pwc-archive/papers-with-abstracts huggingface dataset:
Interference Simulator for the Whole HF Band: Application to CW-Morse
Authors: E. Mendieta-Otero, I. Pérez-Álvarez, B. Pérez-Díaz
Date: 2024-02-07 00:00:00

In this paper, we use jointly a model of narrow band interference and a congestion model to model and implement an interference simulator for the whole HF band. The result is a model to generate interfering signals that could be found in a given frequency allocation, at a given time (past, present, or future) and for a given location. Our model does not require measurements and it is characterized by its ease of use and the freedom it offers to choose scene (modulation, location, week, year, etc.). In addition, we have defined a generic modulating function and the conditions to model a contact continuous wave (CW)-Morse, which meets the usual standards of contest. Consequently, our interference model in conjunction with the CW-Morse modulating function designed results in a specific CW-Morse model for amateur contests. As an example of the simulation model, we simulate the CW-Morse communications on the contest ARR ARRL Field Day 2011.
Device Activity Detection and Channel Estimation for Millimeter-Wave Massive MIMO
Authors: Yinchuan Li, Yuancheng Zhan, Le Zheng, Xiaodong Wang
Date: 2024-02-07 00:00:00
Tasks: Action Detection, Activity Detection, compressed sensing, Super-Resolution

Millimeter-Wave Massive MIMO is important for beyond 5G or 6G wireless communication networks. The goal of this paper is to establish successful communication between the cellular base stations and devices, focusing on the problem of joint user activity detection and channel estimation. Different from traditional compressed sensing (CS) methods that only use the sparsity of user activities, we develop several Approximate Message Passing (AMP) based CS algorithms by exploiting the sparsity of user activities and mmWave channels. First, a group soft-thresholding AMP is presented to utilize only the user activity sparsity. Second, a hard-thresholding AMP is proposed based on the on-grid CS approach. Third, a super-resolution AMP algorithm is proposed based on atomic norm, in which a greedy method is proposed as a super-resolution denoiser. And we smooth the denoiser based on Monte Carlo sampling to have Lipschitz continuity and present state evolution results. Extensive simulation results show that the proposed method outperforms the previous state-of-the-art methods.
Robust Symbol-Level Precoding for Massive MIMO Communication Under Channel Aging
Authors: Yafei Wang, Xinping Yi, Hongwei Hou, Wenjin Wang, Shi Jin
Date: 2024-02-07 00:00:00
Tasks: Robust Design

This paper investigates the robust design of symbol-level precoding (SLP) for multiuser multiple-input multiple-output (MIMO) downlink transmission with imperfect channel state information (CSI) caused by channel aging. By utilizing the a posteriori channel model based on the widely adopted jointly correlated channel model, the imperfect CSI is modeled as the statistical CSI incorporating the channel mean and channel variance information with spatial correlation. With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing and minimum mean square error (MMSE) problems for robust SLP design. The former targets to maximize the minimum SINR across users, while the latter minimizes the mean square error between the received signal and the target constellation point. When it comes to massive MIMO scenarios, the increment in the number of antennas poses a computational complexity challenge, limiting the deployment of SLP schemes. To address such a challenge, we simplify the objective function of the SINR balancing problem and further derive a closed-form SLP scheme. Besides, by approximating the matrix involved in the computation, we modify the proposed algorithm and develop an MMSE-based SLP scheme with lower computation complexity. Simulation results confirm the superiority of the proposed schemes over the state-of-the-art SLP schemes.
On the Accuracy of Phase Extraction from a Known-Frequency Noisy Sinusoidal Signal
Authors: Emmanuel Dervieux, Florian Tilquin, Alexis Bisiaux, Wilfried Uhring
Date: 2024-02-06 00:00:00

Accurate phase extraction from sinusoidal signals is a crucial task in various signal processing applications. While prior research predominantly addresses the case of asynchronous sampling with unknown signal frequency, this study focuses on the more specific situation where synchronous sampling is possible, and the signal's frequency is known. In this framework, a comprehensive analysis of phase estimation accuracy in the presence of both additive and phase noises is presented. A closed-form expression for the asymptotic Probability Density Function (PDF) of the resulting phase estimator is validated by simulations depicting Root Mean Square Error (RMSE) trends in different noise scenarios. This estimator is asymptotically efficient, converging rapidly to its Cram\'er-Rao Lower Bound (CRLB). Three distinct RMSE behaviours were identified based on SNR, sample count (N), and noise level: (i) saturation towards a random guess at low Signal to Noise Ratio (SNR), (ii) linear decrease with the square roots of N and SNR at moderate noise levels, and (iii) saturation at high SNR towards a noise floor dependent on the phase noise level. By quantifying the impact of sample count, additive noise, and phase noise on phase estimation accuracy, this work provides valuable insights for designing systems requiring precise phase extraction, such as phase-based fluorescence assays or system identification.
Joint Beamforming Design for Double Active RIS-assisted Radar-Communication Coexistence Systems
Authors: Mengyu Liu, Hong Ren, Cunhua Pan, Boshi Wang, Zhiyuan Yu, Ruisong Weng, Kangda Zhi, Yongchao He
Date: 2024-02-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) technology has been considered as one of the key candidate technologies in the next-generation wireless communication systems. However, when radar and communication equipment coexist in the same system, i.e. radar-communication coexistence (RCC), the interference from communication systems to radar can be large and cannot be ignored. Recently, reconfigurable intelligent surface (RIS) has been introduced into RCC systems to reduce the interference. However, the "multiplicative fading" effect introduced by passive RIS limits its performance. To tackle this issue, we consider a double active RIS-assisted RCC system, which focuses on the design of the radar's beamforming vector and the active RISs' reflecting coefficient matrices, to maximize the achievable data rate of the communication system. The considered system needs to meet the radar detection constraint and the power budgets at the radar and the RISs. Since the problem is non-convex, we propose an algorithm based on the penalty dual decomposition (PDD) framework. Specifically, we initially introduce auxiliary variables to reformulate the coupled variables into equation constraints and incorporate these constraints into the objective function through the PDD framework. Then, we decouple the equivalent problem into several subproblems by invoking the block coordinate descent (BCD) method. Furthermore, we employ the Lagrange dual method to alternately optimize these subproblems. Simulation results verify the effectiveness of the proposed algorithm. Furthermore, the results also show that under the same power budget, deploying double active RISs in RCC systems can achieve higher data rate than those with single active RIS and double passive RISs.
Auto-Encoder Optimized PAM IM/DD Transceivers for Amplified Fiber Links
Authors: Amir Omidi, Mai Banawan, Erwan Weckenmann, Benoit Paquin, Alireza Geravand, Zibo Zheng, Wei Shi, Ming Zeng, Leslie A. Rusch
Date: 2024-02-06 00:00:00
Tasks: Decoder

We examine pulse amplitude modulation (PAM) for intensity modulation and direct detection systems. Using a straight-forward, mixed noise model, we optimize the constellations with an autoencoder-based neural network (NN), an improve required signal-to-noise ratio of 4 dB for amplified spontaneous emission (ASE)-limited PAM4 and PAM8, without increasing system complexity. Performance can also be improved in O-band wavelength division multiplexing system with semiconductor optical amplifier amplification and chromatic dispersion. We show via simulation that for such a system operating at 53 Gbaud, we can extend the reach of PAM4 by 10-25 km with an optimized constellation and a NN decoder. We present an experimental validation of 4 dB improvement of an ASE-limited PAM4 at 60 Gbaud using an optimized constellation and a NN decoder.
Proactive Blockage Prediction for UAV assisted Handover in Future Wireless Network
Authors: Iftikhar Ahmad, Ahsan Raza Khan, Abdul Jabbar, Muhammad Alquraan, Lina Mohjazi, Masood Ur Rehman, Muhammad Ali Imran, Ahmed Zoha, Sajjad Hussain
Date: 2024-02-06 00:00:00
Tasks: Blocking

The future wireless communication applications demand seamless connectivity, higher throughput, and low latency, for which the millimeter-wave (mmWave) band is considered a potential technology. Nevertheless, line-of-sight (LoS) is often mandatory for mmWave band communication, and it renders these waves sensitive to sudden changes in the environment. Therefore, it is necessary to maintain the LoS link for a reliable connection. One such technique to maintain LoS is using proactive handover (HO). However, proactive HO is challenging, requiring continuous information about the surrounding wireless network to anticipate potential blockage. This paper presents a proactive blockage prediction mechanism where an unmanned aerial vehicle (UAV) is used as the base station for HO. The proposed scheme uses computer vision (CV) to obtain potential blocking objects, user speed, and location. To assess the effectiveness of the proposed scheme, the system is evaluated using a publicly available dataset for blockage prediction. The study integrates scenarios from Vision-based Wireless (ViWi) and UAV channel modeling, generating wireless data samples relevant to UAVs. The antenna modeling on the UAV end incorporates a polarization-matched scenario to optimize signal reception. The results demonstrate that UAV-assisted Handover not only ensures seamless connectivity but also enhances overall network performance by 20%. This research contributes to the advancement of proactive blockage mitigation strategies in wireless networks, showcasing the potential of UAVs as dynamic and adaptable base stations.
Novel IMU-based Adaptive Estimator of the Center of Rotation of Joints for Movement Analysis
Authors: Sara García-de-Villa, Ana Jiménez-Martín, J. Jesús García-Domínguez
Date: 2024-02-06 00:00:00

The location of the center of rotation (COR) of joints is a key parameter in multiple applications of human motion analysis. The aim of this work was to propose a novel real-time estimator of the center of fixed joints using an inertial measurement unit (IMU). Since the distance to this center commonly varies during the joint motion due to soft tissue artifacts (STA), our approach is aimed at adapting to these small variations when the COR is fixed. Our proposal, called ArVEd, to the best of our knowledge, is the first real-time estimator of the IMU-joint center vector based on one IMU. Previous works are off-line and require a complete measurement batch to be solved and most of them are not tested on the real scenario. The algorithm is based on an Extended Kalman Filter (EKF) that provides an adaptive vector to STA motion variations at each time instant, without requiring a pre-processing stage to reduce the level of noise. ArVEd has been tested through different experiments, including synthetic and real data. The synthetic data are obtained from a simulated spherical pendulum whose COR is fixed, considering both a constant and a variable IMU-joint vector, that simulates translational IMU motions due to STA. The results prove that ArVEd is adapted to obtain a vector per sample with an accuracy of 6.8$\pm$3.9 on the synthetic data, that means an error lower than 3.5% of the simulated IMU-joint vector. Its accuracy is also tested on the real scenario estimating the COR of the hip of 5 volunteers using as reference the results from an optical system. In this case, ArVEd gets an average error of 9.5% of the real vector value. In all the experiments, ArVEd outperforms the published results of the reference algorithms.
Collaborative Deep Reinforcement Learning for Resource Optimization in Non-Terrestrial Networks
Authors: Yang Cao, Shao-Yu Lien, Ying-Chang Liang, Dusit Niyato, Xuemin, Shen
Date: 2024-02-06 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning

Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services. Due to the rapid mobility of LEO satellite, inter-beam/satellite handovers happen frequently for a specific user equipment (UE). To tackle this issue, earth-fixed cell scenarios have been under studied, in which the LEO satellite adjusts its beam direction towards a fixed area within its dwell duration, to maintain stable transmission performance for the UE. Therefore, it is required that the LEO satellite performs real-time resource allocation, which however is unaffordable by the LEO satellite with limited computing capability. To address this issue, in this paper, we propose a two-time-scale collaborative deep reinforcement learning (DRL) scheme for beam management and resource allocation in NTNs, in which LEO satellite and UE with different control cycles update their decision-making policies through a sequential manner. Specifically, UE updates its policy subject to improving the value functions of both the agents. Furthermore, the LEO satellite only makes decisions through finite-step rollouts with a reference decision trajectory received from the UE. Simulation results show that the proposed scheme can effectively balance the throughput performance and computational complexity over traditional greedy-searching schemes.
A Novel Local and Hyper-Local Multicast Services Transmission Scheme for Beyond 5G Networks
Authors: Sweta Singh, K. Giridhar
Date: 2024-02-06 00:00:00

The efficiency of the broadcast network is impacted by the different types of services that may be transmitted over it. Global services serve users across the entire network, while local services cater to specific regions, and hyper-local services have even narrower coverage. Multimedia Broadcast over a Single-Frequency Network (MBSFN) is typically used for global service transmission while existing literature extensively discusses schemes for transmitting local or hyper-local services with or without Single Frequency Network (SFN) gain. However, these schemes fall short when network-wide requests for only local and hyper-local services are made, leading operators to scale down to either Single Cell-Point to Multipoint (SCPtM) or Multi-Frequency Network (MFN). SCPtM is highly susceptible to interference, and MFN requires substantial amounts of valuable spectrum. They both employ the Least Channel Gain (LCG) strategy for transmitting hyper-local services without SFN gain. Our proposed Local and Hyper-Local Services (LHS) transmission scheme utilizes the knowledge of user distribution and their corresponding radio link channel quality to schedule single or multi-resolution, local or hyper-local services within a three-cell cluster and aims to enhance spectral efficiency and maximize system throughput. It leverages Scalable Video Coding (SVC) in conjunction with Hierarchical Modulation (HM) for transmitting multi-resolution multimedia content to address the problem of heterogeneity amongst the multicast group users. The proposed scheme also employs macro-diversity combining with optimal HM parameters for each gNB catering to a local service area in order to minimize the service outage. System-level simulation results testify to the better performance achieved by the proposed LHS transmission scheme with respect to SCPtM.
Using Perspective-n-Point Algorithms for a Local Positioning System Based on LEDs and a QADA Receiver
Authors: Elena Aparicio-Esteve, Jesús Ureña, Álvaro Hernández, Daniel Pizarro, David Moltó
Date: 2024-02-06 00:00:00
Tasks: Position

The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to a receiver.The receiver is based on a quadrant photodiode angular diversity aperture (QADA) plus an aperture placed over it.This configuration can be modelled as a perspective camera, where the image position of the transmitters can be used to recover the receiver's 3D pose. This process is known as the perspective-n-point (PnP) problem, which is well known in computer vision and photogrammetry. This work investigates the use of different state-of-the-art PnP algorithms to localize the receiver in a large space based on four co-planar transmitters and with a distance from transmitters to receiver of 3.4 m. Encoding techniques are used to permit the simultaneous emission of all the transmitted signals and their processing in the receiver. In addition, correlation techniques are used to determine the image points projected from each emitter on the QADA. This work uses Monte Carlo simulations to characterize the absolute errors for a grid of test points under noisy measurements, as well as the robustness of the system when varying the 3D location of one transmitter. The IPPE algorithm obtained the best performance in this configuration. The proposal has also been experimentally evaluated in a real setup. The estimation of the receiver's position at three points using the IPPE algorithm achieves average absolute errors of 4.33cm, 3.51cm and 28.90cm in the coordinates x, y and z, respectively. These positioning results are in line with those obtained in previous work using triangulation techniques but with the addition that the complete pose of the receiver is obtained in this proposal.
The beltway problem over orthogonal groups
Authors: Tamir Bendory, Dan Edidin, Oscar Mickelin
Date: 2024-02-06 00:00:00
Tasks: Retrieval

The classical beltway problem entails recovering a set of points from their unordered pairwise distances on the circle. This problem can be viewed as a special case of the crystallographic phase retrieval problem of recovering a sparse signal from its periodic autocorrelation. Based on this interpretation, and motivated by cryo-electron microscopy, we suggest a natural generalization to orthogonal groups: recovering a sparse signal, up to an orthogonal transformation, from its autocorrelation over the orthogonal group. If the support of the signal is collision-free, we bound the number of solutions to the beltway problem over orthogonal groups, and prove that this bound is exactly one when the support of the signal is radially collision-free (i.e., the support points have distinct magnitudes). We also prove that if the pairwise products of the signal's weights are distinct, then the autocorrelation determines the signal uniquely, up to an orthogonal transformation. We conclude the paper by considering binary signals and show that in this case, the collision-free condition need not be sufficient to determine signals up to orthogonal transformation.
An Analytic Solution for Kernel Adaptive Filtering
Authors: Benjamin Colburn, Luis G. Sanchez Giraldo, Kan Li, Jose C. Principe
Date: 2024-02-05 00:00:00

Conventional kernel adaptive filtering (KAF) uses a prescribed, positive definite, nonlinear function to define the Reproducing Kernel Hilbert Space (RKHS), where the optimal solution for mean square error estimation is approximated using search techniques. Instead, this paper proposes to embed the full statistics of the input data in the kernel definition, obtaining the first analytical solution for nonlinear regression and nonlinear adaptive filtering applications. We call this solution the Functional Wiener Filter (FWF). Conceptually, the methodology is an extension of Parzen's work on the autocorrelation RKHS to nonlinear functional spaces. We provide an extended functional Wiener equation, and present a solution to this equation in an explicit, finite dimensional, data-dependent RKHS. We further explain the necessary requirements to compute the analytical solution in RKHS, which is beyond traditional methodologies based on the kernel trick. The FWF analytic solution to the nonlinear minimum mean square error problem has better accuracy than other kernel-based algorithms in synthetic, stationary data. In real world time series, it has comparable accuracy to KAF but displays constant complexity with respect to number of training samples. For evaluation, it is as computationally efficient as the Wiener solution (with a larger number of dimensions than the linear case). We also show how the difference equation learned by the FWF from data can be extracted leading to system identification applications, which extend the possible applications of the FWF beyond optimal nonlinear filtering.
Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform
Authors: Anna Cetera, Ali Rabiee, Sima Ghafoori, Reza Abiri
Date: 2024-02-05 00:00:00
Tasks: Brain Computer Interface, EEG

There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cortices - to grasp execution - dominated by the motor cortices. In this study, we developed a novel vision-based grasping BCI platform that distinguishes different grip types (power and precision) through the phases of plan-to-grasp tasks using EEG signals. Using our platform and extracting features from Filter Bank Common Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains discriminative spatial patterns present in both the observation and movement phases. Support Vector Machine (SVM) classification (power vs precision) yielded high accuracy percentages of 74% and 68% for the observation and movement phases in the alpha band, respectively.
Exact Tensor Completion Powered by Slim Transforms
Authors: Li Ge, Lin Chen, Yudong Chen, Xue Jiang
Date: 2024-02-02 00:00:00

In this work, a tensor completion problem is studied, which aims to perfectly recover the tensor from partial observations. The existing theoretical guarantee requires the involved transform to be orthogonal, which hinders its applications. In this paper, jumping out of the constraints of isotropy and self-adjointness, the theoretical guarantee of exact tensor completion with arbitrary linear transforms is established by directly operating the tensors in the transform domain. With the enriched choices of transforms, a new analysis obtained by the proof discloses why slim transforms outperform their square counterparts from a theoretical level. Our model and proof greatly enhance the flexibility of tensor completion and extensive experiments validate the superiority of the proposed method.