pwc-archive/papers-with-abstracts huggingface dataset:
Understanding UAV-Based WPCN-Aided Capabilities for Offshore Monitoring Applications
Authors: Dick Carrillo, Konstantin Mikhaylov, Pedro J. Nardelli, Sergey Andreev, Daniel B. da Costa
Date: 2020-12-23 00:00:00

Despite the immense progress in the recent years, efficient solutions for monitoring remote areas are still missing today. This is especially notable in the context of versatile maritime and offshore use cases, owing to a broader span of operating regions and a lack of radio network infrastructures. In this article, we address the noted challenge by delivering a conceptual solution based on the convergence of three emerging technologies -- unmanned aerial vehicles (UAVs), battery-less sensors, and wireless powered communication networks (WPCNs). Our contribution offers a systematic description of the ecosystem related to the proposed solution by identifying its key actors and design dimensions together with the relevant resources and performance metrics. A system-level modeling-based evaluation of an illustrative scenario delivers deeper insights into the considered operation and the associated trade-offs. Further, unresolved challenges and perspective directions are underpinned for a subsequent study.
Hybrid Beamforming for Millimeter Wave Full-Duplex under Limited Receive Dynamic Range
Authors: Ian P. Roberts, Jeffrey G. Andrews, Sriram Vishwanath
Date: 2020-12-21 00:00:00

Full-duplex millimeter wave (mmWave) communication has shown increasing promise for self-interference cancellation via hybrid precoding and combining. This paper proposes a novel mmWave multiple-input multiple-output (MIMO) design for configuring the analog and digital beamformers of a full-duplex transceiver. Our design is the first to holistically consider the key practical constraints of analog beamforming codebooks, a minimal number of radio frequency (RF) chains, limited channel knowledge, beam alignment, and a limited receive dynamic range. To prevent self-interference from saturating the receiver of a full-duplex device having limited dynamic range, our design addresses saturation on a per-antenna and per-RF chain basis. Numerical results evaluate our design in a variety of settings and validate the need to prevent receiver-side saturation. These results and the corresponding insights serve as useful design references for practical full-duplex mmWave transceivers.
SuperCell: A Wide-Area Coverage Solution Using High-Gain, High-Order Sectorized Antennas on Tall Towers
Authors: Pratheep Bondalapati, Abhishek Tiwari, Mustafa Emin Sahin, Qi Tang, Srishti Saraswat, Vishvas Suryakumar, Ali Yazdan, Julius Kusuma, Amit Dubey
Date: 2020-11-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this article we introduce a novel solution called SuperCell, which can improve the return on investment (ROI) for rural area network coverage. SuperCell offers two key technical features: it uses tall towers with high-gain antennas for wide coverage and high-order sectorization for high capacity. We show that a solution encompassing a high-elevation platform in excess of 200 meters increases coverage by 5x. Combined with dense frequency reuse by using as many as 36 azimuthal sectors from a single location, our solution can adequately serve the rural coverage and capacity demands. We validate this through propagation analysis, modeling, and experiments. The article gives a design perspective using different classes of antennas: Luneburg lens, active/passive phased array, and spatial multiplexing solutions. For each class, the corresponding analytical model of the resulting signal-to-interference plus noise ratio (SINR) based range and capacity prediction is presented. The spatial multiplexing solution is also validated through field measurements and additional 3D ray-tracing simulation. Finally, in this article we also shed light on two recent SuperCell field trials performed using a Luneburg lens antenna system. The trials took place in rural New Mexico and Mississippi. In the trials, we quantified the coverage and capacity of SuperCell in barren land and in a densely forested location, respectively. In the article, we demonstrate the results obtained in the trials and share the lessons learned regarding green-field and brown-field deployments.
Knowledge-Driven Machine Learning: Concept, Model and Case Study on Channel Estimation
Authors: Daofeng Li, Kaihe Deng, Ming Zhao, Sihai Zhang, Jinkang Zhu
Date: 2020-12-21 00:00:00
Tasks: BIG-bench Machine Learning

The power of big data and machine learning has been drastically demonstrated in many fields during the past twenty years which somehow leads to the vague even false understanding that the huge amount of precious human knowledge accumulated to date no longer seems to matter. In this paper, we are pioneering to propose the knowledge-driven machine learning(KDML) model to exhibit that knowledge can play an important role in machine learning tasks. KDML takes advantage of domain knowledge to processes the input data by space transforming without any training which enable the space of input and the output data of the neural networks to be identical, so that we can simplify the machine learning network structure and reduce training costs significantly. Channel estimation problems considering the time selective and frequency selective fading in wireless communications are taken as a case study, where we choose least square(LS) and minimum meansquare error(MMSE) as knowledge module and Long Short Term Memory(LSTM) as learning module. The performance obtained by KDML channel estimator obviously outperforms that of knowledge processing or conventional machine learning, respectively. Our work sheds light on the new area of machine learning and knowledge processing.
Low Complexity Component Nonlinear Distortions Mitigation Scheme for Probabilistically Shaped 64-QAM Signals
Authors: Yiwen Wu, Mengfan Fu, Huazhi Lun, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Date: 2020-12-21 00:00:00

We propose a degenerated hierarchical look-up table (DH-LUT) scheme to compensate component nonlinearities. For probabilistically shaped 64-QAM signals, it achieves up to 2-dB SNR improvement, while the size of table is only 8.59% compared to the conventional LUT method.
Optimized 2D CA-CFAR for Drone-Mounted Radar Signal Processing Using Integral Image Algorithm
Authors: Budiman P. A. Rohman, Muhammad Bagus Andra, Masahiko Nishimoto
Date: 2020-12-21 00:00:00

Buried survivor detection in the post-disaster environment by employing radar as sensor is an appealing approach. However, the implementation in the real field is challenging especially for large observation missions. Mounting the radar on the flying drone is the most promising solution. In this case, since the limitations of drones such as low computer specification and limited power resources, an efficient radar data processing is crucially required. Hence, this paper study about the implementation of the integral image technique to optimize the computation of the signal processing step of ultra-wideband impulse radar signatures. The evaluation was held on the single board computer mounted on the developed multisensory drone. The results confirm that the developed method can relatively reduce the data processing time.
Experimental Study of Through-the-Wall Respiration Sign Detection Using Ultra-wideband Impulse Radar
Authors: Budiman P. A. Rohman, Muhammad Bagus Andra, Dion Hayu Fandiantoro, Masahiko Nishimoto
Date: 2020-12-20 00:00:00

The through-the-wall human being detection and localization in the complex environments by using the radar system has many possible applications such as for law enforcement, and search and rescue missions in disaster-stricken areas. This paper discusses the experimental study of ultra-wideband impulse radar for obscured human respiration detection through spectrum analysis. We investigate the effect of the range of human-wall and radar-wall to the peak factor value of human respiration sign power spectrum. This result will be useful as a reference for analyzing the possibility of the sign to be detected by the radar with respect to the distance from both sides. According to the experimental results, longer both distance increases gradually the peak factor value. However, the peak became narrower. Besides, at a very short distance between areas and walls, the peak factor became very small because of the strong reflection of both objects. Therefore, an adaptive threshold technique and background suppression are needed in order to maintain the detection performance against any possible conditions.
One-Bit Target Detection in Collocated MIMO Radar and Performance Degradation Analysis
Authors: Yu-Hang Xiao, David Ramírez, Peter J. Schreier, Cheng Qian, Lei Huang
Date: 2020-12-19 00:00:00
Tasks: Form, Quantization

Target detection is an important problem in multiple-input multiple-output (MIMO) radar. Many existing target detection algorithms were proposed without taking into consideration the quantization error caused by analog-to-digital converters (ADCs). This paper addresses the problem of target detection for MIMO radar with one-bit ADCs and derives a Rao's test-based detector. The proposed method has several appealing features: 1) it is a closed-form detector; 2) it allows us to handle sign measurements straightforwardly; 3) there are closed-form approximations of the detector's distributions, which allow us to theoretically evaluate its performance. Moreover, the closed-form distributions allow us to study the performance degradation due to the one-bit ADCs, yielding an approximate $2$ dB loss in the low-signal-to-noise-ratio (SNR) regime compared to $\infty$-bit ADCs. Simulation results are included to showcase the advantage of the proposed detector and validate the accuracy of the theoretical results.
Partition of Unity Methods for Signal Processing on Graphs
Authors: Roberto Cavoretto, Alessandra De Rossi, Wolfgang Erb
Date: 2020-12-19 00:00:00
Tasks: Clustering, Unity

Partition of unity methods (PUMs) on graphs are simple and highly adaptive auxiliary tools for graph signal processing. Based on a greedy-type metric clustering and augmentation scheme, we show how a partition of unity can be generated in an efficient way on graphs. We investigate how PUMs can be combined with a local graph basis function (GBF) approximation method in order to obtain low-cost global interpolation or classification schemes. From a theoretical point of view, we study necessary prerequisites for the partition of unity such that global error estimates of the PUM follow from corresponding local ones. Finally, properties of the PUM as cost-efficiency and approximation accuracy are investigated numerically.
Low-Cost Compact Theft-Detection System using MPU-6050 and Blynk IoT Platform
Authors: Atharva Karnik, Diksha Adke, Pushkar Sathe
Date: 2020-12-18 00:00:00

The system explained in this paper provides a compact smart surveillance system. Recent years have seen the Internet of Things (IoT) dominating in various fields of applications. With devices getting smarter and insurgence of 5G technology, the connectivity of people with devices is increasing. Smarter surveillance systems are more reliable and accessible. A gyroscope is a MEM sensor which detects angular disturbances. The principle is to detect opening or knockdown of the door physically or by a gas cutter. The system is connected to the user via Wi-Fi using ESP8266. Being a system with a low form factor, this system can be implemented on doors, shops, cars, etc. An alarm system is included in the system to alert the neighbors as well as to send a notification to the user via Blynk mobile application. The proposed system is a portable smart home solution for theft detection. The code for this system is available here: https://github.com/atharvakarnik/TheftDetectionMPU.git
Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications
Authors: Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao
Date: 2020-12-18 00:00:00

This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs). As the number of reflecting elements increases, the reflection resource allocation (RRA) will become urgently needed in this context, which is due to the non-ignorable energy consumption. The goal of this paper, therefore, is to realize the RRA besides the active-passive beamforming design, where RRA is based on the introduced modular IRS architecture. The modular IRS consists with multiple modules, each of which has multiple reflecting elements and is equipped with a smart controller, all the controllers can communicate with each other in a point-to-point fashion via fiber links. Consequently, an optimization problem is formulated to maximize the minimum SINR at DTs, subject to the module size constraint and both individual source terminal (ST) transmit power and the reflecting coefficients constraints. Whereas this problem is NP-hard due to the module size constraint, we develop an approximate solution by introducing the mixed row block $\ell_{1,F}$-norm to transform it into a suitable semidefinite relaxation. Finally, numerical results demonstrate the meaningfulness of the introduced modular IRS architecture.
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond
Authors: Mahmoud Nazzal, Mehmet Ali Aygul, Huseyin Arslan
Date: 2020-12-18 00:00:00
Tasks: Compressive Sensing, valid

Applying compressive sensing (CS) allows for sub-Nyquist sampling in several application areas in 5G and beyond. This reduces the associated training, feedback, and computation overheads in many applications. However, the applicability of CS relies on the validity of a signal sparsity assumption and knowing the exact sparsity level. It is customary to assume a foreknown sparsity level. Still, this assumption is not valid in practice, especially when applying learned dictionaries as sparsifying transforms. The problem is more strongly pronounced with multidimensional sparsity. In this paper, we propose an algorithm for estimating the composite sparsity lying in multiple domains defined by learned dictionaries. The proposed algorithm estimates the sparsity level over a dictionary by inferring it from its counterpart with respect to a compact discrete Fourier basis. This inference is achieved by a machine learning prediction. This setting learns the intrinsic relationship between the columns of a dictionary and those of such a fixed basis. The proposed algorithm is applied to estimating sparsity levels in wireless channels, and in cognitive radio spectra. Extensive simulations validate a high quality of sparsity estimation leading to performances very close to the impractical case of assuming known sparsity.
A PMU-Based Machine Learning Application for Fast Detection of Forced Oscillations from Wind Farms
Authors: Mohammed-Ilies Ayachi, Luigi Vanfretti, Shehab Ahmed
Date: 2020-12-17 00:00:00
Tasks: BIG-bench Machine Learning

Today's evolving power system contains an increasing amount of power electronic interfaced energy sources and loads that require a paradigm shift in utility operations. Sub-synchronous oscillations at frequencies around 13-15 Hz, for instance, have been reported by utilities due to wind farm controller interactions with the grid. Dynamics at such frequencies are unobservable by most SCADA tools due to low sampling frequencies and lack of synchronization. Real-time or off-line frequency domain analysis of phasor measurement unit (PMU) data has become a valuable method to identify such phenomena, at the expense of costly power system data and communication infrastructure. This article proposes an alternative machine learning (ML) based application for sub-synchronous oscillation detection in wind farm applications. The application is targeted for real-time implementation at the edge, resulting in significant savings in terms of data and communication requirements. Validation is performed using data from a North American wind farm operator.
Securing Communications with Friendly Unmanned Aerial Vehicle Jammers
Authors: Minsu Kim, Seongjun Kim, Jemin Lee
Date: 2020-12-17 00:00:00

In this paper, we analyze the impact of a friendly unmanned aerial vehicle (UAV) jammer on UAV communications in the presence of multiple eavesdroppers. We first present channel components determined by the line-of-sight (LoS) probability between the friendly UAV jammer and the ground device, and introduce different channel fadings for LoS and non-line-of-sight (NLoS) links. We then derive the secrecy transmission probability satisfying both constraints of legitimate and wiretap channels. We also analyze the secrecy transmission probability in the presence of randomly distributed multiple friendly UAV jammers. Finally, we show the existence of the optimal UAV jammer location, and the impact of the density of eavesdroppers, the transmission power of the UAV jammer, and the density of UAV jammers on the optimal location.
Generalized Circuit Averaging Technique for Two Switch DC-DC Converters
Authors: Sumukh Surya, Janamejaya Channegowda, Kali Naraharisetti
Date: 2020-12-16 00:00:00
Tasks: Vocal Bursts Valence Prediction

Cuk and SEPIC are some of the important DC-DC converters used for charging batteries. In this paper, a generalized circuit averaging technique is employed for Cuk and SEPIC converters. The derived equations are used to obtain the frequency response of open loop transfer function. The ratio of perturbed output voltage to duty cycle ($G_{vd}$) is simulated using LTSpice software package. The derived averaged models of the converters aids in faster and simpler simulation. The behavior of the converters in CCM and DCM was also simulated. The derived expressions can be generalized to power converters with two switches.
Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation
Authors: Francesco Betti Sorbelli, Cristina M. Pinotti, Simone Silvestri, Sajal K. Das
Date: 2020-12-15 00:00:00

Localizing ground devices (GDs) is an important requirement for a wide variety of applications, such as infrastructure monitoring, precision agriculture, search and rescue operations, to name a few. To this end, unmanned aerial vehicles (UAVs) or drones offer a promising technology due to their flexibility. However, the distance measurements performed using a drone, an integral part of a localization procedure, incur several errors that affect the localization accuracy. In this paper, we provide analytical expressions for the impact of different kinds of measurement errors on the ground distance between the UAV and GDs. We review three range-based and three range-free localization algorithms, identify their source of errors, and analytically derive the error bounds resulting from aggregating multiple inaccurate measurements. We then extend the range-free algorithms for improved accuracy. We validate our theoretical analysis and compare the observed localization error of the algorithms after collecting data from a testbed using ten GDs and one drone, equipped with ultra wide band (UWB) antennas and operating in an open field. Results show that our analysis closely matches with experimental localization errors. Moreover, compared to their original counterparts, the extended range-free algorithms significantly improve the accuracy.
A Maximum-Likelihood-based Multi-User LoRa Receiver Implemented in GNU Radio
Authors: Mathieu Xhonneux, Joachim Tapparel, Orion Afisiadis, Alexios Balatsoukas-Stimming, Andreas Burg
Date: 2020-12-15 00:00:00

LoRa is a popular low-power wide-area network (LPWAN) technology that uses spread-spectrum to achieve long-range connectivity and resilience to noise and interference. For energy efficiency reasons, LoRa adopts a pure ALOHA access scheme, which leads to reduced network throughput due to packet collisions at the gateways. To alleviate this issue, in this paper we analyze and implement a LoRa receiver that is able to decode LoRa packets from two interfering users. Our main contribution is a two-user detector derived in a maximum-likelihood fashion using a detailed interference model. As the complexity of the maximum-likelihood sequence estimation is prohibitive, a complexity-reduction technique is introduced to enable a practical implementation of the proposed two-user detector. This detector has been implemented along with an interference-robust synchronization algorithm on the GNU Radio Software-Defined-Radio (SDR) platform. The SDR implementation shows the effectiveness of the proposed method and also allows its experimental evaluation. Measurements indicate that our detector inherently leverages the time offset between the two colliding users to separate and demodulate their contributions.
Hybrid NOMA for Future Radio Access: Design, Potentials and Limitations
Authors: Kuntal Deka, Sanjeev Sharma
Date: 2020-12-15 00:00:00

Next-generation internet of things (IoT) applications need trillions of low-powered wireless mobile devices to connect with each other having ultra-reliability and low-latency. Non-orthogonal multiple access (NOMA) is a promising technology to address massive connectivity for 5G and beyond by accommodating several users within the same orthogonal resource block. Therefore, this article explores hybrid NOMA (HNOMA) for massive multiple access in the uplink scenarios due to its higher spectral efficiency. The HNOMA includes both power domain and code domain NOMA method due to diverse channel conditions in practice. We highlight that polar coded based data transmission can achieve higher reliability and lower latency in HNOMA-based wireless networks. Further, at the base station (BS), channel state information (CSI) of each link is not perfectly available or very complex to estimate due to non-orthogonal links. Therefore, we analyze and review the performance of uplink based system involving HNOMA transmission in the presence of imperfect CSI. Furthermore, we summarize some key technical challenges as well as their potential solutions in futuristic IoT applications using HNOMA transmission. Finally, we offer some design guidelines for HNOMA-based systems using deep learning approach to implement adaptive and efficient wireless networks.
Laser Phase Noise Tolerance of Uniform and Probabilistically-shaped QAM Signals for High Spectral Efficiency Systems
Authors: Takeo Sasai, Asuka Matsushita, Masanori Nakamura, Seiji Okamoto, Fukutaro Hamaoka, Yoshiaki Kisaka
Date: 2020-12-15 00:00:00

We numerically and experimentally investigate the laser phase noise tolerance of probabilistically shaped (PS) and uniformly shaped (US) quadrature amplitude modulation (QAM) signals. In the simulations, we compare PS-64QAM to US-16QAM, PS-256QAM to US-64QAM, and PS-1024QAM to US-256QAM under the same information rate (IR). We confirm that a sufficient shaping gain is observed with narrow linewidth lasers, whereas degradation of the shaping gain is clearly observed when large phase noise and high order modulation formats are assumed. In our experiments, we compare polarization-division-multiplexed (PDM) 16-GBd PS-1024QAM and US-256QAM under the same IR using lasers with 0.1-kHz and 40-kHz linewidths. For carrier phase recovery (CPR), we employ a pilot-assisted digital phase locked loop. Results reveal that PS-1024QAM achieves high performance with the 0.1 kHz-laser or > 5% pilot ratio, whereas US-256QAM outperforms PS-1024QAM when lasers with 40-kHz linewidth and < 5% pilot ratio are used. We also evaluate the pilot ratio dependency of the required optical signal-to-noise ratio at the forward error correction limit and the achievable information rate. Additionally, we compare the performance of two types of CPR updating schemes: updating phase estimation at only the pilot symbol or at all symbols.