pwc-archive/papers-with-abstracts huggingface dataset:
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
Authors: Hengtao He, Rui Wang, Weijie Jin, Shi Jin, Chao-Kai Wen, Geoffrey Ye Li
Date: 2020-06-30 00:00:00
Tasks: Compressive Sensing, Denoising

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the beam squint effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs.
On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 Cases
Authors: Jhony H. Giraldo, Thierry Bouwmans
Date: 2020-07-01 00:00:00

The mathematical modeling of infectious diseases is a fundamental research field for the planning of strategies to contain outbreaks. The models associated with this field of study usually have exponential prior assumptions in the number of new cases, while the exploration of spatial data has been little analyzed in these models. In this paper, we model the number of new cases of the Coronavirus Disease 2019 (COVID-19) as a problem of reconstruction of time-varying graph signals. To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing. Our method outperforms state-of-the-art algorithms in two COVID-19 databases provided by Johns Hopkins University. In the same way, we prove the benefits of the convergence rate of the Sobolev reconstruction method by relying on the condition number of the Hessian associated with the underlying optimization problem of our method.
Ocean Reverberation Suppression via Matrix Completion with Sensor Failure
Authors: Li-ya Xu, Bin Liao, Hao Zhang, Peng Xiao, Jian-jun Huang
Date: 2020-04-26 00:00:00
Tasks: Matrix Completion

In the ocean environment, reverberation is a common and strong interference which significantly degrades the performance of target bearing estimation. Meanwhile, sensor failure is inevitable in actual sonar deployment as the underwater scene is complicated. Therefore, it is a challenge for target detection in the ocean reverberation with sensor failure. To address this issue, we propose an improved approach based on the principle of low rank characteristics of matrix in this paper. Firstly, we utilize Hankel structured matrix to counteract the problem of sensor failure. Then, the algorithm of matrix completion (MC) based on l1-norm and l2-norm are exploited to recover the true signal matrix from the corrupted received matrix. Numerical results demonstrate that compared with other related methods, the l1-norm provides better capability in the probability of recovery and shows the best robustness of bearing estimation with the narrow beam width and low sidelobe. Moreover, the proposed approach verifies the performances of reverberation suppression and achieves high resolution of target detection.
Kernel Learning for High-Resolution Time-Frequency Distribution
Authors: Lei Jiang, Haijian Zhang, Lei Yu, Guang Hua
Date: 2020-07-01 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The design of high-resolution and cross-term (CT) free time-frequency distributions (TFDs) has been an open problem. Classical kernel based methods are limited by the trade-off between TFD resolution and CT suppression, even under optimally derived parameters. To break the current limitation, we propose a data-driven kernel learning model directly based on Wigner-Ville distribution (WVD). The proposed kernel learning based TFD (KL-TFD) model includes several stacked multi-channel learning convolutional kernels. Specifically, a skipping operator is utilized to maintain correct information transmission, and a weighted block is employed to exploit spatial and channel dependencies. These two designs simultaneously achieve high TFD resolution and CT elimination. Numerical experiments on both synthetic and real-world data confirm the superiority of the proposed KL-TFD over traditional kernel function methods.
Multi-Objective Energy Efficient Resource Allocation and User Association for In-band Full Duplex Small-Cells
Authors: Sheyda Zarandi, Ata Khalili, Mehdi Rasti, Hina Tabassum
Date: 2020-07-01 00:00:00

In this paper, we develop a framework to maximize the network energy efficiency (EE) by optimizing joint user-base station~(BS) association,~subchannel assignment, and power control considering an in-band full-duplex (IBFD)-enabled small-cell network. We maximize EE (ratio of network aggregate throughput and power consumption) while guaranteeing a minimum data rate requirement in both the uplink and downlink. The considered problem belongs to the category of mixed-integer non-linear programming problem (MINLP), {\color{black} and thus is NP-hard}. To cope up with this complexity and to derive a trade-off between system throughput and energy utilization, we first restate the considered problem as a multi-objective optimization problem (MOOP) aiming at maximizing system's throughput and minimizing system's energy consumption, simultaneously. This MOOP is then tackled by using $\epsilon$-constraint method. To do so, we first transform the binary subchannel and BS assignment variables into continuous ones without altering the feasible region of the problem and then approximate the non-convex rate functions through majorization-minimization (MM) approach. Simulation results are presented to demonstrate the effectiveness of our proposed algorithm in improving network's EE compared to the existing literature.~Furthermore, simulation results unveil that by employing the IBFD capability in OFDMA networks, our proposed resource allocation algorithm achieves a $69\%$ improvement in the EE as compared to the half-duplex system for practical values of residual self-interference.
UAV-Assisted Attack Prevention, Detection, and Recovery of 5G Networks
Authors: Aly Sabri Abdalla, Keith Powell, Vuk Marojevic, Giovanni Geraci
Date: 2020-07-01 00:00:00

Unmanned aerial vehicles (UAVs) are emerging as enablers for supporting many applications and services, such as precision agriculture, search and rescue, temporary network deployment or coverage extension, and security. UAVs are being considered for integration into emerging 5G networks as aerial users or network support nodes. We propose to leverage UAVs in 5G to assist in the prevention, detection, and recovery of attacks on 5G networks. Specifically, we consider jamming, spoofing, eavesdropping and the corresponding mitigation mechanisms that are enabled by the versatility of UAVs. We introduce the hot zone, safe zone and UAV-based secondary authorization entity, among others, to increase the resilience and confidentiality of 5G radio access networks and services. We present simulation results and discuss open issues and research directions, including the need for experimental evaluation and a research platform for prototyping and testing the proposed technologies.
Review of the State-of-the-Art on Adaptive Protection for Microgrids based on Communications
Authors: Daniel Gutierrez-Rojas, Pedro H. J. Nardelli, Goncalo Mendes, Petar Popovski
Date: 2020-07-01 00:00:00

The dominance of distributed energy resources in microgrids and the associated weather dependency require flexible protection. They include devices capable of adapting their protective settings as a reaction to (potential) changes in system state. Communication technologies have a key role in this system since the reactions of the adaptive devices shall be coordinated. This coordination imposes strict requirements: communications must be available and ultra-reliable with bounded latency in the order of milliseconds. This paper reviews the state-of-the-art in the field and provides a thorough analysis of the main related communication technologies and optimization techniques. We also present our perspective on the future of communication deployments in microgrids, indicating the viability of 5G wireless systems and multi-connectivity to enable adaptive protection.
Fast Computation of Electromagnetic Wave Propagation and Scattering for Quasi-cylindrical Geometry
Authors: Shaolin Liao
Date: 2020-07-01 00:00:00

The cylindrical Taylor Interpolation through FFT (TI-FFT) algorithm for computation of the near-field and far-field in the quasi-cylindrical geometry has been introduced. The modal expansion coefficient of the vector potentials ${\bf F}$ and ${\bf A}$ within the context of the cylindrical harmonics (TE and TM modes) can be expressed in the closed-form expression through the cylindrical addition theorem. For the quasi-cylindrical geometry, the modal expansion coefficient can be evaluated through FFT with the help of the Taylor Interpolation (TI) technique. The near-field on any arbitrary cylindrical surface can be obtained through the Inverse Fourier Transform (IFT). The far-field can be obtained through the Near-Field Far-Field (NF-FF) transform. The cylindrical TI-FFT algorithm has the advantages of $\mathcal{O} \left( \hbox{N} \log_2 \hbox{N} \right)$ computational complexity for $\hbox{N} = \hbox{N}_\phi \times \hbox{N}_z$ computational grid, small sampling rate (large sampling spacing) and no singularity problem.
Throughput Analysis for Virtual MIMO WSNs over Measured MIMO Channels
Authors: I. Dey, M. Majid Butt, N. Marchetti
Date: 2020-06-09 00:00:00

A recently conducted indoor-to-outdoor measurement campaign for investigating the propagation characteristics of an $8 \times 8$ virtual multiple-input-multiple-output (MIMO) based wireless sensor network (WSN) is presented in this paper. The campaign is conducted in an instrumentation room devoid of windows, but filled with different noisy electrical and measuring units. The channel impulse responses are reported when a 20 MHz wide signal is transmitted at 2.53 GHz. Measurements are collected for 15 different spatial combinations of the transmit antennas. After analyzing the collected data, system capacity and achievable transmission rates are calculated for each measurement scenario. Using these values, we examined the best configuration for positioning the sensors that can maximize overall network throughput. Results demonstrated that distributing sensors on all 4 walls of the room can achieve the highest possible information rate.
On the Trade-offs between Coverage Radius, Altitude and Beamwidth for Practical UAV Deployments
Authors: Haneya Naeem Qureshi, Ali Imran
Date: 2020-06-09 00:00:00
Tasks: Sensitivity

Current studies on Unmanned Aerial Vehicle (UAV) based cellular deployment consider UAVs as aerial base stations for air-to-ground communication. However, they analyze UAV coverage radius and altitude interplay while omitting or over-simplifying an important aspect of UAV deployment, i.e., effect of a realistic antenna pattern. This paper addresses the UAV deployment problem while using a realistic 3D directional antenna model. New trade-offs between UAV design space dimensions are revealed and analyzed in different scenarios. The sensitivity of coverage area to both antenna beamwidth and height is compared. The analysis is extended to multiple UAVs and a new packing scheme is proposed for multiple UAVs coverage that offers several advantages compared to prior approaches.
Improved Performance of BitTorrent Traffic Prediction Using Kalman Filter
Authors: Harisankar Sadasivan, Pranav Channakeshava, Pathipati Srihari
Date: 2020-06-09 00:00:00
Tasks: Prediction, Traffic Prediction

Supervising internet traffic is essential for any Internet Service Provider (ISP) to dynamically allocate bandwidth in an optimized manner. BitTorrent is a well-known peer-to-peer file-sharing protocol for bulky file transfer. Its extensive bandwidth consumption affects the Quality of Service (QoS) and causes latency to other applications. There is a strong requirement to predict the BitTorrent traffic to improve the QoS. In this paper, we propose a Kalman filter (KF) based method to predict the network traffic for various traffic data sets. The observed performance of KF is superior in terms of both Mean Squared Error (MSE) and total computation time, when compared to Auto Regressive Moving Average (ARMA) model.
Noisy One-bit Compressed Sensing with Side-Information
Authors: Swatantra Kafle, Thakshila Wimalajeewa, and Pramod K. Varshney
Date: 2020-06-09 00:00:00
Tasks: compressed sensing, Quantization

We consider the problem of sparse signal reconstruction from noisy one-bit compressed measurements when the receiver has access to side-information (SI). We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip error after quantization. A generalized approximate message passing-based method for signal reconstruction from noisy one-bit compressed measurements is proposed, which is then extended for the case where the receiver has access to a signal that aids signal reconstruction, i.e., side-information. Two different scenarios of side-information are considered-a) side-information consisting of support information only, and b) side information consisting of support and amplitude information. SI is either a noisy version of the signal or a noisy estimate of the support of the signal. We develop reconstruction algorithms from one-bit measurements using noisy SI available at the receiver. Laplacian distribution and Bernoulli distribution are used to model the two types of noises which, when applied to the signal and the support, yields the SI for the above two cases, respectively. The Expectation-Maximization algorithm is used to estimate the noise parameters using noisy one-bit compressed measurements and the SI. We show that one-bit compressed measurement-based signal reconstruction is quite sensitive to noise, and the reconstruction performance can be significantly improved by exploiting available side-information at the receiver.
Towards Domain Independence in CNN-based Acoustic Localization using Deep Cross Correlations
Authors: Juan Manuel Vera-Diaz, Daniel Pizarro, Javier Macias-Guarasa
Date: 2020-06-09 00:00:00
Tasks: Decoder, Deep Learning

Time delay estimation is essential in Acoustic Source Localization (ASL) systems. One of the most used techniques for this purpose is the Generalized Cross Correlation (GCC) between a pair of signals and its use in Steered Response Power (SRP) techniques, which estimate the acoustic power at a specific location. Nowadays, Deep Learning strategies may outperform these methods. However, they are generally dependent on the geometric and sensor configuration conditions that are available during the training phases, thus having limited generalization capabilities when facing new environments if no re-training nor adaptation is applied. In this work, we propose a method based on an encoder-decoder CNN architecture capable of outperforming the well known SRP-PHAT algorithm, and also other Deep Learning strategies when working in mismatched training-testing conditions without requiring a model re-training. Our proposal aims to estimate a smoothed version of the correlation signals, that is then used to generate a refined acoustic power map, which leads to better performance on the ASL task. Our experimental evaluation uses three publicly available realistic datasets and provides a comparison with the SRP-PHAT algorithm and other recent proposals based on Deep Learning.
Reconstruction of Potassium Concentrations with the ECG on Imbalanced Datasets
Authors: Nicolas Pilia, Cristiana Corsi, Stefano Severi, Olaf Dössel, Axel Loewe
Date: 2020-06-09 00:00:00

End-stage chronic kidney disease (CKD) patients are facing a 30% rise for the risk of lethal cardiac events (LCE) compared to non-CKD patients. At the same time, these patients undergoing dialysis experience shifts in the potassium concentrations. The increased risk of LCE paired with the concentration changes suggest a connection between LCE and concentration disbalances. To prove this link, a continuous monitoring device for the ionic concentrations, e.g. the ECG, is needed. In this work, we want to answer if an optimised signal processing chain can improve the result quantify the influence of a disbalanced training dataset on the final estimation result. The study was performed on a dataset consisting of 12-lead ECGs recorded during dialysis sessions of 32 patients. We selected three features to find a mapping from ECG features to [K+]o: T-wave ascending slope, T-wave descending slope and T-wave amplitude. A polynomial model of 3rd order was used to reconstruct the concentrations from these features. We solved a regularised weighted least squares problem with a weighting matrix dependent on the frequency of each concentration in the dataset (frequent concentration weighted less). By doing so, we tried to generate a model being suitable for the whole range of the concentrations.With weighting, errors are increasing for the whole dataset. For the data partition with [K+]o<5 mmol/l, errors are increasing, for [K+]o$\geq$5 mmol/l, errors are decreasing. However, and apart from the exact reconstruction results, we can conclude that a model being valid for all patients and not only the majority, needs to be learned with a more homogeneous dataset. This can be achieved by leaving out data points or by weighting the errors during the model fitting. With increasing weighting, we increase the performance on the part of the [K+]o that are less frequent which was desired in our case.
Laser detection using liquid crystal polarization modulators
Authors: David M Benton
Date: 2020-06-09 00:00:00

Lasers can be identified by their relatively long coherence lengths using interferometry. A Mach Zehnder interferometer incorporating liquid crystal polarization modulators is demonstrated as a means of low cost, robust laser detection. Temporal modulations, as a signature of coherence can be induced by modulating polarization changes in liquid crystal modulators, using low voltages. Sensitivities of less than 10nW can be achieved. The suitability as a means of laser detection is discussed.
Regional Robust Secure Precise Wireless Transmission Design for Multi-user UAV Broadcasting System
Authors: Tong Shen, Tingting Liu, Yan Lin, Yongpeng Wu, Feng Shu, Zhu Han
Date: 2019-04-09 00:00:00

In this paper, two regional robust secure precise wireless transmission (SPWT) schemes for multi-user unmanned aerial vehicle (UAV) :1) regional signal-to-leakage-and-noise ratio (SLNR) and artificial-noise-to-leakage-and-noise ratio (ANLNR) (R-SLNR-ANLNR) maximization and 2) point SLNR and ANLNR (P-SLNR-ANLNR) maximization, are proposed to tackle with the estimation errors of the target users' location. In SPWT system, the estimation error for SPWT can not be ignored. However the conventional robust methods in secure wireless communications optimize the beamforming vector in the desired positions only in statistical means and can not guarantee the security for each symbol. Proposed regional robust schemes are designed for optimizing the secrecy performance in the whole error region around the estimated location. Specifically, with known maximal estimation error, we define target region and wiretap region. Then design an optimal beamforming vector and an artificial noise projection matrix, which achieve the confidential signal in the target area having the maximal power while only few signal power is conserved in the potential wiretap region. Instead of considering the statistical distributions of the estimated errors into optimization, we optimize the SLNR and ANLNR of the whole target area, which significantly decreases the complexity. Moreover, the proposed schemes can ensure that the desired users are located in the optimized region, which are more practical than conventional methods. Simulation results show that our proposed regional robust SPWT design is capable of substantially improving the secrecy rate compared to the conventional non-robust method. The P-SLNR-ANLNR maximization-based method has the comparable secrecy performance with a lower complexity than that of the R-SLNR-ANLNR maximization-based method.
Modulation Format Dependent, Closed-Form Formula for Estimating Nonlinear Interference in S+C+L Band Systems
Authors: Daniel Semrau, Lidia Galdino, Eric Sillekens, Domanic Lavery, Robert I. Killey, Polina Bayvel
Date: 2020-06-08 00:00:00
Tasks: Form

A closed-form formula for the nonlinear interference estimation of arbitrary modulation formats in ultra-wideband transmission systems is presented. Enabled by the proposed approach, the formula is applied to the entire S+C+L band (20 THz) and validated by numerical simulations with experimentally measured fibre data.
Discovering Transforms: A Tutorial on Circulant Matrices, Circular Convolution, and the Discrete Fourier Transform
Authors: Bassam Bamieh
Date: 2018-05-15 00:00:00

How could the Fourier and other transforms be naturally discovered if one didn't know how to postulate them? In the case of the Discrete Fourier Transform (DFT), we show how it arises naturally out of analysis of circulant matrices. In particular, the DFT can be derived as the change of basis that simultaneously diagonalizes all circulant matrices. In this way, the DFT arises naturally from a linear algebra question about a set of matrices. Rather than thinking of the DFT as a signal transform, it is more natural to think of it as a single change of basis that renders an entire set of mutually-commuting matrices into simple, diagonal forms. The DFT can then be "discovered" by solving the eigenvalue/eigenvector problem for a special element in that set. A brief outline is given of how this line of thinking can be generalized to families of linear operators, leading to the discovery of the other common Fourier-type transforms, as well as its connections with group representations theory.
Energy-Efficient Fixed-Gain AF Relay Assisted OFDM with Index Modulation
Authors: Jiusi Zhou, Shuping Dang, Basem Shihada, Mohamed-Slim Alouini
Date: 2020-06-08 00:00:00

To broaden the application scenario and reduce energy consumption, we propose an energy-efficient fixed-gain (FG) amplify-and-forward (AF) relay assisted orthogonal frequency-division multiplexing with index modulation (OFDM-IM) scheme in this letter. The proposed system needs neither instantaneous channel state information (CSI) nor complicated processing at the relay node. It operates based on the power allocation scheme that minimizes the sum of transmit power at both source and relay node, given an outage probability constraint. Through a series of problem transformation and simplification, we convert the original power allocation problem to its relaxed version and solve it using convex programming techniques. To reveal the computing efficiency of the proposed power allocation scheme, we analyze its computational complexity. Numerical simulations substantiate that the proposed optimization scheme has a neglectable loss compared with the brute force search, but the computational complexity can be considerably reduced.