pwc-archive/papers-with-abstracts huggingface dataset:
Two-Timescale Design for Movable Antennas Enabled-Multiuser MIMO Systems
Authors: Ziyuan Zheng, Qingqing Wu, Wen Chen, Guojie Hu
Date: 2024-10-08 00:00:00
Tasks: Position

Movable antennas (MAs), which can be swiftly repositioned within a defined region, offer a promising solution to the limitations of fixed-position antennas (FPAs) in adapting to spatial variations in wireless channels, thereby improving channel conditions and communication between transceivers. However, frequent MA position adjustments based on instantaneous channel state information (CSI) incur high operational complexity, making real-time CSI acquisition impractical, especially in fast-fading channels. To address these challenges, we propose a two-timescale transmission framework for MA-enabled multiuser multiple-input-multiple-output (MU-MIMO) systems. In the large timescale, statistical CSI is exploited to optimize MA positions for long-term ergodic performance, whereas, in the small timescale, beamforming vectors are designed using instantaneous CSI to handle short-term channel fluctuations. Within this new framework, we analyze the ergodic sum rate and develop efficient MA position optimization algorithms for both maximum-ratio-transmission (MRT) and zero-forcing (ZF) beamforming schemes. These algorithms employ alternating optimization (AO), successive convex approximation (SCA), and majorization-minimization (MM) techniques, iteratively optimizing antenna positions and refining surrogate functions that approximate the ergodic sum rate. Numerical results show significant ergodic sum rate gains with the proposed two-timescale MA design over conventional FPA systems, particularly under moderate to strong line-of-sight (LoS) conditions. Notably, MA with ZF beamforming consistently outperforms MA with MRT, highlighting the synergy between beamforming and MAs for superior interference management in environments with moderate Rician factors and high user density, while MA with MRT can offer a simplified alternative to complex beamforming designs in strong LoS conditions.
Privacy-Enhanced Over-the-Air Federated Learning via Client-Driven Power Balancing
Authors: Bumjun Kim, Hyowoon Seo, Wan Choi
Date: 2024-10-08 00:00:00
Tasks: Federated Learning

This paper introduces a novel privacy-enhanced over-the-air Federated Learning (OTA-FL) framework using client-driven power balancing (CDPB) to address privacy concerns in OTA-FL systems. In recent studies, a server determines the power balancing based on the continuous transmission of channel state information (CSI) from each client. Furthermore, they concentrate on fulfilling privacy requirements in every global iteration, which can heighten the risk of privacy exposure as the learning process extends. To mitigate these risks, we propose two CDPB strategies -- CDPB-n (noisy) and CDPB-i (idle) -- allowing clients to adjust transmission power independently, without sharing CSI. CDPB-n transmits noise during poor conditions, while CDPB-i pauses transmission until conditions improve. To further enhance privacy and learning efficiency, we show a mixed strategy, CDPB-mixed, which combines CDPB-n and CDPB-i. Our experimental results show that CDPB outperforms traditional approaches in terms of model accuracy and privacy guarantees, providing a practical solution for enhancing OTA-FL in resource-constrained environments.
Long-Range Reading of Multiple Chipless Sensors from the Isoline Processing of 3D Radar Images
Authors: A. Hadj Djilani, Dominique Henry, A. El Sayed Ahmad, Patrick Pons, Hervé Aubert
Date: 2024-10-08 00:00:00

In this paper, we report the long-range and wireless interrogation of multiple chipless sensors from the isoline processing of three-dimensional polarimetric radar images. A Frequency-Modulated Continuous-Wave Radar operating at 24 GHz is used for the indoor interrogation of four sensors in the basement of a Laboratory. In such cluttered environment, the proposed radar image processing based on isolines computation allows the wireless measurement range of sensors up to 5.8m.
A Radio Map Approach for Reduced Pilot CSI Tracking in Massive MIMO Networks
Authors: Yuanshuai Zheng, Junting Chen
Date: 2024-10-08 00:00:00

Massive multiple-input multiple-output (MIMO) systems offer significant potential to enhance wireless communication performance, yet accurate and timely channel state information (CSI) acquisition remains a key challenge. Existing works on CSI estimation and radio map applications typically rely on stationary CSI statistics and accurate location labels. However, the CSI process can be discontinuous due to user mobility and environmental variations, and inaccurate location data can degrade the performance. By contrast, this paper studies radio-map-embedded CSI tracking and radio map construction without the assumptions of stationary CSI statistics and precise location labels. Using radio maps as the prior information, this paper develops a radio-map-embedded switching Kalman filter (SKF) framework that jointly tracks the location and the CSI with adaptive beamforming for sparse CSI observations under reduced pilots. For radio map construction without precise location labels, the location sequence and the channel covariance matrices are jointly estimated based on a Hidden Markov Model (HMM). An unbiased estimator on the channel covariance matrix is found. Numerical results on ray-traced MIMO channel datasets demonstrate that using 1 pilot in every 10 milliseconds, an average of over 97% of capacity over that of perfect CSI can be achieved, while a conventional Kalman filter (KF) can only achieve 76%. Furthermore, the proposed radio-map-embedded CSI model can reduce the localization error from 30 meters from the prior to 6 meters for radio map construction.
Closed-Loop phase selection in EEG-TMS using Bayesian Optimization
Authors: Miriam Kirchhoff, Dania Humaidan, Ulf Ziemann
Date: 2024-10-08 00:00:00
Tasks: Bayesian Optimization, EEG, regression, Rhythm

Research on transcranial magnetic stimulation (TMS) combined with encephalography feedback (EEG-TMS) has shown that the phase of the sensorimotor mu rhythm is predictive of corticospinal excitability. Thus, if the subject-specific optimal phase is known, stimulation can be timed to be more efficient. In this paper, we present a closed-loop algorithm to determine the optimal phase linked to the highest excitability with few trials. We used Bayesian optimization as an automated, online search tool in an EEG-TMS simulation experiment. From a sample of 38 participants, we selected all participants with a significant single-subject phase effect (N = 5) for simulation. We then simulated 1000 experimental sessions per participant where we used Bayesian optimization to find the optimal phase. We tested two objective functions: Fitting a sinusoid in Bayesian linear regression or Gaussian Process (GP) regression. We additionally tested adaptive sampling using a knowledge gradient as the acquisition function compared with random sampling. We evaluated the algorithm's performance in a fast optimization (100 trials) and a long-term optimization (1000 trials). For fast optimization, the Bayesian linear regression in combination with adaptive sampling gives the best results with a mean phase location accuracy of 79 % after 100 trials. With either sampling approach, Bayesian linear regression performs better than GP regression in the fast optimization. In the long-term optimization, Bayesian regression with random sampling shows the best trajectory, with a rather steep improvement and good final performance of 87 % mean phase location accuracy. We show the suitability of closed-loop Bayesian optimization for phase selection. We could increase the speed and accuracy by using prior knowledge about the expected function shape compared with traditional Bayesian optimization with GP regression.
Performance Analysis of Local Partial MMSE Precoding Based User-Centric Cell-Free Massive MIMO Systems and Deployment Optimization
Authors: Peng Jiang, Jiafei Fu, Pengcheng Zhu, Yan Wang, Jiangzhou Wang, Xiaohu You
Date: 2024-10-08 00:00:00

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a Barzilai-Borwein based gradient descent method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.
Low Complexity Joint Channel Estimation and Data Detection for AFDM Receivers With Oversampling
Authors: Kuranage Roche Rayan Ranasinghe, Yao Ge, Giuseppe Thadeu Freitas de Abreu, Yong Liang Guan
Date: 2024-10-07 00:00:00

In this paper, we propose a novel low complexity time domain (TD) oversampling receiver framework under affine frequency division multiplexing (AFDM) waveforms for joint channel estimation and data detection (JCEDD). Leveraging a generalized doubly-dispersive channel model, we first derive the input-output (I/O) relationship for arbitrary waveforms when oversampled in the TD and present the I/O relationship for AFDM as an example. Subsequently, utilizing the multiple sample streams created via the oversampling procedure, we use the parametric bilinear Gaussian belief propagation (PBiGaBP) technique to conduct JCEDD for decoding the transmitted data and estimating the complex channel coefficients. Simulation results verify significant performance improvements both in terms of data decoding and complex channel coefficient estimation with improved robustness against a varying number of pilots over a conventional Nyquist sampling rate receiver.
A novel hybrid and publicly available model for spur gear vibrations based on an efficient dynamic model
Authors: Omri Matania, Lior Bachar, Roee Cohen, Jacob Bortman
Date: 2024-10-07 00:00:00
Tasks: model

Dynamic models hold great potential for research and development in signal processing, machine learning, and digital twin algorithms for diagnosing rotating machinery. Various studies have suggested dynamic models of gears, employing many model approaches. However, there is currently a lack of a computationally efficient and publicly accessible model that accurately represents real-world data. In this study, we propose a novel hybrid model that integrates a realistic and efficiently validated dynamic model of spur gear vibrations with an enhancement process aimed at bridging the gap between simulation and reality. This process minimizes discrepancies between features extracted from simulated and measured data through fine-tuning of the model hyperparameters. The effectiveness of this hybrid model is demonstrated across numerous test apparatuses, encompassing several types of faults, severities, and speeds. The new hybrid model, inclusive of an upgraded dynamic model, generates data swiftly within seconds and is made publicly available with a user-friendly application programming interface and a detailed user manual. The novel suggested hybrid model has great potential to enhance future research in model-based studies, including machine learning, signal processing, and digital twin approaches. The user manual can be found in the following link: https://github.com/PHM-BGU/public_dynamic_model_for_gear_vibrations
Degrees of Freedom of Holographic MIMO in Multi-user Near-field Channels
Authors: Houfeng Chen, Shaohua Yue, Marco Di Renzo, Hongliang Zhang
Date: 2024-10-07 00:00:00
Tasks: Blocking

Holographic multiple-input multiple-output (HMIMO) is an emerging technology for 6G communications, in which numerous antenna units are integrated in a limited space. As the HMIMO array aperture expands, the near-field region of the array is dramatically enlarged, resulting in more users being located in the near-field region. This creates new opportunities for wireless communications. In this context, the evaluation of the spatial degrees of freedom (DoF) of HMIMO multi-user systems in near-field channels is an open problem, as the analytical methods utilized for evaluating the DoF in far-field channels cannot be directly applied. In this paper, we propose a novel method to calculate the DoF of HMIMO in multi-user near-field channels. We first derive the DoF for a single user in the near field, and then extend the analysis to multi-user scenarios. In this latter scenario, we focus on the impact of spatial blocking between HMIMO users. The derived analytical framework reveals that the DoF of HMIMO in multi-user near-field channels is not in general given by the sum of the DoF of the HMIMO single-user setting because of spatial blocking. Simulation results demonstrate the effectiveness of the proposed method. In the considered case study, the number of DoF reduces by $21.2\%$ on average due to spatial blocking.
Near-Field ISAC in 6G: Addressing Phase Nonlinearity via Lifted Super-Resolution
Authors: Sajad Daei, Amirreza Zamani, Saikat Chatterjee, Mikael Skoglund, Gabor Fodor
Date: 2024-10-07 00:00:00
Tasks: ISAC, Super-Resolution

Integrated sensing and communications (ISAC) is a promising component of 6G networks, fusing communication and radar technologies to facilitate new services. Additionally, the use of extremely large-scale antenna arrays (ELAA) at the ISAC common receiver not only facilitates terahertz-rate communication links but also significantly enhances the accuracy of target detection in radar applications. In practical scenarios, communication scatterers and radar targets often reside in close proximity to the ISAC receiver. This, combined with the use of ELAA, fundamentally alters the electromagnetic characteristics of wireless and radar channels, shifting from far-field planar-wave propagation to near-field spherical wave propagation. Under the far-field planar-wave model, the phase of the array response vector varies linearly with the antenna index. In contrast, in the near-field spherical wave model, this phase relationship becomes nonlinear. This shift presents a fundamental challenge: the widely-used Fourier analysis can no longer be directly applied for target detection and communication channel estimation at the ISAC common receiver. In this work, we propose a feasible solution to address this fundamental issue. Specifically, we demonstrate that there exists a high-dimensional space in which the phase nonlinearity can be expressed as linear. Leveraging this insight, we develop a lifted super-resolution framework that simultaneously performs communication channel estimation and extracts target parameters with high precision.
Transmit Beampattern Synthesis for Active RIS-Aided MIMO Radar via Waveform and Beamforming Optimization
Authors: Shengyao Chen, Minghui He, Longyao Ran, Hongtao Li, Feng Xi, Sirui Tian, Zhong Liu
Date: 2024-10-07 00:00:00

In conventional colocated multiple-input multiple-output (MIMO) radars, practical waveform constraints including peak-to-average power ratio, constant or bounded modulus lead to a significant performance reduction of transmit beampattern, especially when the element number is limited. This paper adopts an active reconfigurable intelligent surface (ARIS) to assist the transmit array and discusses the corresponding beampattern synthesis. We aim to minimize the integrated sidelobe-to-mainlobe ratio (ISMR) of beampattern by the codesign of waveform and ARIS reflection coefficients. The resultant problem is nonconvex constrained fractional programming whose objective function and plenty of constraints are variable-coupled. We first convert the fractional objective function into an integral form via Dinkelbach transform, and then alternately optimize the waveform and ARIS reflection coefficients. Three types of waveforms are unifiedly optimized by a consensus alternating direction method of multipliers (CADMM)-based algorithm wherein the global optimal solutions of all subproblems are obtained, while the ARIS reflection coefficients are updated by a concave-convex procedure (CCCP)-based algorithm. The convergence is also analyzed based on the properties of CADMM and CCCP. Numerical results show that ARIS-aided MIMO radars have superior performance than conventional ones due to significant reduction of sidelobe energy.
Direction Modulation Design for UAV Assisted by IRS with discrete phase shift
Authors: Maolin Li, Wei Gao, Qi Wu, Feng Shu, Cunhua Pan, Di wu
Date: 2024-10-07 00:00:00
Tasks: Position

As a physical layer security technology, directional modulation (DM) can be combined with intelligent reflect-ing surface (IRS) to improve the security of drone communications. In this paper, a directional modulation scheme assisted by the IRS is proposed to maximize the transmission rate of unmanned aerial vehicle (UAV) secure communication. Specifically, with the assistance of the IRS, the UAV transmits legitimate information and main-tains its constellation pattern at the location of legitimate users on the ground, while the constellation pattern is disrupted at the eavesdropper's location. In order to solve the joint optimization problem of digital weight coefficients, UAV position, and IRS discrete phase shift, firstly, the digital weight vector and UAV position are optimized through power minimization. Secondly, three methods are proposed to optimize IRS phase shift, namely vector trajectory (VT) method, cross entropy vector trajectory (CE-VT) algorithm, and block coordinate descent vector trajectory (BCD-VT) algorithm. Compared to traditional cross entropy (CE) methods and block coordinate descent (BCD) methods, the proposed CE-VT and BCD-VT algorithms can improve transmission rate performance. The numerical results validate the effectiveness of the optimization scheme in IRS assisted UAV communication.
Large-Scale GNSS Spreading Code Optimization
Authors: Alan Yang, Tara Mina, Stephen Boyd, Grace Gao
Date: 2024-10-06 00:00:00

We propose a bit-flip descent method for optimizing binary spreading codes with large family sizes and long lengths, addressing the challenges of large-scale code design in GNSS and emerging PNT applications. The method iteratively flips code bits to improve the codes' auto- and cross-correlation properties. In our proposed method, bits are selected by sampling a small set of candidate bits and choosing the one that offers the best improvement in performance. The method leverages the fact that incremental impact of a bit flip on the auto- and cross-correlation may be efficiently computed without recalculating the entire function. We apply this method to two code design problems modeled after the GPS L1 C/A and Galileo E1 codes, demonstrating rapid convergence to low-correlation codes. The proposed approach offers a powerful tool for developing spreading codes that meet the demanding requirements of modern and future satellite navigation systems.
Data-driven Framework for Forward and Inverse Problems in Guided Waves-Based Structural Health Monitoring Under Varying Environmental and Operating Conditions
Authors: Yiming Fan, Fotis Kopsaftopoulos
Date: 2024-10-01 00:00:00
Tasks: Structural Health Monitoring

Recently, guided waves-based techniques have garnered increased attention from researchers in the field of Structural Health Monitoring (SHM) for damage detection and quantification. Extracting features that are sensitive to changes in structural conditions has become a critical step. Guided waves, being one of the most widely used techniques, are highly responsive to structural changes caused by various types of damage. However, due to disturbances from environmental and operational conditions (EOCs) and waveforms reflected from boundaries and damage in complex systems, manually extracting features related to the system state is challenging. To address this, various neural networks, capable of automatically deriving features, have been developed and applied to system identification tasks. Among these, Convolutional Autoencoders (CAEs) have proven effective as damage detectors, projecting data efficiently into a latent space. However, existing CAE-based models for damage diagnosis often overlook the influence of EOCs, which can significantly impact model performance. In this work, a scheme that provides accurate damage level classification while estimating EOCs, such as loading conditions, making the model adaptable to varying external factors is introduced. By restructuring and combining the CAE with feedforward neural networks (FFNNs), the proposed scheme also enables signal reconstruction, a potentially valuable feature when data is limited. The method has been validated on an aluminum plate with various damage levels under different loading conditions, achieving near-perfect state classification and low-error signal reconstruction. The framework has also been tested on incomplete datasets to verify its robustness.
Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
Authors: José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, Henk Wymeersch
Date: 2024-10-05 00:00:00
Tasks: Integrated sensing and communication, ISAC, Position

Gain-phase impairments (GPIs) affect both communication and sensing in 6G integrated sensing and communication (ISAC). We study the effect of GPIs in a single-input, multiple-output orthogonal frequency-division multiplexing ISAC system and develop a model-based unsupervised learning approach to simultaneously (i) estimate the gain-phase errors and (ii) localize sensing targets. The proposed method is based on the optimal maximum a-posteriori ratio test for a single target. Results show that the proposed approach can effectively estimate the gain-phase errors and yield similar position estimation performance as the case when the impairments are fully known.