdropping

Physical-layer security (PLS) has the potential to strongly enhance the overall system security as an alternative to or in combination with conventional cryptographic primitives usually implemented at higher network layers. Secret-key generation relying on wireless channel reciprocity is an interesting solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS). Moreover, as opposed to previous works, we take into account the fact that the eavesdropper's observations might be correlated and we consider the high signal-to-noise ratio (SNR) regime where we can find simple analytical expressions for the secret-key capacity. As already found in previous works, we find that RSS-based secret-key generation is heavily penalized as compared to CSI-based systems. At high SNR, we are able to precisely and simply quantify this penalty: a halved pre-log factor and a constant penalty of about 0.69 bit, which disappears as Eve's channel gets highly correlated.

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arXiv2020

Secrecy Capacity of FBMC-OQAM Modulation over Frequency Selective Channel

This paper studies the information-theoretic secrecy capacity of an Offset-QAM-based filterbank multicarrier (FBMC-OQAM) communication over a wiretap frequency selective channel. The secrecy capacity is formulated as an optimization problem which has a closed-form solution in the high signal-to-noise ratio (SNR) regime. Two of the most common equalization strategies in FBMC-OQAM are considered, namely, single-tap and multi-tap equalization. For the sake of comparison, we also consider the secrecy capacity of a generic modulation and a cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) modulation. As a result, we find that FBMC-OQAM is particularly competitive for medium-to-long burst transmissions.

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arXiv2020

WaveComBox: a Matlab Toolbox for Communications using New Waveforms

Future generations of communications systems will target a very wide range of applications. Each application comes with its own set of requirements in terms of data rate, latency, user density, reliability... Accommodating this large variety of specifications implies the need for a novel physical layer technology. In this regard, the most popular modulation nowadays, namely, the orthogonal frequency division multiplexing modulation, is characterized by a poor time-frequency localization, implying strong limitations. In the light of these limitations, communications using new waveforms, relying on more sophisticated signal processing techniques and providing improved time-frequency localization, have attracted a lot of attention for the last decade. At the same time, the higher complexity of these new waveforms, not only in terms of implementation but also conceptually, creates an entrance barrier that slows down their adoption by industries, standardization bodies and more generally in the telecommunication community. The WaveComBox toolbox, freely available at www.wavecombox.com, is a user-friendly, open-source and well documented piece of software aiming at considerably lowering the entrance barrier of recently proposed waveforms. This article first describes the general abstract structure of the toolbox. Secondly, examples are given to illustrate how to use the toolbox and to show some more advanced functionalities.

cs.IT↗

arXiv2020

Impact of Realistic Propagation Conditions on Reciprocity-Based Secret-Key Capacity

Secret-key generation exploiting the channel reciprocity between two legitimate parties is an interesting alternative solution to cryptographic primitives for key distribution in wireless systems as it does not rely on an access infrastructure and provides information-theoretic security. The large majority of works in the literature generally assumes that the eavesdropper gets no side information about the key from her observations provided that (i) it is spaced more than a wavelength away from a legitimate party and (ii) the channel is rich enough in scattering. In this paper, we show that this condition is not always verified in practice and we analyze the secret-key capacity under realistic propagation conditions.

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