pwc-archive/papers-with-abstracts huggingface dataset:
When atomic norm meets the G-filter: A general framework for line spectral estimation
Authors: Bin Zhu, Jiale Tang
Date: 2024-10-16 00:00:00

This paper proposes a novel approach for line spectral estimation which combines Georgiou's filter bank (G-filter) with atomic norm minimization (ANM). A key ingredient is a Carath\'{e}odory--Fej\'{e}r-type decomposition for the covariance matrix of the filter output. The resulting optimization problem can be characterized via semidefinite programming and contains the standard ANM for line spectral estimation as a special case. Simulations show that our approach outperforms the standard ANM in terms of recovering the number of spectral lines when the signal-to-noise ratio is no lower than 0 dB and the G-filter is suitably designed.
Magnetic Distortion Resistant Orientation Estimation
Authors: Sikai Yang, Miaomiao Liu, Wan Du
Date: 2024-10-16 00:00:00

Inertial Measurement Unit (IMU) sensors, including accelerometers, gyroscopes, and magnetometers, are used to estimate the orientation of mobile devices. However, indoor magnetic fields are often distorted, causing the magnetometer's readings to deviate from true north and resulting in inaccurate orientation estimates. Existing solutions either ignore magnetic distortion or avoid using the magnetometer when distortion is detected. In this paper, we develop MDR, a Magnetic Distortion Resistant orientation estimation system that fundamentally models and corrects magnetic distortion. MDR builds a database to record magnetic directions at different locations and uses it to correct orientation estimates affected by magnetic distortion. To avoid the overhead of database preparation, MDR adopts practical designs to automatically update the database in parallel with orientation estimation. Experiments on 27+ hours of arm motion data show that MDR outperforms the state-of-the-art method by 35.34%.
Exploring Dual-Sniffer Passive Localization: Algorithm Design and Experimental Results
Authors: Tuo Wu, Lingyu Hou, Hong Niu, Saihua Xu, Sirajudeen Gulam Razul, Chau Yuen
Date: 2024-10-16 00:00:00
Tasks: Position

In this paper, we explore a dual-sniffer passive localization system that detects the timing difference of signals from both commercial base station (eNb) and user equipment (UE) to the sniffers. We design two localization schemes for UE localization: a time of arrival (ToA) based scheme and a time difference of arrival (TDoA) based scheme. In the ToA-based scheme, we derive two ellipse equations from measured arrival times at two sniffers, enabling direct numerical computation of the estimated position. For the TDoA-based scheme, we relocate one sniffer to a different position to obtain two sets of TDoA measurements, resulting in hyperbola equations. We then apply a least squares (LS) algorithm to analytically estimate the UE's position. Simulation results validate the effectiveness of the proposed TDoA-based scheme, demonstrating improved accuracy in UE positioning.We build a platform based on the considered localization system and conduct real-world experiments. The experimental results confirm the accuracy and practicality of the TDoA-based dual-sniffer localization scheme, demonstrating improved precision in passive localization.
Reduced Overhead Channel Estimation for OTFS With Split Pilot
Authors: Danilo Lelin Li, Sanoopkumar P. S., Arman Farhang
Date: 2024-10-15 00:00:00

Orthogonal time frequency space modulation (OTFS) is currently one of the most robust modulation techniques for high Doppler channels. However, to reap the benefits of OTFS, an accurate channel estimation is crucial. To this mean, the widely used embedded pilot structures use twice the channel length size as a delay guard to avoid interference between the pilot and data symbols. Hence, incurring a large spectral efficiency loss, especially in wideband systems where the channel length is large. To reduce the pilot overhead, we propose a novel split pilot structure with two impulse pilots. With two pilots, we can use one to cancel the other, thus, capable of removing the pilot interference over data. To remove the data interference from the pilot, we also propose an iterative joint channel estimation and detection technique tailored to the proposed split pilot structure. With the interference caused by the delay spread solved, we reduce the number of delay guards in our system by half, significantly improving the spectral efficiency. To corroborate our claims, we numerically demonstrate that our proposed method can achieve performance levels comparable to that of the full-guard method while using only half the delay guard. Additionally, we show that our proposed iterative channel estimating technique has a fast convergence speed, requiring only two iterations.
Bistatic Information Fusion for Positioning and Tracking in Integrated Sensing and Communication
Authors: Maximilian Bauhofer, Marcus Henninger, Thorsten Wild, Stephan ten Brink, Silvio Mandelli
Date: 2024-10-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

The distributed nature of cellular networks is one of the main enablers for integrated sensing and communication (ISAC). For target positioning and tracking, making use of bistatic measurements is non-trivial due to their non-linear relationship with Cartesian coordinates. Most of the literature proposes geometric-based methods to determine the target's location by solving a well-defined set of equations stemming from the available measurements. The error covariance to be used for Bayesian tracking is then derived from local Taylor expansions. In our work we adaptively fuse any subset of bistatic measurements using a maximum likelihood (ML) framework, allowing to incorporate every possible combination of available measurements, i.e., transmitter angle, receiver angle and bistatic range. Moreover, our ML approach is intrinsically flexible, as it can be extended to fuse an arbitrary number of measurements by multistatic setups. Finally, we propose both a fixed and dynamic way to compute the covariance matrix for the position error to be fed to Bayesian tracking techniques, like a Kalman filter. Numerical evaluations with realistic cellular communications parameters at mmWave frequencies show that our proposal outperforms the considered baselines, achieving a location and velocity root mean square error of 0.25 m and 0.83 m/s, respectively.
Spiking Neural Belief Propagation Decoder for Short Block Length LDPC Codes
Authors: Alexander von Bank, Eike-Manuel Edelmann, Sisi Miao, Jonathan Mandelbaum, Laurent Schmalen
Date: 2024-10-15 00:00:00
Tasks: Decoder

Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This paper proposes a novel decoding algorithm for low-density parity-check (LDPC) codes that integrates SNNs into belief propagation (BP) decoding by approximating the check node update equations using SNNs. For the (273,191) and (1023,781) finite-geometry LDPC code, the proposed decoder outperforms sum-product decoder at high signal-to-noise ratios (SNRs). The decoder achieves a similar bit error rate to normalized sum-product decoding with successive relaxation. Furthermore, the novel decoding operates without requiring knowledge of the SNR, making it robust to SNR mismatch.
EEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface
Authors: Pradyot Anand, Anant Jain, Suriya Prakash Muthukrishnan, Shubhendu Bhasin, Sitikantha Roy, Lalan Kumar
Date: 2024-10-15 00:00:00
Tasks: Brain Computer Interface, Dimensionality Reduction, EEG, feature selection

Electroencephalography (EEG)--based turn intention prediction for lower limb movement is important to build an efficient brain-computer interface (BCI) system. This study investigates the feasibility of intention detection of left-turn, right-turn, and straight walk by utilizing EEG signals obtained before the event occurrence. Synchronous data was collected using 31-channel EEG and IMU-based motion capture systems for nine healthy participants while performing left-turn, right-turn, and straight walk movements. EEG data was preprocessed with steps including Artifact Subspace Reconstruction (ASR), re-referencing, and Independent Component Analysis (ICA) to remove data noise. Feature extraction from the preprocessed EEG data involved computing various statistical measures (mean, median, standard deviation, skew, and kurtosis), and Hjorth parameters (activity, mobility, and complexity). Further, the feature selection was performed using the Random forest algorithm for the dimensionality reduction. The feature set obtained was utilized for 3-class classification using XG boost, gradient boosting, and support vector machine (SVM) with RBF kernel classifiers in a five-fold cross-validation scheme. Using the proposed intention detection methodology, the SVM classifier using an EEG window of 1.5 s and 0 s time-lag has the best decoding performance with mean accuracy, precision, and recall of 81.23%, 85.35%, and 83.92%, respectively, across the nine participants. The decoding analysis shows the feasibility of turn intention prediction for lower limb movement using the EEG signal before the event onset.
Adaptive Power Allocation in Spaceborne Assisted NOMA Systems for Integrated Terrestrial Communications
Authors: M Khalil, Ke Wang, Jinho Choi
Date: 2024-10-15 00:00:00
Tasks: Management

This study introduces an innovative approach for adaptive power allocation in Non-Orthogonal Multiple Access (NOMA) systems, enhanced by the integration of spaceborne and terrestrial signals through a Reconfigurable Intelligent Surface (RIS). We develop an adaptive mechanism to adjust the power distribution between spaceborne and terrestrial signals according to variations in environmental conditions and elevation angles. This mechanism employs a sophisticated transition model that combines Gaussian Mixture Models with Log-Normal distributions to adaptively counteract the detrimental impacts of atmospheric attenuation and urban shadowing. These adaptive power adjustments significantly enhance system capacity, particularly improving the Signal-to-Interference-plus-Noise Ratio under diverse operational scenarios. Simulation studies confirm the efficacy of our method within an RIS-enhanced framework, showing an approximate 20\% increase in system capacity through optimized power management between spaceborne and terrestrial signals.
6G RIS-aided Single-LEO Localization with Slow and Fast Doppler Effects
Authors: Sharief Saleh, Musa Furkan Keskin, Basuki Priyanto, Martin Beale, Pinjun Zheng, Tareq Y. Al-Naffouri, Gonzalo Seco-Granados, Henk Wymeersch
Date: 2024-10-14 00:00:00
Tasks: Autonomous Driving

6G networks aim to enable applications like autonomous driving by providing complementary localization services through key technologies such as non-terrestrial networks (NTNs) with low Earth orbit (LEO) satellites and reconfigurable intelligent surfaces (RIS). Prior research in 6G localization using single LEO, multi-LEO, and multi-LEO multi-RIS setups has limitations: single LEO lacks the required accuracy, while multi-LEO/RIS setups demand many visible satellites and RISs, which is not always feasible in practice. This paper explores the novel problem of localization with a single LEO satellite and a single RIS, bridging these research areas. We present a comprehensive signal model accounting for user carrier frequency offset (CFO), clock bias, and fast and slow Doppler effects. Additionally, we derive a low-complexity estimator that achieves theoretical bounds at high signal-to-noise ratios (SNR). Our results demonstrate the feasibility and accuracy of RIS-aided single-LEO localization in 6G networks and highlight potential research directions.
WAND: Wavelet Analysis-based Neural Decomposition of MRS Signals for Artifact Removal
Authors: Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Kyung Min Nam, Ruud J. G. van Sloun, Alex A. Bhogal
Date: 2024-10-14 00:00:00

Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the quantification of low-concentration metabolites, limiting MRS reliability. This paper introduces wavelet analysis-based neural decomposition (WAND), a novel data-driven method designed to decompose MRS signals into their constituent components: metabolite-specific signals, baseline, and artifacts. WAND takes advantage of the enhanced separability of these components within the wavelet domain. The method employs a neural network, specifically a U-Net architecture, trained to predict masks for wavelet coefficients obtained through the continuous wavelet transform. These masks effectively isolate desired signal components in the wavelet domain, which are then inverse-transformed to obtain separated signals. Notably, an artifact mask is created by inverting the sum of all known signal masks, enabling WAND to capture and remove even unpredictable artifacts. The effectiveness of WAND in achieving accurate decomposition is demonstrated through numerical evaluations using simulated spectra. Furthermore, WAND's artifact removal capabilities significantly enhance the quantification accuracy of linear combination model fitting. The method's robustness is further validated using data from the 2016 MRS Fitting Challenge and in-vivo experiments.
Performance Analysis of OTFS-NOMA System with Fractional Doppler
Authors: Wafa Hedhly, Leila Musavian, Nikolaos Thomos
Date: 2024-10-14 00:00:00

In this work, we investigate the effect of fractional Doppler on the performance of a system using orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access (NOMA) where users have different mobility profiles. Fractional Doppler results in inter-Doppler interference (IDI) and degrades the quality of OTFS-modulated signals. We consider a downlink (DL) communication scenario where multiple users are distinguished based on their mobility profiles into a single high-mobility (HM) user and multiple low-mobility (LM) users. OTFS modulation is implemented for the HM user by embedding its information symbols in the delay-Doppler domain, while LM users' symbols are represented in the time-frequency (TF) domain. The LM users' signals are kept orthogonal to each other in the frequency domain by accessing disjoint subcarriers. Further, NOMA spectrum sharing is implemented between the HM user and the KM users to achieve higher spectral efficiency. Performance analysis in terms of DL spectral efficiency and outage probability is conducted for different system parameters. The numerical results show that IDI has a noticeable performance impact on the HM user, depending on the NOMA parameters.
Caching Content Placement and Beamforming Co-design for IRS-Aided MIMO Systems with Imperfect CSI
Authors: Meng Gao, Yang Wang, Huafu Li, Junqi Guo
Date: 2024-10-14 00:00:00

When offloading links encounter deep fading and obstruction, edge caching cannot fully enhance wireless network performance and improve the QoS of edge nodes, as it fails to effectively reduce backhaul burden. The emerging technology of intelligent reflecting surfaces (IRS) compensates for this disadvantage by creating a smart and reconfigurable wireless environment. Subsequently, we jointly design content placement and active/passive beamforming to minimize network costs under imperfect channel state information (CSI) in the IRS-oriented edge caching system. This minimization problem is decomposed into two subproblems. The content placement subproblem is addressed by applying KKT optimality conditions. We then develop the alternating optimization method to resolve precoder and reflection beamforming. Specifically, we reduce transmission power by first fixing the phase shift, reducing the problem to a convex one relative to the precoder, which is solved through convex optimization. Next, we fix the precoder and resolve the resulting reflection beamforming problem using the penalty convex-concave procedure (CCP) method. Results demonstrate that our proposed method outperforms uniform caching and random phase approaches in reducing transmission power and saving network costs. Eventually, the proposed approach offers potential improvements in the caching optimization and transmission robustness of wireless communication with imperfect CSI.
Optimizing Fingerprint-Spectrum-Based Synchronization in Integrated Sensing and Communications
Authors: Xiao-Yang Wang, Shaoshi Yang, Hou-Yu Zhai, Christos Masouros, J. Andrew Zhang
Date: 2024-10-14 00:00:00
Tasks: Super-Resolution

Asynchronous radio transceivers often lead to significant range and velocity ambiguity, posing challenges for precise positioning and velocity estimation in passive-sensing perceptive mobile networks (PMNs). To address this issue, carrier frequency offset (CFO) and time offset (TO) synchronization algorithms have been studied in the literature. However, their performance can be significantly affected by the specific choice of the utilized window functions. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We first derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then develop a practical window selection criterion and test a special window generated by the super-resolution algorithm. Numerical simulation has verified our analysis.
Source Localization of an Unknown Transmission in Dense Multipath Environments
Authors: Asaf Afriat, Dan Raphaeli, Oded Bialer
Date: 2024-10-13 00:00:00
Tasks: Position

Accurately estimating the position of a wireless emitter in a multipath environment based on samples received at various base stations (in known locations) has been extensively explored in the literature. Existing approaches often assume that the emitted signal is known to the location system, while in some applications, such as locating surveillance or intelligence systems, it usually remains unknown. In this paper, we propose a novel estimator for determining the position of an emitter transmitting an unknown signal in a dense multipath environment with a given power-delay profile. We also derive the Carmer-Rao lower bound (CRLB) to evaluate the estimator's performance. Our approach is based on approximating the dense multipath channel in the frequency domain as a Gaussian random vector using the central limit theorem, formulating a log-likelihood cost function for the position and some features of the transmitted signal, and applying a maximum search over both. The optimization problem is non-convex and has no known analytical solutions, which makes it computationally infeasible for multidimensional brute-force search. To address this challenge, we developed a practical optimization algorithm that overcomes the computational complexity, using reasonable approximations, that provides a feasible position estimator. Through extensive evaluations, we demonstrate that the proposed estimator outperforms other state-of-the-art estimators. Moreover, as the number of base stations and SNR increase, our estimator approaches the CRLB, indicating its effectiveness and efficiency.
Heterogeneous Graph Neural Network for Cooperative ISAC Beamforming in Cell-Free MIMO Systems
Authors: Zihuan Wang, Vincent W. S. Wong
Date: 2024-10-13 00:00:00
Tasks: Graph Neural Network, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is one of the usage scenarios for the sixth generation (6G) wireless networks. In this paper, we study cooperative ISAC in cell-free multiple-input multiple-output (MIMO) systems, where multiple MIMO access points (APs) collaboratively provide communication services and perform multi-static sensing. We formulate an optimization problem for the ISAC beamforming design, which maximizes the achievable sum-rate while guaranteeing the sensing signal-to-noise ratio (SNR) requirement and total power constraint. Learning-based techniques are regarded as a promising approach for addressing such a nonconvex optimization problem. By taking the topology of cell-free MIMO systems into consideration, we propose a heterogeneous graph neural network (GNN), namely SACGNN, for ISAC beamforming design. The proposed SACGNN framework models the cell-free MIMO system for cooperative ISAC as a heterogeneous graph and employs a transformer-based heterogeneous message passing scheme to capture the important information of sensing and communication channels and propagate the information through the graph network. Simulation results demonstrate the performance gain of the proposed SACGNN framework over a conventional null-space projection based scheme and a deep neural network (DNN)-based baseline scheme.
Sum Rate Maximization for Movable Antenna Enhanced Multiuser Covert Communications
Authors: Haobin Mao, Xiangyu Pi, Lipeng Zhu, Zhenyu Xiao, Xiang-Gen Xia, Rui Zhang
Date: 2024-10-12 00:00:00
Tasks: Position

In this letter, we propose to employ movable antenna (MA) to enhance covert communications with noise uncertainty, where the confidential data is transmitted from an MA-aided access point (AP) to multiple users with a warden attempting to detect the existence of the legal transmission. To maximize the sum rate of users under covertness constraint, we formulate an optimization problem to jointly design the transmit beamforming and the positions of MAs at the AP. To solve the formulated non-convex optimization problem, we develop a block successive upper-bound minimization (BSUM) based algorithm, where the proximal distance algorithm (PDA) and the successive convex approximation (SCA) are employed to optimize the transmit beamforming and the MAs' positions, respectively. Simulation results show that the proposed MAs-aided system can significantly increase the covert sum rate via antenna position optimization as compared to conventional systems with fixed-position antennas (FPAs).
Exploiting Moving Arrays for Near-Field Sensing
Authors: Yilong Chen, Zixiang Ren, Xianghao Yu, Lei Liu, Jie Xu
Date: 2024-10-12 00:00:00

This letter exploits moving arrays to enable nearfield multiple-input multiple-output (MIMO) sensing via a limited number of antenna elements. We consider a scenario where a base station (BS) is equipped with a uniform linear array (ULA) on a moving platform. The objective is to locate a point target in the two-dimensional (2D) space by leveraging the near-field channel characteristics created by the movement of antenna arrays. Under this setup, we analyze the Cramer-Rao bound (CRB) for estimating the target's 2D coordinate, which provides the fundamental sensing performance limits for localization. It is revealed that our proposed design with a moving array achieves a CRB that is proportional to the CRB obtained by an equivalent extremely large ULA matching the platform's size. This shows that the movement of antenna array significantly enlarges its effective aperture to enable near-field sensing. Numerical results show that the proposed moving array design substantially enhances the target estimation performance compared to the conventional fixed array benchmark.