pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Learning of Occupancy Grids
Authors: Christopher Robbiano, Edwin K. P. Chong, Mahmood R. Azimi-Sadjadi, Louis L. Scharf, Ali Pezeshki
Date: 2019-11-18 00:00:00

Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells. The problem is to recursively update the probability that each cell in the grid is occupied, based on a sequence of sensor measurements from a moving platform. In this paper, we provide a new Bayesian framework for generating these probabilities that does not assume statistical independence between the occupancy state of grid cells. This approach is made analytically tractable through the use of binary asymmetric channel models that capture the errors associated with observing the occupancy state of a grid cell. Binary-valued measurement vectors are the thresholded output of a sensor in a radar, sonar, or other sensory system. We compare the performance of the proposed framework to that of the classical formulation for occupancy grids. The results show that the proposed framework identifies occupancy grids with lower false alarm and miss detection rates, and requires fewer observations of the surrounding area, to generate an accurate estimate of occupancy probabilities when compared to conventional formulations.
A Track-Before-Detect Approach to Multi-Target Tracking on Automotive Radar Sensor Data
Authors: David Meister, Martin F. Holder, Hermann Winner
Date: 2020-06-04 00:00:00
Tasks: State Estimation

In recent years, Bayes filter methods in the labeled random finite set formulation have become increasingly powerful in the multi-target tracking domain. One of the latest outcomes is the Generalized Labeled Multi-Bernoulli (GLMB) filter which allows for stable cardinality and target state estimation as well as target identification in a unified framework. In contrast to the initial context of the GLMB filter, this paper makes use of it in the Track-Before-Detect (TBD) scheme and thus, avoids information loss due to thresholding and other data preprocessing steps. This paper provides a TBD GLMB filter design under the separable likelihood assumption that can be applied to real world scenarios and data in the automotive radar context. Its applicability to real sensor data is demonstrated in an exemplary scenario. To the best of the authors' knowledge, the GLMB filter is applied to real radar data in a TBD framework for the first time.
Datasets of ionospheric parameters provided by SCINDA GNSS receiver from Lisbon airport area
Authors: Tatiana Barlyaeva, Teresa Barata, Anna Morozova
Date: 2020-02-17 00:00:00

Here we present datasets provided by a SCINDA GNSS receiver installed in the Lisbon airport area from November of 2014 to July of 2019. The installed equipment is a NovAtel EURO4 with a JAVAD Choke-Ring antenna. The data are in an archived format and include the general messages on quality of records (*.msg), RANGE files (*.rng), raw observables as the signal-to-noise (S/N) ratios, pseudoranges and phases (*.obs), receiver position information (*.psn), ionosphere scintillations monitor (ISMRB; *.ism) and ionospheric parameters: total electron content (TEC), rate of change of TEC index (ROTI), and the scintillation index S4 (*.scn). The presented data cover the full 2015 year. The raw data are of 1-minute resolution and available for each of the receiver-satellite pairs. The processing and the analysis of the ionosphere scintillation datasets can be done using a specific "SCINDA-Iono" toolbox for the MATLAB developed by T. Barlyaeva (2019) and available online via MathWorks File Exchange system. The toolbox calculates 1-hour means for ionospheric parameters for each of the available receiver-satellite pairs and averaged over all available satellites during the analyzed hour. Here we present the processed data for the following months in 2015: March, June, October, and December. The months were selected as containing most significant geomagnetic events of 2015. The 1-hour means for other months can be obtained from the raw data using the aforementioned toolbox. The provided datasets are interesting for the GNSS and ionosphere based scientific communities.
Graph Signal Processing: Vertex Multiplication
Authors: Aykut Koç, Yigit E. Bayiz
Date: 2020-07-09 00:00:00
Tasks: Time Series, Time Series Analysis

On the Euclidean domains of classical signal processing, linking of signal samples to the underlying coordinate structure is straightforward. While graph adjacency matrices totally define the quantitative associations among the underlying graph vertices, a major problem in graph signal processing is the lack of explicit association of vertices with an underlying quantitative coordinate structure. To make this link, we propose an operation, called the vertex multiplication, which is defined for graphs and can operate on graph signals. Vertex multiplication, which generalizes the coordinate multiplication operation in time series signals, can be interpreted as an operator which assigns a coordinate structure to a graph. By using the graph domain extension of differentiation and graph Fourier transform (GFT), vertex multiplication is defined such that it shows Fourier duality, which states that differentiation and coordinate multiplication operations are duals of each other under Fourier transformation (FT). The proposed definition is shown to reduce to coordinate multiplication for graphs corresponding to time series. Numerical examples are also presented.
Achievable Rates of Opportunistic Cognitive Radio Systems Using Reconfigurable Antennas with Imperfect Sensing and Channel Estimation
Authors: Hassan Yazdani, Azadeh Vosoughi, Xun Gong
Date: 2020-07-08 00:00:00

We consider an opportunistic cognitive radio (CR) system in which secondary transmitter (SUtx) is equipped with a reconfigurable antenna (RA). Utilizing the beam steering capability of the RA, we regard a design framework for integrated sector-based spectrum sensing and data communication. In this framework, SUtx senses the spectrum and detects the beam corresponding to active primary user's (PU) location. SUtx also sends training symbols (prior to data symbols), to enable channel estimation at secondary receiver (SUrx) and selection of the strongest beam between SUtx-SUrx for data transmission. We establish a lower bound on the achievable rates of SUtx-SUrx link, in the presence of spectrum sensing and channel estimation errors, and errors due to incorrect detection of the beam corresponding to PU's location and incorrect selection of the strongest beam for data transmission. We formulate a novel constrained optimization problem, aiming at maximizing the derived achievable rate lower bound subject to average transmit and interference power constraints. We optimize the durations of spatial spectrum sensing and channel training as well as data symbol transmission power. Our numerical results demonstrate that between optimizing spectrum sensing and channel training durations, the latter is more important for providing higher achievable rates.
Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
Authors: Marcel Schloz, Thomas C. Pekin, Zhen Chen, Wouter Van den Broek, David A. Muller, Christoph T. Koch
Date: 2020-05-04 00:00:00

The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings. Furthermore, reconstruction of the sample-induced phase shift is typically limited by uncertainty in the experimental parameters and finite sample thicknesses. Presented is a conjugate gradient descent algorithm, regularized optimization for ptychography (ROP), that recovers the partially known experimental parameters along with the phase shift, improves resolution by incorporating the multislice formalism to treat finite sample thicknesses, and includes regularization in the optimization process, thus achieving reliable results from noisy data with severely reduced and underdetermined information.
A Novel Heap-based Pilot Assignment for Full Duplex Cell-Free Massive MIMO with Zero-Forcing
Authors: Hieu V. Nguyen, Van-Dinh Nguyen, Octavia A. Dobre, Shree Krishna Sharma, Symeon Chatzinotas, Björn Ottersten, Oh-Soon Shin
Date: 2020-07-08 00:00:00
Tasks: Robust Design

This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multipleoutput (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources. To enable the incorporation of FD technology in CF-mMIMO systems, we propose a novel heapbased pilot assignment algorithm, which not only can mitigate the effects of pilot contamination but also reduce the involved computational complexity. Then, we formulate a robust design problem for spectral efficiency (SE) maximization in which the power control and AP-UE association are jointly optimized, resulting in a difficult mixed-integer nonconvex programming. To solve this problem, we derive a more tractable problem before developing a very simple iterative algorithm based on inner approximation method with polynomial computational complexity. Numerical results show that our proposed methods with realistic parameters significantly outperform the existing approaches in terms of the quality of channel estimate and SE.
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing
Authors: Valentin Leplat, Nicolas Gillis, Cédric Févotte
Date: 2020-07-08 00:00:00
Tasks: blind source separation, Hyperspectral Unmixing

Many datasets are obtained as a resolution trade-off between two adversarial dimensions; for example between the frequency and the temporal resolutions for the spectrogram of an audio signal, and between the number of wavelengths and the spatial resolution for a hyper/multi-spectral image. To perform blind source separation using observations with different resolutions, a standard approach is to use coupled nonnegative matrix factorizations (NMF). Most previous works have focused on the least squares error measure, which is the $\beta$-divergence for $\beta = 2$. In this paper, we formulate this multi-resolution NMF problem for any $\beta$-divergence, and propose an algorithm based on multiplicative updates (MU). We show on numerical experiments that the MU are able to obtain high resolutions in both dimensions on two applications: (1) blind unmixing of audio spectrograms: to the best of our knowledge, this is the first time a coupled NMF model is used in this context, and (2) the fusion of hyperspectral and multispectral images: we show that the MU compete favorable with state-of-the-art algorithms in particular in the presence of non-Gaussian noise.
Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information
Authors: Rasoul Shafipour, Gonzalo Mateos
Date: 2020-07-07 00:00:00
Tasks: Graph Learning, Link Prediction

We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and effect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations' covariance matrix and the so-called graph shift operator (GSO -- a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations' empirical covariance matrix. For streaming data said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.
Using Bluetooth Low Energy (BLE) Signal Strength Estimation to Facilitate Contact Tracing for COVID-19
Authors: Gary F. Hatke, Monica Montanari, Swaroop Appadwedula, Michael Wentz, John Meklenburg, Louise Ivers, Jennifer Watson, Paul Fiore
Date: 2020-06-28 00:00:00
Tasks: Privacy Preserving

The process of contact tracing to reduce the spread of highly infectious and life-threatening diseases has traditionally been a primarily manual process managed by public health entities. This process becomes challenged when faced with a pandemic of the proportions of SARS-CoV2. Digital contact tracing has been proposed as way to augment manual contact tracing and lends itself to widely proliferated devices such as cell phones and wearables. This paper describes a method and analysis of determining whether two cell phones, carried by humans, were in persistent contact of no more than 6 feet over 15 minutes using Bluetooth Low Energy signals. The paper describes the approach to detecting these signals, as well as a data-driven performance analysis showing that larger numbers of samples coupled with privacy preserving auxiliary information improves detection performance.
Crossterm-Free Time-Frequency Representation Exploiting Deep Convolutional Neural Network
Authors: Shuimei Zhang, Yimin D. Zhang
Date: 2020-07-07 00:00:00
Tasks: Compressive Sensing

Bilinear time-frequency representations (TFRs) provide high-resolution time-varying frequency characteristics of nonstationary signals. However, they suffer from crossterms due to the bilinear nature. Existing crossterm-reduced TFRs focus on optimized kernel design which amounts to low-pass weighting or masking in the ambiguity function domain. Optimization of fixed and adaptive kernels are difficult, particularly for complicated signals whose autoterms and crossterms overlap in the ambiguity function. In this letter, we develop a new method to offer high-resolution TFRs of nonstationary signals with crossterms effectively suppressed. The proposed method exploits a deep convolutional neural network which is trained to construct crossterm-free TFRs. The effectiveness of the proposed method is verified by simulation results which clearly show desirable autoterm preservation and crossterm mitigation capabilities. The proposed technique significantly outperforms state-of-the-art time-frequency analysis algorithms based on adaptive kernels and compressive sensing techniques.
STBC-Aided Cooperative NOMA with Timing Offsets, Imperfect Successive Interference Cancellation, and Imperfect Channel State Information
Authors: Muhammad Waseem Akhtar, Syed Ali Hassan, Sajid Saleem, Haejoon Jung
Date: 2020-07-07 00:00:00
Tasks: Fairness

The combination of non-orthogonal multiple access(NOMA) and cooperative communications can be a suitable solution for fifth generation (5G) and beyond 5G (B5G) wireless systems with massive connectivity, because it can provide higher spectral efficiency, lower energy consumption, and improved fairness compared to the non-cooperative NOMA. However,the receiver complexity in the conventional cooperative NOMA increases with increasing number of users owing to successive interference cancellation (SIC) at each user. Space time block code-aided cooperative NOMA (STBC-CNOMA) offers less numbers of SIC as compared to that of conventional cooperative NOMA. In this paper, we evaluate the performance of STBC-CNOMA under practical challenges such as imperfect SIC, imperfect timing synchronization between distributed cooperating users, and imperfect channel state information (CSI). We derive closed-form expressions of the received signals in the presence of such realistic impairments and then use them to evaluate outage probability. Further, we provide intuitive insights into the impact of each impairment on the outage performance through asymptotic analysis at high transmit signal-to-noise ratio. We also compare the complexity of STBC-CNOMA with existing cooperative NOMA protocols for a given number of users. In addition, through analysis and simulation, we observe that the impact of the imperfect SIC on the outage performance of STBC-CNOMA is more significant compared to the other two imperfections. Therefore, considering the smaller number of SIC in STBC-CNOMA compared to the other cooperative NOMA protocols, STBC-CNOMA is an effective solution to achieve high reliability for the same SIC imperfection condition.
Off-grid Multi-Source Passive Localization Using a Moving Array
Authors: Dan Bao, Changlong Wang, Jingjing Cai
Date: 2020-07-07 00:00:00
Tasks: Compressive Sensing

A novel direct passive localization technique through a single moving array is proposed in this paper using the sparse representation of the array covariance matrix in spatial domain. The measurement is constructed by stacking the vectorized version of all the array covariance matrices at different observing positions. First, an on-grid compressive sensing (CS) based method is developed, where the dictionary is composed of the steering vectors from the searching grids to the observing positions. Convex optimization is applied to solve the `1-norm minimization problem. Second, to get much finer target positions, we develop an on-grid CS based method, where the majorization-minimization technique replaces the atan-sum objective function in each iteration by a quadratic convex function which can be easily minimized. The objective function,atan-sum, is more similar to `0-norm, and more sparsity encouraging than the log-sum function.This method also works more robustly at conditions of low SNR, and fewer observing positions are needed than in the traditional ones. The simulation experiments verify the promises of the proposed algorithm.
Relay Assisted OFDM with Subcarrier Number Modulation in Multi-Hop Cooperative Networks
Authors: Shuping Dang, Jiusi Zhou, Basem Shihada, Mohamed-Slim Alouini
Date: 2020-07-07 00:00:00
Tasks: Diversity

Orthogonal frequency-division multiplexing (OFDM) with subcarrier number modulation (OFDM-SNM) manifests its superior nature of high spectral efficiency (SE) and low complexity for signal estimation. To exploit the spatial gain of OFDM-SNM, we propose a relay assisted OFDM-SNM scheme for multi-hop cooperative systems in this letter. It is stipulated that a relay operated by decode-and-forward (DF) and half-duplex (HD) protocols exists in each hop. We analyze the outage performance of the relay assisted OFDM-SNM system. The average outage probability is approximated in closed form. Moreover, to reveal the diversity and coding gains of relay assisted OFDM-SNM, we explore the asymptotic outage performance at high signal-to-noise ratio (SNR) by power series expansion. To verify the improvement on outage performance and energy efficiency, we carry out the comparison among different multi-hops systems with traditional OFDM-SNM fixing a certain distance from source to destination. Simulation results corroborate the derived outage probabilities and provide insight into the proposed system in this letter.
On the Construction of Polar Codes in the Middleton Class-A Channels
Authors: Ammar Hadi, Emad Alsusa, Khaled M. Rabie
Date: 2020-07-07 00:00:00

Although power line communication (PLC) systems are available everywhere, unfortunately these systems are not suitable for information transmission due to the effects of the impulsive noise. Therefore, many previous studies on channel codes have been carried out for the purpose of reducing the impulsive noise in such channels. This paper investigates some methods for the construction of polar codes under PLC systems in the presence of Middleton class-A noise. We discuss here the most feasible construction methods which already have been adopted with other channels. In addition, we present an illustrative example for the construction in these methods and also we discuss a comparison between the methods in terms of performance.
Full Duplex Integrated Access and Backhaul for 5G NR: Analyses and Prototype Measurements
Authors: Gee Yong Suk, Soo-Min Kim, Jongwoo Kwak, Seop Hur, Eunyong Kim, Chan-Byoung Chae
Date: 2020-07-07 00:00:00

Integrated access and backhaul (IAB) frameworks for 5G new radio (NR) as a cost-effective alternative to the wired backhaul have been investigated by 3GPP. A promising solution for this framework is the integration of full duplex (FD) technologies to enhance the spectral efficiency and make an efficient use of the network resources-termed FD IAB. However, FD IAB presents significant technical challenges, such as self-interference (SI) in the IAB framework, which may cast doubt over the feasibility of FD IAB. In this article, we present a brief tutorial of the FD IAB framework and its enabling technologies. We then numerically evaluate and discuss the link-level SI reduction and the system-level downlink throughput performance of the FD IAB. Finally, we validate the feasibility of FD IAB using 28 GHz hardware prototype measurements of propagation-domain SI suppression. Our numerical evaluations and prototype measurements confirm that the FD IAB serves as a promising framework for 5G NR.
Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes
Authors: Alberto Natali, Mario Coutino, Geert Leus
Date: 2020-07-07 00:00:00

Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart grids, social networks, etc., the lack of well-defined interaction weights hinders the ability to model the observed networked data using graph filters. Therefore, in this paper, we focus on the joint identification of coefficients and graph weights defining the graph filter that best models the observed input/output network data. While these two problems have been mostly addressed separately, we here propose an iterative method that exploits the knowledge of the support of the graph for the joint identification of graph filter coefficients and edge weights. We further show that our iterative scheme guarantees a non-increasing cost at every iteration, ensuring a globally-convergent behavior. Numerical experiments confirm the applicability of our proposed approach.