pwc-archive/papers-with-abstracts huggingface dataset:
Frequency-Domain Signal Processing for Spectrally-Enhanced CP-OFDM Waveforms in 5G New Radio
Authors: Juha Yli-Kaakinen, AlaaEddin Loulou, Toni Levanen, Kari Pajukoski, Arto Palin, Markku Renfors, Mikko Valkama
Date: 2020-08-03 00:00:00

Orthogonal frequency-division multiplexing (OFDM) has been selected as the basis for the fifth-generation new radio (5G-NR) waveform developments. However, effective signal processing tools are needed for enhancing the OFDM spectrum in various advanced transmission scenarios. In earlier work, we have shown that fast-convolution (FC) processing is a very flexible and efficient tool for filtered-OFDM signal generation and receiver-side subband filtering, e.g., for the mixed-numerology scenarios of the 5G-NR. FC filtering approximates linear convolution through effective fast Fourier transform (FFT)-based circular convolutions using partly overlapping processing blocks. However, with the continuous overlap-and-save and overlap-and-add processing models with fixed block-size and fixed overlap, the FC-processing blocks cannot be aligned with all OFDM symbols of a transmission frame. Furthermore, 5G-NR numerology does not allow to use transform lengths shorter than 128 because this would lead to non-integer cyclic prefix (CP) lengths. In this article, we present new FC-processing schemes which solve the mentioned limitations. These schemes are based on dynamically adjusting the overlap periods and extrapolating the CP samples, which make it possible to align the FC blocks with each OFDM symbol, even in case of variable CP lengths. This reduces complexity and latency, e.g., in mini-slot transmissions and, as an example, allows to use 16-point transforms in case of a 12-subcarrier-wide subband allocation, greatly reducing the implementation complexity. On the receiver side, the proposed scheme makes it possible to effectively combine cascaded inverse and forward FFT units in FC-filtered OFDM processing. Transform decomposition is used to simplify these computations. Very extensive set of numerical results is also provided, in terms of radio-link performance and associated processing complexity.
BlueFMCW: Random Frequency Hopping Radar for Mitigation of Interference and Spoofing
Authors: Thomas Moon, Jounsup Park, Seungmo Kim
Date: 2020-08-03 00:00:00

Traffic safety is the foremost value that automotive radar systems aim to pursue. Unlike in mobile communication systems, the literature for radar systems did not adequately address inter-radar interference and security threats such as jamming and spoofing, which in turn threatens the traffic safety. In this context, we introduce a novel frequency-modulated continuous-wave (FMCW) radar scheme (namely, BlueFMCW) that mitigates both interference and spoofing signals. BlueFMCW randomly hops frequency to avoid interference and spoofing signals. Our phase alignment algorithm is capable of removing the phase discontinuity while combining the beat signals from the randomly-hopped chirps, and thereby radar's resolution is not compromised. The simulation results show that BlueFMCW can efficiently mitigate the interference and spoofing signals in various scenarios without paying its resolution.
A Novel Physics-based Channel Model for Reconfigurable Intelligent Surface-assisted Multi-user Communication Systems
Authors: Jiaqi Xu, Yuanwei Liu
Date: 2020-08-03 00:00:00
Tasks: Quantization

The reconfigurable intelligent surface (RIS) is one of the promising technologies contributing to the next generation smart radio environment. A novel physics-based RIS channel model is proposed. Particularly, we consider the RIS and the scattering environment as a whole by studying the signal's multipath propagation, as well as the radiation pattern of the RIS. The model suggests that the RIS-assisted wireless channel can be approximated by a Rician distribution. Analytical expressions are derived for the shape factor and the scale factor of the distribution. For the case of continuous phase shifts, the distribution depends on the number of elements of the RIS and the observing direction of the receiver. For the case of continuous phase shifts, the distribution further depends on the quantization level of the RIS phase error. The scaling law of the average received power is obtained from the scale factor of the distribution. For the application scenarios where RIS functions as an anomalous reflector, we investigate the performance of single RIS-assisted multiple access networks for time-division multiple access (TDMA), frequency-division multiple access (FDMA) and non-orthogonal multiple access (NOMA). Closed-form expressions for the outage probability of the proposed channel model are derived. It is proved that a constant diversity order exists, which is independent of the number of RIS elements. Simulation results are presented to confirm that the proposed model applies effectively to the phased-array implemented RISs.
Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO
Authors: Malong Ke, Zhen Gao, Yongpeng Wu, Xiqi Gao, Robert Schober
Date: 2019-06-24 00:00:00
Tasks: Compressive Sensing

This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation problems as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize simultaneous active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity presented in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.
Signal Processing on Directed Graphs
Authors: Antonio G. Marques, Santiago Segarra, Gonzalo Mateos
Date: 2020-08-02 00:00:00
Tasks: Causal Inference

This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs). Directionality is inherent to many real-world (information, transportation, biological) networks and it should play an integral role in processing and learning from network data. We thus lay out a comprehensive review of recent advances in SP on digraphs, offering insights through comparisons with results available for undirected graphs, discussing emerging directions, establishing links with related areas in machine learning and causal inference in statistics, as well as illustrating their practical relevance to timely applications. To this end, we begin by surveying (orthonormal) signal representations and their graph frequency interpretations based on novel measures of signal variation for digraphs. We then move on to filtering, a central component in deriving a comprehensive theory of SP on digraphs. Indeed, through the lens of filter-based generative signal models, we explore a unified framework to study inverse problems (e.g., sampling and deconvolution on networks), statistical analysis of random signals, and topology inference of digraphs from nodal observations.
All-Digital FPGA-based DAC with None or Few External Components
Authors: Luis E. Rueda G., Edward Silva, Andres Centeno, Elkim Roa
Date: 2020-08-02 00:00:00
Tasks: All

One of the many limitations with the mixed-signal design is physically testing circuit ideas. While it is easier to test digital circuits with FPGAs, this can not be done usually with mixed-signal circuits. Although some FPGAs have built-in analog-to-digital and digital-to-analog converters, regular commercial FPGAs development boards and low-cost FPGAs lack built-in data converters. Here we introduce an all-digital FPGA-based DAC, which is one of the main blocks to enable mixed-signal experiments. The DAC can be synthesized entirely in an FPGA and does not require the use of external components. Furthermore, and to extend its range of applications, a discussion regarding the proposed DAC's problems and possible solutions is presented. Experimental demonstration of a 4-bit and a 5-bit DAC corroborate the theoretical analysis developed in this work. This work also suggests a scheme which includes few external resistors to improve the linearity (DNL$\leq$0.25LSB and an INL$\leq$0.5LSB), and the power consumption (5X improvement over the standalone configuration).
Adaptive Compressive Sampling for Mid-infrared Spectroscopic Imaging
Authors: Mahsa Lotfollahi, Nguyen Tran, Sebastian Berisha, Chalapathi Gajjela, Zhu Han, David Mayerich, Rohith Reddy
Date: 2020-08-02 00:00:00

Minfrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.
Discrimination of Internal Faults and Other Transients in an Interconnected System with Power Transformers and Phase Angle Regulators
Authors: Pallav Kumar Bera, Can Isik, Vajendra Kumar
Date: 2020-04-13 00:00:00

This study solves the problem of accurate detection of internal faults and classification of transients in a 5-bus interconnected system for Phase Angle Regulators (PAR) and Power Transformers. The analysis prevents mal-operation of differential relays in case of transients other than faults which include magnetizing inrush, sympathetic inrush, external faults with CT saturation, capacitor switching, non-linear load switching, and ferroresonance. A gradient boosting classifier (GBC) is used to distinguish the internal faults from the transient disturbances based on 1.5 cycles of 3-phase differential currents registered by a change detector. After the detection of an internal fault, GBCs are used to locate the faulty unit (Power Transformer, PAR series, or exciting unit) and identify the type of fault. In case a transient disturbance is detected, another GBC classifies them into the six transient disturbances. Five most relevant frequency and time domain features obtained using Information Gain are used to train and test the classifiers. The proposed algorithm distinguishes the internal faults from the other transients with a balanced accuracy of 99.95%. The faulty transformer unit is located with a balanced accuracy of 99.5% and the different transient disturbances are identified with a balanced accuracy of 99.3%. Moreover, the reliability of the scheme is verified for different rating and connection of the transformers involved, CT saturation, and noise levels in the signals. These GBC classifiers can work together with a conventional differential relay and offer a supervisory control over its operation. PSCAD/EMTDC software is used for simulation of the transients and to develop the two and three-winding transformer models for creating the internal faults including inter-turn and inter-winding faults.
FDTD-Based Diffuse Scattering and Transmission Models for Ray-Tracing of Millimeter-Wave Communication Systems
Authors: Stefanos Bakirtzis, Takahiro Hashimoto, Costas D. Sarris
Date: 2020-08-02 00:00:00

At millimeter-wave frequencies, diffuse scattering from rough surfaces is an important propagation mechanism. Including this mechanism in radio propagation modeling tools,such as ray-tracing, is a key step towards realizing accurate propagation models for 5G and beyond systems. We propose a two-stage solution to this problem. First, we model reflection and transmission through rough slabs, such as doors, walls and windows with the FDTD method. Our results indicate the influence of roughness and whether this influence is measurable, either reducing the magnitude of the reflected and transmitted waves, or (most importantly) generating diffuse scattering components. In the latter case, the surface effectively acts as a secondary source, whose pattern is computed by full-wave analysis. Then, this pattern is embedded in a ray-tracer, enabling the computation and tracing of diffuse scattering field components. We demonstrate this approach in the ray-tracing analysis of a 28 GHz indoor environment.
Selection of Robust Digital Communication Techniques for the Vehicle to Vehicle Communication
Authors: Subrahmanya Gunaga, Varad Vinod Prabhu, Rahul M. S., Akash Kulkarni, Nalini C. Iyer
Date: 2020-08-02 00:00:00

V2V, Vehicle to Vehicle communication has become one of the key features in achieving complete autonomy for self-driving vehicles. We use digital communication as a backbone to deliver a vehicle to vehicle communication. The primary building blocks of a digital communication system are source coding, error correction and detection, and channel coding. Choosing optimal techniques for each block plays a significant role in the performance of the entire communication system. Five Source coding techniques, three Error control, and Channel coding techniques, respectively, have been considered for the implementation. The methods were evaluated based on different comparison parameters for each of the building blocks. Based on the obtained result, the robust techniques were chosen for our application.
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter
Authors: Cong-Thanh Do, Tran Thien Dat Nguyen, Diluka Moratuwage, Changbeom Shim, Yon Dohn Chung
Date: 2020-08-02 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on locations where the objects appear, their detection probabilities, and the statistics of the sensor's false alarms significantly influence the tracking accuracy of the filter. However, this information is usually assumed to be known and provided by the users. In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information. Experimental results show that the performance of the proposed filter is comparable to an ideal GLMB filter supplied with correct information of the tracking scenarios.
Millimeter-Wave Angle Estimation of Multiple Targets Using Space-Time Modulation and Interferometric Antenna Arrays
Authors: Stavros Vakalis, Jeffrey A. Nanzer
Date: 2020-08-01 00:00:00

A new method of angle estimation of multiple targets using a distributed interferometric antenna array and wideband space-time modulation is presented in this work. Interferometric array measurements of angle of arrival are generally ambiguous in the presence of one or more targets. We propose a new method of mitigating ambiguities in interferometric measurements by multiplying the angle pseudo-spectra from multiple antenna baselines, resulting in detections at only the angles of the targets. Using a single linear frequency modulated (LFM) transmitter and a receive interferometric array with $N$ elements we show a simple and computationally efficient technique to estimate the angle of up to $\mathcal{O}(N^2)$ targets. We describe the theory behind the technique, present detailed simulations, and an experimental verification using a three-element millimeter-wave measurement system.
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
Authors: Teng Liu, Wenhao Tan, Xiaolin Tang, Jinwei Zhang, Yang Xing, Dongpu Cao
Date: 2020-07-16 00:00:00
Tasks: energy management, Management

Motivated by the concerns on transported fuel consumption and global air pollution, industrial engineers, and academic researchers have made many efforts to construct more efficient and environment-friendly vehicles. Hybrid electric vehicles (HEVs) are the representative ones because they can satisfy the power demand by coordinating energy supplements among different energy storage devices. To achieve this goal, energy management approaches are crucial technology, and driving cycles are the critical influence factor. Therefore, this paper aims to summarize driving cycle-driven energy management strategies (EMSs) for HEVs. First, the definition and significance of driving cycles in the energy management field are clarified, and the recent literature in this research domain is reviewed and revisited. In addition, according to the known information of driving cycles, the EMSs are divided into three categories, and the relevant study directions, such as standard driving cycles, long-term driving cycle generation (LT-DCG) and short-term driving cycle prediction (ST-DCP) are illuminated and analyzed. Furthermore, the existing database of driving cycles in highway and urban aspects are displayed and discussed. Finally, this article also elaborates on the future prospects of energy management technologies related to driving cycles. This paper focusing on helping the relevant researchers realize the state-of-the-art of HEVs energy management field and also recognize its future development direction.
Sparse Array Design via Fractal Geometries
Authors: Regev Cohen, Yonina C. Eldar
Date: 2020-01-05 00:00:00

Sparse sensor arrays have attracted considerable attention in various fields such as radar, array processing, ultrasound imaging and communications. In the context of correlation-based processing, such arrays enable to resolve more uncorrelated sources than physical sensors. This property of sparse arrays stems from the size of their difference coarrays, defined as the differences of element locations. Thus, the design of sparse arrays with large difference coarrays is of great interest. In addition, other array properties such as symmetry, robustness and array economy are important in different applications. Numerous studies have proposed diverse sparse geometries, focusing on certain properties while lacking others. Incorporating multiple properties into the design task leads to combinatorial problems which are generally NP-hard. For small arrays these optimization problems can be solved by brute force, however, in large scale they become intractable. In this paper, we propose a scalable systematic way to design large sparse arrays considering multiple properties. To that end, we introduce a fractal array design in which a generator array is recursively expanded according to its difference coarray. Our main result states that for an appropriate choice of the generator such fractal arrays exhibit large difference coarrays. Furthermore, we show that the fractal arrays inherit their properties from their generators. Thus, a small generator can be optimized according to desired requirements and then expanded to create a fractal array which meets the same criteria. This approach paves the way to efficient design of large arrays of hundreds or thousands of elements with specific properties.
For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning
Authors: Peiyang Song, Nan Zhang, Lin Cai, Guo Li, Fengkui Gong
Date: 2020-08-01 00:00:00

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes intelligent variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). Also, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the closed-form SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.
Information-Theoretic Approach to Navigation for Efficient Detection and Classification of Underwater Objects
Authors: Christopher Robbiano, Edwin K. P. Chong, Mahmood R. Azimi-Sadjadi
Date: 2020-07-09 00:00:00
Tasks: General Classification

This paper addresses an autonomous exploration problem in which a mobile sensor, placed in a previously unseen search area, utilizes an information-theoretic navigation cost function to dynamically select the next sensing action, i.e., location from which to take a measurement, to efficiently detect and classify objects of interest within the area. The information-theoretic cost function proposed in this paper consist of two \textit{information gain} terms, one for detection and localization of objects and the other for sequential classification of the detected objects. We present a novel closed-form representation for the cost function, derived from the definition of mutual information. We evaluate three different policies for choosing the next sensing action: lawn mower, greedy, and non-greedy. For these three policies, we compare the results from our information-theoretic cost functions to the results of other information-theoretic inspired cost functions. Our simulation results show that search efficiency is greater using the proposed cost functions compared to those of the other methods, and that the greedy and non-greedy policies outperform the lawn mower policy.