arXiv subjects

Martin Willame

Publications and source records attributed to Martin Willame.

9 recordsLinked to original sources

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

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 Cram\'er-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

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

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

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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arXiv2024

Hybrid Fusion for 802.11ax Wi-Fi-based Passive Radars Exploiting Beamforming Feedbacks

Passive Wi-Fi-based radars (PWRs) are devices that enable the localization of targets using Wi-Fi signals of opportunity transmitted by an access point. Unlike active radars that optimize their transmitted waveform for localization, PWRs align with the 802.11 amendments. Specifically, during the channel sounding session preceding a multi-user multiple-input multiple-output downlink transmission, an access point isotropically transmits a null data packet (NDP) with a known preamble. From these known symbols, client user equipments derive their channel state information and transmit an unencrypted beamforming feedback (BFF) back to the access point. The BFF comprises the right singular matrix of the channel and the corresponding stream gain for each subcarrier, which allows the computation of a beamforming matrix at the access point. In a classical PWR processing, only the preamble symbols from the NDP are exploited during the channel sounding session. In this study, we investigate multiple target localization by a PWR exploiting hybrid information sources. On one hand, the joint angle-of-departure and angle-of-arrival evaluated from the NDP. On another hand, the line-of-sight angle-of-departures inferred from the BFFs. The processing steps at the PWR are defined and an optimal hybrid fusion rule is derived in the maximum likelihood framework. Monte-Carlo simulations assess the enhanced accuracy of the proposed combination method compared to classical PWR processing based solely on the NDP, and compare the localisation performance between client and non-client targets.

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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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