arXiv subjects

Harpreet S. Dhillon

Publications and source records attributed to Harpreet S. Dhillon.

At least 19 recordsLinked to original sources

arXiv2026

Analysis of Integrated Communication and Sensing in Cylindrical Aerial Corridors

In unmanned aerial vehicle (UAV) networks, integrated communication and sensing (ISAC) is emerging as a key enabler to support extended and continuous communication and sensing capabilities for sixth-generation (6G) services among UAVs operating in swarms. In this paper, a comprehensive framework is presented to investigate the ISAC coverage probability of a reference UAV-base station (BS) in a UAV network. A fixed number of UAV user equipment (UAVUEs) and UAV-BSs are deployed in an aerial corridor modeled as a finite cylinder. The three-dimensional (3D) binomial point process (BPP) is used to model both the 3D spatial locations of the UAV-UEs and the UAV-BSs. By adopting realistic 3D third generation partnership project (3GPP) antenna patterns for both the UAV-UEs and UAV-BSs, the UAVs' boresight direction of the maximum antenna gain is modeled as a random unit vector. All UAV-BSs have sensing capabilities, so an arbitrary yet fixed reference UAV-BS acts as a monostatic radar to acquire sensing measurements associated with a target UAV-UE at a given time snapshot while the target UAVUE communicates with the reference UAV-BS. Consequently, considering both clutter and communication interference, exact analytical expressions for ISAC coverage probability are derived. Low complexity baseline analytical expressions are also derived by ignoring clutter interference and directional characteristics of the UAV-UEs. Finally, motivated by the vision for intelligent UAV networks, a DeepSets-based model is developed and trained to estimate the optimal location of the reference UAV-BS that maximizes ISAC performance given a spatial deployment of the UAVs. To the best of our knowledge, this is the first work to rigorously investigate ISAC performance in UAV networks while incorporating the directionality characteristics of the UAV antennas.

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arXiv2026

LEO-based Carrier-Phase Positioning for 6G: Design Insights and Comparison with GNSS

The integration of non-terrestrial networks (NTN) into 5G new radio (NR) enables a new class of positioning capabilities based on cellular signals transmitted by Low-Earth Orbit (LEO) satellites. In this paper, we investigate joint delay-and-carrier-phase positioning for LEO-based NR-NTN systems and provide a convergence-centric comparison with Global Navigation Satellite Systems (GNSS). We show that the rapid orbital motion of LEO satellites induces strong temporal and geometric diversity across observation epochs, thereby improving the conditioning of multi-epoch carrier-phase models and enabling significantly faster integer-ambiguity convergence. To enable robust carrier-phase tracking under intermittent positioning reference signal (PRS) transmissions, we propose a dual-waveform design that combines wideband PRS for delay estimation with a continuous narrowband carrier for phase tracking. Using a realistic simulation framework incorporating LEO orbit dynamics, we demonstrate that LEO-based joint delay-and-carrier-phase positioning achieves cm-level accuracy with convergence times on the order of a few seconds, whereas GNSS remains limited to meter-level accuracy over comparable short observation windows. These results establish LEO-based cellular positioning as a strong complement and potential alternative to GNSS for high-accuracy positioning, navigation, and timing (PNT) services in future wireless networks.

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arXiv2026

Joint Communication and Sensing in Aerial Corridors: A Novel Stochastic Geometry Framework

In unmanned aerial vehicle (UAVs) networks, joint communication and sensing (JCAS) is emerging as a key enabler to support extended and continuous communication and sensing capabilities for sixth-generation (6G) services among UAVs operating in swarms. Within this framework, aerial corridors provide a structured environment for supporting coordinated and reliable operations. In this paper, a comprehensive frame- work is presented to investigate the JCAS coverage probability (CP) of a UAV-base station (BS) in an aerial corridor populated by UAV-user equipments (UEs). The corridor is modeled as a finite cylinder, within which a fixed number of UAV-UEs are spatially distributed according to a three-dimensional (3D) binomial point process (BPP). The UAV-BS is assumed to be equipped with a realistic 3D third generation partnership project (3GPP) antenna pattern and exploits radar sensing capabilities to track a known UAV-UE. Subsequently, the tracked UAV-UE is assumed to perform uplink communication with the UAV-BS. Accordingly, the JCAS CP at the UAV-BS is analyzed under the presence of clutter and uplink communication interference, and exact-form analytical expressions are derived. To the best of our knowledge, this is the first work to develop a stochastic geometry framework for the rigorous analysis of JCAS performance in aerial corridors, with UAV-UE locations modeled as a 3D BPP. Among several insights, results show that increasing the directivity of the UAV-BS antenna beams leads to notable JCAS performance gains, particularly for shorter UAV corridors.

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arXiv2026

Optimal Anchor Placement for Wireless Localization in Mixed LOS and NLOS Scenarios

This paper develops a general optimal experimental design framework for anchor placement in localization and related estimation problems whose Fisher information matrix (FIM) can be expressed as a weighted sum of rank-one matrices. Within this framework, anchor locations are selected to optimize Cramér--Rao lower bound (CRLB)-based performance measures, while accounting jointly for measurement geometry and information strength. As a challenging motivating application, we consider localization in diffraction-dominated non-line-of-sight (NLOS) outdoor-to-indoor (O2I) environments, where the direct path is severely attenuated or blocked. Although diffraction-based time-of-arrival (TOA) measurements can provide useful positioning information, optimal anchor placement is less intuitive than in conventional line-of-sight (LOS) settings because both the measurement directions and their information strengths depend on the propagation environment. The proposed $S_n,r_n$ -based FIM representation compactly captures this interaction between geometry and signal strength. For a single target, it yields a polygon-closure interpretation and characterizes the conditions under which A-, D-, and E-optimal anchor geometries coincide. We then extend the framework to region-wide anchor placement by constructing a precomputed anchor--target dictionary and formulating min--max E- and D-optimal anchor-selection problems as mixed-integer second-order cone programs. Numerical results demonstrate the resulting anchor layouts, localization bounds, and computational tradeoffs in a mixed-LOS/NLOS O2I environment. While the analysis is instantiated and evaluated for diffraction-based O2I localization, its underlying results apply broadly to estimation and anchor-placement problems with weighted rank-one-sum FIMs.

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arXiv2026

On the Analysis of Platooned Vehicular Networks on Highways

Vehicular platooning refers to coordinated and close movement of vehicular users (VUs) traveling together along a common route segment, offering strategic benefits such as reduced fuel costs, lower emissions, and improved traffic flow. {Highways offer a natural setting for platooning due to extended travel distances, yet their potential remains underexplored, particularly in terms of communication and connectivity. Given that effective platooning relies on robust vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, understanding connectivity dynamics on highways is essential.} In this paper, we analyze the dynamics of vehicular platooning on a highway and its impact on the performance of two forms of vehicular communications -- namely V2V and V2I communication -- compared to independent vehicle movement on a highway. The vehicular networks consists of road-side units (RSUs), modeled as a 1D Poisson point process (PPP), to provide the vehicular connectivity to the VUs. VUs are modeled as 1D PPP under the non-platooned traffic scenario (N-PTS) and as a 1D Matern cluster process (MCP) under the platooned traffic scenario (PTS). We evaluate the distribution on the per-RSU load, representing the number of VUs served, for the typical and tagged RSU. Additionally, we derive coverage probability (CP) and rate coverage (RC), which measures the probability of the signal-to-interference-plus-noise ratio (SINR) and achievable rate above a specified threshold at the typical VU along with their meta distribution (MD), providing a deeper understanding of the reliability and variability of these metrics across different spatial distributions of VUs and RSUs. Finally, we validate our theoretical findings through simulations and provide numerical insights into the impact of different traffic patterns on RSU load distribution, CP, and RC performance.

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arXiv2026

Fourier Preconditioning for Neural Feature Learning

Mutual information (MI)-inspired feature learning techniques are capable of generating low-dimensional embeddings that retain nonlinear dependence structures, but direct estimations of MI suffer from noisy probability distribution estimates in the low-data regime. The H-Score objective, computed from second-order statistics, provides a practical proxy metric for training feature extraction networks. We prove that H-Score is invariant to invertible transformations in the unrestricted functional setting, but becomes sensitive to input basis rotations under constrained approximation classes. Consequently, we study unitary preconditioning for H-Score networks and show that selecting an appropriate basis rotation reduces finite-width truncation error by concentrating predictive dependence into fewer dominant modes. We identify the fast Fourier transform (FFT) as an effective data-independent, low-cost preconditioner for approximately stationary processes, where spectral structure induces concentration of the cross-covariance singular value spectrum. We introduce training-free metrics based on spectral entropy and cumulative dependence energy to quantify basis suitability and predict downstream inference gains prior to network training. Experiments across eight multivariate datasets demonstrate that FFT preconditioning is particularly useful in resource-constrained regimes, achieving up to 50% normalized mean squared error (NMSE) reduction, while the proposed metrics correlate with observed performance gains and correctly identify cases where spectral preconditioning is detrimental.

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arXiv2026

Björck Sequences: Extension to Arbitrary Lengths, Correlation Analysis, and Applications to Wireless Systems

In this paper, we propose a sequence construction framework that extends prime-length Björck sequences, a class of Constant Amplitude Zero Autocorrelation (CAZAC) sequences, to arbitrary lengths using Goldbach's conjecture for even and odd integers. The framework is generic and applies to any CAZAC family defined for prime lengths and supports extensions to both cyclically shifted sequences and sequences with different root indices. We analytically characterize the resulting correlation behavior and show that the construction preserves orthogonality among cyclic shifts while maintaining favorable zero-lag cross-correlation across different root-index sequences. We further investigate Björck sequences as candidates for reference signals in next-generation wireless systems. Using the proposed framework, we extend Björck sequences to arbitrary lengths and evaluate their time- and frequency-offset estimation performance in terrestrial (TNs) and non-terrestrial networks (NTNs). Results show performance comparable to Zadoff--Chu (ZC) sequences in low-Doppler TN environments and improved robustness in high-Doppler NTN scenarios due to superior ambiguity-function properties. We also identify an inherent Doppler-dependent behavior that can cause sequence misidentification under large Doppler shifts. To address this, we propose two mitigation strategies: (i) leveraging coarse Doppler estimates prior to detection, and (ii) selecting appropriately spaced subsets of orthogonal sequences. Ambiguity function-based analysis demonstrates the effectiveness of these approaches in improving estimation reliability. Overall, this work enables practical arbitrary-length CAZAC sequence design and establishes Björck sequences as a strong alternative for reference signal design in high-Doppler environments.

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arXiv2026

A New Location Estimator for Mixed LOS & NLOS scenarios

Time-of-arrival (TOA)-based localization in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments is challenging because conventional Euclidean range models do not capture diffraction-dominated propagation. We show that the diffraction path-length model smoothly transitions between LOS and diffraction-dominated NLOS conditions, eliminating the need for explicit path classification. Although this model provides a unified geometric description of mixed LOS/NLOS propagation, the resulting 3D maximum-likelihood problem is nonconvex, and a direct Gauss--Newton estimator based on this model can converge to suboptimal local minima. This motivates the development of a class of structure-exploiting estimators. For known target height, the model induces a virtual-anchor representation of the reduced 2D problem, enabling estimators that exhibit a clear complexity--performance tradeoff: surrogate formulations provide structure and computational efficiency, while a semidefinite-relaxation formulation more faithfully preserves the original likelihood at higher cost. Building on this same structure, we develop 3D sample--polish--select estimators that reduce the global search to one dimension, solve the associated fixed-height 2D subproblems, and then apply local nonlinear refinement in 3D. The proposed estimators achieve near-Cramér--Rao lower bound (CRLB) performance with substantially lower complexity than multistart Gauss--Newton, while also being far more robust to initialization than a direct single-start Gauss--Newton estimator.

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arXiv2026

Full Motion State Localization with Extra Large Aperture Arrays

Conventional localization techniques typically assume far-field (FF) propagation characterized by planar wavefronts and simplified spatial relationships. The use of higher carrier frequencies has given rise to the paradigm of extra large aperture arrays (ELAAs) which consist of a large number of tightly packed antenna elements. These arrays have a large electrical aperture which increases the Fraunhofer distance making the FF assumption restrictive. As a result, near-field (NF) effects, such as spherical wavefront curvature, direction dependent gains, and spatial variations in Doppler and delay, become significant even at distances previously regarded as FF. This paradigm shift opens up new opportunities: the rich multi-parametric structure of NF models if properly exploited can enable superior localization accuracy. In this work, we investigate the potential of multi-snapshot, full-motion state (3D position, 3D velocity, and 2D orientation) estimation using delay and Doppler measurements for a mobile receiver equipped with a linear ELAA in an environment comprising a number of wideband anchors. We develop a signal model that captures both the NF propagation geometry and spatially varying Doppler effects. We perform an information-theoretic analysis to establish Cramer-Rao lower bounds (CRLB) on the achievable position error bound (PEB), velocity error bound (VEB), and orientation error bound (OEB), respectively. We reveal that delay measurements carry richer information than Doppler measurements, and standalone Doppler measurements cannot overcome information losses due to unknown channel gains and frequency offsets, enabling only coarse estimation capabilities. We also propose a maximum-likelihood (ML) approach to jointly estimate the 8D position parameters from measured channel characteristics.

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arXiv2025

Mutual Information-Driven Visualization and Clustering for Core KPI Selection in O-RAN Testing

O-RAN testing is becoming increasingly difficult with the exponentially growing number of performance measurements as the system grows more complex, with additional units, interfaces, applications, and possible implementations and configurations. To simplify the testing procedure and improve system design for O-RAN systems, it is important to identify the dependencies among various performance measurements, which are inherently time-series and can be modeled as realizations of random processes. While information theory can be utilized as a principled foundation for mapping these dependencies, the robust estimation of such measures for random processes from real-world data remains challenging. This paper introduces AMIF-MDS, which employs aggregate mutual Information in frequency (AMIF), a practical proxy for directed information (DI), to quantify similarity and visualize inter-series dependencies with multidimensional scaling (MDS). The proposed quantile-based AMIF estimator is applied to O-RAN time-series testing data to identify dependencies among various performance measures so that we can focus on a set of ``core'' performance measures. Applying density-based spatial clustering of applications with noise (DBSCAN) to the MDS embedding groups mutually informative metrics, organically reveals the link-adaptation indicators among other clusters, and yields a ``core'' performance measure set for future learning-driven O-RAN testing.

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arXiv2025

Two-Stage Weighted Projection for Reliable Low-Complexity Cooperative and Non-Cooperative Localization

In this paper, we propose a two-stage weighted projection method (TS-WPM) for time-difference-of-arrival (TDOA)-based localization, providing provable improvements in positioning accuracy, particularly under high geometric dilution of precision (GDOP) and low signal-to-noise ratio (SNR) conditions. TS-WPM employs a two-stage iterative refinement approach that dynamically updates both range and position estimates, effectively mitigating residual errors while maintaining computational efficiency. Additionally, we extend TS-WPM to support cooperative localization by leveraging two-way time-of-arrival (TW-TOA) measurements, which enhances positioning accuracy in scenarios with limited anchor availability. To analyze TS-WPM, we derive its error covariance matrix and mean squared error (MSE), establishing conditions for its optimality and robustness. To facilitate rigorous evaluation, we develop a 3rd Generation Partnership Project (3GPP)-compliant analytical framework, incorporating 5G New Radio (NR) physical layer aspects as well as large-scale and small-scale fading. As part of this, we derive a generalized Cramér-Rao lower bound (CRLB) for multipath propagation and introduce a novel non-line-of-sight (NLOS) bias model that accounts for propagation conditions and SNR variations. Our evaluations demonstrate that TS-WPM achieves near-CRLB performance and consistently outperforms state-of-the-art weighted nonlinear least squares (WNLS) in high GDOP and low SNR scenarios. Moreover, cooperative localization with TS-WPM significantly enhances accuracy, especially when an insufficient number of anchors (such as 2) are visible. Finally, we analyze the computational complexity of TS-WPM, showing its balanced trade-off between accuracy and efficiency, making it a scalable solution for real-time localization in next-generation networks.

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arXiv2025

Multi-Target Detection for Cognitive MIMO Radar Networks

In this work, we develop centralized and decentralized signal fusion techniques for constant false alarm rate (CFAR) multi-target detection with a cognitive radar network in unknown noise and clutter distributions. Further, we first develop a detection statistic for co-located monostatic MIMO radar in unknown noise and clutter distributions which is asymptotically CFAR as the number of received pulses over all antennas grows large, and we provide conditions under which this detection statistic is valid. We leverage reinforcement learning (RL) for improved multi-target detection performance, where the radar learns likely target locations in a search area. These results are then generalized to the setting of cognitive radar networks, where radars collaborate to learn where targets are likely to appear in a search area. We show a fundamental tradeoff between the spatial and temporal domain for CFAR detection in unknown noise and clutter distributions; in other words, we show a tradeoff between the number of radar antennas and the number of temporal samples. We show the benefits and tradeoffs with centralized and decentralized detection with a network of cognitive radars.

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arXiv2025

Impact of Frequency on Diffraction-Aided Wireless Positioning

This paper tackles the challenge of accurate positioning in Non-Line-of-Sight (NLoS) environments, with a focus on indoor public safety scenarios where NLoS bias severely impacts localization performance. We explore Diffraction MultiPath Components (MPC) as a critical mechanism for Outdoor-to-Indoor (O2I) signal propagation and its role in positioning. The proposed system comprises outdoor Uncrewed Aerial Vehicle (UAV) transmitters and indoor receivers that require localization. To facilitate diffraction-based positioning, we develop a method to isolate diffraction MPCs at indoor receivers and validate its effectiveness using a ray-tracing-generated dataset, which we have made publicly available. Our evaluation across the FR1, FR2, and FR3 frequency bands within the 5G/6G spectrum confirms the viability of diffraction-based positioning techniques for next-generation wireless networks.

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arXiv2025

Deterministic and Statistical Analysis of the DoF of Continuous Linear Arrays in the Near Field

This paper examines the number of communication modes, that is, the degrees of freedom (DoF) in a wireless line-of-sight channel comprising a small continuous linear intelligent antenna array in the near field of a large one. The framework allows for any orientations between the arrays and any positions in a two-dimensional space assuming that the transmitting array is placed at the origin. Therefore, apart from the length of the two continuous arrays, four key parameters determine the DoF and are hence considered in the analysis: the Cartesian coordinates of the center of the receiving array and two angles that model the rotation of each array around its center. The paper starts with the calculation of the deterministic DoF for a generic geometric setting, which extends beyond the widely studied paraxial case. Subsequently, a stochastic geometry framework is proposed to study the statistical DoF, as a first step towards the investigation of the system-level performance in near field networks. Numerical results applied to millimeter wave networks reveal the large number of DoF provided by near-field communications and unveil key system-level insights. A comparison of the proposed method with the singular value decomposition-based method is illustrated to validate the model.

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arXiv2025

LEO-based Positioning: Foundations, Signal Design, and Receiver Enhancements for 6G NTN

The integration of non-terrestrial networks (NTN) into 5G new radio (NR) has opened up the possibility of developing a new positioning infrastructure using NR signals from Low-Earth Orbit (LEO) satellites. Compared to existing Global Navigation Satellite Systems (GNSS), LEO-based cellular positioning offers several advantages, such as a superior link budget, higher operating bandwidth, and large forthcoming constellations. Due to these factors, LEO-based positioning, navigation, and timing (PNT) is a potential enhancement for NTN in 6G cellular networks. However, extending the existing terrestrial cellular positioning methods to LEO-based NTN positioning requires key fundamental enhancements. These include creating broad positioning beams orthogonal to conventional communication beams, time-domain processing at the user equipment (UE) to resolve large delay and Doppler uncertainties, and efficiently accommodating positioning reference signals (PRS) from multiple satellites within the communication resource grid. In this paper, we present the first set of design insights by incorporating these enhancements and thoroughly evaluating LEO-based positioning, considering the constraints and capabilities of the NR-NTN physical layer. To evaluate the performance of LEO-based NTN positioning, we develop a comprehensive NR-compliant simulation framework, including LEO orbit simulation, transmission (Tx) and receiver (Rx) architectures, and a positioning engine incorporating the necessary enhancements. Our findings suggest that LEO-based NTN positioning could serve as a complementary infrastructure to GNSS and, with appropriate enhancements, may also offer a viable alternative.

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arXiv2025

A New Statistical Approach to Calibration-Free Localization Using Unlabeled Crowdsourced Data

Fingerprinting-based indoor localization methods typically require labor-intensive site surveys to collect signal measurements at known reference locations and frequent recalibration, which limits their scalability. This paper addresses these challenges by presenting a novel approach for indoor localization that utilizes crowdsourced data without location labels. We leverage the statistical information of crowdsourced data and propose a cumulative distribution function (CDF) based distance estimation method that maps received signal strength (RSS) to distances from access points. This approach overcomes the limitations of conventional distance estimation based on the empirical path loss model by efficiently capturing the impacts of shadow fading and multipath. Compared to fingerprinting, our unsupervised statistical approach eliminates the need for signal measurements at known reference locations. The estimated distances are then integrated into a three-step framework to determine the target location. The localization performance of our proposed method is evaluated using RSS data generated from ray-tracing simulations. Our results demonstrate significant improvements in localization accuracy compared to methods based on the empirical path loss model. Furthermore, our statistical approach, which relies on unlabeled data, achieves localization accuracy comparable to that of the supervised approach, the $k$-Nearest Neighbor ($k$NN) algorithm, which requires fingerprints with location labels. For reproducibility and future research, we make the ray-tracing dataset publicly available at [2].

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arXiv2025

Diffraction Aided Wireless Positioning

Wireless positioning in Non-Line-of-Sight (NLoS) scenarios presents significant challenges due to multipath effects that lead to biased measurements and reduced positioning accuracy. This paper revisits electromagnetic field theory related to diffraction and in the context of wireless positioning and proposes a novel positioning technique that greatly improves accuracy in NLoS environments dominated by diffraction. The method is applied to a critical public safety use case: precisely locating at-risk individuals within buildings, with a particular focus on improving 3D positioning and z-axis accuracy. By leveraging the Geometrical Theory of Diffraction (GTD), the approach introduces an innovative NLoS path length model and a new NLOS positioning technique. Using Fisher information analysis, we establish the conditions required for 3D positioning and derive lower bounds on positioning performance for both 3D and z-axis estimates for the proposed NLOS positioning technique. Additionally, we propose an algorithmic implementation of the proposed NLoS positioning method using non-linear least squares estimation, which we term D-NLS. The positioning performance of our proposed NLOs positioning technique is validated using an extensive ray-tracing simulation. The numerical results highlight the superiority of our approach in outdoor-to-indoor environments, which directly estimates NLoS path lengths and delivers significant performance enhancements over existing methods for both 3D and z-axis positioning scenarios.

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arXiv2025

Indoor Positioning for Public Safety: Role of UAVs, LEOs, and Propagation-Aware Techniques

Effective indoor positioning is critical for public safety, enabling first responders to locate at-risk individuals accurately during emergency scenarios. However, traditional Global Navigation Satellite Systems (GNSS) often perform poorly indoors due to poor coverage and non-line-of-sight (NLOS) conditions. Moreover, relying on fixed cellular infrastructure, such as terrestrial networks (TNs), may not be feasible, as indoor signal coverage from a sufficient number of base stations or WiFi access points cannot be guaranteed for accurate positioning. In this paper, we propose a rapidly deployable indoor positioning system (IPS) leveraging mobile anchors, including uncrewed aerial vehicles (UAVs) and Low-Earth-Orbit (LEO) satellites, and discuss the role of GNSS and LEOs in localizing the mobile anchors. Additionally, we discuss the role of sidelink-based positioning, which is introduced in 3rd Generation Partnership Project (3GPP) Release 18, in enabling public safety systems. By examining outdoor-to-indoor (O2I) signal propagation, particularly diffraction-based approaches, we highlight how propagation-aware positioning methods can outperform conventional strategies that disregard propagation mechanism information. The study highlights how emerging 5G Advanced and Non-Terrestrial Networks (NTN) features offer new avenues to improve positioning in challenging indoor environments, ultimately paving the way for cost-effective and resilient IPS solutions tailored to public safety applications.

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