pwc-archive/papers-with-abstracts huggingface dataset:
A Robust Anchor-based Method for Multi-Camera Pedestrian Localization
Authors: Wanyu Zhang, JiaQi Zhang, Dongdong Ge, Yu Lin, Huiwen Yang, Huikang Liu, Yinyu Ye
Date: 2024-10-25 00:00:00
Tasks: Position

This paper addresses the problem of vision-based pedestrian localization, which estimates a pedestrian's location using images and camera parameters. In practice, however, calibrated camera parameters often deviate from the ground truth, leading to inaccuracies in localization. To address this issue, we propose an anchor-based method that leverages fixed-position anchors to reduce the impact of camera parameter errors. We provide a theoretical analysis that demonstrates the robustness of our approach. Experiments conducted on simulated, real-world, and public datasets show that our method significantly improves localization accuracy and remains resilient to noise in camera parameters, compared to methods without anchors.
Geometric Correction and Mosaic Generation of Geo High Resolution Camera Images
Authors: Ankur Garg, Nitesh Thapa, Ghansham Sangar, Neha Gaur, Meenakshi Sarkar, S. Manthira Moorthi, Debajyoti Dhar
Date: 2024-10-25 00:00:00
Tasks: Earth Observation

The Geo High Resolution Camera (GHRC) aboard ISRO GSAT-29 satellite is a state-of-the-art 6-band Visible and Near Infrared (VNIR) imager in geostationary orbit at 55degE longitude. It provides a ground sampling distance of 55 meters at nadir, covering 110x110 km at a time, and can image the entire Earth disk using a scan mirror mechanism. To cover India, GHRC uses a two-dimensional raster scanning technique, resulting in over 1,000 scenes that must be stitched into a seamless mosaic. This paper presents the geolocation model and examines potential sources of targeting error, with an assessment of location accuracy. Challenges in inter-band registration and inter-frame mosaicing are addressed through algorithms for geometric correction, band-to-band registration, and seamless mosaic generation. In-flight geometric calibration, including adjustments to the instrument interior alignment angles using ground reference images, has improved pointing and location accuracy. A backtracking algorithm has been developed to correct frame-to-frame mosaicing errors for large-scale mosaics, leveraging geometric models, image processing, and space resection techniques. These advancements now enable the operational generation of full India mosaics with 100-meter resolution and high geometric fidelity, enhancing the GHRC capabilities for Earth observation and monitoring applications.
Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT
Authors: Liam Moroy, Guillaume Bourmaud, Frédéric Champagnat, Jean-François Giovannelli
Date: 2024-10-21 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, SSIM

Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be concentrated around a single mode, and consequently are evaluated using image-to-image metrics such as PSNR/SSIM. Instead, we are interested in reconstructing compressible flow images from sinograms having a small number of projections, which results in a posterior distribution no longer concentrated or even multimodal. Thus, in this paper, we aim at evaluating the approximate posterior of PnP diffusion models and introduce two posterior evaluation properties. We quantitatively evaluate three PnP diffusion methods on three different datasets for several numbers of projections. We surprisingly find that, for each method, the approximate posterior deviates from the true posterior when the number of projections decreases.
Fusion of Information in Multiple Particle Filtering in the Presence of Unknown Static Parameters
Authors: Xiaokun Zhao, Marija Iloska, Yousef El-Laham, Mónica F. Bugallo
Date: 2024-10-31 00:00:00
Tasks: State Space Models

An important and often overlooked aspect of particle filtering methods is the estimation of unknown static parameters. A simple approach for addressing this problem is to augment the unknown static parameters as auxiliary states that are jointly estimated with the time-varying parameters of interest. This can be impractical, especially when the system of interest is high-dimensional. Multiple particle filtering (MPF) methods were introduced to try to overcome the curse of dimensionality by using a divide and conquer approach, where the vector of unknowns is partitioned into a set of subvectors, each estimated by a separate particle filter. Each particle filter weighs its own particles by using predictions and estimates communicated from the other filters. Currently, there is no principled way to implement MPF methods where the particle filters share unknown parameters or states. In this work, we propose a fusion strategy to allow for the sharing of unknown static parameters in the MPF setting. Specifically, we study the systems which are separable in states and observations. It is proved that optimal Bayesian fusion can be obtained for state-space models with non-interacting states and observations. Simulations are performed to show that MPF with fusion strategy can provide more accurate estimates within fewer time steps comparing to existing algorithms.
In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models
Authors: Zihang Song, Matteo Zecchin, Bipin Rajendran, Osvaldo Simeone
Date: 2024-10-31 00:00:00
Tasks: In-Context Learning, State Space Models

Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done without requiring any explicit optimization and by leveraging not only short pilot sequences but also contextual information such as long-term channel statistics. The operating principle underlying automatic adaptation is in-context learning (ICL), an emerging property of sequence models. Prior art adopted transformer-based sequence models, which, however, have a computational complexity scaling quadratically with the context length due to batch processing. Recently, state-space models (SSMs) have emerged as a more efficient alternative, affording a linear inference complexity in the context size. This work explores the potential of SSMs for ICL-based equalization in cell-free massive MIMO systems. Results show that selective SSMs achieve comparable performance to transformer-based models while requiring approximately eight times fewer parameters and five times fewer floating-point operations.
A Peaceman-Rachford Splitting Approach with Deep Equilibrium Network for Channel Estimation
Authors: Dingli Yuan, Shitong Wu, Haoran Tang, Lu Yang, Chenghui Peng
Date: 2024-10-31 00:00:00

Multiple-input multiple-output (MIMO) is pivotal for wireless systems, yet its high-dimensional, stochastic channel poses significant challenges for accurate estimation, highlighting the critical need for robust estimation techniques. In this paper, we introduce a novel channel estimation method for the MIMO system. The main idea is to construct a fixed-point equation for channel estimation, which can be implemented into the deep equilibrium (DEQ) model with a fixed network. Specifically, the Peaceman-Rachford (PR) splitting method is applied to the dual form of the regularized minimization problem to construct fixed-point equation with non-expansive property. Then, the fixed-point equation is implemented into the DEQ model with a fixed layer, leveraging its advantage of the low training complexity. Moreover, we provide a rigorous theoretical analysis, demonstrating the convergence and optimality of our approach. Additionally, simulations of hybrid far- and near-field channels demonstrate that our approach yields favorable results, indicating its ability to advance channel estimation in MIMO system.
Unauthorized UAV Countermeasure for Low-Altitude Economy: Joint Communications and Jamming based on MIMO Cellular Systems
Authors: Zhuoran Li, Zhen Gao, Kuiyu Wang, Yikun Mei, Chunli Zhu, Lei Chen, Xiaomei Wu, Dusit Niyato
Date: 2024-10-30 00:00:00

To ensure the thriving development of low-altitude economy, countering unauthorized unmanned aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming. In light of this, this paper investigates the joint design of beamforming to simultaneously support communication with legitimate users and countermeasure against unauthorized UAVs based on dual-functional multiple-input multiple-output (MIMO) cellular systems. We first formulate a joint communication and jamming (JCJ) problem, relaxing it through semi-definite relaxation (SDR) to obtain a tractable semi-definite programming (SDP) problem, with SDR providing an essential step toward simplifying the complex JCJ design. Although the solution to the relaxed SDP problem cannot directly solve the original problem, it offers valuable insights for further refinement. Therefore, we design a novel constraint specifically tailored to the structure of the SDP problem, ensuring that the solution adheres to the rank-1 constraint of the original problem. Finally, we validate effectiveness of the proposed JCJ scheme through extensive simulations. Simulation codes are provided to reproduce the results in this paper: https://github.com/LiZhuoRan0. The results confirm that the proposed JCJ scheme can operate effectively when the total number of legitimate users and unauthorized UAVs exceeds the number of antennas.
Power Modeling in mm-Wave and Terahertz CMOS Transmitters for Wireless Network-on-Chip
Authors: Mohammad Shahmoradi, Korkut Kaan Tokgoz, Eduard Alarcon, Sergi Abadal
Date: 2024-10-30 00:00:00

Wireless Network-on-Chip (WNoC) systems, which interconnect chips using wireless links, face significant challenges in area and power consumption. To tackle these constraints, behavioral models (BMs) are crucial for assessing system performance under various conditions and optimizing parameters like data throughput and power consumption. Building transceivers (TRXs) physically is costly and time-consuming, making modeling a more practical approach. This paper develops a power consumption model for the sub-blocks of a WNoC transmitter (TX) at the chip level. By integrating these BMs with MATLAB, we aim to create a power model for TXs in WNoC architectures, optimized for CMOS technology operating at millimeter-wave and terahertz frequencies.
Chernoff fusion of Bernoulli Gaussian max filters
Authors: Zhijin Chen, Branko Ristic, Du Yong Kim
Date: 2024-10-30 00:00:00

Statistical dependencies between information sources are rarely known, yet in practical distributed tracking schemes, they must be taken into account in order to prevent track divergences. Chernoff fusion is well-known and universally accepted method that can address the problem of track fusion when the statistical dependence between the fusing sources is unknown. In this paper we derive the exact Chernoff fusion equations for Bernoulli Gaussian max filters. These filters have been recently derived in the framework of possibility theory, as the analog of the Bernoulli Gaussian sum filters. The main motivation for the possibilistic approach is that it effectively deals with imprecise mathematical models (e.g. dynamics, measurements) used in tracking algorithms. The paper also demonstrates the proposed possibilistic fusion scheme in the absence of knowledge about statistical dependence.
Location and Map-Assisted Wideband Phase and Time Calibration Between Distributed Antennas
Authors: Yibo Wu, Musa Furkan Keskin, Ulf Gustavsson, Gonzalo Seco-Granados, Erik G. Larsson, Henk Wymeersch
Date: 2024-10-30 00:00:00

Distributed massive multiple-input multiple-output networks utilize a large number of distributed access points (APs) to serve multiple user equipments (UEs), offering significant potential for both communication and localization. However, these networks require frequent phase and time calibration between distributed antennas due to oscillator phase drifts, crucial for reciprocity-based coherent beamforming and accurate localization. While this calibration is typically performed through bi-directional measurements between antennas, it can be simplified to unidirectional measurement under perfect knowledge of antenna locations. This paper extends a recent phase calibration narrowband line-of-sight (LoS) model to a phase and time calibration wideband orthogonal frequency division multiplexing model, including both LoS and reflection paths and allowing for joint phase and time calibrations. We explore different scenarios, considering whether or not prior knowledge of antenna locations and the map is available. For each case, we introduce a practical maximum likelihood estimator and conduct Cramer-Rao lower bound (CRLB) analyses to benchmark performance. Simulations validate our estimators against the CRLB in these scenarios.
RIS-Aided Dual-Polarized MIMO: How Large a Surface is Needed to Beat Single Polarization?
Authors: Zizhou Zheng, Huan Huang, Hongliang Zhang, A. Lee Swindlehurst
Date: 2024-10-30 00:00:00
Tasks: Diversity

Dual-polarized (DP) multiple-input-multiple-output (MIMO) systems have been widely adopted in commercial mobile wireless communications. Such systems achieve multiplexing and diversity gain by exploiting the polarization dimension. However, existing studies have shown that the capacity of DP MIMO may not surpass that of single-polarized (SP) MIMO systems due to the cross-polarization coupling induced by the propagation environment. In this letter, we employ reconfigurable intelligent surfaces (RISs) to address this issue and investigate how large the surface should be to ensure a better performance for DP MIMO. Specifically, we first derive the capacities of DP and SP MIMO systems with an RIS, and then study the influence of the RIS size on the system capacity. Our analyses reveal how to deploy the RIS in a DP MIMO scenario.
Subset Random Sampling of Finite Time-vertex Graph Signals
Authors: Hang Sheng, Qinji Shu, Hui Feng, Bo Hu
Date: 2024-10-30 00:00:00

Time-varying data with irregular structures can be described by finite time-vertex graph signals (FTVGS), which represent potential temporal and spatial relationships among multiple sources. While sampling and corresponding reconstruction of FTVGS with known spectral support are well investigated, methods for the case of unknown spectral support remain underdeveloped. Existing random sampling schemes may acquire samples from any vertex at any time, which is uncommon in practical applications where sampling typically involves only a subset of vertices and time instants. In sight of this requirement, this paper proposes a subset random sampling scheme for FTVGS. We first randomly select some rows and columns of the FTVGS to form a submatrix, and then randomly sample within the submatrix. Theoretically, we prove sufficient conditions to ensure that the original FTVGS is reconstructed with high probability. Also, we validate the feasibility of reconstructing the original FTVGS by experiments.
Channel-Coded Precoding for Multi-User MISO Systems
Authors: Ly V. Nguyen, Junil Choi, Bjorn Ottersten, A. Lee Swindlehurst
Date: 2024-10-30 00:00:00

Precoding is a critical and long-standing technique in multi-user communication systems. However, the majority of existing precoding methods do not consider channel coding in their designs. In this paper, we consider the precoding problem in multi-user multiple-input single-output (MISO) systems, incorporating channel coding into the design. By leveraging the error-correcting capability of channel codes we increase the degrees of freedom in the transmit signal design, thereby enhancing the overall system performance. We first propose a novel data-dependent precoding framework for coded MISO systems, referred to as channel-coded precoding (CCP), which maximizes the probability that information bits can be correctly recovered by the channel decoder. This proposed CCP framework allows the transmit signals to produce data symbol errors at the users' receivers, as long as the overall information BER performance can be improved. We develop the CCP framework for both one-bit and multi-bit error-correcting capacity and devise a projected gradient-based approach to solve the design problem. We also develop a robust CCP framework for the case where knowledge of perfect channel state information (CSI) is unavailable at the transmitter, taking into account the effect of both noise and channel estimation errors. Finally, we conduct numerous simulations to verify the effectiveness of the proposed CCP and its superiority compared to existing precoding methods, and we identify situations where the proposed CCP yields the most significant gains.
Multi-Target Integrated Sensing and Communications in Massive MIMO Systems
Authors: Ozan Alp Topal, Özlem Tuğfe Demir, Emil Björnson, Cicek Cavdar
Date: 2024-10-29 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) allows networks to perform sensing alongside data transmission. While most ISAC studies focus on single-target, multi-user scenarios, multi-target sensing is scarcely researched. This letter examines the monostatic sensing performance of a multi-target massive MIMO system, aiming to minimize the sum of Cram\'er-Rao lower bounds (CRLBs) for target direction-of-arrival estimates while meeting user equipment (UE) rate requirements. We propose several precoding schemes, comparing sensing performance and complexity, and find that sensing-focused precoding with power allocation for communication achieves near-optimal performance with 20 times less complexity than joint precoding. Additionally, time-sharing between communication and sensing outperforms simple time division, highlighting the benefits of resource-sharing for ISAC.
AI-assisted Agile Propagation Modeling for Real-time Digital Twin Wireless Networks
Authors: Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Ian C. Wong, Pedram Johari, Eric W. Anderson, Stefano Basagni, Tommaso Melodia
Date: 2024-10-29 00:00:00
Tasks: Computational Efficiency, CPU, GPU

Accurate channel modeling in real-time faces remarkable challenge due to the complexities of traditional methods such as ray tracing and field measurements. AI-based techniques have emerged to address these limitations, offering rapid, precise predictions of channel properties through ground truth data. This paper introduces an innovative approach to real-time, high-fidelity propagation modeling through advanced deep learning. Our model integrates 3D geographical data and rough propagation estimates to generate precise path gain predictions. By positioning the transmitter centrally, we simplify the model and enhance its computational efficiency, making it amenable to larger scenarios. Our approach achieves a normalized Root Mean Squared Error of less than 0.035 dB over a 37,210 square meter area, processing in just 46 ms on a GPU and 183 ms on a CPU. This performance significantly surpasses traditional high-fidelity ray tracing methods, which require approximately three orders of magnitude more time. Additionally, the model's adaptability to real-world data highlights its potential to revolutionize wireless network design and optimization, through enabling real-time creation of adaptive digital twins of real-world wireless scenarios in dynamic environments.
MU-MIMO Symbol-Level Precoding for QAM Constellations with Maximum Likelihood Receivers
Authors: X. Tong, A. Li, L. Lei, X. Hu, F. Dong, S. Chatzinotas, C. Masouros
Date: 2024-10-29 00:00:00

In this paper, we investigate symbol-level precoding (SLP) and efficient decoding techniques for downlink transmission, where we focus on scenarios where the base station (BS) transmits multiple QAM constellation streams to users equipped with multiple receive antennas. We begin by formulating a joint symbol-level transmit precoding and receive combining optimization problem. This coupled problem is addressed by employing the alternating optimization (AO) method, and closed-form solutions are derived by analyzing the obtained two subproblems. Furthermore, to address the dependence of the receive combining matrix on the transmit signals, we switch to maximum likelihood detection (MLD) method for decoding. Notably, we have demonstrated that the smallest singular value of the precoding matrix significantly impacts the performance of MLD method. Specifically, a lower value of the smallest singular value results in degraded detection performance. Additionally, we show that the traditional SLP matrix is rank-one, making it infeasible to directly apply MLD at the receiver end. To circumvent this limitation, we propose a novel symbol-level smallest singular value maximization problem, termed SSVMP, to enable SLP in systems where users employ the MLD decoding approach. Moreover, to reduce the number of variables to be optimized, we further derive a more generic semidefinite programming (SDP)-based optimization problem. Numerical results validate the effectiveness of our proposed schemes and demonstrate that they significantly outperform the traditional block diagonalization (BD)-based method.
LiquiRIS: A Major Step Towards Fast Beam Switching in Liquid Crystal-based RISs
Authors: Luis F. Abanto-Leon, Robin Neuder, Waqar Ahmed, Alejandro Jimenez Saez, Vahid Jamali, Arash Asadi
Date: 2024-10-28 00:00:00

Reconfigurable intelligent surfaces (RISs) offer enhanced control over propagation through phase and amplitude manipulation but face practical challenges like cost and power usage, especially at high frequencies. This is specifically a major problem at high frequencies (Ka- and V-band) where the high cost of semiconductor components (i.e., diodes, varactors, MEMSs) can make RISs prohibitively costly. In recent years, it is shown that liquid crystals (LCs) are low-cost and low-energy alternative which can address the aforementioned challenges but at the cost of lower response time. In LiquiRIS, we enable leveraging LC-based RIS in mobile networks. Specifically, we devise techniques that minimize the beam switching time of LC-based RIS by tapping into the physical properties of LCs and the underlying mathematical principles of beamforming. We achieve this by modeling and optimizing the beamforming vector to account for the rotation characteristics of LC molecules to reduce their transition time from one state to another. In addition to prototyping the proposed system, we show via extensive experimental analysis that LiquiRIS substantially reduces the response time (up to 70.80%) of liquid crystal surface (LCS).