pwc-archive/papers-with-abstracts huggingface dataset:
Channel Modeling and Signal Processing for Array-based Visible Light Communication System in Misalignment
Authors: Jiaqi Wei, Chen Gong, Nuo Huang, Zhengyuan Xu
Date: 2021-01-10 00:00:00

This paper proposes an indoor visible light communication (VLC) system with multiple transmitters and receivers. Due to diffusivity of LED light beams, photodiode receive signals from many directions. We use one concave and one convex lens as optical antenna, and obtain the optimal lens structure by optimizing which corresponds to the minimum condition number of channel gain matrix. In this way the light emitted by different LED can be separated well from each other then minimize signal interference. However, interference increases in the case of system deviation, so we explore the system mobility. Then subsequent signal processing is carried out, including signal combining and successive interference cancellation (SIC). We combine the same signal received by different receivers to improve signal to interference noise ratio (SINR). And SIC can effectively restore interference and eliminate its impact. The simulation results show that channel capacity can be increased by more than 5 times and up to 20 times under the condition of receiver and transmitter alignment. In the case of movement, channel capacity can also be increased by about 4 times on average. Moreover, the mobile range of system is also significantly expanded.
A Two-Stage Wavelet Decomposition Method for Instantaneous Power Quality Indices Estimation Considering Interharmonics and Transient Disturbances
Authors: Yiqing Yu, Wei Zhao, Shisong Li, Songling Huang
Date: 2021-01-10 00:00:00

As the complexity increases in modern power systems, power quality analysis considering interharmonics has become a challenging and important task. This paper proposes a novel decomposition and estimation method for instantaneous power quality indices (PQIs) monitoring in single-phase and three-phase systems with interharmonics and transient disturbances. To separate the interharmonic components, a set of new scaling filter and wavelet filter with narrow transition bands are designed for the undecimated wavelet packet transform (UWPT). Further, a two-stage decomposition method for multi-tone voltage and current signals is proposed. The Hilbert transform (HT) is applied to calculate the instantaneous amplitude and phase of each frequency component, which accordingly allows the monitoring of different PQI parameters. Numerical tests are conducted to check the performance of the proposed method. The test results show that compared to other conventional approaches, instantaneous PQIs estimated by the proposed method present significant advances for tracking transitory changes in power systems, and could be considered as a helpful tool for high-accuracy PQ detections.
On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation
Authors: Andreas Toftegaard Kristensen, Alexios Balatsoukas-Stimming, Andreas Burg
Date: 2021-01-09 00:00:00

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.
Channel Estimation for IRS-aided Multiuser Communications with Reduced Error Propagation
Authors: Yi Wei, Ming-Min Zhao, Min-Jian Zhao, Yunlong Cai
Date: 2021-01-09 00:00:00

Intelligent reflecting surface (IRS) has emerged as a promising paradigm to improve the capacity and reliability of a wireless communication system by smartly reconfiguring the wireless propagation environment. To achieve the promising gains of IRS, the acquisition of the channel state information (CSI) is essential, which however is practically difficult since the IRS does not employ any transmit/receive radio frequency (RF) chains in general and it has limited signal processing capability. In this paper, we study the uplink channel estimation problem for an IRS-aided multiuser single-input multi-output (SIMO) system, and propose a novel two-phase channel estimation (2PCE) strategy which can alleviate the negative effects caused by error propagation in the existing three-phase channel estimation approach, i.e., the channel estimation errors in previous phases will deteriorate the estimation performance in later phases, and enhance the channel estimation performance with the same amount of channel training overhead as in the existing approach. Moreover, the asymptotic mean squared error (MSE) of the 2PCE strategy is analyzed when the least-square (LS) channel estimation method is employed, and we show that the 2PCE strategy can outperform the existing approach. Finally, extensive simulation results are presented to validate the effectiveness of the 2PCE strategy.
Quantization optimized with respect to the Haar basis
Authors: Shu Nakamura
Date: 2021-01-09 00:00:00
Tasks: Quantization

We propose a method of data quantization of finite discrete-time signals which optimizes the error estimate of low frequency Haar coefficients. We also discuss the error/noise bounds of this quantization in the Fourier space. Our result shows one can quantize any discrete-time analog signal with high precision at low frequencies. Our method is deterministic, and it employs no statistical arguments, nor any probabilistic assumptions.
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Authors: Guanhua Ye, Hongzhi Yin, Tong Chen, Hongxu Chen, Lizhen Cui, Xiangliang Zhang
Date: 2021-01-08 00:00:00
Tasks: Sleep apnea detection

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy. In this paper, we advance RR-interval based OSA detection by considering its real-world practicality from energy perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped on smart wrist-worn wearable devices (e.g., smart watches and wristbands), the energy efficiency of the detection model is crucial to fully support an overnight observation on patients. This creates challenges as the PPG sensors are unable to keep collecting continuous signals due to the limited battery capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency Extraction Network (FENet), which can extract features from different frequency bands of the input RR-interval signals and generate continuous detection results with downsampled, discontinuous RR-interval signals. With the help of the one-to-multiple structure, FENet requires only one-third of the operation time of the PPG sensor, thus sharply cutting down the energy consumption and enabling overnight diagnosis. Experimental results on real OSA datasets reveal the state-of-the-art performance of FENet.
Secrecy Rate Maximization for Hardware Impaired Untrusted Relaying Network with Deep Learning
Authors: Hamed Bastami, Majid Moradikia, Hamid Behroozi, Rodrigo C. de Lamare, Ahmed Abdelhadi, Zhigou Ding
Date: 2021-01-07 00:00:00

This paper investigates the physical layer security design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Taking into account the hardware impairments, and power budget constraints, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aimed at maximizing average secrecy rate (ASR). The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to improve the ASR while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases.
A Wave Trapping Dispersive Delay Structure Based on Substrate Integrated Waveguide (SIW)
Authors: Hossein Nazemi-Rafi, Masoud Movahhedi
Date: 2021-01-07 00:00:00

A dispersive delay structure (DDS) is a device that creates a specified group delay function over frequency bandwidth. In this paper, the idea of trapping the electromagnetic waves in a feedback loop is proposed to achieve higher group delay swing. The proposed structure includes two transmission lines and two junctions that act as power divider/combiner. The transmission lines can be either dispersive or non-dispersive. The group delay in the proposed configuration is proportional to the injected power into the feedback loop. So, an increase in the group delay swing can be done by improving the junctions or by adding more feedback loops to the structure. Based on this configuration, a novel DDS, using the substrate integrated waveguide (SIW) transmission line, is introduced. According to the experimental results, the proposed device with a relatively compact size creates high group delay swings (9 ns), and its insertion loss is about 7 dB.
An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems
Authors: Mehmet Özgün Demir, Ozan Alp Topal, Ali Emre Pusane, Guido Dartmann, Gerd Ascheid, Güneş Karabulut Kurt
Date: 2021-01-07 00:00:00
Tasks: Reinforcement Learning (RL)

Being capable of sensing and behavioral adaptation in line with their changing environments, cognitive cyber-physical systems (CCPSs) are the new form of applications in future wireless networks. With the advancement of the machine learning algorithms, the transmission scheme providing the best performance can be utilized to sustain a reliable network of CCPS agents equipped with self-decision mechanisms, where the interactions between each agent are modeled in terms of service quality, security, and cost dimensions. In this work, first, we provide network utility as a reliability metric, which is a weighted sum of the individual utility values of the CCPS agents. The individual utilities are calculated by mixing the quality of service (QoS), security, and cost dimensions with the proportions determined by the individualized user requirements. By changing the proportions, the CCPS network can be tuned for different applications of next-generation wireless networks. Then, we propose a secure transmission policy selection (STPS) mechanism that maximizes the network utility by using the Markov-decision process (MDP). In STPS, the CCPS network jointly selects the best performing physical layer security policy and the parameters of the selected secure transmission policy to adapt to the changing environmental effects. The proposed STPS is realized by reinforcement learning (RL), considering its real-time decision mechanism where agents can decide automatically the best utility providing policy in an altering environment.
An Adaptive All-Pass Filter for Time-Varying Delay Estimation
Authors: Beth Jelfs, Shuai Sun, Kamran Ghorbani, Christopher Gilliam
Date: 2021-01-07 00:00:00
Tasks: All

The focus of this paper is the estimation of a delay between two signals. Such a problem is common in signal processing and particularly challenging when the delay is non-stationary in nature. Our proposed solution is based on an all-pass filter framework comprising of two elements: a time delay is equivalent to all-pass filtering and an all-pass filter can be represented in terms of a ratio of a finite impulse response (FIR) filter and its time reversal. Using these elements, we propose an adaptive filtering algorithm with an LMS style update that estimates the FIR filter coefficients and the time delay. Specifically, at each time step, the algorithm updates the filter coefficients based on a gradient descent update and then extracts an estimate of the time delay from the filter. We validate our algorithm on synthetic data demonstrating that it is both accurate and capable of tracking time-varying delays.
Re-configurable Intelligent Surface-based VLC Receivers Using Tunable Liquid-crystals: The Concept
Authors: Alain R. Ndjiongue, Telex M. N. Ngatched, Octavia A. Dobre, Harald Haas
Date: 2021-01-07 00:00:00

Visible light communication (VLC) enables access to huge unlicensed bandwidth, a higher security level, and no radio frequency interference. With these advantages, VLC emerges as a complementary solution to radio frequency communications. VLC systems have primarily been designed for indoor scenarios with typical transmission distances between 2 and 5 m. Different designs would be required for larger distances. This paper proposes for the first time the use of a liquid crystal (LC)-based re-configurable intelligent surface (RIS) on improving the VLC signal detection and transmission range. An LC-based RIS presents multiple advantages, including the tunability of its photo-refractive parameters. Another advantage is its light amplification capabilities when under the influence of an externally applied field. In this paper, we analyze an LC-based RIS structure to amplify the detected light and improve the VLC signal detection and transmission range. Results show that mixing LC with 4 to 8 wt\% concentration of a dye such as the terthiophene (3T-2MB) improves the VLC transmission range of about 0.20 to 1.08 m. This improvement can reach 6.56 m if we combine 8 wt\% concentration of 3T-2MB and 0.1 wt\% concentration of trinitrofluorenone.
A generalized efficiency mismatch attack to bypass detection-scrambling countermeasure
Authors: M A Ruhul Fatin, Shihan Sajeed
Date: 2021-01-07 00:00:00

The ability of an eavesdropper to compromise the security of a quantum communication system by changing the angle of the incoming light is well-known. Randomizing the role of the detectors has been proposed to be an efficient countermeasure to this type of attack. Here we show that the proposed countermeasure can be bypassed if the attack is generalized by including more attack variables. Using the experimental data from existing literature, we show how randomization effectively prevents the initial attack but fails to do so when Eve generalizes her attack strategy. Our result and methodology could be used to security-certify a free-space quantum communication receiver against all types of detector-efficiency-mismatch type attacks.
Cross-domain Joint Dictionary Learning for ECG Inference from PPG
Authors: Xin Tian, Qiang Zhu, Yuenan Li, Min Wu
Date: 2021-01-07 00:00:00
Tasks: Clinical Knowledge, Dictionary Learning

The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis has limited fidelity for uncommon ECG waveform shapes due to the lack of rich representative power. In this paper, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL) and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, our proposed joint dictionary learning frameworks aim to maximize the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with 34,000+ ECG/PPG cycle pairs containing a variety of ECG morphologies and cardiovascular diseases. We demonstrate both visually and quantitatively that our proposed frameworks can achieve better inference performance than previous methods, suggesting an encouraging potential for ECG screening using PPG based on the proactive learned PPG-ECG relationship.
Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks
Authors: Yixuan Zou, Zhijin Qin, Yuanwei Liu
Date: 2021-01-07 00:00:00
Tasks: Action Detection, Activity Detection

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks. In the grant-free NOMA systems with a massive number of users, user activity detection is of great importance. Existing multi-user detection (MUD) techniques rely on complicated update steps which may cause latency in signal detection. In this paper, we propose a generative neural network-based MUD (GenMUD) framework to utilize low-complexity neural networks, which are trained to reconstruct signals in a small fixed number of steps. By exploiting the uncorrelated user behaviours, we design a network architecture to achieve higher recovery accuracy with a low computational cost. Experimental results show significant performance gains in detection accuracy compared to conventional solutions under different channel conditions and user sparsity levels. We also provide a sparsity estimator through extensive experiments. Simulation results of the sparsity estimator showed high estimation accuracy, strong robustness to channel variations and neglectable impact on support detection accuracy.
Residential smart plug with bluetooth communication
Authors: Thales Ruano Barros de Souza, Gabriel Goes Rodrigues, Luan da Silva Serrao, Renata do Nascimento Mota Macambira, Celso Barbosa Carvalho
Date: 2021-01-06 00:00:00

Electricity forms the backbone of the modern world but increasing energy demand with the growth of urban areas in recent decades has overwhelmed the current power grid ecosystem. So, there is a need to move towards a more efficient and interconnected smart grid infrastructure. The growing popularity of the Internet of Things(IoT) has increased the demand for smart and connected devices. In this work we developed a hardware device based on the ATmega2560 microcontroller that can estimate the power consumption and control the state of electro-electronic devices interconnected to it through Bluetooth wireless technology. The developed hardware is a smart plug focusing on smart home applications. As a result, by using a smartphone device with Bluetooth communication, one can control and measure electrical parameters of the interconnected electro-electronic hardware such as the RMS (Root Mean Square) current and RMS power been consumed. The obtained results showed the technical viability in the construction of energy consumption measuring device using modules and components available in the Brazilian market.
A Pilot Study of Smart Agricultural Irrigation using Unmanned Aerial Vehicles and IoT-Based Cloud System
Authors: Mohamed Esmail Karar, Faris Alotaibi, Abdullah AL Rasheed, Omar Reyad
Date: 2021-01-06 00:00:00

This article introduces a new mobile-based application of modern information and communication technology in agriculture based on Internet of Things (IoT), embedded systems and an unmanned aerial vehicle (UAV). The proposed agricultural monitoring system was designed and implemented using Arduino microcontroller boards, Wi-Fi modules, water pumps and electronic environmental sensors, namely temperature, humidity and soil moisture. The role of UAV in this study is to collect these environmental data from different regions of the farm. Then, the quantity of water irrigation is automatically computed for each region in the cloud. Moreover, the developed system can monitor the farm conditions including the water requirements remotely on Android mobile application to guide the farmers. The results of this study demonstrated that our proposed IoT-based embedded system can be effective to avoid unnecessary and wasted water irrigation within the framework of smart agriculture.
Study on MCS Selection and Spectrum Allocation for URLLC Traffic under Delay and Reliability Constraint in 5G Network
Authors: Yuehong Gao, Changhao Sun, Xiaonan Zhang, Xiao Hong
Date: 2021-01-06 00:00:00

To support Ultra-Reliable and Low Latency Communications (URLLC) is an essential character of the 5th Generation (5G) communication system. Unlike the other two use cases defined in 5G, e.g. enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC), URLLC traffic has strict delay and reliability requirement. In this paper, an analysis model for URLLC traffic is proposed from the generation of a URLLC traffic until its transmission over a wireless channel, where channel quality, coding scheme with finite coding length, modulation scheme and allocated spectrum resource are taken into consideration. Then, network calculus analysis is applied to derive the delay guarantee for periodical URLLC traffic. Based on the delay analysis, the admission region is found under certain delay and reliability requirement, which gives a lower bound on required spectrum resource. Theoretical results in the scenario of a 5G New Radio system are presented, where the SNR thresholds for adaptive modulation and coding scheme selection, transmission rate and delay, as well as admission region under different configurations are discussed. In addition, simulation results are obtained and compared with theoretical results, which validates that the admission region derived in this work provides a lower spectrum allocation bound.