pwc-archive/papers-with-abstracts huggingface dataset:
Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network
Authors: Xukai Zhong, Yiming Huo, Xiaodai Dong, Zhonghua Liang
Date: 2020-08-07 00:00:00

Unmanned aerial vehicle (UAV)-assisted communications is a promising solution to improve the performance of future wireless networks, where UAVs are deployed as base stations for enhancing the quality of service (QoS) provided to ground users when traditional terrestrial base stations are unavailable or not sufficient. An effective framework is proposed in this paper to manage the dynamic movement of multiple unmanned aerial vehicles (UAVs) in response to ground user mobility, with the objective to maximize the sum data rate of the ground users. First, we discuss the relationship between the air-to-ground (A2G) path loss (PL) and the location of UAVs. Then a deep Q-network (DQN) based method is proposed to adjust the locations of UAVs to maximize the sum data rate of the user equipment (UE). Finally, simulation results show that the proposed method is capable of adjusting UAV locations in a real-time condition to improve the QoS of the entire network.
Experimental Analysis on Variations and Accuracy of Crosstalk in Trench-Assisted Multi-core Fibers
Authors: Hui Yuan, Alessandro Ottino, Yunnuo Xu, Arsalan Saljoghei, Tetsuya Hayashi, Tetsuya Nakanishi, Eric Sillekens, Lidia Galdino, Polina Bayvel, Zhixin Liu, Georgios Zervas
Date: 2020-08-07 00:00:00

Space division multiplexing using multi-core fiber (MCF) is a promising solution to cope with the capacity crunch in standard single-mode fiber based optical communication systems. Nevertheless, the achievable capacity of MCF is limited by inter-core crosstalk (IC-XT). Many existing researches treat IC-XT as a static interference, however, recent research shows that IC-XT varies with time, wavelength and baud rate. This inherent stochastic feature requires a comprehensive characterization of the behaviour of MCF to its application in practical transmission systems and the theoretical understanding of IC-XT phenomenon. In this paper, we experimentally investigate the IC-XT behaviour of an 8-core trench-assisted MCF in a temperature-controlled environment, using popular modulation formats. We compare the measured results with the theoretical prediction to validate the analytical IC-XT models previously developed. Moreover, we explore the effects of the measurement configurations on the IC-XT accuracy and present an analysis on the IC-XT step distribution. Our results indicate that a number of transmission parameters have significant influence on the strength and volatility of IC-XT. Moreover, the averaging time of the power meter and the observation time window can affect the value of the observed IC-XT, the degrees of the effects vary with the type of the source signals.
QoS-Compliant 3D Deployment Optimization Strategy for UAV Base Stations
Authors: Xukai Zhong, Yiming Huo, Xiaodai Dong, Zhonghua Liang
Date: 2020-08-07 00:00:00

Unmanned aerial vehicle (UAV) is being integrated as an active element in 5G and beyond networks. Because of its flexibility and mobility, UAV base stations (UAV-BSs) can be deployed according to the ground user distributions and their quality of service (QoS) requirement. Although there has been quite some prior research on the UAV deployment, no work has studied this problem in a 3 dimensional (3D) setting and taken into account the UAV-BS capacity limit and the quality of service (QoS) requirements of ground users. Therefore, in this paper, we focus on the problem of deploying UAV-BSs to provide satisfactory wireless communication services, with the aim to maximize the total number of covered user equipment (UE) subject to user data rate requirements and UAV-BSs' capacity limit. First, we model the relationship between the air-to-ground (A2G) path loss (PL) and the location of UAV-BSs in both horizontal and vertical dimensions which has not been considered in previous works. Unlike the conventional UAV deployment problem formulation, the 3D deployment problem is decoupled into a 2D horizontal placement and altitude determination connected by path loss requirement and minimization. Then, we propose a novel genetic algorithm (GA) based 2D placement approach in which UAV-BSs are placed to have maximum coverage of the users with consideration of data rate distribution. Finally, numerical and simulation results show that the proposed approach has enabled a better coverage percentage comparing with other schemes.
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition
Authors: Runfeng Miao, Li Zhang, Qiang Sun
Date: 2020-08-07 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), SSVEP, Transfer Learning

In this study, an advanced CCA-based algorithn called hybrid template canonical correlation analysis (HTCCA) was proposed to improve the performance of brain-computer interface (BCI) based on steady state visual evoked potential (SSVEP) uuder data-linited condition. The HTCCA method combines the training data from several subjects to construct SSVEP templates. The experinental results evaluated on two public benchmark datasets showed that the proposed method outperforms the compared methods in both detection accuracy and information transfer rate when the number of tuials is small.Considering that user-friendly experience will become a key factor for BCI in practical application, it is very necessary to develop effective methods based on limited EEG samples. This study demonstrates that the proposed method has great potential in the application of SSVEP-based brain-computer interfaces.
On the Application of Error Backpropagation to the Background Calibration of Time Interleaved ADC for Digital Communication Receivers
Authors: Fredy Solis, Benjamín T. Reyes, Damián A. Morero, Mario R. Hueda
Date: 2020-08-07 00:00:00

This paper introduces a backpropagation-based technique for the calibration of the mismatch errors of time-interleaved analog to digital converters (TI-ADCs). This technique is applicable to digital receivers such as those used in coherent optical communications. The error at the slicer of the receiver is processed using a modified version of the well known backpropagation algorithm from machine learning. The processed slicer error can be directly applied to compensate the TI-ADC mismatch errors with an adaptive equalizer, or it can be used to digitally estimate and correct said mismatch errors using analog techniques such as delay cells and programmable gain amplifiers (PGA). The main advantages of the technique proposed here compared to prior art are its robustness, its speed of convergence, and the fact that it always works in background mode, independently of the oversampling factor and the properties of the input signal, as long as the receiver converges. Moreover, this technique enables the joint compensation of impairments not addressed by traditional TI-ADC calibration techniques, such as I/Q skew in quadrature modulation receivers. Simulations are presented to demonstrate the effectiveness of the technique, and low complexity implementation options are discussed.
Improper Gaussian Signaling for the $K$-user MIMO Interference Channels with Hardware Impairments
Authors: Mohammad Soleymani, Ignacio Santamaria, Peter J. Schreier
Date: 2020-01-28 00:00:00

This paper investigates the performance of improper Gaussian signaling (IGS) for the $K$-user multiple-input, multiple-output (MIMO) interference channel (IC) with hardware impairments (HWI). HWI may arise due to imperfections in the devices like I/Q imbalance, phase noise, etc. With I/Q imbalance, the received signal is a widely linear transformation of the transmitted signal and noise. Thus, the effective noise at the receivers becomes improper, which means that its real and imaginary parts are correlated and/or have unequal powers. IGS can improve system performance with improper noise and/or improper interference. In this paper, we study the benefits of IGS for this scenario in terms of two performance metrics: achievable rate and energy efficiency (EE). We consider the rate region, the sum-rate, the EE region and the global EE optimization problems to fully evaluate the IGS performance. To solve these non-convex problems, we employ an optimization framework based on majorization-minimization algorithms, which allow us to obtain a stationary point of any optimization problem in which either the objective function and/or constraints are linear functions of rates. Our numerical results show that IGS can significantly improve the performance of the $K$-user MIMO IC with HWI and I/Q imbalance, where its benefits increase with the number of users, $K$, and the imbalance level, and decrease with the number of antennas.
Geometric Sequence Decomposition with $k$-simplexes Transform
Authors: Woong-Hee Lee, Jong-Ho Lee, Ki Won Sung
Date: 2019-10-31 00:00:00

This paper presents a computationally efficient technique for decomposing non-orthogonally superposed $k$ geometric sequences. The method, which is named as geometric sequence decomposition with $k$-simplexes transform (GSD-ST), is based on the concept of transforming an observed sequence to multiple $k$-simplexes in a virtual $k$-dimensional space and correlating the volumes of the transformed simplexes. Hence, GSD-ST turns the problem of decomposing $k$ geometric sequences into one of solving a $k$-th order polynomial equation. Our technique has significance for wireless communications because sampled points of a radio wave comprise a geometric sequence. This implies that GSD-ST is capable of demodulating randomly combined radio waves, thereby eliminating the effect of interference. To exemplify the potential of GSD-ST, we propose a new radio access scheme, namely non-orthogonal interference-free radio access (No-INFRA). Herein, GSD-ST enables the collision-free reception of uncoordinated access requests. Numerical results show that No-INFRA effectively resolves the colliding access requests when the interference is dominant.
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare
Authors: William Taylor, Syed Aziz Shah, Kia Dashtipour, Adnan Zahid, Qammer H. Abbasi, Muhammad Ali Imran
Date: 2020-08-06 00:00:00
Tasks: Activity Recognition, BIG-bench Machine Learning, Human Activity Recognition, Motion Detection

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real time monitoring by deploying equipment on a person's body. However, putting devices on a person's body all the time make it uncomfortable and the elderly tends to forget it to wear as well in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals presents particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine learning model was able to achieve 96.70 % accuracy using the Random Forest algorithm using 10 fold cross validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90 %. The machine learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.
Point Cloud Segmentation based on Hypergraph Spectral Clustering
Authors: Songyang Zhang, Shuguang Cui, Zhi Ding
Date: 2020-01-21 00:00:00
Tasks: Clustering, Point Cloud Segmentation, Segmentation

Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis. The surface of a three-dimensional (3D) point cloud and the multilateral relationship among their points can be naturally captured by the high-dimensional hyperedges. This work investigates the power of hypergraph spectral analysis in unsupervised segmentation of 3D point clouds. We estimate and order the hypergraph spectrum from observed point cloud coordinates. By trimming the redundancy from the estimated hypergraph spectral space based on spectral component strengths, we develop a clustering-based segmentation method. We apply the proposed method to various point clouds, and analyze their respective spectral properties. Our experimental results demonstrate the effectiveness and efficiency of the proposed segmentation method.
3D Spectrum Mapping Based on ROI-Driven UAV Deployment
Authors: Qihui Wu, Feng Shen, Zheng Wang, Guoru Ding
Date: 2020-08-06 00:00:00

Given the explosive growth of Internet of Things (IoT) devices ranging from the two-dimensional (2D) ground to the three-dimensional (3D) space, it is a necessity to establish a 3D spectrum map to comprehensively present and effectively manage the 3D spatial spectrum resources in smart city infrastructures. By leveraging the popularity and location flexibility of the unmanned aerial vehicles (UAVs), we are able to execute spatial sampling with these emerging flying spectrum-monitoring devices (SMDs) at will. In this paper, we first present a brief survey to show the state-of-the-art studies on spectrum mapping. Then, we introduce the 3D spectrum mapping model. Next, we propose a 3D spectrum mapping framework which is composed of pre-sampling, spectrum situation estimation, UAV deployment and spectrum recovery. Therein we develop a Region of Interest (ROI)-driven UAV deployment scheme, which selects new sampling points of the highest estimated interest and the lowest energy cost iteratively. Meanwhile, we slice the entire 3D spectrum map into a series of "images" and "repair" those unsampled locations. Furthermore, we provide an exemplary case study on the 3D spectrum mapping, where, for example, an important event is being held and the entire spectrum situation needs to be monitored in real time to deal with malicious interference sources. Lastly, the challenges and open issues are discussed.
MetaRadar: Indoor Localization by Reconfigurable Metamaterials
Authors: Haobo Zhang, Jingzhi Hu, Hongliang Zhang, Boya Di, Kaigui Bian, Zhu Han, Lingyang Song
Date: 2020-08-06 00:00:00
Tasks: Indoor Localization

Indoor localization has drawn much attention owing to its potential for supporting location based services. Among various indoor localization techniques, the received signal strength (RSS) based technique is widely researched. However, in conventional RSS based systems where the radio environment is unconfigurable, adjacent locations may have similar RSS values, which limits the localization precision. In this paper, we present MetaRadar, which explores reconfigurable radio reflection with a surface/plane made of metamaterial units for multi-user localization. By changing the reflectivity of metamaterial, MetaRadar modifies the radio channels at different locations, and improves localization accuracy by making RSS values at adjacent locations have significant differences. However, in MetaRadar, it is challenging to build radio maps for all the radio environments generated by metamaterial units and select suitable maps from all the possible maps to realize a high accuracy localization. To tackle this challenge, we propose a compressive construction technique which can predict all the possible radio maps, and propose a configuration optimization algorithm to select favorable metamaterial reflectivities and the corresponding radio maps. The experimental results show a significant improvement from a decimeter-level localization error in the traditional RSS-based systems to a centimeter-level one in MetaRadar.
Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features
Authors: Michael Saxon, Ayush Tripathi, Yishan Jiao, Julie Liss, Visar Berisha
Date: 2019-11-26 00:00:00
Tasks: BIG-bench Machine Learning

Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal speech from one disease and evaluating on hypernasal speech from another disease (e.g. training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered speech but evaluating on cleft palate speech.
Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines
Authors: Zhe Wan, Tianyi Wang, Yiming Zhou, Subramanian S. Iyer, Vwani P. Roychowdhury
Date: 2020-08-05 00:00:00

Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNN) to improve energy efficiency. Such architectures, however, leverage charge conservation, an operation with infinite resolution, and thus are susceptible to errors. The computations in DNN realized by analog NVM thus have high uncertainty due to the device stochasticity. Several reports have demonstrated the use of analog NVM for CIM in a limited scale. It is unclear whether the uncertainties in computations will prohibit large-scale DNNs. To explore this critical issue of scalability, this paper first presents a simulation framework to evaluate the feasibility of large-scale DNNs based on CIM architecture and analog NVM. Simulation results show that DNNs trained for high-precision digital computing engines are not resilient against the uncertainty of the analog NVM devices. To avoid such catastrophic failures, this paper introduces the analog floating-point representation for the DNN, and the Hessian-Aware Stochastic Gradient Descent (HA-SGD) training algorithm to enhance the inference accuracy of trained DNNs. As a result of such enhancements, DNNs such as Wide ResNets for the CIFAR-100 image recognition problem are demonstrated to have significant performance improvements in accuracy without adding cost to the inference hardware.
Optimal Data Detection and Signal Estimation in Systems with Input Noise
Authors: Ramina Ghods, Charles Jeon, Arian Maleki, Christoph Studer
Date: 2020-08-05 00:00:00
Tasks: Compressive Sensing

Practical systems often suffer from hardware impairments that already appear during signal generation. Despite the limiting effect of such input-noise impairments on signal processing systems, they are routinely ignored in the literature. In this paper, we propose an algorithm for data detection and signal estimation, referred to as Approximate Message Passing with Input noise (AMPI), which takes into account input-noise impairments. To demonstrate the efficacy of AMPI, we investigate two applications: Data detection in large multiple-input multiple output (MIMO) wireless systems and sparse signal recovery in compressive sensing. For both applications, we provide precise conditions in the large-system limit for which AMPI achieves optimal performance. We furthermore use simulations to demonstrate that AMPI achieves near-optimal performance at low complexity in realistic, finite-dimensional systems.
Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion
Authors: Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love, James V. Krogmeier
Date: 2020-08-05 00:00:00
Tasks: Position

In this paper, a data-driven position-aided approach is proposed to reduce the training overhead in MIMO systems, by leveraging side information and on-the-field measurements. A data tensor is constructed by collecting beam-training measurements on a subset of positions and beams, and a hybrid noisy tensor completion (HNTC) algorithm is proposed to predict the received power across the coverage area, which exploits both the spatial smoothness and the low-rank property of MIMO channels. A recommendation algorithm based on the completed tensor, beam subset selection (BSS), is proposed to achieve fast and accurate beam-training. Besides, a grouping-based BSS algorithm is proposed to combat the detrimental effect of noisy positional information. Numerical results evaluated with the Quadriga channel simulator at 60 GHz millimeter-wave channels show that the proposed BSS recommendation algorithm in combination with HNTC achieve accurate received power predictions, enabling beam-alignment with small overhead: given power measurements on 40% of possible discretized positions, HNTC-based BSS attains a probability of correct alignment of 91%, with only 2% of trained beams, as opposed to a state-of-the-art position-aided beam-alignment scheme which achieves 54% correct alignment in the same configuration. Finally, an online HNTC method via warm-start is proposed, that alleviates the computational complexity by 50%, with no degradation in prediction accuracy.
Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links
Authors: Pavel Skvortcov, Ian Phillips, Wladek Forysiak, Toshiaki Koike-Akino, Keisuke Kojima, Kieran Parsons, David S. Millar
Date: 2020-08-05 00:00:00

Huffman-coded sphere shaping (HCSS) is an algorithm for finite-length probabilistic constellation shaping, which provides nearly optimal energy efficiency at low implementation complexity. In this paper, we experimentally study the nonlinear performance of HCSS employing dual-polarization 64-ary quadrature amplitude modulation (DP-64QAM) in an extended reach single-span link comprising 200 km of standard single mode fiber (SSMF). We investigate the effects of shaping sequence length, dimensionality of symbol mapping, and shaping rate. We determine that the na\"ive approach of Maxwell-Boltzmann distribution matching - which is optimal in the additive white Gaussian noise channel - provides a maximum achievable information rate gain of 0.18 bits/4D-symbol in the infinite length regime. Conversely, HCSS can achieve a gain of 0.37 bits/4Dsymbol using amplitude sequence lengths of 32, which may be implemented without multiplications, using integer comparison and addition operations only. Coded system performance, with a net data rate of approximately 425 Gb/s for both shaped and uniform inputs, is also analyzed.