pwc-archive/papers-with-abstracts huggingface dataset:
Wiener-Hammerstein model and its learning for nonlinear digital pre-distortion of optical transmitters
Authors: Takeo Sasai, Masanori Nakamura, Etsushi Yamazaki, Asuka Matsushita, Seiji Okamoto, Kengo Horikoshi, Yoshiaki Kisaka
Date: 2020-12-15 00:00:00

We present a simple nonlinear digital pre-distortion (DPD) of optical transmitter components, which consists of concatenated blocks of a finite impulse response (FIR) filter, a memoryless nonlinear function and another FIR filter. The model is a Wiener-Hammerstein (WH) model and has essentially the same structure as neural networks or multilayer perceptions. This awareness enables one to achieve complexity-efficient DPD owing to the model-aware structure and exploit the well-developed optimization scheme in the machine learning field. The effectiveness of the method is assessed by electrical and optical back-to-back (B2B) experiments, and the results show that the WH DPD offers a 0.52-dB gain in signal-to-noise ratio (SNR) and 6.0-dB gain in optical modulator output power at a fixed SNR over linear-only DPD.
Pointing Error Analysis of Optically Pre-Amplified Pulse Position Modulation Receivers
Authors: Konstantinos Yiannopoulos, Nikolaos C. Sagias, Anthony C. Boucouvalas
Date: 2020-12-14 00:00:00
Tasks: Position

We present analytical results on the effect of pointing errors on the average bit-error probability (ABEP) of optically pre-amplified pulse-position modulation (PPM) receivers. The results show that the beam width plays a key role in the ABEP and that a significant power penalty is introduced by utilizing sub-optimal widths, especially when pointing errors incorporate a jitter component. We also present the optimisation the beam width for a number of pointing error scenarios and show that increasing beam widths are required as additional signal energy becomes available. The optimal beam width is also affected by the PPM modulation order and the optical noise modes, with more energy efficient (higher modulation order and lower noise) systems allowing for broader beams.
Is FFT Fast Enough for Beyond-5G Communications?
Authors: Saulo Queiroz, João P. Vilela, Edmundo Monteiro
Date: 2020-12-14 00:00:00
Tasks: Open-Ended Question Answering

In this paper, we study the impact of computational complexity on the throughput limits of the {\color{black}fast Fourier transform (FFT)} algorithm for {\color{black}orthogonal frequency division multiplexing(OFDM)} waveforms. Based on the spectro-computational {\color{\corcorrecao}complexity} (SC) analysis, {\color{\corcorrecao} we verify that the complexity of an $N$-point FFT grows faster than the number of bits in the OFDM symbol.} Thus, we show that FFT nullifies the OFDM throughput on $N$ unless the $N$-point discrete Fourier transform (DFT) problem verifies as $\Omega(N)$, which remains a "fascinating" open question in theoretical computer science. Also, because FFT demands $N$ to be a power of two $2^i$ ($i>0$), the spectrum widening leads to an exponential complexity on $i$, i.e. $O(2^ii)$. To overcome these limitations, {\color{\corcorrecao} we consider the alternative frequency-time transform formulation of vector OFDM (V-OFDM), in which an $N$-point FFT is replaced by $N/L$ ($L$$>$$0$) smaller {\color{\corcorrecao}$L$-point} FFTs to mitigate the cyclic prefix overhead of OFDM. Building on that, we replace FFT by the straightforward DFT algorithm to release the V-OFDM parameters from growing as powers of two and to benefit from flexible numerology (e.g., $L=3$, $N=156$). Besides, by setting $L$ to $\Theta(1)$, the resulting solution can run linearly on $N$ (rather than exponentially on $i$) while sustaining a non null throughput as $N$ grows. }
On Low-Rank Hankel Matrix Denoising
Authors: Mingzhou Yin, Roy S. Smith
Date: 2020-12-14 00:00:00
Tasks: Denoising

The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure. This makes it possible to denoise the data by estimating the underlying structured low-rank matrix. However, standard low-rank approximation approaches are not guaranteed to perform well in estimating the noise-free matrix. In this paper, recent results in matrix denoising by singular value shrinkage are reviewed. A novel approach is proposed to solve the low-rank Hankel matrix denoising problem by using an iterative algorithm in structured low-rank approximation modified with data-driven singular value shrinkage. It is shown numerically in both the input-output trajectory denoising and the impulse response denoising problems, that the proposed method performs the best in terms of estimating the noise-free matrix among existing algorithms of low-rank matrix approximation and denoising.
Fiber Nonlinearity Mitigation by Short-Length Probabilistic Constellation Shaping for Pilot-Aided Signaling
Authors: Tobias Fehenberger, Helmut Griesser, Jörg-Peter Elbers
Date: 2020-12-14 00:00:00

Probabilistic constellation shaping (PCS) offers a significant performance improvement over uniform signaling. It was recently discovered that long blocks are not required to achieve maximum shaping gain when transmitting over the nonlinear fiber channel because short-length PCS effectively mitigates fiber nonlinear interference (NLI). The reason for this behavior is that short-length PCS implicitly induces some temporal properties in the shaped transmit sequence that are beneficial for the fiber-optic channel. To achieve robust data-aided digital signal processing of high-order QAM, periodic quaternary phase shift keying pilots are typically inserted into the high-order QAM transmit sequence. In this work, we investigate in simulations the effect of such pilot-aided signaling on NLI mitigation. Albeit modifying the temporal properties of the shaped transmit sequence, a pilot rate of 1/32 is found to not alter the beneficial effects of short-length PCS. The operation meaning of this finding is that even with pilot-aided signaling, long PCS block lengths are not required for maximum shaping gain.
Energy Efficient Competitive Resource Allocation in MIMO networks
Authors: Guillaume Thiran, Ivan Stupia, Luc Vandendorpe
Date: 2020-12-14 00:00:00

This paper considers the competitive resource allocation problem in Multiple-Input Multiple-Output (MIMO) interfering channels, when users maximize their energy efficiency. Considering each transmitter-receiver pair as a selfish player, conditions on the existence and uniqueness of the Nash equilibrium of the underlying noncooperative game are obtained. A decentralized asynchronous algorithm is proven to converge towards this equilibrium under the same conditions. Two frameworks are considered for the analysis of this game. On the one hand, the game is rephrased as a Quasi-Variational Inequality (QVI). On the other hand, the best response of the players is analyzed in light of the contraction mappings. For this problem, the contraction approach is shown to lead to tighter results than the QVI one. When specializing the obtained results to OFDM networks, the obtained conditions appear to significantly outperform state-of-the-art works, and to lead to much simpler decentralized algorithms. Numerical results finally assess the obtained conditions in different settings.
Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP
Authors: Xianming Li, Yongwei Huang, Wing-Kin Ma
Date: 2020-12-14 00:00:00
Tasks: Quantization, valid

Consider a robust multiple-input single-output downlink beamforming optimization problem in a frequency division duplexing system. The base station (BS) sends training signals to the users, and every user estimates the channel coefficients, quantizes the gain and the direction of the estimated channel and sends them back to the BS. Suppose that the channel state information at the transmitter is imperfectly known mainly due to the channel direction quantization errors, channel estimation errors and outdated channel effects. The actual channel is modeled as in an uncertainty set composed of two inequality homogeneous and one equality inhomogeneous quadratic constraints, in order to account for the aforementioned errors and effects. Then the transmit power minimization problem is formulated subject to robust signal-to-noise-plus-interference ratio constraints. Each robust constraint is transformed equivalently into a quadratic matrix inequality (QMI) constraint with respect to the beamforming vectors. The transformation is accomplished by an equivalent phase rotation process and the strong duality result for a quadratically constrained quadratic program. The minimization problem is accordingly turned into a QMI problem, and the problem is solved by a restricted linear matrix inequality relaxation with additional valid convex constraints. Simulation results are presented to demonstrate the performance of the proposed method, and show the efficiency of the restricted relaxation.
Adaptive User Pairing for Downlink NOMA System with Imperfect SIC
Authors: Nemalidinne Siva Mouni, Abhinav Kumar, Prabhat K. Upadhyay
Date: 2020-12-13 00:00:00

Non-orthogonal multiple access (NOMA) has been recognized as a key driving technology for the fifth generation (5G) and beyond 5G cellular networks. For a practical dowlink NOMA system with imperfect successive interference cancellation (SIC), we derive bounds on channel coefficients and power allocation factors between NOMA users to achieve higher rates than an equivalent orthogonal multiple access (OMA) system. We propose an adaptive user pairing (A-UP) algorithm for NOMA systems. Through extensive simulations, we show that NOMA with imperfect SIC is not always superior to OMA. Further, the proposed A-UP algorithm results in better performance than state-of-the-art NOMA pairing algorithms in presence of SIC imperfections.
Uplink Achievable Rate of Intelligent Reflecting Surface-Aided Millimeter-Wave Communications with Low-Resolution ADC and Phase Noise
Authors: Kangda Zhi, Cunhua Pan, Hong Ren, Kezhi Wang
Date: 2020-08-02 00:00:00
Tasks: Quantization

In this paper, we derive the uplink achievable rate expression of intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) systems, taking into account the phase noise at IRS and the quantization error at base stations (BSs). We show that the performance is limited only by the resolution of analog-digital converters (ADCs) at BSs when the number of IRS reflectors grows without bound. On the other hand, if BSs have ideal ADCs, the performance loss caused by IRS phase noise is a constant. Finally, our results validate the feasibility of using low-precision hardware at the IRS when BSs are equipped with low-resolution ADCs.
Highly accurate closed-form approximation for the probability of detection of Weibull fluctuating targets in non-coherent detectors
Authors: Fernando Darío Almeida García, Andrea Carolina Flores Rodriguez, Gustavo Fraidenraich
Date: 2020-12-12 00:00:00
Tasks: Form

In this paper, we derive a highly accurate approximation for the probability of detection (PD) of a non-coherent detector operating with Weibull fluctuation targets. To do so, we assume a pulse-to-pulse decorrelation during the coherent processing interval (CPI). Specifically, the proposed approximation is given in terms of: i) a closed-form expression derived in terms of the Fox's H-function, for which we also provide a portable and efficient MATHEMATICA routine; and ii) a fast converging series obtained through a comprehensive calculus of residues. Both solutions are fast and provide very accurate results. In particular, our series representation, besides being a more tractable solution, also exhibits impressive savings in computational load and computation time compared to previous studies. Numerical results and Monte-Carlo simulations corroborated the validity of our expressions.
Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG
Authors: Manali Saini, Udit Satija, Madhur Deo Upadhayay
Date: 2020-12-12 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), General Classification, Multi-class Classification

Mental task identification and classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in the design of portable brain-computer interface (BCI) and neurofeedback (NFB) systems. However, the real-time recorded EEG signals are often contaminated with noises such as ocular artifacts (OAs) and muscle artifacts (MAs), which deteriorate the hand-crafted features extracted from EEG signal, resulting inadequate identification and classification of mental tasks. Therefore, we investigate the use of recent deep learning techniques which do not require any manual feature extraction or artifact suppression step. In this paper, we propose a light-weight one-dimensional convolutional neural network (1D-CNN) architecture for mental task identification and classification. The robustness of the proposed architecture is evaluated using artifact-free and artifact-contaminated EEG signals taken from two publicly available databases (i.e, Keirn and Aunon ($K$) database and EEGMAT ($E$) database) and in-house ($R$) database recorded using single-channel neurosky mindwave mobile 2 (MWM2) EEG headset in performing not only mental/non-mental binary task classification but also different mental/mental multi-tasks classification. Evaluation results demonstrate that the proposed architecture achieves the highest subject-independent classification accuracy of $99.7\%$ and $100\%$ for multi-class classification and pair-wise mental tasks classification respectively in database $K$. Further, the proposed architecture achieves subject-independent classification accuracy of $99\%$ and $98\%$ in database $E$ and the recorded database $R$ respectively. Comparative performance analysis demonstrates that the proposed architecture outperforms existing approaches not only in terms of classification accuracy but also in robustness against artifacts.
Channel Modeling for UAV Communications: State of the Art, Case Studies, and Future Directions
Authors: Zhuangzhuang Cui, Ke Guan, César Briso-Rodríguez, Bo Ai, Zhangdui Zhong, Claude Oestges
Date: 2020-12-12 00:00:00

As essential aerial platforms, unmanned aerial vehicles (UAVs) play an increasingly important role in broad wireless connectivity and high-data-rate transmission for future communication systems. Notably, various communication scenarios are involved in UAV communications, such as intercommunications between UAVs and communications with the ground user equipment, the cellular base station, and the ground station, to name a few. However, existing works mostly focus on a single communication scenario, a designated channel type, and a specific operating frequency, thus urgently requiring a comprehensive understanding of multi-scenario, multi-frequency, and multi-type UAV channels. This article pours attention into the essentials of corresponding air-to-air (A2A) and air-to-ground (A2G) channels in UAV communications. We first identify the latest key challenges of channel modeling for UAV communications. We then provide the state of the art for A2A and A2G channel properties and models based on extensive measurement campaigns. In particular, we conduct realistic case studies to further demonstrate critical channel characterizations and machine learning-based modeling methods. Last but not least, potential directions are widely discussed for paving the way towards more accurate and effective channel models for UAV communications.
Focus optimization in a Computational Confocal Microscope
Authors: Keith Dillon
Date: 2020-12-11 00:00:00

We consider the numerical optimization of performance for a computational extension of a confocal microscope. Using a system where the pinhole detector is replaced with a detector array, we seek to exploit this additional information for each point in the scan. We derive an optimal estimate of the light at focus which minimizes the contribution of out-of-focus light. We estimate the amount of improvement that would be theoretically possible in point-scanning and line-scanning systems and demonstrate with simulation. We find that even with a large degree of regularization, a significant improvement is possible, especially for line-scanning systems.
Exploiting the Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery
Authors: Wanli Ma, Alin Achim, Oktay Karakuş
Date: 2020-12-11 00:00:00

In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes. This is achieved by promoting linear features in the images. For the inverse problem-solving stage, we propose a penalty function, which combines the dual-tree complex wavelet transform (DT-CWT) with the non-convex Cauchy penalty function. The solution to this inverse problem is based on the forward-backward (FB) splitting algorithm to obtain enhanced images in the Radon domain. The proposed method achieves the best results and leads to significant improvement in terms of various performance metrics, compared to state-of-the-art ship wake detection methods. The accuracy of detecting ship wakes in SAR images with different frequency bands and spatial resolution reaches more than 90%, which clearly demonstrates an accuracy gain of 7% compared to the second-best approach.
A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface
Authors: Oktay Karakuş, Igor Rizaev, Alin Achim
Date: 2020-12-11 00:00:00

The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc. Speckle noise constitutes however the main reason for reduced performance in applications such as classification, ship detection, target tracking and so on. This paper presents an investigation into the despeckling of SAR images of the ocean that include ship wake structures, via sparse regularisation using the Cauchy proximal operator. We propose a closed-form expression for calculating the proximal operator for the Cauchy prior, which makes it applicable in generic proximal splitting algorithms. In our experiments, we simulate SAR images of moving vessels and their wakes. The performance of the proposed method is evaluated in comparison to the L1 and TV norm regularisation functions. The results show a superior performance of the proposed method for all the utilised images generated.
Interpolation and Gap Filling of Landsat Reflectance Time Series
Authors: Alvaro Moreno-Martinez, Marco Maneta, Gustau Camps-Valls, Luca Martino, Nathaniel Robinson, Brady Allred, Steven W Running
Date: 2020-12-11 00:00:00
Tasks: Time Series, Time Series Analysis

Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions. Image fusion in general and downscaling/blending in particular allow to combine different multiresolution datasets. We present here an optimal interpolation approach to generate smoothed and gap-free time series of Landsat reflectance data. We fuse MODIS (moderate-resolution imaging spectroradiometer) and Landsat data globally using the Google Earth Engine (GEE) platform. The optimal interpolator exploits GEE ability to ingest large amounts of data (Landsat climatologies) and uses simple linear operations that scale easily in the cloud. The approach shows very good results in practice, as tested over five sites with different vegetation types and climatic characteristics in the contiguous US.
Signal processing with a distribution of graph operators
Authors: Feng Ji, Wee Peng Tay
Date: 2020-12-11 00:00:00

In this paper, we develop a signal processing framework of a network without explicit knowledge of the network topology. Instead, we make use of knowledge on the distribution of operators on the network. This makes the framework flexible and useful when accurate knowledge of graph topology is unavailable. Moreover, the usual graph signal processing is a special case of our framework by using the delta distribution. The main elements of the theory include Fourier transform, theory of filtering and sampling.
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Authors: Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay, Gustau Camps-Valls
Date: 2020-12-11 00:00:00
Tasks: Earth Observation, regression, Time Series, Time Series Analysis

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index derived from MODIS and Vegetation Optical Depth derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment.
Point-to-Point Communication in Integrated Satellite-Aerial Networks: State-of-the-art and Future Challenges
Authors: Nasir Saeed, Heba Almorad, Hayssam Dahrouj, Tareq Y. Al-Naffouri, Jeff S. Shamma, Mohamed-Slim Alouini
Date: 2020-12-11 00:00:00

This paper overviews point-to-point (P2P) links for integrated satellite-aerial networks, which are envisioned to be among the key enablers of the sixth-generation (6G) of wireless networks vision. The paper first outlines the unique characteristics of such integrated large-scale complex networks, often denoted by spatial networks, and focuses on two particular space-air infrastructures, namely, satellites networks and high-altitude platforms (HAPs). The paper then classifies the connecting P2P communications links as satellite-to-satellite links at the same layer (SSLL), satellite-to-satellite links at different layers (SSLD), and HAP-to-HAP links (HHL). The paper overviews each layer of such spatial networks separately, and highlights the possible natures of the connecting links (i.e., radio-frequency or free-space optics) with a dedicated overview to the existing link-budget results. The paper, afterwards, presents the prospective merit of realizing such an integrated satellite-HAP network towards providing broadband services in under-served and remote areas. Finally, the paper sheds light on several future research directions in the context of spatial networks, namely large-scale network optimization, intelligent offloading, smart platforms, energy efficiency, multiple access schemes, and distributed spatial networks.