pwc-archive/papers-with-abstracts huggingface dataset:
A Grant-based Random Access Protocol in Extra-Large Massive MIMO System
Authors: Otávio Seidi Nishimura, José Carlos Marinello Filho, Taufik Abrão
Date: 2020-09-05 00:00:00

Extra-large massive multiple-input multiple-output (XL-MIMO) systems is a new concept, where spatial non-stationarities allow activate a high number of user equipments (UEs). This paper focuses on a grant-based random access (RA) approach in the novel XL-MIMO channel scenarios. Modifications in the classical Strongest User Collision Resolution (SUCRe) protocol have been aggregated to explore the visibility regions (VRs) overlapping in XL-MIMO. The proposed grant-based RA protocol takes advantage of this new degree of freedom for improving the number of access attempts and accepted UEs. As a result, the proposed grant-based protocol for XL-MIMO systems is capable of reducing latency in the pilot allocation step.
Data Augmentation for Electrocardiogram Classification with Deep Neural Network
Authors: Naoki Nonaka, Jun Seita
Date: 2020-09-05 00:00:00
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification

Electrocardiogram (ECG) is the most crucial monitoring modality to diagnose cardiovascular events. Precise and automatic detection of abnormal ECG patterns is beneficial to both physicians and patients. In the automatic detection of abnormal ECG patterns, deep neural networks (DNNs) have shown significant achievements. However, DNNs require large amount of labeled data, which are often expensive to obtain. On the other hand, recent research have shown by randomly combining data augmentations can improve image classification accuracy. Thus, in this work we explore data augmentation suitable for ECG data and propose ECG Augment. We show by introducing ECG Augment, we can improve classification of atrial fibrillation with single lead ECG data, without changing an architecture of DNN.
Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications
Authors: Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu
Date: 2020-05-14 00:00:00

In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals. It is often challenging for the prior art of frequency tracking to i)achieve a high accuracy under noisy conditions, ii)detect and track multiple frequency components efficiently, or iii)strike a good trade-off of the processing delay versus the resilience and the accuracy of tracking. To address these issues, we propose Adaptive Multi-Trace Carving (AMTC), a unified approach for detecting and tracking one or more subtle frequency components under very low signal-to-noise ratio (SNR) conditions and in near real time. AMTC takes as input a time-frequency representation of the system's preprocessing results (such as the spectrogram), and identifies frequency components through iterative dynamic programming and adaptive trace compensation. The proposed algorithm considers relatively high energy traces sustaining over a certain duration as an indicator of the presence of frequency/oscillation components of interest and track their time-varying trend. Extensive experiments using both synthetic data and real-world forensic data of power signatures and physiological monitoring reveal that the proposed method outperforms representative prior art under low SNR conditions, and can be implemented in near real-time settings. The proposed AMTC algorithm can empower the development of new information forensic technologies that harness very small signals.
Evolving Intelligent Reflector Surface towards 6G for Public Health: Application in Airborne Virus Detection
Authors: Harun Šiljak, Nouman Ashraf, Michael Taynnan Barros, Daniel Perez Martins, Bernard Butler, Arman Farhang, Nicola Marchetti, Sasitharan Balasubramaniam
Date: 2020-09-04 00:00:00

While metasurface based intelligent reflecting surfaces (IRS) are an important emerging technology for future generations of wireless connectivity in its own right, the plans for the mass deployment of these surfaces motivate the question of their integration with other new and emerging technologies that would require mass proliferation. This question of integration and the vision of future communication systems as an invaluable component for public health motivated our new concept of Intelligent Reflector-Viral Detectors (IR-VD). In this novel scheme, we propose deployment of intelligent reflectors with strips of receptor-based viral detectors placed between the reflective surface tiles. Our proposed approach encodes information of the virus by flicking the angle of the reflected beams, using time variations between the beam deviations to represent the messages. This information includes the presence of the virus, its location and load size. The paper presents simulation to demonstrate the encoding process based on varying quantity of virus that have bound onto the IR-VD.
Lorentzian Peak Sharpening and Sparse Blind Source Separation for NMR Spectroscopy
Authors: Yuanchang Sun, Jack Xin
Date: 2020-09-04 00:00:00
Tasks: blind source separation

In this paper, we introduce a preprocessing technique for blind source separation (BSS) of nonnegative and overlapped data. For Nuclear Magnetic Resonance spectroscopy (NMR), the classical method of Naanaa and Nuzillard (NN) requires the condition that source signals to be non-overlapping at certain locations while they are allowed to overlap with each other elsewhere. NN's method works well with data signals that possess stand alone peaks (SAP). The SAP does not hold completely for realistic NMR spectra however. Violation of SAP often introduces errors or artifacts in the NN's separation results. To address this issue, a preprocessing technique is developed here based on Lorentzian peak shapes and weighted peak sharpening. The idea is to superimpose the original peak signal with its weighted negative second order derivative. The resulting sharpened (narrower and taller) peaks enable NN's method to work with a more relaxed SAP condition, the so called dominant peaks condition (DPS), and deliver improved results. To achieve an optimal sharpening while preserving the data nonnegativity, we prove the existence of an upper bound of the weight parameter and propose a selection criterion. Numerical experiments on NMR spectroscopy data show satisfactory performance of our proposed method.
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Authors: Chang Liu, Jiahui Sun, Haiming Jin, Meng Ai, Qun Li, Cheng Zhang, Kehua Sheng, Guobin Wu, XiaoHu Qie, Xinbing Wang
Date: 2020-09-04 00:00:00

Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the strategy that dispatches orders over the entire city at a uniform time interval. However, the uneven spatio-temporal order distributions in real-world ridesharing systems indicate that such an approach is suboptimal in practice. Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time. Specifically, we propose a hierarchical approach, which generates clusters of geographical areas suitable to share the same dispatching intervals, and then makes online decisions of selecting the appropriate time instances for order dispatch within each spatial cluster. Technically, we prove the impossibility of designing constant-competitive-ratio algorithms for the online adaptive interval problem, and propose online algorithms under partial or even zero future order knowledge that significantly improve the platform's profit over existing approaches. We conduct extensive experiments with a large-scale ridesharing order dataset, which contains all of the over 3.5 million ridesharing orders in Beijing, China, received by Didi Chuxing from October 1st to October 31st, 2018. The experimental results demonstrate that our proposed algorithms outperform existing approaches.
Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDM
Authors: Seyed Hadi Mirfarshbafan, Mahdi Shabany, Seyed Alireza Nezamalhosseini, Christoph Studer
Date: 2020-09-04 00:00:00
Tasks: Quantization

The use of low-resolution data converters in the radio-frequency (RF) chains of all-digital massive multiple-input multiple-output (MIMO) basestations promises significant reductions in power consumption, hardware costs, and interconnect bandwidth. We propose a quantization-aware data-detection algorithm which mitigates the performance loss of 1-bit quantized massive MIMO orthogonal frequency-division multiplexing (OFDM) systems. Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm. We show that the proposed algorithms significantly outperform linear data detectors and channel estimators in terms of bit error rate. For the proposed nonlinear data detection algorithm, we develop a very large scale integration (VLSI) architecture and present implementation results on a Xilinx Virtex-7 field programmable gate array (FPGA). Our implementation results are, to the best of our knowledge, the first for 1-bit massive MU-MIMO-OFDM systems and demonstrate comparable hardware efficiency with respect to state-of-the-art linear data detectors designed for systems with high-resolution data converters, while achieving lower bit error rate.
Learning Constellation Map with Deep CNN for Accurate Modulation Recognition
Authors: Van-Sang Doan, Thien Huynh-The, Cam-Hao Hua, Quoc-Viet Pham, Dong-Seong Kim
Date: 2020-09-04 00:00:00
Tasks: Classification, General Classification

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades for identifying the modulation format of an incoming signal, they often reveal the obstacle of learning radio characteristics for most traditional machine learning algorithms. To overcome this drawback, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. Particularly, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image. The deep network is specified by multiple processing blocks, where several grouped and asymmetric convolutional layers in each block are organized by a flow-in-flow structure for feature enrichment. These blocks are connected via skip-connection to prevent the vanishing gradient problem while effectively preserving the information identify throughout the network. Regarding several intensive simulations on the constellation image dataset of eight digital modulations, the proposed deep network achieves the remarkable classification accuracy of approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further outperforms some state-of-the-art deep models of constellation-based modulation classification.
Temporal optical neurons for serial deep learning
Authors: Zhixing Lin, Shuqian Sun, Jose Azana, Wei Li, Ninghua Zhu, Ming Li
Date: 2020-09-04 00:00:00
Tasks: Deep Learning

Deep learning is able to functionally simulate the human brain and thus, it has attracted considerable interest. Optics-assisted deep learning is a promising approach to improve the forward-propagation speed and reduce the power consumption. However, present methods are based on a parallel processing approach that is inherently ineffective in dealing with serial data signals at the core of information and communication technologies. Here, we propose and demonstrate a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. By utilizing ultra-short coherent optical pulses as the information carriers, the neurons are distributed at different time slots in a serial pattern, and interconnected to each other through group delay dispersion. A 4-layer serial optical neural network (SONN) was constructed and trained for classification of both analog and digital signals with simulated accuracy rates of over 90% with proper individuality variance rates. Furthermore, we performed a proof-of-concept experiment of a pseudo-3-layer SONN to successfully recognize the ASCII (American Standard Code for Information Interchange) codes of English letters at a data rate of 12 Gbps. This concept represents a novel one-dimensional realization of artificial neural networks, enabling an efficient application of optical deep learning methods to the analysis and processing of serial data signals, while offering a new overall perspective for the temporal signal processing.
Chain-Net: Learning Deep Model for Modulation Classification Under Synthetic Channel Impairment
Authors: Thien Huynh-The, Van-Sang Doan, Cam-Hao Hua, Quoc-Viet Pham, Dong-Seong Kim
Date: 2020-09-04 00:00:00
Tasks: General Classification

Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of effectively learning weakly discriminative modulation patterns. This paper proposes a robust modulation classification method by taking advantage of deep learning to capture the meaningful information of modulation signal at multi-scale feature representations. To this end, a novel architecture of convolutional neural network, namely Chain-Net, is developed with various asymmetric kernels organized in two processing flows and associated via depth-wise concatenation and element-wise addition for optimizing feature utilization. The network is evaluated on a big dataset of 14 challenging modulation formats, including analog and high-order digital techniques. The simulation results demonstrate that Chain-Net robustly classifies the modulation of radio signals suffering from a synthetic channel deterioration and further performs better than other deep networks.
RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring
Authors: John M. Bernabei, Olaoluwa Owoputi, Shyon D. Small, Nathaniel T. Nyema, Elom Dumenyo, Joongwon Kim, Steven N. Baldassano, Christopher Painter, Erin C. Conrad, Taneeta M. Ganguly, Ramani Balu, Kathryn A. Davis, Jay Pathmanathan, Brian Litt
Date: 2020-09-03 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Seizure Detection

Objective: Continuous EEG (cEEG) monitoring is associated with lower mortality in critically ill patients, however it is underutilized due to the difficulty of manually interpreting prolonged streams of cEEG data. Here we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures (RAMSES) that dramatically reduces the amount of manual EEG review. Methods: We developed a custom data reduction algorithm using a random forest, and deployed it within an online cloud-based platform which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We validate RAMSES on cEEG recordings from 77 patients undergoing routine scalp ICU EEG monitoring. Results: On subjects with seizures we achieved >80% overall data reduction, while detecting a mean of 84% of seizures across all validation patients, with 19/27 patients achieving 100% seizure detection. On seizure free-patients, the majority of cEEG records, we reduced data requiring manual review by >83%. Conclusion: This study validates a platform for machine-learning assisted data reduction. Significance: This work represents a meaningful step towards improving utility and decreasing cost for cEEG monitoring We also make our high-quality annotated dataset of 77 ICU cEEG recordings public for others to validate and improve upon our methods.
Model-Free Design of Control Systems over Wireless Fading Channels
Authors: Vinicius Lima, Mark Eisen, Konstantinos Gatsis, Alejandro Ribeiro
Date: 2020-09-03 00:00:00

Wireless control systems replace traditional wired communication with wireless networks to exchange information between actuators, plants and sensors in a control system. The noise in wireless channels renders ideal control policies suboptimal, and their performance is moreover directly dependent on the way in which wireless resources are allocated between control loops. Proper design of the control policy and the resource allocation policy based on both plant states and wireless fading states is then critical to achieve good performance. The resulting problem of co-designing control-aware resource allocation policies and communication-aware controllers, however, is challenging due to its infinite dimensionality, existence of system constraints and need for explicit knowledge of the plants and wireless network models. To overcome those challenges, we rely on constrained reinforcement learning algorithms to propose a model-free approach to the design of wireless control systems. We demonstrate the near optimality of control system performance and stability using near-universal policy parametrizations and present a practical model-free algorithm to learn the co-design policy. Numerical experiments show the strong performance of learned policies over baseline solutions.
A Derivation of Identifiable Condition for Non-Uniform Linear Array DOA Estimation
Authors: Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri
Date: 2020-08-31 00:00:00

Phase ambiguity happens in uniform linear arrays (ULAs) when the sensor distance is greater than $\lambda/2$. This problem in direction of arrival (DOA) estimation and can be solved by designing a proper sensor configuration. In this work, we derive the identifiable condition for ULA DOA estimation.
Channel estimation: unified view of optimal performance and pilot sequences
Authors: Luc Le Magoarou, Stéphane Paquelet
Date: 2020-02-11 00:00:00

Channel estimation is of paramount importance in most communication systems in order to optimize the data rate/energy consumption tradeoff. In modern systems, the possibly large number of transmit/receive antennas and subcarriers makes this task difficult. Designing pilot sequences of reasonable size yielding good performance is thus critical. Classically, the number of pilots is reduced by viewing the channel as a random vector and assuming knowledge of its distribution. In practice, this requires estimating the channel covariance matrix, which can be computationally costly and not adapted to scenarios with high mobility. In this paper, an alternative view is considered, in which the channel is a function of unknown deterministic parameters. In this setting, the problem of designing optimal pilot sequences of smallest possible size is studied for any parametric channel model. To do so, the Cram{\'e}r-Rao bound (CRB) for this general channel estimation problem is given, highlighting its key dependency on the introduced variation space. Then, the minimal size of pilot sequences and minimal value of the CRB are determined. Moreover, a general strategy to build optimal minimal length power constrained pilots sequences is given, based on an estimation of the variation space. The theoretical results are finally illustrated in a massive MIMO system context. They conveniently allow to retrieve well known previous results, but also to exhibit minimal length optimal pilot sequences for a new strategy based on a nonlinear physical model.
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer
Authors: Jing-Xuan Zhang, Li-Juan Liu, Yan-Nian Chen, Ya-Jun Hu, Yuan Jiang, Zhen-Hua Ling, Li-Rong Dai
Date: 2020-09-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Voice Conversion

With the development of automatic speech recognition (ASR) and text-to-speech synthesis (TTS) technique, it's intuitive to construct a voice conversion system by cascading an ASR and TTS system. In this paper, we present a ASR-TTS method for voice conversion, which used iFLYTEK ASR engine to transcribe the source speech into text and a Transformer TTS model with WaveNet vocoder to synthesize the converted speech from the decoded text. For the TTS model, we proposed to use a prosody code to describe the prosody information other than text and speaker information contained in speech. A prosody encoder is used to extract the prosody code. During conversion, the source prosody is transferred to converted speech by conditioning the Transformer TTS model with its code. Experiments were conducted to demonstrate the effectiveness of our proposed method. Our system also obtained the best naturalness and similarity in the mono-lingual task of Voice Conversion Challenge 2020.
Designing the Waveform Bandwidth and Time Duration of Automotive Radars for Better Collision Warning Performance
Authors: Hang Ruan, Yimin Liu, Tianyao Huang, Xiqin Wang
Date: 2020-09-03 00:00:00

Automotive radar is a key component in an ADAS. The increasing number of radars implemented in vehicles makes interference between them a noteworthy issue. One method of interference mitigation is to limit the TBP of radar waveforms. However, the problems of how much TBP is necessary and how to optimally utilize the limited TBP have not been addressed. We take CWS as an example and propose a method of designing the radar waveform parameters oriented by the performance of CWS We propose a metric to quantify the CWS performance and study how the radar waveform parameters (bandwidth and duration) influence this metric. Then, the waveform parameters are designed with a limit on the TBP to optimize the system performance. Numerical results show that the proposed design outperforms the state-of-the-art parameter settings in terms of system performance and resource or energy efficiency.
Angular Clustering of Millimeter-Wave Propagation Channels with Watershed Transformation
Authors: Pengfei Lyu, Aziz Benlarbi-Delaï, Zhuoxiang Ren, Julien Sarrazin
Date: 2020-09-02 00:00:00
Tasks: Clustering

An angular clustering method based on image processing is proposed in this paper. It is used to identify clusters in 2D representations of propagation channels. The approach uses operations such as watershed segmentation and is particularly well suited for clustering directional channels obtained by beam-steering at millimeter-wave. This situation occurs for instance with electronic beam-steering using analog antenna arrays during beam training process or during channel modeling measurements using either electronic or mechanical beam-steering. In particular, the proposed technique is used here to cluster two-dimensional power angular spectrum maps. The proposed clustering is unsupervised and is well suited to preserve the shape of clusters by considering the angular connection between neighbor samples, which is useful to obtain more accurate descriptions of channel angular properties. The approach is found to outperform approaches based on K-Power- Means in terms of accuracy as well as computational resource . The technique is assessed in simulation using IEEE 802.11ad channel model and in measurement using experiments conducted at 60 GHz in an indoor environment.