pwc-archive/papers-with-abstracts huggingface dataset:
On The Synergy Between Nonconvex Extensions of The Tensor Nuclear Norm for Tensor Recovery
Authors: Kaito Hosono, Shunsuke Ono, Takamichi Miyata
Date: 2020-09-08 00:00:00

Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank--e.g. the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank. Since the Tucker rank is nonconvex and discontinuous, many relaxations of the Tucker rank have been proposed, e.g., the tensor nuclear norm, weighted tensor nuclear norm, and weighted tensor Schatten-$p$ norm. In particular, the weighted tensor Schatten-p norm has two parameters, the weight and $p$, and the tensor nuclear norm and weighted tensor nuclear norm are special cases of these parameters. However, there has been no detailed discussion of whether the effects of the weighting and $p$ are synergistic. In this paper, we propose a novel low-rank tensor completion model using the weighted tensor Schatten-$p$ norm to reveal the relationships between the weight and $p$. To clarify whether complex methods such as the weighted tensor Schatten-$p$ norm are necessary, we compare them with a simple method using rank-constrained minimization. It was found that the simple methods did not outperform the complex methods unless the rank of the original tensor could be accurately known. If we can obtain the ideal weight, $p = 1$ is sufficient, although it is necessary to set $p<1$ when using the weights obtained from observations. These results are consistent with existing reports.
Machine learning-based method for linearization and error compensation of an absolute rotary encoder
Authors: Lorenzo Iafolla, Massimiliano Filipozzi, Sara Freund, Azhar Zam, Georg Rauter, Philippe Claude Cattin
Date: 2020-09-07 00:00:00
Tasks: BIG-bench Machine Learning

The main objective of this work is to develop a miniaturized, high accuracy, single-turn absolute, rotary encoder called ASTRAS360. Its measurement principle is based on capturing an image that uniquely identifies the rotation angle. To evaluate this angle, the image first has to be classified into its sector based on its color, and only then can the angle be regressed. In-spired by machine learning, we built a calibration setup, able to generate labeled training data automatically. We used these training data to test, characterize, and compare several machine learning algorithms for the classification and the regression. In an additional experiment, we also characterized the tolerance of our rotary encoder to eccentric mounting. Our findings demonstrate that various algorithms can perform these tasks with high accuracy and reliability; furthermore, providing extra-inputs (e.g. rotation direction) allows the machine learning algorithms to compensate for the mechanical imperfections of the rotary encoder.
A Distributed Power Control Algorithm for Energy Efficiency Maximization in Wireless Cellular Networks
Authors: Rojin Aslani, Mehdi Rasti
Date: 2020-09-07 00:00:00

In this paper, we propose a distributed power control algorithm for addressing the global energy efficiency (GEE) maximization problem subject to satisfying a minimum target SINR for all user equipments (UEs) in wireless cellular networks. We state the problem as a multi-objective optimization problem which targets minimizing total power consumption and maximizing total throughput, simultaneously, while a minimum target SINR is guaranteed for all UEs. We propose an iterative scheme executed in the UEs to control their transmit power using individual channel state information (CSI) such that the GEE is maximized in a distributed manner. We prove the convergence of the proposed iterative algorithm to its corresponding unique fixed point also shown by our numerical results. Additionally, simulation results demonstrate that our proposed scheme outperforms other algorithms in the literature and performs like the centralized algorithm executed in the base station and maximizes the GEE using the global CSI.
Evaluation of gps/glonass patch versus rf gps (L1) patch antenna performance parameter
Authors: Gholam Aghashirin, Hoda S. Abdel Aty Zohdy, Mohamed A. Zohdy, Darrell Schmidt, Adam Timmons
Date: 2020-09-07 00:00:00

In any wireless communication network and system an antenna is an important element along the propagation path of an electrical signals. Antenna module is a vital component of automated driving systems, it should function as needed in dGPS, HD map correction services, and radio and navigation systems. The main scope of this engineering research work involves the evaluation and determining the performance parameter and characteristic of the GPS/GLONASS patch vs RF GPS L1(1.57542 GHz) patch antenna characteristic. FEKO simulation studies are carried out to extensively compare, make an assessment and evaluate the characteristic and performance parameter, such as the average/passive gain of the proposed antenna in the presence of background noise. Prior to the start of the FEKO simulation studies, a physical mechanical dimension measurements via a Digital instrumentation were conducted for the following: Radiating Element Size: The actual length (L), and width (W), Substrate Material Size: The substrate length (Lsub), width (Wsub), and height (h). The proposed antenna model for GPS only patch antenna operating at 1.57542 GHz and the dual band patch antenna resonating at 1.5925 GHz are developed. To be specific, this work presents the design, modeling, determining passive gain of the RF GPS L1 patch vs. dual band patch antenna with intended targeted applications within the automotive system and space. Simulation are undertaken to generate the RF GPS L1 patch and dual band patch antenna structure respectively for the sole purpose of evaluating the performance of the proposed dual band antenna. Simulation are performed rather than mathematical modelling. The emphasis of this paper is how to obtain the equivalent amount of total passive gain in a GPS vs. that of dual band antenna.
Auction Based Approach For Resource Allocation In D2D Communication
Authors: Saumya Borwankar
Date: 2020-09-07 00:00:00

Device to device communication has prevailed as an issue for small cell networks. Here we have implemented a new scheme that allows us to improve spectral capabilities of mobiles communicating with each other (peer to peer network) for downlink cellular network. Previously the spectral capabilities were handled by Reverse Iterative combinatorial auction mechanism, where the cellular uses used to bid for d2d links. We have made a comparison between Reverse Iterative combinatorial Auction (R-ICA) and New Auction method on the basis of plots on sum rate over SINR and number of d2d users.
End-to-End CNN+LSTM Deep Learning Approach for Bearing Fault Diagnosis
Authors: Amin Khorram, Mohammad Khalooei, Mansoor Rezghi
Date: 2019-09-16 00:00:00
Tasks: Fault Detection, Fault Diagnosis, Temporal Sequences

Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering potential faults in its early stages. Intelligent fault diagnosis is a promising tool due to its ability to rapidly and efficiently processing collected signals and providing accurate diagnosis results. Although many studies have developed machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the results have generally been limited to relatively small train and test datasets and the input data has been manipulated (selective features used) to reach high accuracy. In this work, the raw data, collected from accelerometers (time-domain features) are taken as the input of a novel temporal sequence prediction algorithm to present an end-to-end method for fault detection. We use equivalent temporal sequences as the input of a novel Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) to detect the bearing fault with the highest accuracy in the shortest possible time. The method can reach the highest accuracy in the literature, to the best knowledge of the authors of the present paper, voiding any sort of pre-processing or manipulation of the input data. Effectiveness and feasibility of the fault diagnosis method are validated by applying it to two commonly used benchmark real vibration datasets and comparing the result with the other intelligent fault diagnosis methods.
A DoA Estimation Based Robust Beam Forming Method for UAV-BS Communication
Authors: Tianxiao Zhao, Chunbo Luo, Geyong Min, Jianming Zhou, Dechun Guo, Wang Miao, Yang Mi
Date: 2019-05-13 00:00:00

High data rate communication with Unmanned Aerial Vehicles (UAV) is of growing demand among industrial and commercial applications since the last decade. In this paper, we investigate enhancing beam forming performance based on signal Direction of Arrival (DoA) estimation to support UAV-cellular network communication. We first study UAV fast moving scenario where we found that drone's mobility cause degradation of beam forming algorithm performance. Then, we propose a DoA estimation algorithm and a steering vector adaptive receiving beam forming method. The DoA estimation algorithm is of high precision with low computational complexity. Also it enables a beam former to timely adjust steering vector value in calculating beam forming weight. Simulation results show higher SINR performance and more stability of proposed method than traditional method based on Multiple Signal Classification (MUSIC) DoA estimation algorithm.
Designing sequence set with minimal peak side-lobe level for applications in high resolution RADAR imaging
Authors: Surya Prakash Sankuru, R Jyothi, Prabhu Babu, Mohammad Alaee-Kerahroodi
Date: 2020-09-07 00:00:00

Constant modulus sequence set with low peak side-lobe level is a necessity for enhancing the performance of modern active sensing systems like Multiple Input Multiple Output (MIMO) RADARs. In this paper, we consider the problem of designing a constant modulus sequence set by minimizing the peak side-lobe level, which can be cast as a non-convex minimax problem, and propose a Majorization-Minimization technique based iterative monotonic algorithm. The iterative steps of our algorithm are computationally not very demanding and they can be efficiently implemented via Fast Fourier Transform (FFT) operations. We also establish the convergence of our proposed algorithm and discuss the computational and space complexities of the algorithm. Finally, through numerical simulations, we illustrate the performance of our method with the state-of-the-art methods. To highlight the potential of our approach, we evaluate the performance of the sequence set designed via our approach in the context of probing sequence set design for MIMO RADAR angle-range imaging application and show results exhibiting good performance of our method when compared with other commonly used sequence set design approaches.
Deep Learning-Based Single-Ended Objective Quality Measures for Time-Scale Modified Audio
Authors: Timothy Roberts, Aaron Nicolson, Kuldip K. Paliwal
Date: 2020-09-07 00:00:00

Objective evaluation of audio processed with Time-Scale Modification (TSM) is seeing a resurgence of interest. Recently, a labelled time-scaled audio dataset was used to train an objective measure for TSM evaluation. This DE measure was an extension of Perceptual Evaluation of Audio Quality, and required reference and test signals. In this paper, two single-ended objective quality measures for time-scaled audio are proposed that do not require a reference signal. Data driven features are created by either a convolutional neural network (CNN) or a bidirectional gated recurrent unit (BGRU) network and fed to a fully-connected network to predict subjective mean opinion scores. The proposed CNN and BGRU measures achieve an average Root Mean Squared Error of 0.608 and 0.576, and a mean Pearson correlation of 0.771 and 0.794, respectively. The proposed measures are used to evaluate TSM algorithms, and comparisons are provided for 16 TSM implementations. The objective measure is available at https://www.github.com/zygurt/TSM.
Ultra-Massive MIMO Systems at Terahertz Bands: Prospects and Challenges
Authors: Alice Faisal, Hadi Sarieddeen, Hayssam Dahrouj, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
Date: 2019-02-28 00:00:00

Terahertz (THz)-band communications are currently being celebrated as a key technology that could fulfill the increasing demands for wireless data traffic in the upcoming sixth-generation (6G) of wireless communications. Many challenges, such as high propagation losses and power limitations, which result in short communication distances, have yet to be addressed for this technology to be realized. Ultra-massive multiple-input, multiple-output (UM-MIMO) antenna systems have emerged as practical means for combatting this distance problem, thereby increasing system capacity. Towards that end, graphene-based nano-antennas have recently been proposed, as they can be individually tuned and collectively controlled in compact UM-MIMO array-of-sub-arrays architectures. In this paper, we present a holistic overview of THz UM-MIMO systems. We assess recent advancements in transceiver design and channel modeling, and discuss the major challenges and shortcomings of such designs by deriving the relationship between communication range, array dimensions, and system performance. We further highlight several research advances that could enhance resource allocation at the THz band, including waveform designs, multi-carrier configurations, and spatial modulations. Based on this discussion, we highlight prospective use cases that can bring THz UM-MIMO into reality in the context of sensing, data centers, cell-free systems, and mid-range wireless communications.
CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion
Authors: Emre Gönültaş, Eric Lei, Jack Langerman, Howard Huang, Christoph Studer
Date: 2020-09-06 00:00:00
Tasks: Outdoor Positioning

Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions. In this paper, we propose a positioning pipeline for wireless LAN MIMO-OFDM systems which uses uplink CSI measurements obtained from one or more unsynchronized access points (APs). For each AP receiver, novel features are first extracted from the CSI that are robust to system impairments arising in real-world transceivers. These features are the inputs to a NN that extracts a probability map indicating the likelihood of a UE being at a given grid point. The NN output is then fused across multiple APs to provide a final position estimate. We provide experimental results with real-world indoor measurements under line-of-sight (LoS) and non-LoS propagation conditions for an 80MHz bandwidth IEEE 802.11ac system using a two-antenna transmit UE and two AP receivers each with four antennas. Our approach is shown to achieve centimeter-level median distance error, an order of magnitude improvement over a conventional baseline.
System Modelling and Design Aspects of Next Generation High Throughput Satellites
Authors: Shree Krishna Sharma, Jorge Querol, Nicola Maturo, Symeon Chatzinotas, Bjorn Ottersten
Date: 2020-09-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Future generation wireless networks are targeting the convergence of fixed, mobile and broadcasting systems with the integration of satellite and terrestrial systems towards utilizing their mutual benefits. Satellite Communications (Sat- Com) is envisioned to play a vital role to provide integrated services seamlessly over heterogeneous networks. As compared to terrestrial systems, the design of SatCom systems require a different approach due to differences in terms of wave propagation, operating frequency, antenna structures, interfering sources, limitations of onboard processing, power limitations and transceiver impairments. In this regard, this letter aims to identify and discuss important modeling and design aspects of the next generation High Throughput Satellite (HTS) systems. First, communication models of HTSs including the ones for multibeam and multicarrier satellites, multiple antenna techniques, and for SatCom payloads and antennas are highlighted and discussed. Subsequently, various design aspects of SatCom transceivers including impairments related to the transceiver, payload and channel, and traffic-based coverage adaptation are presented. Finally, some open topics for the design of next generation HTSs are identified and discussed.
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots
Authors: Chenghong Bian, Yuwen Yang, Feifei Gao, Geoffrey Ye Li
Date: 2020-09-06 00:00:00
Tasks: Beam Prediction, Data Augmentation

In this paper, we propose a new downlink beamforming strategy for mmWave communications using uplink sub-6GHz channel information and a very few mmWave pilots. Specifically, we design a novel dual-input neural network, called FusionNet, to extract and exploit the features from sub-6GHz channel and a few mmWave pilots to accurately predict mmWave beam. To further improve the beamforming performance and avoid over-fitting, we develop two data pre-processing approaches utilizing channel sparsity and data augmentation. The simulation results demonstrate superior performance and robustness of the proposed strategy compared to the existing one that purely relies on the sub-6GHz information, especially in the low signal-to-noise ratio (SNR) regions.
Is Third-Party Provided Travel Time Helpful to Estimate Freeway Performance Measures?
Authors: Sakib Mahmud Khan, Anthony David Patire
Date: 2020-09-06 00:00:00

Transportation agencies monitor freeway performance using various measures such as VMT (Vehicle Miles Traveled), VHD (Vehicle Hours of Delay), and VHT (Vehicle Hours Traveled). Public transportation agencies typically rely on point detector data to estimate these freeway performance measures. Point detectors, such as inductive loops cannot capture the travel time for a corridor, which can lead to inaccurate performance measure estimation. This research develops a hybrid method, which estimates of freeway performance measures using a mix of probe data from third-parties and data from traditional point detectors. Using a simulated I-210 model, the overall framework using multiple data sources is evaluated, and compared with the traditional point detector-based estimation method. In the traditional method, point speeds are estimated with the flow and occupancy values using the g-factors. Data from 5% of the total vehicle are used to generate the third-party vendor provided travel time data. The analysis is conducted for multiple scenarios, including peak and off-peak periods. Findings suggest that fusing data from both third-party vendors and point detectors can help estimate performance measures better, compared to the traditional method, in scenarios that have noticeable traffic demand on freeways.
Adaptive and Fast Combined Waveform-Beamforming Design for mmWave Automotive Joint Communication-Radar
Authors: Preeti Kumari, Nitin Jonathan Myers, Robert W. Heath Jr
Date: 2020-09-06 00:00:00
Tasks: Autonomous Driving, compressed sensing

Millimeter-wave (mmWave) joint communication-radar (JCR) will enable high data rate communication and high-resolution radar sensing for applications such as autonomous driving. Prior JCR systems that are based on the mmWave communications hardware, however, suffer from a limited angular field-of-view and low estimation accuracy for radars due to the employed directional communication beam. In this paper, we propose an adaptive and fast combined waveform-beamforming design for the mmWave automotive JCR with a phased-array architecture that permits a trade-off between communication and radar performances. To rapidly estimate the mmWave automotive radar channel in the Doppler-angle domain with a wide field-of-view, our JCR design employs a few circulant shifts of the transmit beamformer and apply two-dimensional partial Fourier compressed sensing technique. We optimize these circulant shifts to achieve minimum coherence in compressed sensing. We evaluate the JCR performance trade-offs using a normalized mean square error (MSE) metric for radar estimation and a distortion MSE metric for data communication, which is analogous to the distortion metric in the rate distortion theory. Additionally, we develop a MSE-based weighted average optimization problem for the adaptive JCR combined waveform-beamforming design. Numerical results demonstrate that our proposed JCR design enables the estimation of short- and medium-range radar channels in the Doppler-angle domain with a low normalized MSE, at the expense of a small degradation in the communication distortion MSE.
XL-MIMO Energy-Efficient Antenna Selection under Non-Stationary Channels
Authors: Gabriel Avanzi Ubiali, Taufik Abrão
Date: 2020-09-05 00:00:00

Massive multiple-input-multiple-output (M-MIMO) is a key technology for 5G networks. Within this research area, new types of deployment are arising, such as the extremely-large regime (XL- MIMO), where the antenna array at the base station (BS) has extreme dimensions. As a consequence, spatial non-stationary properties appear as the users see only a portion of the antenna array, which is called visibility region (VR). In this challenging transmission-reception scenario, an algorithm to select the appropriate antenna-elements for processing the received signal of a given user in the uplink (UL), as well as to transmit the signal of this user during downlink (DL) is proposed. The advantage of not using all the available antenna-elements at the BS is the computational burden and circuit power consumption reduction, improving the energy efficiency (EE) substantially. Numerical results demonstrate that one can increase the EE without compromising considerably the spectral efficiency (SE). Under few active users scenario, the performance of the XL-MIMO system shows that the EE is maximized using less than 20% of the antenna-elements of the array, without compromising the SE severely.
Stochastic Channel Models for Massive and XL-MIMO Systems
Authors: Lígia May Taniguchi, Taufik Abrão
Date: 2020-09-05 00:00:00

In this paper, stochastic channel models for massive MIMO (M-MIMO) and extreme large MIMO (XL- MIMO) system applications are described, evaluated and systematically compared. This work aims to cover new aspects of massive MIMO stochastic channel models in a comprehensive and systematic way. For that, we compare different models, presenting graphically and intuitively the behavior of each model. Each massive MIMO channel model emulates the environment using different methodologies and properties. Using metrics such as capacity, SINR, singular values decomposition (SVD), and condition number, one can understand the influence of each characteristic on the modelling and how it differentiates from other models. Moreover, in new XL-MIMO scenarios, where the near-field and visible region (VR) effects arise, our finding demonstrate that for the two assumed schemes of clusters distribution, the clusters location influences the performance of the conjugate beamforming and zero-forcing (ZF) precoding due to the correlation effect, which have been analysed from the geometric massive MIMO channel models.