pwc-archive/papers-with-abstracts huggingface dataset:
MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving
Authors: Xiangyu Gao, Sumit Roy, Guanbin Xing
Date: 2021-01-22 00:00:00
Tasks: Autonomous Driving, Radar odometry

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform.
Multi-objective design of multilayer microwave dielectric filters using artificial bee colony algorithm
Authors: Abdurrahim Toktas
Date: 2021-01-22 00:00:00
Tasks: Electrical Engineering, Metaheuristic Optimization

Artificial bee colony algorithm (ABC) developed by inspiring the foraging phenomena of the natural honey bees is a simple and powerful metaheuristic optimization algorithm. The performance of single objective ABC performance has been well demonstrated by implemented to different design optimization problems from elec-trical and mechanical to civil engineering. Its efficacy is anticipated in the multi-objective design optimization of multilayer microwave dielectric filters (MMDFs) which is a challenging multi-objective problem falls into the context of electromagnetic (EM) design in electrical engineering. The MMDF is composed of superimposed multiple dielectric layers in order to pass or stop the EM wave at the desired frequency bands. An accurate dual-objective EM model of MMDF is constituted to be the total reflection coefficients for oblique incident wave angular range with transverse electric (TE) or transverse magnetic (TM) polarizations. The objective functions depend on the total reflection (TR) at the pass and stop band re-gions. Three types of the MDFs that are low pass (LP), high pass (HP) and band pass (BP) MMDFs are optimally designed across the constituted dual-objective EM model with a frequency dependent material database through using the multi-objective ABC (MO-ABC) strategy. The Pareto optimal solutions are refined within the possible solutions for synchronously minimizing the objective func-tions. The global optimal MMDF designs are selected from the Pare-to optimal solutions by providing the trade-off therein between the objective functions. The performance study regarding the frequen-cy characteristics of the designed MMDFs are well presented via numerical and graphical results. It is hence demonstrated that MO-ABC is even versatile and robust in the multi-objective design of MMDFs.
Efficient Near-Field Imaging Using Cylindrical MIMO Arrays
Authors: Shiyong Li, Shuoguang Wang, Moeness G. Amin, Guoqiang Zhao
Date: 2021-01-22 00:00:00
Tasks: ARC

Multiple-input multiple-output (MIMO) array based millimeter-wave (MMW) imaging has a tangible prospect in applications of concealed weapons detection. A near-field imaging algorithm based on wavenumber domain processing is proposed for a cylindrical MIMO array scheme with uniformly spaced transmit and receive antennas over both the vertical and horizontal-arc directions. The spectrum aliasing associated with the proposed MIMO array is analyzed through a zero-filling discrete-time Fourier transform. The analysis shows that an undersampled array can be used in recovering the MMW image by a wavenumber domain algorithm. The requirements for the antenna inter-element spacing of the MIMO array are delineated. Numerical simulations as well as comparisons with the backprojection (BP) algorithm are provided to demonstrate the effectiveness of the proposed method.
Ultra-wideband free-space optical phase stabilisation
Authors: Benjamin P. Dix-Matthews, David R. Gozzard, Skevos F. E. Karpathakis, Charles T. Gravestock, Sascha W. Schediwy
Date: 2020-10-08 00:00:00

Free-space optical (FSO) communications has the potential to revolutionize wireless communications due to its advantages of inherent security, high-directionality, high available bandwidth and small physical footprint. The effects of atmospheric turbulence currently limit the performance of FSO communications. In this letter, we demonstrate a system capable of indiscriminately suppressing the atmospheric phase noise encountered by independent optical signals spread over a range of 7.2 THz (encompassing the full optical C-Band), by actively phase stabilizing a primary optical signal at 193.1 THz (1552 nm). We show ~30 dB of indiscriminate phase stabilization over the full range, down to average phase noise at 10 Hz of -39.6 dBc/Hz when using an acousto-optic modulator (AOM) as a Doppler actuator, and -39.9 dBc/Hz when using a fiber-stretcher as group-delay actuator to provide the phase-stabilization system's feedback. We demonstrate that this suppression is limited by the noise of the independent optical signals, and that the expected achievable suppression is more than 40 dB greater, reaching around -90 dB/Hz at 10 Hz. We conclude that 40 Tbps ground-to-space FSO transmission would be made possible with the combination of our stabilization system and other demonstrated technologies.
Minimum Length Scheduling for Multi-cell Full Duplex Wireless Powered Communication Networks
Authors: Muhammad Shahid Iqbal, Yalcin Sadi, Sinem Coleri
Date: 2021-01-20 00:00:00
Tasks: Scheduling

In this paper, we investigate a novel minimum length scheduling problem to determine the optimal power control, and scheduling for constant and continuous rate models, while considering concurrent transmission of users, energy causality, maximum transmit power and traffic demand constraints. The formulated optimization problems are shown to be non-convex and combinatorial in nature, thus, difficult to solve for the global optimum. As a solution strategy, first, we propose optimal polynomial time algorithms for the power control problem considering constant and continuous rate models based on the evaluation of Perron-Frobenius conditions and usage of bisection method, respectively. Then, the proposed optimal power control solutions are used to determine the optimal transmission time for a subset of users that will be scheduled by the scheduling algorithms. For the constant rate scheduling problem, we propose a heuristic algorithm that aims to maximize the number of concurrently transmitting users by maximizing the allowable interference on each user without violating the signal-to-noise-ratio (SNR) requirements. For the continuous rate scheduling problem, we define a penalty function representing the advantage of concurrent transmission over individual transmission of the users. Following the optimality analysis of the penalty metric and demonstration of the equivalence between schedule length minimization and minimization of the sum of penalties, we propose a heuristic algorithm based on the allocation of the concurrently transmitting users with the goal of minimizing the sum penalties over the schedule. Through extensive simulations, we demonstrate that the proposed algorithm outperforms the successive transmission and concurrent transmission of randomly selected users for different HAP transmit powers, network densities and network size.
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem
Authors: Yiran He, Hoi-To Wai
Date: 2021-01-20 00:00:00
Tasks: Denoising, Graph Learning

This paper is concerned with the blind identification of graph filters from graph signals. Our aim is to determine if the graph filter generating the graph signals is first-order lowpass without knowing the graph topology. Notice that lowpass graph filter is a common prerequisite for applying graph signal processing tools for sampling, denoising, and graph learning. Our method is inspired by the Perron Frobenius theorem, which observes that for first-order lowpass graph filter, the top eigenvector of output covariance would be the only eigenvector with elements of the same sign. Utilizing this observation, we develop a simple detector that answers if a given data set is produced by a first-order lowpass graph filter. We analyze the effects of finite-sample, graph size, observation noise, strength of lowpass filter, on the detector's performance. Numerical experiments on synthetic and real data support our findings.
Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification
Authors: Alessandro Bria, Claudio Marrocco, Francesco Tortorella
Date: 2021-01-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Motor Imagery

Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor images recognized from the electroencephalogram (EEG) to control commands. EEG patterns of different imagination tasks, e.g. hand and foot movements, are effectively classified with machine learning techniques using band power features. Recently, also Convolutional Neural Networks (CNNs) that learn both effective features and classifiers simultaneously from raw EEG data have been applied. However, CNNs have two major drawbacks: (i) they have a very large number of parameters, which thus requires a very large number of training examples; and (ii) they are not designed to explicitly learn features in the frequency domain. To overcome these limitations, in this work we introduce Sinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass and depthwise convolutional filters. Experimental results obtained on the publicly available BCI Competition IV Dataset 2a show that our approach outperforms reference methods in terms of classification accuracy.
Improved Coefficients for the Karagiannidis-Lioumpas Approximations and Bounds to the Gaussian Q-Function
Authors: Islam M. Tanash, Taneli Riihonen
Date: 2021-01-19 00:00:00

We revisit the Karagiannidis-Lioumpas (KL) approximation of the Q-function by optimizing its coefficients in terms of absolute error, relative error and total error. For minimizing the maximum absolute/relative error, we describe the targeted uniform error functions by sets of nonlinear equations so that the optimized coefficients are the solutions thereof. The total error is minimized with numerical search. We also introduce an extra coefficient in the KL approximation to achieve significantly tighter absolute and total error at the expense of unbounded relative error. Furthermore, we extend the KL expression to lower and upper bounds with optimized coefficients that minimize the error measures in the same way as for the approximations.
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems
Authors: Bo Lin, Feifei Gao, Shun Zhang, Ting Zhou, Ahmed Alkhateeb
Date: 2021-01-18 00:00:00
Tasks: Combinatorial Optimization

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.
Performance Analysis and Codebook Design for mmWave Beamforming System with Beam Squint
Authors: Hongkang Yu, Pengxin Guan, Yiru Wang, Yuping Zhao
Date: 2021-01-18 00:00:00

Beamforming technology is widely used in millimeter wave systems to combat path losses, and beamformers are usually selected from a predefined codebook. Unfortunately, the traditional codebook design neglects the beam squint effect, and this will cause severe performance degradation when the bandwidth is large. In this letter, we consider that a codebook with fixed size is adopted in the wideband beamforming system. First, we analyze how beam squint affects system performance when all beams have the same width. The expression of average spectrum efficiency is derived based on the ideal beam pattern. Next, we formulate the optimization problem to design the optimal codebook. Simulation results demonstrate that the proposed codebook deals with beam squint by spreading the beam coverage and significantly mitigates the performance degradation.
Learning to Select for MIMO Radar based on Hybrid Analog-Digital Beamforming
Authors: Zhaoyi Xu, Fan Liu, Konstantinos Diamantaras, Christos Masouros, Athina Petropulu
Date: 2021-01-18 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we propose an energy-efficient radar beampattern design framework for a Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a machine learning approach to synthesize the probing beampattern based on a small number of RF chains and antennas. By leveraging a combination of softmax neural networks, the proposed solution is able to achieve a desirable beampattern with high accuracy.
Sparse Array Beamformer Design for Active and Passive Sensing
Authors: Syed Ali Hamza
Date: 2021-01-18 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, parameter estimation

Sparse sensor placement, with various design objectives, has successfully been employed in diverse application areas, particularly for enhanced parameter estimation and receiver performance. The sparse array design criteria are generally categorized into environment-independent and environment-dependent performance metrics. The former are largely benign to the underlying environment and, in principle, seek to maximize the spatial degrees of freedom by extending the coarray aperture. Environment-dependent objectives, on the other hand, consider the operating conditions characterized by emitters and targets in the array field of view, in addition to receiver noise. In this regard, applying such objectives renders the array configuration as well as the array weights time-varying in response to dynamic and changing environment. This work is geared towards designing environment-dependent sparse array beamformer to improve the output signal-to-interference and noise ratio using both narrowband and wideband signal platforms. One key challenge in implementing the data-dependent approaches is the lack of knowledge of exact or estimated values of the data autocorrelation function across the full sparse array aperture. At the core of this work is to address the aforementioned issues by devising innovative solutions using convex optimization and machine learning tools, structured sparsity concepts, low rank matrix completion schemes and fusing the environment-dependent and environment-independent deigns by developing a hybrid approach.
Robust Energy-Efficient Resource Management, SIC Ordering, and Beamforming Design for MC MISO-NOMA Enabled 6G
Authors: Abolfazl Zakeri, Ata Khalili, Mohammad Reza Javan, Nader Mokari, Eduard A Jorswieck
Date: 2021-01-17 00:00:00
Tasks: Management

This paper studies a novel approach for successive interference cancellation (SIC) ordering and beamforming in a multiple antennas non-orthogonal multiple access (NOMA) network with multi-carrier multi-user setup. To this end, we formulate a joint beamforming design, subcarrier allocation, user association, and SIC ordering algorithm to maximize the worst-case energy efficiency (EE). The formulated problem is a non-convex mixed integer non-linear programming (MINLP) which is generally difficult to solve. To handle it, we first adopt the linearizion technique as well as relaxing the integer variables, and then we employ the Dinkelbach algorithm to convert it into a more mathematically tractable form. The adopted non-convex optimization problem is transformed into an equivalent rank-constrained semidefinite programming (SDP) and is solved by SDP relaxation and exploiting sequential fractional programming. Furthermore, to strike a balance between complexity and performance, a low complex approach based on alternative optimization is adopted. Numerical results unveil that the proposed SIC ordering method outperforms the conventional existing works addressed in the literature.
An embedded multichannel sound acquisition system for drone audition
Authors: Michael Clayton, Lin Wang, Andrew McPherson, Andrea Cavallaro
Date: 2021-01-17 00:00:00

Microphone array techniques can improve the acoustic sensing performance on drones, compared to the use of a single microphone. However, multichannel sound acquisition systems are not available in current commercial drone platforms. To encourage the research in drone audition, we present an embedded sound acquisition and recording system with eight microphones and a multichannel sound recorder mounted on a quadcopter. In addition to recording and storing locally the sound from multiple microphones simultaneously, the embedded system can connect wirelessly to a remote terminal to transfer audio files for further processing. This will be the first stage towards creating a fully embedded solution for drone audition. We present experimental results obtained by state-of-the-art drone audition algorithms applied to the sound recorded by the embedded system.
Fourier, Gabor, Morlet or Wigner: Comparison of Time-Frequency Transforms
Authors: Stefan Scholl
Date: 2021-01-17 00:00:00
Tasks: 2D Object Detection

In digital signal processing time-frequency transforms are used to analyze time-varying signals with respect to their spectral contents over time. Apart from the commonly used short-time Fourier transform, other methods exist in literature, such as the Wavelet, Stockwell or Wigner-Ville transform. Consequently, engineers working on digital signal processing tasks are often faced with the question which transform is appropriate for a specific application. To address this question, this paper first briefly introduces the different transforms. Then it compares them with respect to the achievable resolution in time and frequency and possible artifacts. Finally, the paper contains a gallery of time-frequency representations of numerous signals from different fields of applications to allow for visual comparison.
Design and Implementation of Low Complexity Reconfigurable Filtered-OFDM based LDACS
Authors: N. Agrawal, A. Ambede, S. J. Darak, A. P. Vinod, A. S. Madhukumar
Date: 2020-09-21 00:00:00

L-band Digital Aeronautical Communication System (LDACS) aims to exploit vacant spectrum in L-band via spectrum sharing, and orthogonal frequency division multiplexing (OFDM) is the currently accepted LDACS waveform. Recently, various works dealing with improving the spectrum utilization of LDACS via filtering/windowing are being explored. In this direction, we propose an improved and low complexity reconfigurable filtered OFDM (LRef-OFDM) based LDACS using novel interpolation and masking based multi-stage digital filter. The proposed filter is designed to meet the stringent non-uniform spectral attenuation requirements of LDACS standard. It offers significantly lower complexity as well as higher transmission bandwidth than state-of-the-art approaches. We also integrate the proposed filter in our end-to-end LDACS testbed realized using Zynq System on Chip and analyze the performance in the presence of $L$-band legacy user interference as well as LDACS specific wireless channels. Via extensive experimental results, we demonstrate the superiority of the proposed LRef-OFDM over OFDM and Filtered-OFDM based LDACS in terms of power spectral density, bit error rate, implementation complexity, and group delay parameters.
A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond
Authors: Ji Bian, Cheng-Xiang Wang, Xiqi Gao, Xiaohu You, Minggao Zhang
Date: 2021-01-17 00:00:00

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.
A General 3D Non-Stationary Massive MIMO GBSM for 6G Communication Systems
Authors: Yi Zheng, Long Yu, Runruo Yang, Cheng-Xiang Wang
Date: 2021-01-17 00:00:00

A general three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) geometry-based stochastic model (GBSM) for the sixth generation (6G) communication systems is proposed in the paper. The novelty of the model is that the model is designed to cover a variety of channel characteristics, including space-time-frequency (STF) non-stationarity, spherical wavefront, spatial consistency, channel hardening, etc. Firstly, the introduction of the twin-cluster channel model is given in detail. Secondly, the key statistical properties such as space-time-frequency correlation function (STFCF), space cross-correlation function (CCF), temporal autocorrelation function (ACF), frequency correlation function (FCF), and performance indicators, e.g., singular value spread (SVS), and channel capacity are derived. Finally, the simulation results are given and consistent with some measurements in relevant literatures, which validate that the proposed channel model has a certain value as a reference to model massive MIMO channel characteristics.