pwc-archive/papers-with-abstracts huggingface dataset:
Manifold Optimization for High Accuracy Spatial Location Estimation Using Ultrasound Waves
Authors: Mohammed H. AlSharif, Ahmed Douik, Mohanad Ahmed, Tareq Y. Al-Naffouri, Babak Hassibi
Date: 2021-03-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper reports the design of a high-accuracy spatial location estimation method using ultrasound waves by exploiting the fixed geometry of the transmitters. Assuming an isosceles triangle antenna configuration, where three antennas are placed as the vertices of an isosceles triangle, the spatial location problem can be formulated as a non-convex optimization problem whose interior is shown to admit a Riemannian manifold structure. Our investigation of the geometry of the newly introduced manifold (i.e., the manifold of all isosceles triangles in R3) enables the design of highly efficient optimization algorithms. Simulations are presented to compare the performance of the proposed approach with popular methods from the literature. The results suggest that the proposed Riemannian-based methods outperform the state-of-the-art methods. Furthermore, the proposed Riemannian methods require much less computation time compared to popular generic non-convex approaches.
ELM-based Frame Synchronization in Nonlinear Distortion Scenario Using Superimposed Training
Authors: Chaojin Qing, Wang Yu, Shuhai Tang, Chuangui Rao, Jiafan Wang
Date: 2021-03-27 00:00:00

The requirement of high spectrum efficiency puts forward higher requirements on frame synchronization (FS) in wireless communication systems. Meanwhile, a large number of nonlinear devices or blocks will inevitably cause nonlinear distortion. To avoid the occupation of bandwidth resources and overcome the difficulty of nonlinear distortion, an extreme learning machine (ELM)-based network is introduced into the superimposed training-based FS with nonlinear distortion. Firstly, a preprocessing procedure is utilized to reap the features of synchronization metric (SM). Then, based on the rough features of SM, an ELM network is constructed to estimate the offset of frame boundary. The analysis and experiment results show that, compared with existing methods, the proposed method can improve the error probability of FS and bit error rate (BER) of symbol detection (SD). In addition, this improvement has its robustness against the impacts of parameter variations.
Towards Fine-Grained Indoor Localization based on Massive MIMO-OFDM System: Experiment and Analysis
Authors: Chenglong Li, Sibren De Bast, Emmeric Tanghe, Sofie Pollin, Wout Joseph
Date: 2021-03-27 00:00:00
Tasks: Indoor Localization

Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution.
Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains
Authors: John Shi, Jose M. F. Moura
Date: 2021-03-19 00:00:00

Vertex based and spectral based GSP sampling has been studied recently. The literature recognizes that methods in one domain do not have a counterpart in the other domain. This paper shows that in fact one can develop a unified graph signal sampling theory with analogous interpretations in both domains just like sampling in traditional DSP. To achieve it, we introduce a spectral shift $M$ acting in the spectral domain rather than shift $A$ that acts in the vertex domain. This leads to a GSP theory that starts from the spectral domain, for example, linear shift invariant (LSI) filtering in the spectral domain is with polynomials $P(M)$. We then develop GSP vertex and spectral domain dual versions for each of the four standard sampling steps of subsampling, decimation, upsampling, and interpolation. We show how GSP sampling reduces to DSP sampling when the graph is the directed time cycle graph. Simple examples illustrate the impact of choices that are available in GSP sampling.
Design of Novel Hybrid CPDM-CO-OFDM FSO Communication System and its Performance Analysis under Diverse Weather Conditions
Authors: Ruhin Chowdhury, A. K. M. Sharoar Jahan Choyon
Date: 2021-03-25 00:00:00

A comprehensive novel design is proposed for the free-space optical (FSO) communication system by hybridizing circular polarization division multiplexing (CPDM) with coherent optical orthogonal frequency division multiplexing (CO-OFDM) and its performance is investigated realistically under diverse turbulent weather conditions of Bangladesh. Here we consider Gamma-Gamma (G-G) distribution for the turbulent FSO channel model. Moreover, the proposed scheme presents an excellent performance since CPDM technique not only maximizes the link capacity of FSO system but also enhances the spectral efficiency (SE) of the system. Besides, multipath-fading, which is appeared during the FSO transmission, is significantly mitigated by OFDM modulation. The outcomes from simulation confirm the advantages of the proposed hybrid scheme and also it can serve as a reference for the FSO application even in the turbulent weather conditions. Performance analysis of the proposed model is described in terms of optical power spectrum (OPS), optical signal to noise ratio (OSNR), bit error rate (BER), Q factor, constellation diagrams, and eye diagrams.
Distributed DoS Attack Detection in SDN: Trade offs in Resource Constrained Wireless Networks
Authors: Gustavo A. Nunez Segura, Arsenia Chorti, Cintia Borges Margi
Date: 2021-03-25 00:00:00
Tasks: Change Point Detection, Management

The Software-defined networking(SDN) paradigm centralizes control decisions to improve programmability and simplify network management. However, this centralization turns the network vulnerable to denial of service (DoS) attacks, and in the case of resource constrained networks, the vulnerabilities escalate. The main shortcoming in current security solutions is the trade off between detection rate and complexity. In this work, we propose a DoS attack detection algorithm for SDN resource constrained networks, based on recent results on non-parametric real-time change point detection, and lightweight enough to run on individual resource constrained devices. Our experiment results show detection rates and attacker identification probabilities equal or over 0.93.
Nocturnal Seizure Detection Using Off-the-Shelf WiFi
Authors: Belal Korany, Yasamin Mostofi
Date: 2021-03-25 00:00:00
Tasks: Management, Seizure Detection

Detection of nocturnal seizures in epilepsy patients is essential, both for the quick management of the seizure complications, and for the assessment of the ongoing seizure treatment. Traditional seizure detection products (e.g., wearables), however, are either very costly, uncomfortable, or unreliable. In this paper, we then propose to utilize everyday WiFi signals for robust, fast, and non-invasive detection of nocturnal seizures. We first present a new and rigorous mathematical characterization for the spectral content/bandwidth of the WiFi signal, measured on a WiFi device placed near a sleeping patient, during different kinds of sleep motions: seizures, normal movements (e.g. posture adjustments), and breathing. Based on this mathematical modeling, we propose a novel pipeline for processing the received WiFi signals to robustly detect all nocturnal non-breathing movements, and then classify them into normal body movements or seizures. In order to validate this, we carry out extensive experiments in 7 different typical bedroom locations, where a set of 20 actors simulate the state of having seizures (total of 260 instances), as well as normal sleep movements (total of 410 instances). Our proposed system detects 93.85% of the seizures with a mean response time of only 5.69 seconds since the onset of the seizure. Moreover, our proposed system achieves a probability of false alarm of only 0.0097, when classifying normal sleep movements. Overall, our new mathematical modeling and experimental results show the great potential the ubiquitous WiFi signals have for detecting nocturnal seizures, which can provide better support for epilepsy patients and their caregivers.
Enhanced Robust Adaptive Beamforming Designs for General-Rank Signal Model via an Induced Norm of Matrix Errors
Authors: Yongwei Huang, Sergiy A. Vorobyov
Date: 2021-03-24 00:00:00
Tasks: CPU

The robust adaptive beamforming (RAB) problem for general-rank signal model with an uncertainty set defined through a matrix induced norm is considered. The worst-case signal-to-interference-plus-noise ratio (SINR) maximization RAB problem is formulated by decomposing the presumed covariance of the desired signal into a product between a matrix and its Hermitian, and putting an error term into the matrix and its Hermitian. In the literature, the norm of the matrix errors often is the Frobenius norm in the maximization problem. Herein, the closed-form optimal value for a minimization problem of the least-squares residual over the matrix errors with an induced $l_{p,q}$-norm constraint is first derived. Then, the worst-case SINR maximization problem is reformulated into the maximization of the difference between an $l_2$-norm function and a $l_q$-norm function, subject to a convex quadratic constraint. It is shown that for any $q$ in the set of rational numbers greater than or equal to one, the maximization problem can be approximated by a sequence of second-order cone programming (SOCP) problems, with the ascent optimal values. The resultant beamvector for some $q$ in the set, corresponding to the maximal actual array output SINR, is treated as the best candidate such that the RAB design is improved the most. In addition, a generalized RAB problem of maximizing the difference between an $l_p$-norm function and an $l_q$-norm function subject to the convex quadratic constraint is studied, and the actual array output SINR is further enhanced by properly selecting $p$ and $q$. Simulation examples are presented to demonstrate the improved performance of the robust beamformers for certain matrix induced $l_{p,q}$-norms, in terms of the actual array output SINR and the CPU-time for the sequential SOCP approximation algorithm.
New Closed-form Joint Localization and Synchronization using Sequential One-way TOAs
Authors: Ningyan Guo, Sihao Zhao, Xiao-Ping Zhang, Zheng Yao, Xiaowei Cui, Mingquan Lu
Date: 2021-01-30 00:00:00
Tasks: Form

It is an essential technique for the moving user nodes (UNs) with clock offset and clock skew to resolve the joint localization and synchronization (JLAS) problem. Existing iterative maximum likelihood methods using sequential one-way time-of-arrival (TOA) measurements from the anchor nodes' (AN) broadcast signals require a good initial guess and have a computational complexity that grows with the number of iterations, given the size of the problem. In this paper, we propose a new closed-form JLAS approach, namely CFJLAS, which achieves the asymptotically optimal solution in one shot without initialization when the noise is small, and has a low computational complexity. After squaring and differencing the sequential TOA measurement equations, we devise two intermediate variables to reparameterize the non-linear problem. In this way, we convert the problem to a simpler one of solving two simultaneous quadratic equations. We then solve the equations analytically to obtain a raw closed-form JLAS estimation. Finally, we apply a weighted least squares (WLS) step to optimize the estimation. We derive the Cramer-Rao lower bound (CRLB), analyze the estimation error, and show that the estimation accuracy of the CFJLAS reaches the CRLB under the small noise condition. The complexity of the new CFJLAS is only determined by the size of the problem, unlike the conventional iterative method, whose complexity is additionally multiplied by the number of iterations. Simulations in a 2D scene verify that the estimation accuracies of the new CFJLAS method in position, velocity, clock offset, and clock skew all reach the CRLB under the small noise condition. Compared with the conventional iterative method, the proposed new CFJLAS method does not require initialization, obtains the optimal solution under the small noise condition, and has a low computational complexity.
A Time-Temperature Dataset for the Strawberry Cold Chain Across Multiple Shipments and Locations
Authors: Alla Abdella, Jeffrey K. Brecht, Ismail Uysal
Date: 2021-03-23 00:00:00

This article describes location aware temperature profiles from six strawberry shipments across the continental United States. Three pallets were instrumented in each shipment with three vertically placed loggers to take a longitudinal and latitudinal snapshot of 9 strategically different locations (including the top, middle and bottom layers of the pallets placed in the back, middle and the front of the shipping container) for a combined 54 measurement points across shipments of varying lengths. The sensors were instrumented in the field, right at the point of harvest, recorded temperatures every every 5 to 10 minutes depending on the shipment, and uploaded their data periodically via cellular radios on each device. The data is a result of significant collaboration between stakeholders from farmers to distributors to retailers to academics, which can play an important role for researchers and educators in food engineering, cold-chain, machine learning, and data mining, as well as in other disciplines related to food and transportation.
FBMC Receiver Design and Analysis for Medium and Large Scale Antenna Systems
Authors: Hamed Hosseiny, Arman Farhang, Behrouz Farhang-Boroujeny
Date: 2021-03-23 00:00:00

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users' channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis.
Precoding for PAPR Reduction in UW-OFDM
Authors: Morteza Rajabzadeh, Heidi Steendam
Date: 2021-03-23 00:00:00

Unique Word Orthogonal Frequency Division multiplexing (UW-OFDM) is a variant of the multicarrier technique that has shown better bit error performance compared to cyclic prefixed (CP-)OFDM. Like other multicarrier techniques, UW-OFDM suffers from the high peak-to-average-power ratio (PAPR) problem, which is an obstacle ahead of practical implementation of UW-OFDM. In this paper, the generator matrix, which is inherent to UW-OFDM, is not only used to create the specific UW-OFDM system, but also to reduce the PAPR. To do so, a suitable optimization problem is developed, and solved using the Procrustes method. The main advantage of the proposed method is that the built-in generator matrix should be calculated only once for each system setting, and the PAPR reduction (PAPR-R) is achieved without any overhead redundancy. Simulation results are provided for evaluating the PAPR-R performance of the proposed method.
An Interpretable Mapping from a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization
Authors: Zhiqun Zhai, Hexun Jiang, Mengfan Fu, Lei Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Date: 2021-03-23 00:00:00

In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.
Communication Technologies for Smart Grid: A Comprehensive Survey
Authors: Fredrik Ege Abrahamsen, Yun Ai, Michael Cheffena
Date: 2021-03-22 00:00:00
Tasks: Survey

With the ongoing trends in the energy sector such as vehicular electrification and renewable energy, smart grid is clearly playing a more and more important role in the electric power system industry. One essential feature of the smart grid is the information flow over the high-speed, reliable and secure data communication network in order to manage the complex power systems effectively and intelligently. Smart grids utilize bidirectional communication to function where traditional power grids mainly only use one-way communication. The communication requirements and suitable technique differ depending on the specific environment and scenario. In this paper, we provide a comprehensive and up-to-date survey on the communication technologies used in the smart grid, including the communication requirements, physical layer technologies, network architectures, and research challenges. This survey aims to help the readers identify the potential research problems in the continued research on the topic of smart grid communications.
DCT and DST Filtering with Sparse Graph Operators
Authors: Keng-Shih Lu, Antonio Ortega, Debargha Mukherjee, Yue Chen
Date: 2021-03-22 00:00:00

Graph filtering is a fundamental tool in graph signal processing. Polynomial graph filters (PGFs), defined as polynomials of a fundamental graph operator, can be implemented in the vertex domain, and usually have a lower complexity than frequency domain filter implementations. In this paper, we focus on the design of filters for graphs with graph Fourier transform (GFT) corresponding to a discrete trigonometric transform (DTT), i.e., one of 8 types of discrete cosine transforms (DCT) and 8 discrete sine transforms (DST). In this case, we show that multiple sparse graph operators can be identified, which allows us to propose a generalization of PGF design: multivariate polynomial graph filter (MPGF). First, for the widely used DCT-II (type-2 DCT), we characterize a set of sparse graph operators that share the DCT-II matrix as their common eigenvector matrix. This set contains the well-known connected line graph. These sparse operators can be viewed as graph filters operating in the DCT domain, which allows us to approximate any DCT graph filter by a MPGF, leading to a design with more degrees of freedom than the conventional PGF approach. Then, we extend those results to all of the 16 DTTs as well as their 2D versions, and show how their associated sets of multiple graph operators can be determined. We demonstrate experimentally that ideal low-pass and exponential DCT/DST filters can be approximated with higher accuracy with similar runtime complexity. Finally, we apply our method to transform-type selection in a video codec, AV1, where we demonstrate significant encoding time savings, with a negligible compression loss.
Sinusoidal Parameter Estimation from Signed Measurements via Majorization-Minimization Based RELAX
Authors: Jiaying Ren, Tianyi Zhang, Jian Li, Petre Stoica
Date: 2021-03-21 00:00:00
Tasks: Computational Efficiency, parameter estimation

We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with fixed as well as time-varying thresholds. In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function. However, the exhaustive search procedure used in each iteration of 1bRELAX is time-consuming. In this paper, we present a majorization-minimization (MM) based 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational efficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the likelihood function iteratively using simple FFT operations instead of the more computationally intensive search used by 1bRELAX. Both simulated and experimental results are presented to show that 1bMMRELAX can significantly reduce the computational cost of 1bRELAX while maintaining its excellent estimation accuracy.
Hankel Singular Value Decomposition as a method of preprocessing the Magnetic Resonance Spectroscopy
Authors: Michał Staniszewski, Andrzej Polański
Date: 2021-03-21 00:00:00

The signal resulting from magnetic resonance spectroscopy is occupied by noises and irregularities so in the further analysis preprocessing techniques have to be introduced. The main idea of the paper is to develop a model of a signal as a sum of harmonics and to find its parameters. Such an approach is based on singular value decomposition applied to the data arranged in the Hankel matrix (HSVD) and can be used in each step of preprocessing techniques. For that purpose a method has was tested on real phantom data.
Comparison of Short Blocklength Slepian-Wolf Coding for Key Reconciliation
Authors: Mahdi Shakiba-Herfeh, Arsenia Chorti
Date: 2021-03-21 00:00:00

We focus Slepian-Wolf (SW) coding in the short blocklength for reconciliation in secret key generation and physical unclonable functions. In the problem formulation, two legitimate parties wish to generate a common secret key from a noisy observation of a common random source in the presence of a passive eavesdropper. We consider three different families of codes for key reconciliation. The selected codes show promising performances in information transmission in the short block-length regime. We implement and compare the performance of different codes for SW reconciliation in the terms of reliability and decoding complexity.