pwc-archive/papers-with-abstracts huggingface dataset:
Einstein from Noise: Statistical Analysis
Authors: Amnon Balanov, Wasim Huleihel, Tamir Bendory
Date: 2024-07-07 00:00:00
Tasks: Template Matching

``Einstein from noise" (EfN) is a prominent example of the model bias phenomenon: systematic errors in the statistical model that lead to spurious but consistent estimates. In the EfN experiment, one falsely believes that a set of observations contains noisy, shifted copies of a template signal (e.g., an Einstein image), whereas in reality, it contains only pure noise observations. To estimate the signal, the observations are first aligned with the template using cross-correlation, and then averaged. Although the observations contain nothing but noise, it was recognized early on that this process produces a signal that resembles the template signal! This pitfall was at the heart of a central scientific controversy about validation techniques in structural biology. This paper provides a comprehensive statistical analysis of the EfN phenomenon above. We show that the Fourier phases of the EfN estimator (namely, the average of the aligned noise observations) converge to the Fourier phases of the template signal, explaining the observed structural similarity. Additionally, we prove that the convergence rate is inversely proportional to the number of noise observations and, in the high-dimensional regime, to the Fourier magnitudes of the template signal. Moreover, in the high-dimensional regime, the Fourier magnitudes converge to a scaled version of the template signal's Fourier magnitudes. This work not only deepens the theoretical understanding of the EfN phenomenon but also highlights potential pitfalls in template matching techniques and emphasizes the need for careful interpretation of noisy observations across disciplines in engineering, statistics, physics, and biology.
Phase-Only Beam Shaping for Transmitting Array Antennas in Radar Applications
Authors: Lior Maman, Shlomo Zach, Amir Boag
Date: 2024-07-06 00:00:00
Tasks: valid

Two beam broadening methods for active electronically scanned array (AESA) antennas with uniform amplitude excitation are proposed and compared: phase tapering optimization (PTO) and a novel time-varying phase tapering (TPT). The PTO is a simple and efficient approach assuming continuous polynomial phase distribution and requiring optimization of only few parameters. The TPT is valid mainly for radar applications, taking advantage of the fact that radars typically transmit pulse trains for coherent integration. By varying the array elements' phases from pulse to pulse, the TPT achieves effective amplitude tapering, thus providing a method of beam shaping, occasionally with a simple analytic form. The TPT also makes it possible to produce beam shaping with very low side lobe levels in comparison to the PTO. As a preliminary step, the dimensionality of the radiation pattern characterization for all scan directions is reduced from five to only two variables. This is crucial for efficient optimization of the radiation pattern which needs to be evaluated over a judiciously specified two-dimensional domain.
A Marginal Distributionally Robust Kalman Filter for Centralized Fusion
Authors: Weizhi Chen, Yaowen Li, Yu Liu, You He
Date: 2024-07-06 00:00:00
Tasks: State Estimation

State estimation is a fundamental problem for multi-sensor information fusion, essential in applications such as target tracking, power systems, and control automation. Previous research mostly ignores the correlation between sensors and assumes independent or known distributions. However, in practice, these distributions are often correlated and difAcult to estimate. This paper proposes a novel moment constrained marginal distributionally robust Kalman Alter (MC-MDRKF) for centralized state estimation in multi-sensor systems. First, we introduce a marginal distributional uncertainty set using a moment-constrained approach, which can better capture the uncertainties of Gaussian noises compared to Kullback-Leibler (KL) divergence-based methods. Based on that, a minimax optimization problem is formulated to identify the least favorable joint distribution and the optimal MMSE estimator thereunder. It is proved that this problem can be reformulated as a convex optimization problem, allowing for efficient solution Anding. Subsequently, by accounting for marginal distributional uncertainty within the state space model, the proposed MC-MDRKF is devised in a minimax approach. Simulation result demonstrates the robustness and superiority of the proposed method in a multi-sensor target tracking scenario.
Extremely Large-Scale Dynamic Metasurface Antennas (XL-DMAs): Near-Field Modeling and Channel Estimation
Authors: Songjie Yang, Wanting Lyu, Boyu Ning, Yue Xiu, Youzhi Xiong, Hua Chen, Chadi Assi, Chau Yuen
Date: 2024-07-06 00:00:00
Tasks: Compressive Sensing, parameter estimation

Dynamic metasurface antennas (DMAs) represent a novel transceiver array architecture for extremely large-scale (XL) communications, offering the advantages of reduced power consumption and lower hardware costs compared to conventional arrays. This paper focuses on near-field channel estimation for XL-DMAs. We begin by analyzing the near-field characteristics of uniform planar arrays (UPAs) and introducing the Oblong Approx. model. This model decouples elevation-azimuth (EL-AZ) parameters for XL-DMAs, providing an effective means to characterize the near-field effect. It offers simpler mathematical expressions than the second-order Taylor expansion model, all while maintaining negligible model errors for oblong-shaped arrays. Building on the Oblong Approx. model, we propose an EL-AZ-decoupled estimation framework that involves near- and far-field parameter estimation for AZ/EL and EL/AZ directions, respectively. The former is formulated as a distributed compressive sensing problem, addressed using the proposed off-grid distributed orthogonal least squares algorithm, while the latter involves a straightforward parallelizable search. Crucially, we illustrate the viability of decoupled EL-AZ estimation for near-field UPAs, exhibiting commendable performance and linear complexity correlated with the number of metasurface elements. Moreover, we design an measurement matrix optimization method with the Lorentzian constraint on DMAs and highlight the estimation performance degradation resulting from this constraint.
Defensive Reconfigurable Intelligent Surface (D-RIS) Based on Non-Reciprocal Channel Links
Authors: Kun Chen-Hu, Petar Popovski
Date: 2024-07-06 00:00:00

A reconfigurable intelligent surface (RIS) is commonly made of low-cost passive and reflective meta-materials with excellent beam steering capabilities. It is applied to enhance wireless communication systems as a customizable signal reflector. However, RIS can also be adversely employed to disrupt the existing communication systems by introducing new types of vulnerability to the physical layer. We consider the \emph{RIS-In-The-Middle (RITM) attack}, in which an adversary uses RIS to jeopardize the direct channel between two transceivers by providing an alternative one with higher signal quality. This adversary can eavesdrop on all exchanged data by the legitimate users, but also perform a false data injection to the receiver. This work devises anti-attack techniques based on a non-reciprocal channel produced by a defensive RIS (D-RIS). The proposed precoding and combining methods and the channel estimation procedure for a non-reciprocal link are effective against potential adversaries while keeping the existing advantages of the RIS. We analyse the robustness of the system against attacks in terms of achievable secrecy rate and probability of detecting fake data. We believe that this defensive role of RIS can be a basis for new protocols and algorithms in the area.
Critical Near-Field Impedance Matrices
Authors: Peter Krämer, Christian Schindelhauer
Date: 2024-07-05 00:00:00

We investigate the theoretical impedance equations for several near-field antenna positions. In the standard model one computes the currents at the antennas for given voltages using the impedance matrix of the antennas, which is only possible if the determinant of the impedance matrix is non-zero. We consider Hertzian group antennas, its relative corresponding impedance and two approximations (mid and far) of it. For the approximations we show that for many situations the determinant is zero. We find three antenna configurations for three antennas, i.e., on a line, on a right triangle, and an isosceles triangle, which result in a zero determinant of the impedance for the far-field approximation. This means that with existing methods, one cannot determine the behavior of this antenna system. For the better mid approximation, we find a configuration of 15 triangular-positioned antennas resulting in a singular impedance matrix. Furthermore, we investigate $n\times n$ grid placed antennas in the more accurate Hertzian impedance model and find that for $d \approx 0.65$ wavelengths of grid distance for n = 2, ..., 8 the absolute value of the determinant of the corresponding impedance matrix decreases by an order of magnitude with each increased grid size.
Hybrid Receiver Design for Massive MIMO-OFDM with Low-Resolution ADCs and Oversampling
Authors: Mengyuan Ma, Nhan Thanh Nguyen, Italo Atzeni, Markku Juntti
Date: 2024-07-05 00:00:00
Tasks: Quantization

Low-resolution analog-to-digital converters (ADCs) and hybrid beamforming have emerged as efficient solutions to reduce power consumption with satisfactory spectral efficiency (SE) in massive multiple-input multiple-output (MIMO) systems. In this paper, we investigate the performance of a hybrid receiver in massive MIMO orthogonal frequency-division multiplexing (OFDM) uplink systems with low-resolution ADCs and oversampling. Considering both the temporal and spatial correlation of the quantization distortion (QD), we derive a closed-form approximation of the frequency-domain QD covariance matrix, which facilitates the evaluation of the system's SE. Then we jointly design the analog and digital combiners of the hybrid receiver to maximize the SE. The formulated problem is challenging due to the constant-modulus constraint of the analog combiner and its coupling with the digital one. To overcome these challenges, we transform the objective function into an equivalent but more tractable form and then iteratively update the analog and digital combiners. Numerical simulations verify the superiority of the proposed algorithm over the considered benchmarks and show the resilience of the hybrid receiver to beam squint with low-resolution ADCs. Furthermore, the proposed hybrid receiver design with oversampling can achieve significantly higher energy efficiency compared with the fully digital one.
Detection and Multi-Parameter Estimation for NLOS Targets: An IRS-assisted Framework
Authors: Zhouyuan Yu, Xiaoling Hu, Chenxi Liu, Qin Tao, Mugen Peng
Date: 2024-07-04 00:00:00
Tasks: parameter estimation

Intelligent reflecting surface (IRS) has the potential to enhance sensing performance, due to its capability of reshaping the echo signals. Different from the existing literature, which has commonly focused on IRS beamforming optimization, in this paper, we pay special attention to designing effective signal processing approaches to extract sensing information from IRS-reshaped echo signals. To this end, we investigate an IRS-assisted non-line-of-sight (NLOS) target detection and multi-parameter estimation problem in orthogonal frequency division multiplexing (OFDM) systems. To address this problem, we first propose a novel detection and direction estimation framework, including a low-overhead hierarchical codebook that allows the IRS to generate three-dimensional beams with adjustable beam direction and width, a delay spectrum peak-based beam training scheme for detection and direction estimation, and a beam refinement scheme for further enhancing the accuracy of the direction estimation. Then, we propose a target range and velocity estimation scheme by extracting the delay-Doppler information from the IRS-reshaped echo signals. Numerical results demonstrate that the proposed schemes can achieve 99.7% target detection rate, a 10^{-3}-rad level direction estimation accuracy, and a 10^{-6}-m/10^{-5}-m/s level range/velocity estimation accuracy.
Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs
Authors: Mengyuan Ma, Nhan Thanh Nguyen, Italo Atzeni, Markku Juntti
Date: 2024-07-04 00:00:00
Tasks: Quantization

Low-resolution analog-to-digital converters (ADCs) have emerged as a promising technology for reducing power consumption and complexity in massive multiple-input multiple-output (MIMO) systems while maintaining satisfactory spectral and energy efficiencies (SE/EE). In this work, we first identify the essential properties of optimal quantization and leverage them to derive a closed-form approximation of the covariance matrix of the quantization distortion. The theoretical finding facilitates the system SE analysis in the presence of low-resolution ADCs. We then focus on the joint optimization of the transmit-receive beamforming and bit allocation to maximize the SE under constraints on the transmit power and the total number of active ADC bits. To solve the resulting mixed-integer problem, we first develop an efficient beamforming design for fixed ADC resolutions. Then, we propose a low-complexity heuristic algorithm to iteratively optimize the ADC resolutions and beamforming matrices. Numerical results for a $64 \times 64$ MIMO system demonstrate that the proposed design offers $6\%$ improvement in both SE and EE with $40\%$ fewer active ADC bits compared with the uniform bit allocation. Furthermore, we numerically show that receiving more data streams with low-resolution ADCs can achieve higher SE and EE compared to receiving fewer data streams with high-resolution ADCs.
Enhanced Support Vector Machine Based Signal Recovery in Bandwidth-Limited 50-100 Gbit/s Flexible DS-PON
Authors: Liyan Wu, Yanlu Huang, Kai Jin, Shangya Han, Kun Xu, Yanni Ou
Date: 2024-07-04 00:00:00

We proposed an adaptive signal recovery algorithm with reduced complexity based on the SVM principle for flexible downstream PON. Experimental results indicate a record-high link power budget of 24 dB for bandwidth-limited 100 Gbit/s direct-detection transmission@1E-3.
Moving Target Detection Method Based on Range? Doppler Domain Compensation and Cancellation for UAV-Mounted Radar
Authors: Xiaodong Qu, Xiaolong Sun, Feiyang Liu, Hao Zhang, Shichao Zhong, Xiaopeng Yang
Date: 2024-07-04 00:00:00

Combining unmanned aerial vehicle (UAV) with through-the-wall radar can realize moving targets detection in complex building scenes. However, clutters generated by obstacles and static objects are always stronger and non-stationary, which results in heavy impacts on moving targets detection. To address this issue, this paper proposes a moving target detection method based on Range-Doppler domain compensation and cancellation for UAV mounted dual channel radar. In the proposed method, phase compensation is performed on the dual channel in range-Doppler domain and then cancellation is utilized to achieve roughly clutters suppression. Next, a filter is constructed based on the cancellation result and the raw echoes, which is used to suppress stationary clutter furthermore. Finally, mismatch imaging is used to focus moving target for detection. Both simulation and UAV-based experiment results are analyzed to verify the efficacy and practicability of the proposed method.
Enhanced Over-the-Air Federated Learning Using AI-based Fluid Antenna System
Authors: Mohsen Ahmadzadeh, Saeid Pakravan, Ghosheh Abed Hodtani, Ming Zeng, Jean-Yves Chouinard, Leslie A. Rusch
Date: 2024-07-03 00:00:00
Tasks: Federated Learning, Position

This paper investigates an over-the-air federated learning (OTA-FL) system that employs fluid antennas (FAs) at an access point. The system enhances learning performance by leveraging the additional degrees of freedom provided by antenna mobility. We analyze the convergence of the OTA-FL system and derive the optimality gap to illustrate the influence of FAs on learning performance. With these results, we formulate a nonconvex optimization problem to minimize the optimality gap by jointly optimizing the positions of the FAs, the beamforming vector, and the transmit power allocation at each user. To address the dynamic environment, we cast this optimization problem as a Markov decision process and propose the recurrent deterministic policy gradient (RDPG) algorithm. Finally, extensive simulations show that the FA-assisted OTA-FL system outperforms systems with fixed-position antennas and that the RDPG algorithm surpasses the existing methods.
A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs
Authors: Wee Kiat New, Kai-Kit Wong, Hao Xu, Chao Wang, Farshad Rostami Ghadi, Jichen Zhang, Junhui Rao, Ross Murch, Pablo Ramírez-Espinosa, David Morales-Jimenez, Chan-Byoung Chae, Kin-Fai Tong
Date: 2024-07-03 00:00:00
Tasks: Position

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capabilities beyond communication, including intelligence and sensing. To achieve these ambitious goals, it is apparent that 6G networks need to incorporate the state-of-the-art technologies. One of the technologies that has garnered rising interest is fluid antenna system (FAS) which represents any software-controllable fluidic, conductive, or dielectric structure capable of dynamically changing its shape and position to reconfigure essential radio-frequency (RF) characteristics. Compared to traditional antenna systems (TASs) with fixed-position radiating elements, the core idea of FAS revolves around the unique flexibility of reconfiguring the radiating elements within a given space. One recent driver of FAS is the recognition of its position-flexibility as a new degree of freedom (dof) to harness diversity and multiplexing gains. In this paper, we provide a comprehensive tutorial, covering channel modeling, signal processing and estimation methods, information-theoretic insights, new multiple access techniques, and hardware designs. Moreover, we delineate the challenges of FAS and explore the potential of using FAS to improve the performance of other contemporary technologies. By providing insights and guidance, this tutorial paper serves to inspire researchers to explore new horizons and fully unleash the potential of FAS.
Classification of Power Quality Disturbances Using Resnet with Channel Attention Mechanism
Authors: Su Pan, Xingyang Nie, Xiaoyu Zhai, Biao Wang, Huilin Ge, Cheng He, Zhenping Ding
Date: 2024-07-02 00:00:00
Tasks: Computational Efficiency, Diagnostic

The detection and classification of power quality disturbances (PQDs) carries significant importance for power systems. In response to this imperative, numerous intelligent diagnostic methods have been developed. However, existing identification methods usually concentrate on single-type signals or on complex signals with two types, rendering them susceptible to noisy labels and environmental effects. This study proposes a novel method for the classification of PQDs, termed ST-GSResNet, which utilizes the S-Transform and an improved residual neural network (ResNet) with a channel attention mechanism. The ST-GSResNet approach initially uses the S-Transform to transform a time-series signal into a 2D time-frequency image for feature enhancement. Then, an improved ResNet model is introduced, which employs grouped convolution instead of the traditional convolution operation. This improvement aims to facilitate learning with a block-diagonal structured sparsity on the channel dimension, the highly-correlated filters are learned in a more structured way in the networks with filter groups. By reducing the number of parameters in the network in this significant manner, the model becomes less prone to overfitting. Furthermore, the SE module concentrates on primary components, which enhances the model's robustness in recognition and immunity to noise. Experimental results demonstrate that, compared to existing deep learning models, our approach has advantages in computational efficiency and classification accuracy.
Hierarchical Decoupling Capacitor Optimization for Power Distribution Network of 2.5D ICs with Co-Analysis of Frequency and Time Domains Based on Deep Reinforcement Learning
Authors: Yuanyuan Duan, Haiyang Feng, Zhiping Yu, Hanming Wu, Leilai Shao, Xiaolei Zhu
Date: 2024-07-02 00:00:00
Tasks: Deep Reinforcement Learning

With the growing need for higher memory bandwidth and computation density, 2.5D design, which involves integrating multiple chiplets onto an interposer, emerges as a promising solution. However, this integration introduces significant challenges due to increasing data rates and a large number of I/Os, necessitating advanced optimization of the power distribution networks (PDNs) both on-chip and on-interposer to mitigate the small signal noise and simultaneous switching noise (SSN). Traditional PDN optimization strategies in 2.5D systems primarily focus on reducing impedance by integrating decoupling capacitors (decaps) to lessen small signal noises. Unfortunately, relying solely on frequency-domain analysis has been proven inadequate for addressing coupled SSN, as indicated by our experimental results. In this work, we introduce a novel two-phase optimization flow using deep reinforcement learning to tackle both the on-chip small signal noise and SSN. Initially, we optimize the impedance in the frequency domain to maintain the small signal noise within acceptable limits while avoiding over-design. Subsequently, in the time domain, we refine the PDN to minimize the voltage violation integral (VVI), a more accurate measure of SSN severity. To the best of our knowledge, this is the first dual-domain optimization strategy that simultaneously addresses both the small signal noise and SSN propagation through strategic decap placement in on-chip and on-interposer PDNs, offering a significant step forward in the design of robust PDNs for 2.5D integrated systems.
SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs
Authors: Yisheng Li, Shuqiang Wang
Date: 2024-07-01 00:00:00
Tasks: EEG, Motor Imagery, Representation Learning

Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating both types of sensors on the same scalp surface. This physical constraint complicates the acquisition of high-quality hybrid signals, thereby limiting the widespread application of hybrid MI-BCIs. To facilitate the acquisition of hybrid EEG-fNIRS signals, this study proposes the spatio-temporal controlled diffusion model (SCDM) as a framework for cross-modal generation from EEG to fNIRS. The model utilizes two core modules, the spatial cross-modal generation (SCG) module and the multi-scale temporal representation (MTR) module, which adaptively learn the respective latent temporal and spatial representations of both signals in a unified representation space. The SCG module further maps EEG representations to fNIRS representations by leveraging their spatial relationships. Experimental results show high similarity between synthetic and real fNIRS signals. The joint classification performance of EEG and synthetic fNIRS signals is comparable to or even better than that of EEG with real fNIRS signals. Furthermore, the synthetic signals exhibit similar spatio-temporal features to real signals while preserving spatial relationships with EEG signals. Experimental results suggest that the SCDM may represent a promising paradigm for the acquisition of hybrid EEG-fNIRS signals in MI-BCI systems.