pwc-archive/papers-with-abstracts huggingface dataset:
Integrated MIMO Passive Radar Target Detection
Authors: Amir Zaimbashi, Maria Sabrina Greco, Fulvio Gini
Date: 2024-02-26 00:00:00

Integrated passive radar (IPR) can be regarded as next generation passive radar technology, which aims to integrate communication and radar systems. Unlike conventional passive radar, which does not prioritize communication-centric radar technology, IPR technology places a higher priority on incorporating specific radar constraints to develop waveforms that are better suited for radar applications. This paper deals with the problem of distributed MIMO IPR target detection under uncalibrated surveillance/reference receivers. We focus on a communication-centric radar system consisting of several opportunity transmitters non-overlapping in frequency with the same bandwidth and several spatially separated receivers. Five new detectors are devised according to the likelihood ratio test (LRT), Rao, Wald, Gradient and Durbin criteria. Although, it is shown that these detectors are asymptotically equivalent, they provide different performance in the presence of noisy reference channels. The invariance principle is applied in this paper to show that all uncertainties affecting threshold setting can be unified in the direct-path signal power-to-noise power ratio (DNR) of the reference channels. To ensure effective detection threshold setting regardless of the DNR values in the reference channels, we introduce a novel strategy to adjust the level of the proposed detectors rather than their sizes. Then, we examine false alarm regulations and detection performance of the fixed-level proposed detectors to demonstrate their effectiveness compared to several existing detectors. Thus, we create a unified framework for uncalibrated MIMO IPR target detection in the presence of noisy reference channels.
Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning
Authors: Luca Barbieri, Bernardo Camajori Tedeschini, Mattia Brambilla, Monica Nicoli
Date: 2024-02-26 00:00:00
Tasks: Deep Learning, Simultaneous Localization and Mapping

Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigation and imaging sensors via Vehicle-to-Everything (V2X) communications for joint positioning and environmental perception. In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors. Vehicles process their LiDAR point clouds using a Deep Neural Network (DNN), namely a 3D object detector, to identify and localize possible static objects present in the driving environment. Data are then aggregated by a centralized infrastructure that performs Data Association (DA) using a Message Passing Neural Network (MPNN) and runs the Implicit Cooperative Positioning (ICP) algorithm. The proposed approach is evaluated using two realistic driving scenarios generated by a high-fidelity automated driving simulator. The results show that CLS-MPNN outperforms a conventional non-cooperative localization algorithm based on Global Navigation Satellite System (GNSS) and a state-of-the-art cooperative Simultaneous Localization and Mapping (SLAM) method while approaching the performances of an oracle system with ideal sensing and perfect association.
Sounding-Based Evaluation of Multi-Sensor ISAC Networks for Drone Applications: Measurement and Simulation Perspectives
Authors: Julia Beuster, Carsten Jan Smeenk, Saw James Myint, Reza Faramarzahanagri, Carsten Andrich, Sebastian Giehl, Christian Schneider, Reiner S. Thomä
Date: 2024-02-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

With the upcoming multitude of commercial and public applications envisioned in the mobile 6G radio landscape using unmanned aerial vehicles (UAVs), integrated sensing and communication (ISAC) plays a key role to enable the detection and localization of passive objects with radar sensing, while optimizing the utilization of scarce resources. To explore the potential of future ISAC architectures with UAVs as mobile nodes in distributed multi-sensor networks, the system's fundamental capability to detect static and dynamic objects that reveal themselves by their bi-static back-scattering needs to be evaluated. Therefore, this paper addresses simulation- and measurement based data acquisition methods to gather knowledge about the bistatic reflectivity of single objects including their Micro-Doppler signature for object identification as well as the influence of multipath propagation in different environments on the localization accuracy and radar tracking performance. We show exemplary results from simulation models, bi-static reflectivity measurements in laboratory environment and real-flight channel sounding experiments in selected scenarios showcasing the potential of synthetic and measured data sets for development and evaluation of ISAC algorithms. The presented measurement data sets are publicly available to encourage the academic RF community to validate future algorithms using realistic scenarios alongside simulations models.
Time vs. Frequency Domain DPD for Massive MIMO: Methods and Performance Analysis
Authors: Yibo Wu, Ulf Gustavsson, Mikko Valkama, Alexandre Graell i Amat, Henk Wymeersch
Date: 2024-02-26 00:00:00

The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs). While the complexity for conventional time domain (TD) DPD scales with the number of PAs, frequency domain (FD) DPD has a complexity scaling with the number of user equipments (UEs). In this work, we provide a comprehensive analysis of different state-of-the-art TD and FD-DPD schemes in terms of complexity and linearization performance in both rich scattering and line-of-sight (LOS) channels and with antenna crosstalk. We propose a novel low-complexity FD convolutional neural network (CNN) DPD. We also propose a learning algorithm for any FD-DPDs with differentiable structure. The analysis shows that FD-DPD, particularly the proposed FD CNN, is preferable in LOS scenarios with few users, due to the favorable trade-off between complexity and linearization performance. On the other hand, in scenarios with more users or isotropic scattering channels, significant intermodulation distortions among UEs degrade FD-DPD performance, making TD-DPD more suitable. The proposed learning algorithm allows FD-DPDs to outperform TD-DPD optimized by indirect learning architecture under antenna crosstalk.
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement Learning
Authors: Alea Schröder, Steffen Gracla, Maik Röper, Dirk Wübben, Carsten Bockelmann, Armin Dekorsy
Date: 2024-02-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates interference among satellite beams, boosting spectral efficiency. While optimal SDMA precoding solutions have been proposed for ideal channel knowledge in various scenarios, addressing robust precoding with imperfect channel information has primarily been limited to simplified models. However, these models might not capture the complexity of LEO satellite applications. We use the Soft Actor-Critic (SAC) deep Reinforcement Learning (RL) method to learn robust precoding strategies without the need for explicit insights into the system conditions and imperfections. Our results show flexibility to adapt to arbitrary system configurations while performing strongly in terms of achievable rate and robustness to disruptive influences compared to analytical benchmark precoders.
Distortion-Controlled Dithering with Reduced Recompression Rate
Authors: Morriel Kasher, Michael Tinston, Predrag Spasojevic
Date: 2024-02-26 00:00:00
Tasks: Data Compression, Image Compression, Quantization

Dithering is a technique that can improve human perception of low-resolution data by reducing quantization artifacts. In this work we formalize and analytically justify two metrics for quantization artifact prominence, using them to design a novel dithering method for distortion-controlled data compression. We present theoretical entropy calculations for this dither and experimentally validate its performance on a low-rate image compression task. The result is a drastic improvement in the perceptual quality of quantized images with a lower recompression entropy than any state-of-the-art dither technique, achieving 45 points lower PIQUE at the same rate or 40% lower rate at the same PIQUE. The proposed dither is an adaptable tool applicable for use in any lossy compression system, permitting precise control of rate-distortion characteristics for both compression and recompression.
Indoor Localization of Smartphones Thanks to Zero-Energy-Devices Beacons
Authors: Shanglin Yang, Yohann Benedic, Dinh-Thuy Phan-Huy, Jean-Marie Gorce, Guillaume Villemaud
Date: 2024-02-26 00:00:00
Tasks: Indoor Localization

In this paper, we present a new ultra-low power method of indoor localization of smartphones (SM) based on zero-energy-devices (ZEDs) beacons instead of active wireless beacons. Each ZED is equipped with a unique identification number coded into a bit-sequence, and its precise position on the map is recorded. An SM inside the building is assumed to have access to the map of ZEDs. The ZED backscatters ambient waves from base stations (BSs) of the cellular network. The SM detects the ZED message in the variations of the received ambient signal from the BS. We accurately simulate the ambient waves from a BS of Orange 4G commercial network, inside an existing large building covered with ZED beacons, thanks to a ray-tracing-based propagation simulation tool. Our first performance evaluation study shows that the proposed localization system enables us to determine in which room a SM is located, in a realistic and challenging propagation scenario.
Analyzing Downlink Coverage in Clustered Low Earth Orbit Satellite Constellations: A Stochastic Geometry Approach
Authors: Miyeon Lee, Sucheol Kim, Minje Kim, Dong-Hyun Jung, Junil Choi
Date: 2024-02-26 00:00:00
Tasks: Point Processes

Satellite networks are emerging as vital solutions for global connectivity beyond 5G. As companies such as SpaceX, OneWeb, and Amazon are poised to launch a large number of satellites in low Earth orbit, the heightened inter-satellite interference caused by mega-constellations has become a significant concern. To address this challenge, recent works have introduced the concept of satellite cluster networks where multiple satellites in a cluster collaborate to enhance the network performance. In order to investigate the performance of these networks, we propose mathematical analyses by modeling the locations of satellites and users using Poisson point processes, building on the success of stochastic geometry-based analyses for satellite networks. In particular, we suggest the lower and upper bounds of the coverage probability as functions of the system parameters, including satellite density, satellite altitude, satellite cluster area, path loss exponent, and Nakagami parameter $m$. We validate the analytical expressions by comparing them with simulation results. Our analyses can be used to design reliable satellite cluster networks by effectively estimating the impact of system parameters on the coverage performance.
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization
Authors: Liangqi Liu, Wenqiang Pu, Yingru Li, Bo Jiu, Zhi-Quan Luo
Date: 2024-02-26 00:00:00
Tasks: Active Learning

The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics. In this paper, the competition between a frequency agile radar and a Digital Radio Frequency Memory (DRFM)-based intelligent jammer is considered. We introduce an Online Convex Optimization (OCO) framework designed to illustrate this adversarial interaction. Notably, traditional OCO algorithms exhibit suboptimal sample efficiency due to the limited information obtained per round. To address the limitations, two refined algorithms are proposed, utilizing unbiased gradient estimators that leverage the unique attributes of the jammer system. Sub-linear theoretical results on both static regret and universal regret are provided, marking a significant improvement in OCO performance. Furthermore, simulation results reveal that the proposed algorithms outperform common OCO baselines, suggesting the potential for effective deployment in real-world scenarios.
Radio Maps for Beam Alignment in mmWave Communications with Location Uncertainty
Authors: Tien Ngoc Ha, Daniel Romero, Roberto López-Valcarce
Date: 2024-02-25 00:00:00

Next generation communication systems require accurate beam alignment to counteract the impairments that characterize propagation in high-frequency bands. The overhead of the pilot sequences required to select the best beam pair is prohibitive when codebooks contain a large number of beams, as is the case in practice. To remedy this issue, some schemes exploit information about the user location to predict the best beam pair. However, these schemes (i) involve no measurements whatsoever, which generally results in a highly suboptimal predicted beam, and (ii) are not robust to localization errors. To address these limitations, this paper builds upon the notion of radio map to develop two algorithms that attain a balance between the quality of the obtained beam pair and measurement overhead. The proposed algorithms predict the received power corresponding to each pair and measure just the Q pairs with highest prediction. While the first algorithm targets simplicity, the second one relies on a Bayesian approach to endow the prediction process with robustness to localization error. The performance of both algorithms is shown to widely outperform existing methods using ray-tracing data.
Localization in Reconfigurable Intelligent Surface Aided mmWave Systems: A Multiple Measurement Vector Based Channel Estimation Method
Authors: Kunlun Li, Jiguang He, Mohammed El-Hajjar, Lie-Liang Yang
Date: 2024-02-25 00:00:00

The sparsity of millimeter wave (mmWave) channels in the angular and temporal domains is beneficial to channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage poses a significant challenge on the localization in mmWave systems, potentially leading to substantial positioning errors. A promising solution is to employ reconfigurable intelligent surface (RIS) to generate the virtual line-of-sight (VLoS) paths to aid localization. Consequently, wireless localization in the RIS-assisted mmWave systems has become the essential research issue. In this paper, a multiple measurement vector (MMV) model is constructed and a two-stage channel estimation based localization scheme is proposed. During the first stage, by exploiting the beamspace sparsity and employing a random RIS phase shift matrix, the channel parameters are estimated, based on which the precoder at base station and combiner at user equipment (UE) are designed. Then, in the second stage, based on the designed precoding and combining matrices, the optimal phase shift matrix for RIS is designed using the proposed modified temporally correlated multiple sparse Bayesian learning (TMSBL) algorithm. Afterwards, the channel parameters, such as angle of reflection, time-of-arrival, etc., embedding location information are estimated for finally deriving the location of UE. We demonstrate the achievable performance of the proposed algorithm and compare it with the state-of-the-art algorithms. Our studies show that the proposed localization scheme is capable of achieving centimeter level localization accuracy, when LoS path is blocked. Furthermore, the proposed algorithm has a low computational complexity and outperforms the legacy algorithms in different perspectives.
Spherical Geometry Algorithm for Space-borne Synthetic Aperture Radar Imaging
Authors: Xinhua Mao
Date: 2024-02-25 00:00:00
Tasks: Image Reconstruction

Higher spatial resolution and larger imaging scene are always the goals pursued by advanced space-borne SAR system.High resolution and wide swath SAR imaging can provide more information about the illuminated scene of interest on one hand,but also come with some new challenges on the other hand.The induced new challenging problems include curved orbit,Earth rotation,and spherical ground surface,etc.Most existing image formation algorithms suffer from performance deficiency in these challenging cases,either in focus accuracy or computational efficiency.In this paper,an accurate Fourier transform relationship between the phase history domain data and the scene reflectivity function is derived under arbitrary radar trajectory by exploiting the spherical geometry property of the space-borne SAR data collection.Using the derived new data model,an image reconstruction algorithm based on Fourier inversion is proposed.The new algorithm has the inherent capability of correcting for the curved orbit and spherical ground surface effect.Meanwhile,the out-of-plane motion effect induced by the Earth's rotation can also be compensated by a two-step phase correction and data projection procedure embedded in the Fourier inversion reconstruction.The new algorithm inherits the merit of both time domain and frequency domain algorithms,has excellent performance in both focus accuracy and computational efficiency.Both simulation and real data processing results validate the effectiveness of the proposed imaging algorithm.
MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion
Authors: Xiangyu Gao, Youchen Luo, Ali Alansari, Yaping Sun
Date: 2024-02-24 00:00:00
Tasks: Human Detection, object-detection, Object Detection, Transfer Learning

This paper introduces MMW-Carry, a system designed to predict the probability of individuals carrying various objects using millimeter-wave radar signals, complemented by camera input. The primary goal of MMW-Carry is to provide a rapid and cost-effective preliminary screening solution, specifically tailored for non-super-sensitive scenarios. Overall, MMW-Carry achieves significant advancements in two crucial aspects. Firstly, it addresses localization challenges in complex indoor environments caused by multi-path reflections, enhancing the system's overall robustness. This is accomplished by the integration of camera-based human detection, tracking, and the radar-camera plane transformation for obtaining subjects' spatial occupancy region, followed by a zooming-in operation on the radar images. Secondly, the system performance is elevated by leveraging long-term observation of a subject. This is realized through the intelligent fusion of neural network results from multiple different-view radar images of an in-track moving subject and their carried objects, facilitated by a proposed knowledge-transfer module. Our experiment results demonstrate that MMW-Carry detects objects with an average error rate of 25.22\% false positives and a 21.71\% missing rate for individuals moving randomly in a large indoor space, carrying the common-in-everyday-life objects, both in open carry or concealed ways. These findings affirm MMW-Carry's potential to extend its capabilities to detect a broader range of objects for diverse applications.
ELAA Near-Field Localization and Sensing with Partial Blockage Detection
Authors: Hui Chen, Pinjun Zheng, Yu Ge, Ahmed Elzanaty, Jiguang He, Tareq Y. Al-Naffouri, Henk Wymeersch
Date: 2024-02-24 00:00:00

High-frequency communication systems bring extremely large aperture arrays (ELAA) and large bandwidths, integrating localization and (bi-static) sensing functions without extra infrastructure. Such systems are likely to operate in the near-field (NF), where the performance of localization and sensing is degraded if a simplified far-field channel model is considered. However, when taking advantage of the additional geometry information in the NF, e.g., the encapsulated information in the wavefront, localization and sensing performance can be improved. In this work, we formulate a joint synchronization, localization, and sensing problem in the NF. Considering the array size could be much larger than an obstacle, the effect of partial blockage (i.e., a portion of antennas are blocked) is investigated, and a blockage detection algorithm is proposed. The simulation results show that blockage greatly impacts performance for certain positions, and the proposed blockage detection algorithm can mitigate this impact by identifying the blocked antennas.
Linear short rate model with several delays
Authors: Alet Roux, Álvaro Guinea Juliá
Date: 2024-02-26 00:00:00
Tasks: Form, model

This paper introduces a short rate model in continuous time that adds one or more memory (delay) components to the Merton model (Merton 1970, 1973) or the Vasi\v{c}ek model (Vasi\v{c}ek 1977) for the short rate. The distribution of the short rate in this model is normal, with the mean depending on past values of the short rate, and a limiting distribution exists for certain values of the parameters. The zero coupon bond price is an affine function of the short rate, whose coefficients satisfy a system of delay differential equations. This system can be solved analytically, obtaining a closed formula. An analytical expression for the instantaneous forward rate is given: it satisfies the risk neutral dynamics of the Heath-Jarrow-Morton model. Formulae for both forward looking and backward looking caplets on overnight risk free rates are presented. Finally, the proposed model is calibrated against forward looking caplets on SONIA rates and the United States yield curve.
Pricing of geometric Asian options in the Volterra-Heston model
Authors: Florian Aichinger, Sascha Desmettre
Date: 2024-02-24 00:00:00

Geometric Asian options are a type of options where the payoff depends on the geometric mean of the underlying asset over a certain period of time. This paper is concerned with the pricing of such options for the class of Volterra-Heston models, covering the rough Heston model. We are able to derive semi-closed formulas for the prices of geometric Asian options with fixed and floating strikes for this class of stochastic volatility models. These formulas require the explicit calculation of the conditional joint Fourier transform of the logarithm of the stock price and the logarithm of the geometric mean of the stock price over time. Linking our problem to the theory of affine Volterra processes, we find a representation of this Fourier transform as a suitably constructed stochastic exponential, which depends on the solution of a Riccati-Volterra equation. Finally we provide a numerical study for our results in the rough Heston model.