pwc-archive/papers-with-abstracts huggingface dataset:
Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges
Authors: Yang Yang, Mingzhe Chen, Yufei Blankenship, Jemin Lee, Zabih Ghassemlooy, Julian Cheng, Shiwen Mao
Date: 2024-03-18 00:00:00

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both the academic and industrial perspectives. The paper provides a comprehensive overview of positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of exisitng efforts and new trends in the field of positioning from both the academic and industrial communities would be a useful reference to researchers in the field.
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
Authors: Jun Lei, Yuxi Zhou, Xue Tian, Qinghao Zhao, Qi Zhang, Shijia Geng, Qingbo Wu, Shenda Hong
Date: 2024-03-18 00:00:00
Tasks: Rhythm

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. Existing algorithms can only distinguish ``AF rhythm in AF patients'' from ``sinus rhythm in normal individuals'' . However, AF patients do not always exhibit AF rhythm, posing a challenge for diagnosis when the AF rhythm is absent. To address this, this paper proposes a novel artificial intelligence (AI) algorithm to distinguish ``sinus rhythm in AF patients'' and ``sinus rhythm in normal individuals'' in beat-level. We introduce beat-level risk interpreters, trend risk interpreters, addressing the interpretability issues of deep learning models and the difficulty in explaining AF risk trends. Additionally, the beat-level information fusion decision is presented to enhance model accuracy. The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314. By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0.7591. Compared to previous segment-level algorithms, we utilized beats as input, reducing data dimensionality and making the model more lightweight, facilitating deployment on portable medical devices. Furthermore, we draw new and interesting findings through average beat analysis and subgroup analysis, considering varying risk levels.
Principles and Optimization of Reflective Intelligent Surface Assisted mmWave Systems
Authors: Lie-Liang Yang
Date: 2024-03-17 00:00:00

A conceptual example is first analyzed to show that efficient wireless communications is possible, when user equipment (UE) receiver, BS transmitter or/and the scatter (reflector) in wireless channels employ the required channel state information (CSI) to remove the randomness of signal phase. Then, the principles and optimization of three reflective intelligent surface (RIS) assisted mmWave (RIS-mmWave) models are introduced. The first model assumes one BS, one RIS and one UE; the second one assumes one BS, one RIS and multiple UEs; while the third RIS-mmWave model assumes one BS, multiple RISs and multiple UEs. Furthermore, the optimization of BS precoder and RIS phase-shifts is addressed in the context of the massive RIS-mmWave scenarios, where the number of BS antennas and that of RIS reflection elements are significantly larger than the number of supported UEs. The analyses demonstrate that, while the deployment of RISs with mmWave is capable of solving the blockage problem and has the potential to significantly improve efficiency, finding the near-optimum solutions for RIS phase-shifts is highly challenging in practice.
Modeling and Coverage Analysis of K-Tier Integrated Satellite-Terrestrial Downlink Networks
Authors: Jungbin Yim, Jeonghun Park, Namyoon Lee
Date: 2024-03-17 00:00:00
Tasks: Point Processes

Integrated satellite-terrestrial networks (ISTNs) can significantly expand network coverage while diminishing reliance on terrestrial infrastructure. Despite the enticing potential of ISTNs, there is no comprehensive mathematical performance analysis framework for these emerging networks. In this paper, we introduce a tractable approach to analyze the downlink coverage performance of multi-tier ISTNs, where each network tier operates with orthogonal frequency bands. The proposed approach is to model the spatial distribution of cellular and satellite base stations using homogeneous Poisson point processes arranged on concentric spheres with varying radii. Central to our analysis is a displacement principle that transforms base station locations on different spheres into projected rings while preserving the distance distribution to the typical user. By incorporating the effects of Shadowed-Rician fading on satellite channels and employing orthogonal frequency bands, we derive analytical expressions for coverage in the integrated networks while keeping full generality. Our primary discovery is that network performance reaches its maximum when selecting the optimal density ratio of users associated with the network according to the density and the channel parameters of each network. Through simulations, we validate the precision of our derived expressions.
Nonlinear Self-Interference Cancellation With Learnable Orthonormal Polynomials for Full-Duplex Wireless Systems
Authors: Hyowon Lee, Jungyeon Kim, Geon Choi, Ian P. Roberts, Jinseok Choi, Namyoon Lee
Date: 2024-03-17 00:00:00

Nonlinear self-interference cancellation (SIC) is essential for full-duplex communication systems, which can offer twice the spectral efficiency of traditional half-duplex systems. The challenge of nonlinear SIC is similar to the classic problem of system identification in adaptive filter theory, whose crux lies in identifying the optimal nonlinear basis functions for a nonlinear system. This becomes especially difficult when the system input has a non-stationary distribution. In this paper, we propose a novel algorithm for nonlinear digital SIC that adaptively constructs orthonormal polynomial basis functions according to the non-stationary moments of the transmit signal. By combining these basis functions with the least mean squares (LMS) algorithm, we introduce a new SIC technique, called as the adaptive orthonormal polynomial LMS (AOP-LMS) algorithm. To reduce computational complexity for practical systems, we augment our approach with a precomputed look-up table, which maps a given modulation and coding scheme to its corresponding basis functions. Numerical simulation indicates that our proposed method surpasses existing state-of-the-art SIC algorithms in terms of convergence speed and mean squared error when the transmit signal is non-stationary, such as with adaptive modulation and coding. Experimental evaluation with a wireless testbed confirms that our proposed approach outperforms existing digital SIC algorithms.
Markovian Block Sparse Signal Detection Using One Bit Measurements
Authors: Alireza Hariri, Hadi Zayyani, Mehdi Korki
Date: 2024-03-17 00:00:00
Tasks: Form

This paper presents a novel sparse signal detection scheme designed for a correlated Markovian Bernoulli-Gaussian sparse signal model, which can equivalently be viewed as a block sparse signal model. Despite the inherent complexity of the model, our approach yields a closed-form detection criterion. Theoretical analyses of the proposed detector are provided, including the computation of false alarm probability and detection probability through closed-form formulas. Simulation results compellingly demonstrate the advantages of our proposed detector compared to an existing detector in the literature.
Double-Private Distributed Estimation Algorithm Using Differential Privacy and a Key-Like Proportionate Matrix with Its Performance Analysis
Authors: Mehdi Korki, Fatemehsadat Hosseiniamin, Hadi Zayyani, Mehdi Bekrani
Date: 2024-03-17 00:00:00
Tasks: Privacy Preserving

In this brief, we present an enhanced privacy-preserving distributed estimation algorithm, referred to as the ``Double-Private Algorithm," which combines the principles of both differential privacy (DP) and cryptography. The proposed algorithm enhances privacy by introducing DP noise into the intermediate estimations of neighboring nodes. Additionally, we employ an inverse of a closed-form reproducible proportionate gain matrix as the cryptographic key matrix to fortify the privacy protection within the proposed double private algorithm. \textcolor{blue}{We improve the algorithm by transmitting alternative variable vectors instead of raw measurements, resulting in enhanced key matrix reconstruction performance. This innovative approach mitigate noise impact, enhancing overall algorithm effectiveness.} We also establish an upper bound for the norm of the error between the non-private Diffusion Least Mean Square (DLMS) algorithm and our double private algorithm. Further, we determine a sufficient condition for the step-size to ensure the mean convergence of the proposed algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm, particularly its ability to attain the final Mean Square Deviation (MSD) comparable to that of the non-private DLMS.
Beamforming Design for Double-Active-RIS-aided Communication Systems with Inter-Excitation
Authors: Boshi Wang, Cunhua Pan, Hong Ren, Zhiyuan Yu, Yang Zhang, Mengyu Liu, Gui Zhou
Date: 2024-03-17 00:00:00

In this paper, we investigate a double-active-reconfigurable intelligent surface (RIS)-aided downlink wireless communication system, where a multi-antenna base station (BS) serves multiple single-antenna users with both double reflection and single reflection links. Due to the signal amplification capability of active RISs, they can effectively mitigate the multiplicative fading effect. However, this also induces signal bouncing between the two active RISs that cannot be ignored. This phenomenon is termed as the "inter-excitation" effect and is characterized in the received signal by proposing a feedback-type model. Based on the signal model, we formulate a weighted sum rate (WSR) maximization problem by jointly optimizing the beamforming matrix at the BS and the reflecting coefficient matrices at the two active RISs, subject to power constraints at the BS and active RISs, as well as the maximum amplification gain constraints of the active RISs. To solve this non-convex problem, we first transform the problem into a more tractable form using the fractional programming (FP) method. Then, by introducing auxiliary variables, the problem can be converted into an equivalent form that can be solved by using a penalty dual decomposition (PDD) algorithm. Finally, simulation results indicate that it proposed scheme outperforms benchmark schemes with single active RIS and double passive RISs in terms of achievable rate. Furthermore, the results demonstrate that the proposed scheme can enhance the WSR by 30\% compared to scenarios that do not take this effect into account when the maximum amplification gain is 40 dB. Additionally, the proposed scheme is capable of achieving high WSR performance at most locations where double active RISs are deployed between the BS and the users, thereby providing greater flexibility in their positioning.
Second-Order Nonlinearity Estimated and Compensated Diffusion LMS Algorithm: Theoretical Upper Bound, Cramer-Rao Lower bound, and Convergence Analysis
Authors: Hadi Zayyani, Mehdi Korki
Date: 2024-03-17 00:00:00

In this paper, an algorithm for estimation and compensation of second-order nonlinearity in wireless sensor setwork (WSN) in distributed estimation framework is proposed. First, the effect of second-order nonlinearity on the performance of Diffusion Least Mean Square (DLMS) algorithm is investigated and an upper bound for $l^2$-norm of the error due to nonlinearity is derived mathematically. Second, mean convergence analysis of the DLMS algorithm in presence of second-order nonlinearity is derived. Third, a distributed algorithm is suggested which consists of extra nonlinearity estimation and compensation units. Moreover, considering the second-order nonlinearity, the Cramer-Rao bound (CRB) for estimating both the unknown vector and nonlinearity coefficient vector is calculated, in which the Fisher information matrix is obtained in a closed-form formula. Simulation results demonstrate the effectiveness of the proposed algorithm in improving the performance of distributed estimation in the presence of nonlinear sensors in a WSN.
Integration of 5G and Motion Sensors for Vehicular Positioning: A Loosely-Coupled Approach
Authors: Sharief Saleh, Qamar Bader, Malek Karaim, Mohamed Elhabiby, Aboelmagd Noureldin
Date: 2024-03-16 00:00:00
Tasks: Autonomous Vehicles, Sensor Fusion

Autonomous vehicles (AVs) are poised to revolutionize the transportation industry by enhancing traffic efficiency and road safety. However, achieving optimal vehicular autonomy demands an uninterrupted and precise positioning solution, especially in deep urban environments. 5G mmWave holds immense potential to provide such a service due to its accurate range and angle measurements. Yet, as mmWave signals are prone to signal blockage, severe positioning errors will occur. Most of the 5G positioning literature relies on constant motion models to bridge such 5G outages, which do not capture the true dynamics of the vehicle. Few proposed methodologies rely on inertial measurement units (IMUs) to bridge such gaps, where they predominantly use tightly coupled (TC) integration schemes, introducing a nonlinear 5G measurement model. Such approaches, which rely on Kalman filtering, necessitate the linearization of the measurement model, leading to pronounced positioning errors. In this paper, however, we propose a loosely coupled (LC) sensor fusion scheme to integrate 5G, IMUs, and odometers to mitigate linearization errors. Additionally, we propose a novel method to design the process covariance matrix of the extended Kalman filter (EKF). Moreover, we propose enhancements to the mechanization of the IMU data to enhance the standalone IMU solution. The proposed methodologies were tested using a novel setup comprising 5G measurements from Siradel's S_5G simulation tool and real IMU and odometer measurements from an hour-long trajectory. The proposed method resulted in 14 cm of error for 95% of the time compared to 1 m provided by the traditional constant velocity model approach.
PnP Restoration with Domain Adaptation for SANS
Authors: Shirin Shoushtari, Edward P. Chandler, Jialiang Zhang, Manjula Senanayake, Sai Venkatesh Pingali, Marcus Foston, Ulugbek S. Kamilov
Date: 2024-03-15 00:00:00
Tasks: Domain Adaptation

Small Angle Neutron Scattering (SANS) is a non-destructive technique utilized to probe the nano- to mesoscale structure of materials by analyzing the scattering pattern of neutrons. Accelerating SANS acquisition for in-situ analysis is essential, but it often reduces the signal-to-noise ratio (SNR), highlighting the need for methods to enhance SNR even with short acquisition times. While deep learning (DL) can be used for enhancing SNR of low quality SANS, the amount of experimental data available for training is usually severely limited. We address this issue by proposing a Plug-and-play Restoration for SANS (PR-SANS) that uses domain-adapted priors. The prior in PR-SANS is initially trained on a set of generic images and subsequently fine-tuned using a limited amount of experimental SANS data. We present a theoretical convergence analysis of PR-SANS by focusing on the error resulting from using inexact domain-adapted priors instead of the ideal ones. We demonstrate with experimentally collected SANS data that PR-SANS can recover high-SNR 2D SANS detector images from low-SNR detector images, effectively increasing the SNR. This advancement enables a reduction in acquisition times by a factor of 12 while maintaining the original signal quality.
Beam Pattern Modulation Embedded mmWave Hybrid Transceiver Design Towards ISAC
Authors: Boxun Liu, Shijian Gao, Zonghui Yang, Xiang Cheng
Date: 2024-03-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widespread adoption of hybrid architecture in mmWave systems compromises multiplexing gain due to limited radio-frequency chains, resulting in mediocre performance when embedding sensing functionality. To avoid sacrificing the spectrum efficiency in hybrid structures while addressing performance bottlenecks in its extension to ISAC, we present an optimized beam pattern modulation-embedded ISAC (BPM-ISAC). BPM-ISAC applies index modulation over beamspace by selectively activating communication beams, aiming to minimize sensing beampattern mean squared error (MSE) under communication MSE constraints through dedicated hybrid transceiver design. Optimization involves the analog part through a min-MSE-based beam selection algorithm, followed by the digital part using an alternating optimization algorithm. Convergence and asymptotic pairwise error probability (APEP) analyses accompany numerical simulations, validating its overall enhanced ISAC performance over existing alternatives.
Bias Control and Linearization of the Transfer Function of Electro-optic and Acousto-optic Modulators
Authors: Clemens Neumüller, Frank Obernosterer, Raimund Meyer, Robert Koch, Gerd Kilian
Date: 2024-03-15 00:00:00

In several types of quantum computers light is one of the main tools to control both the position and the quantum state of the atoms used for computing. In practical systems laser light is applied to manipulate quantum states of qubits in the desired way. Beside physical effects like decoherence and quantum noise the precision of qubit manipulation has a significant impact on the achievable quantum computing error rate. One of the key optical components beside the laser is the optical modulator, which modulates or switches a constant power laser light in order to provide light pulses or pulse sequences with a desired envelope. Acousto-optic (AOM) and electro-optic (EOM) modulators can be applied, which are both voltage controlled. However, there is neither a simple linear relationship between their control signal and the precise modulator output, nor can they be considered to have time-invariant characteristics. The aim of this paper is to describe techniques to generate AOM and EOM control signals in such a way that almost arbitrary target output waveforms (i. e. optical power versus time) are achieved with high accuracy.
Joint Optimization for Achieving Covertness in MIMO Over-the-Air Computation Networks
Authors: Junteng Yao, Tuo Wu, Ming Jin, Cunhua Pan, Quanzhong Li, Jinhong Yuan
Date: 2024-03-15 00:00:00

This paper investigates covert data transmission within a multiple-input multiple-output (MIMO) over-the-air computation (AirComp) network, where sensors transmit data to the access point (AP) while guaranteeing covertness to the warden (Willie). Simultaneously, the AP introduces artificial noise (AN) to confuse Willie, meeting the covert requirement. We address the challenge of minimizing mean-square-error (MSE) of the AP, while considering transmit power constraints at both the AP and the sensors, as well as ensuring the covert transmission to Willie with a low detection error probability (DEP). However, obtaining globally optimal solutions for the investigated non-convex problem is challenging due to the interdependence of optimization variables. To tackle this problem, we introduce an exact penalty algorithm and transform the optimization problem into a difference-of-convex (DC) form problem to find a locally optimal solution. Simulation results showcase the superior performance in terms of our proposed scheme in comparison to the benchmark schemes.
A Belief Propagation Algorithm for Multipath-based SLAM with Multiple Map Features: A mmWave MIMO Application
Authors: Xuhong LI, Xuesong Cai, Erik Leitinger, Fredrik Tufvesson
Date: 2024-03-15 00:00:00
Tasks: Simultaneous Localization and Mapping

In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts mulitiple map feature (MF) models describing specularly reflected multipath components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobile agent's state including position and orientation. The Bayesian model is represented by a factor graph enabling the use of belief propagation (BP) for efficient computation of the marginal posterior distributions. The algorithm also exploits amplitude information enabling reliable detection of weak MFs associated with MPCs of very low signal-to-noise ratios (SNRs). The performance of the proposed algorithm is evaluated using real millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) measurements with single base station setup. Results demonstrate the excellent localization and mapping performance of the proposed algorithm in challenging dynamic outdoor scenarios.
NR-Surface: NextG-ready $μ$W-reconfigurable mmWave Metasurface
Authors: Minseok Kim, Namjo Ahn, Song Min Kim
Date: 2024-03-15 00:00:00

Metasurface has recently emerged as an economic solution to expand mmWave coverage. However, their pervasive deployment remains a challenge, mainly due to the difficulty in reaching the tight 260ns NR synchronization requirement and real-time wireless reconfiguration while maintaining multi-year battery life. This paper presents NR-Surface, the first real-time reconfigurable metasurface fully compliant with the NR standard, operating at 242.7 $\mu$W for a 2.1-year lifetime on an AA battery. NR-Surface incorporates (i) a new extremely low-power (14KHz sampling) reconfiguration interface, NarrowBand Packet Unit (NBPU), for synchronization and real-time reconfiguration, and (ii) a highly responsive and low-leakage metasurface designed for low-duty cycled operation, by carefully leveraging the structure and the periodicity of the NR beam management procedure in the NR standard. NR-Surface is prototyped and evaluated end-to-end with NR BS built on srsRAN to demonstrate diverse usage scenarios including multiple NR-Surface per BS, multiple UE per NR-Surface, and 3D beamforming. Around-the-corner UE evaluations showcase NR-Surface efficacy under different user mobility patterns (20.3dB gain) and dynamic blockage (22.2dB gain).