pwc-archive/papers-with-abstracts huggingface dataset:
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition
Authors: Shelly Vishwakarma, Wenda Li, Chong Tang, Karl Woodbridge, Raviraj Adve, Kevin Chetty
Date: 2021-03-02 00:00:00
Tasks: Activity Recognition, Classification, Diversity, General Classification, Human Animation

This work presents a simulation framework to generate human micro-Dopplers in WiFi based passive radar scenarios, wherein we simulate IEEE 802.11g complaint WiFi transmissions using MATLAB's WLAN toolbox and human animation models derived from a marker-based motion capture system. We integrate WiFi transmission signals with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics, and the sensor parameters. In this paper, we consider five human activities. We uniformly benchmark the classification performance of multiple machine learning and deep learning models against a common dataset. Further, we validate the classification performance using the real radar data captured simultaneously with the motion capture system. We present experimental results using simulations and measurements demonstrating good classification accuracy of $\geq$ 95\% and $\approx$ 90\%, respectively.
Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces
Authors: Bo Yang, Xuelin Cao, Chongwen Huang, Chau Yuen, Lijun Qian, Marco Di Renzo
Date: 2021-03-02 00:00:00

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach.
Ambient PMU Data Based System Oscillation Analysis Using Multivariate Empirical Mode Decomposition
Authors: Shutang You
Date: 2021-01-12 00:00:00

Wide-area synchrophasor ambient measurements provide a valuable data source for real-time oscillation mode monitoring and analysis. This paper introduces a novel method for identifying inter-area oscillation modes using wide-area ambient measurements. Based on multivariate empirical mode decomposition (MEMD), which can analyze multi-channel non-stationary and nonlinear signals, the proposed method is capable of detecting the common oscillation mode that exists in multiple synchrophasor measurements at low amplitudes. Test results based on two real-world datasets validate the effectiveness of the proposed method.
Local Change Point Detection and Cleaning of EEMD Signals with Application to Acoustic Shockwaves
Authors: Kentaro Hoffman, Jonathan M. Lees, Kai Zhang
Date: 2021-03-01 00:00:00
Tasks: Change Point Detection

The Ensemble Empirical Mode Decomposition (EEMD) has become a preferred technique to decompose nonlinear and non-stationary signals due to its ability to create time-varying basis functions. However, current EEMD signal cleaning techniques are unable to deal with situations where a signal only occurs for a portion of the entire recording length. By combining change point detection and statistical hypothesis testing, we demonstrate how to clean a signal to emphasize unique local changes within each basis function. This not only allows us to observe which frequency bands are undergoing a change, but also leads to improved recovery of the underlying information. Using this technique, we demonstrate improved signal cleaning performance for acoustic shockwave signal detection. The technique is implemented in R via the LCDSC package.
MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things
Authors: Chengxiao Liu, Wei Feng, Xiaoming Tao, Ning Ge
Date: 2021-03-01 00:00:00
Tasks: Edge-computing

In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) keeps increasing. As conventional cellular technologies are hard to be directly used for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures including satellites and unmanned aerial vehicles (UAVs), where a non-terrestrial network (NTN) can be built under the cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN is required to be empowered by mobile edge computing (MEC) while providing oasis-oriented on-demand coverage for the devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of complex propagation environment in the MEC-empowered NTN, which makes it hard to orchestrate the resources. In this paper, we propose a process-oriented framework to design the communication and MEC systems in a time-division manner. Under this framework, the large-scale channel state information (CSI) is used to characterize the complex propagation environment with an affordable cost, where a non-convex latency minimization problem is formulated. After that, the approximated problem is given and it can be decomposed into subproblems. These subproblems are further solved in an iterative way. Simulation results demonstrate the superiority of the proposed process-oriented scheme over other algorithms. These results also indicate that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of resource use. Furthermore, the results imply that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.
Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-varying Channels
Authors: Qihang Huang, Wei Li, Weicheng Zhan, Yuhang Wang, Rongrong Guo
Date: 2021-03-01 00:00:00

In this work, we focus on the model-mismatch problem for model-based subspace channel tracking in the correlated underwater acoustic channel. A model based on the underwater acoustic channel's correlation can be used as the state-space model in the Kalman filter to improve the underwater acoustic channel tracking compared that without a model. Even though the data support the assumption that the model is slow-varying and uncorrelated to some degree, to improve the tracking performance further, we can not ignore the model-mismatch problem because most channel models encounter this problem in the underwater acoustic channel. Therefore, in this work, we provide a dynamic time-variant state-space model for underwater acoustic channel tracking. This model is tolerant to the slight correlation after decorrelation. Moreover, a forward-backward Kalman filter is combined to further improve the tracking performance. The performance of our proposed algorithm is demonstrated with the same at-sea data as that used for conventional channel tracking. Compared with the conventional algorithms, the proposed algorithm shows significant improvement, especially in rough sea conditions in which the channels are fast-varying.
Distribution Grid Modeling Using Smart Meter Data
Authors: Yifei Guo, Yuxuan Yuan, Zhaoyu Wang
Date: 2021-02-28 00:00:00

The knowledge of distribution grid models, including topologies and line impedances, is essential to grid monitoring, control and protection. However, this information is often unavailable, incomplete or outdated. The increasing deployment of smart meters (SMs) provides a unique opportunity to address this issue. This paper proposes a two-stage data-driven framework for distribution grid modeling using SM data. In the first stage, we propose to identify the topology via reconstructing a weighted Laplacian matrix of distribution networks, which is mathematically proven to be robust against moderately heterogeneous R/X profiles. In the second stage, we develop nonlinear least absolute deviations (LAD) and least squares (LS) regression models to estimate line impedances of single branches based on a nonlinear inverse power flow, which is then embedded within a bottom-up sweep algorithm to achieve the identification across the network in a branch-wise manner. Because the estimation models are inherently non-convex programs and NP-hard, we specially address their tractable convex relaxations and verify the exactness. In addition, we design a conductor library to significantly narrow down the solution space. Numerical results on the modified IEEE 13-bus, 37-bus and 69-bus test feeders validate the effectiveness of the proposed methods.
Communications Standards for Unmanned Aircraft Systems: The 3GPP Perspective and Research Drivers
Authors: Aly Sabri Abdalla, Vuk Marojevic
Date: 2020-09-08 00:00:00
Tasks: Management

An unmanned aircraft system (UAS) consists of an unmanned aerial vehicle (UAV) and its controller which use radios to communicate. While the remote controller (RC) is traditionally operated by a person who is maintaining visual line of sight with the UAV it controls, the trend is moving towards long-range control and autonomous operation. To enable this, reliable and widely available wireless connectivity is needed because it is the only way to manually control a UAV or take control of an autonomous UAV flight. This article surveys the ongoing Third Generation Partnership Project (3GPP) standardization activities for enabling networked UASs. In particular, we present the requirements, envisaged architecture and services to be offered to/by UAVs and RCs, which will communicate with one another, with the UAS Traffic Management (UTM), and with other users through cellular networks. Critical research directions relate to security and spectrum coexistence, among others. We identify major R\&D platforms that will drive the standardization of cellular communications networks and applications.
Rejection of Smooth GPS Time Synchronization Attacks via Sparse Techniques
Authors: Erick Schmidt, Junhwan Lee, Nikolaos Gatsis, David Akopian
Date: 2021-02-28 00:00:00

This paper presents a novel time synchronization attack (TSA) model for the Global Positioning System (GPS) based on clock data behavior changes in a higher-order derivative domain. Further, the time synchronization attack rejection and mitigation based on sparse domain (TSARM-S) is presented. TSAs affect stationary GPS receivers in applications where precise timing is required, such as cellular communications, financial transactions, and monitoring of the electric power grid. In the present work, the clock bias and clock drift are monitored at higher-order clock data derivatives where the TSA is seen as a sparse spike-like event. The smoothness of the attack relates to the derivative order where the sparsity is observed. The proposed method jointly estimates a dynamic solution for GPS timing and rejects behavior changes based on such sparse events. An evaluation procedure is presented for two testbeds, namely a commercial receiver and a software-defined radio. Further, the proposed method is evaluated against distinct real-dataset Texas Spoofing Test Battery (TEXBAT) scenarios. Combined synthetic and real-data results show an average RMS clock bias error of 12.08 m for the SDR platform, and 45.74 m for the commercial device. Further, the technique is evaluated against state-of-the-art mitigation techniques and in a spoofing-plus-multipath scenario for robustness. Finally, TSARM-S can be potentially optimized and implemented in commercial devices via a firmware upgrade.
Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations
Authors: Yagiz Savas, Abolfazl Hashemi, Abraham P. Vinod, Brian M. Sadler, Ufuk Topcu
Date: 2021-02-28 00:00:00

We study the problem of securely communicating a sequence of information bits with a client in the presence of multiple adversaries at unknown locations in the environment. We assume that the client and the adversaries are located in the far-field region, and all possible directions for each adversary can be expressed as a continuous interval of directions. In such a setting, we develop a periodic transmission strategy, i.e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission. We formulate a series of nonconvex semi-infinite optimization problems to synthesize the transmission strategy. We show that the semi-definite program (SDP) relaxations of these nonconvex problems are exact under an efficiently verifiable sufficient condition. We approximate the SDP relaxations, which are subject to infinitely many constraints, by randomly sampling a finite subset of the constraints and establish the probability with which optimal solutions to the obtained finite SDPs and the semi-infinite SDPs coincide. We demonstrate with numerical simulations that the proposed periodic strategy can ensure the security of communication in scenarios in which all stationary strategies fail to guarantee security.
Digital Interference Mitigation in Space Division Multiplexing Self-Homodyne Coherent Detection
Authors: Hanzi Huang, Yetian Huang, Haoshuo Chen, Qianwu Zhang, Jian Chen, Nicolas K. Fontaine, Mikael Mazur, Roland Ryf, Junho Cho, Yingxiong Song
Date: 2021-02-28 00:00:00

We propose a digital interference mitigation scheme to reduce the impact of mode coupling in space division multiplexing self-homodyne coherent detection and experimentally verify its effectiveness in 240-Gbps mode-multiplexed transmission over 3-mode multimode fiber.
The Property of Frequency Shift in 2D-FRFT Domain with Application to Image Encryption
Authors: Lei Gao, Lin Qi, Ling Guan
Date: 2021-02-28 00:00:00
Tasks: Image Reconstruction

The Fractional Fourier Transform (FRFT) has been playing a unique and increasingly important role in signal and image processing. In this letter, we investigate the property of frequency shift in two-dimensional FRFT (2D-FRFT) domain. It is shown that the magnitude of image reconstruction from phase information is frequency shift-invariant in 2D-FRFT domain, enhancing the robustness of image encryption, an important multimedia security task. Experiments are conducted to demonstrate the effectiveness of this property against the frequency shift attack, improving the robustness of image encryption.
Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link
Authors: Lei Wang, Pengcheng Xu, Zhaoyang Qu, Xiaoyong Bo, Yunchang Dong, Zhenming Zhang, Yang Li
Date: 2021-02-27 00:00:00
Tasks: Cyber Attack Detection

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber-Physical Power System (CPPS). Two-step principal component analysis (PCA) is used for classifying the system's operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories' samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.
A Low-Complexity ADMM-based Massive MIMO Detectors via Deep Neural Networks
Authors: Isayiyas Nigatu Tiba, Quan Zhang, Jing Jiang, Yongchao Wang
Date: 2021-02-27 00:00:00

An alternate direction method of multipliers (ADMM)-based detectors can achieve good performance in both small and large-scale multiple-input multiple-output (MIMO) systems. However, due to the difficulty of choosing the optimal penalty parameters, their performance is limited. This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation. It exploits the unfolding technique and learns to estimate the penalty parameters. Additionally, a computationally cheaper detector is also proposed. The proposed methods can handle the higher-order modulation signals. Numerical results are presented to demonstrate the performances of the proposed methods compared with the existing works.
A matrix-free Levenberg-Marquardt algorithm for efficient ptychographic phase retrieval
Authors: Saugat Kandel, S. Maddali, Youssef S G Nashed, Stephan O Hruszkewycz, Chris Jacobsen, Marc Allain
Date: 2021-02-27 00:00:00
Tasks: Retrieval

The phase retrieval problem, where one aims to recover a complex-valued image from far-field intensity measurements, is a classic problem encountered in a range of imaging applications. Modern phase retrieval approaches usually rely on gradient descent methods in a nonlinear minimization framework. Calculating closed-form gradients for use in these methods is tedious work, and formulating second order derivatives is even more laborious. Additionally, second order techniques often require the storage and inversion of large matrices of partial derivatives, with memory requirements that can be prohibitive for data-rich imaging modalities. We use a reverse-mode automatic differentiation (AD) framework to implement an efficient matrix-free version of the Levenberg-Marquardt (LM) algorithm, a longstanding method that finds popular use in nonlinear least-square minimization problems but which has seen little use in phase retrieval. Furthermore, we extend the basic LM algorithm so that it can be applied for general constrained optimization problems beyond just the least-square applications. Since we use AD, we only need to specify the physics-based forward model for a specific imaging application; the derivative terms are calculated automatically through matrix-vector products, without explicitly forming any large Jacobian or Gauss-Newton matrices. We demonstrate that this algorithm can be used to solve both the unconstrained ptychographic object retrieval problem and the constrained "blind" ptychographic object and probe retrieval problems, under both the Gaussian and Poisson noise models, and that this method outperforms best-in-class first-order ptychographic reconstruction methods: it provides excellent convergence guarantees with (in many cases) a superlinear rate of convergence, all with a computational cost comparable to, or lower than, the tested first-order algorithms.
Validating Clustering Frameworks for Electric Load Demand Profiles
Authors: Mayank Jain, Tarek Alskaif, Soumyabrata Dev
Date: 2021-02-26 00:00:00
Tasks: Clustering, Dimensionality Reduction

Large-scale deployment of smart meters has made it possible to collect sufficient and high-resolution data of residential electric demand profiles. Clustering analysis of these profiles is important to further analyze and comment on electricity consumption patterns. Although many clustering techniques have been proposed in the literature over the years, it is often noticed that different techniques fit best for different datasets. To identify the most suitable technique, standard clustering validity indices are often used. These indices focus primarily on the intrinsic characteristics of the clustering results. Moreover, different indices often give conflicting recommendations which can only be clarified with heuristics about the dataset and/or the expected cluster structures -- information that is rarely available in practical situations. This paper presents a novel scheme to validate and compare the clustering results objectively. Additionally, the proposed scheme considers all the steps prior to the clustering algorithm, including the pre-processing and dimensionality reduction steps, in order to provide recommendations over the complete framework. Accordingly, the proposed strategy is shown to provide better, unbiased, and uniform recommendations as compared to the standard Clustering Validity Indices.
DC-Assisted Stabilization of Internal Oscillations for Improved Symbol Transitions in a Direct Antenna Modulation Transmitter
Authors: Danyang Huang, Kurt Schab, Joseph Dusenbury, Brandon Sluss, Jacob Adams
Date: 2021-02-26 00:00:00

Internal oscillations in switched antenna transmitters cause undesirable fluctuations of the stored energy in the system, reducing the effectiveness of time-varying broadbanding methods, such as energy-synchronous direct antenna modulation. To mitigate these parasitic oscillations, a modified direct antenna modulation system with an auxiliary DC source is introduced to stabilize energy storage on the antenna. A detailed circuit model for a direct antenna modulation system is used to identify the origin of the oscillations and to justify the selection of the DC source. Measured phase shift keyed waveforms transmitted by the modified system show significant increases in signal fidelity, including a 10-20 dB reduction in error vector magnitude compared to a time-invariant system. Comparison to an equivalent, scalable time-invariant antenna suggests that the switched transmitter behaves as though it has 2-3 times lower radiation Q-factor and 20% higher radiation efficiency.
Near-Field Millimeter-Wave Imaging via Circular-Arc MIMO Arrays
Authors: Shiyong Li, Shuoguang Wang, Guoqiang Zhao, Houjun Sun
Date: 2021-02-26 00:00:00
Tasks: ARC

Millimeter-wave (MMW) imaging has a wide prospect in application of concealed weapons detection. We propose a circular-arc multiple-input multiple-output (MIMO) array scheme with uniformly spaced transmit and receive antennas along the horizontal-arc direction, while scanning along the vertical direction. The antenna beams of the circular-arc MIMO array can provide more uniform coverage of the imaging scene than those of the linear or planar MIMO arrays. Further, a near-field three-dimensional (3-D) imaging algorithm, based on the spatial frequency domain processing, is presented with analysis of sampling criteria and resolutions. Numerical simulations, as well as comparisons with the back-projection (BP) algorithm, are provided to show the efficacy of the proposed approach.
A silicon photonics feed-forward neural network for nonlinear distortion mitigation in an optical link
Authors: Mattia Mancinelli, Paolo Bettotti, Lorenzo Pavesi
Date: 2020-12-10 00:00:00

We design and model a single-layer, passive, all-optical silicon photonics neural network to mitigate optical link nonlinearities. The network nodes are formed by silicon microring resonators whose transfer function has been experimentally measured. Both the transmitted amplitude and phase maps of the nonlinear response of the microrings are parametrized as a function of the wavelength and of the signal power to form tunable activation functions of the single nodes in the complex valued network. Training of the network is achieved by a particle swarm optimizer which selects the complex weights and the activation functions. We demonstrate that a single feed-forward layer with a single node perceptron is effective in compensating linear and nonlinear distortions over a broad range of signal-to-noise-ratio and propagation lengths. We propose to implement this simple neuronal network as an optical link transparent layer to correct signal distortions.