pwc-archive/papers-with-abstracts huggingface dataset:
Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming
Authors: Yongjun Ahn, jinhong Kim, Seungnyun Kim, Kyuhong Shim, Jiyoung Kim, Sangtae Kim, Byonghyo Shim
Date: 2022-09-06 00:00:00
Tasks: Management, Quantization

Beamforming technique realized by the multiple-input-multiple-output (MIMO) antenna arrays has been widely used to compensate for the severe path loss in the millimeter wave (mmWave) bands. In 5G NR system, the beam sweeping and beam refinement are employed to find out the best beam codeword aligned to the mobile. Due to the complicated handshaking and finite resolution of the codebook, today's 5G-based beam management strategy is ineffective in various scenarios in terms of the data rate, energy consumption, and also processing latency. An aim of this article is to introduce a new type of beam management framework based on the computer vision (CV) technique. In this framework referred to as computer vision-aided beam management (CVBM), a camera attached to the BS captures the image and then the deep learning-based object detector identifies the 3D location of the mobile. Since the base station can directly set the beam direction without codebook quantization and feedback delay, CVBM achieves the significant beamforming gain and latency reduction. Using the specially designed dataset called Vision Objects for Beam Management (VOBEM), we demonstrate that CVBM achieves more than 40% improvement in the beamforming gain and 40% reduction in the beam training overhead over the 5G NR beam management.
Robust Wideband Channel Estimation for mmWave Massive MIMO Systems With Beam Squint
Authors: Li Ge, Lin Chen, Xue Jiang, Weifeng Zhu, Qibo Qin, Xingzhao Liu
Date: 2022-09-06 00:00:00

This paper investigates the robust wideband channel estimation problem in the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In such a scenario, the beam squint effect that the array response vectors vary with different frequencies and the impulsive noise are in existence, which pose great challenges for accurate channel estimation. Directly applying the existing channel estimation methods usually suffers significant performance degradation, since they are proposed based on the assumptions of frequency-invariant array response vectors and Gaussian distributed noise. To address these issues, this paper proposes a novel wideband channel estimation method with robustness to impulsive noise. Specifically, the proposed method incorporates a cyclic refinement step to overcome the estimation inaccuracy caused by the greedy nature of matching-pursuit-esque algorithms. In particular, the generalized $\ell_p$-norm minimization criterion is adopted in Newton's method to improve the performance robustness against the non-Gaussian impulsive noise. Numerical results are provided to verify the superior performance of the proposed method over the existing representative benchmarks.
Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method
Authors: Tianfu Qi, Jun Wang, Xiaonan Chen, Wei Huang
Date: 2022-09-06 00:00:00
Tasks: blind source separation, parameter estimation

In many scenarios, the communication system suffers from both Gaussian white noise and non-Gaussian impulsive noise. In order to design optimal signal detection method, it is necessary to estimate the parameters of mixed Gaussian-impulsive noise. Even though this issue can be well tackled with respect to pure mixed noise, it is quite challenging based on the received single-channel signal including both transmitting signal and mixed noise. To mitigate the negative impact of transmitting signal, we propose a parameter estimation method by utilizing a neural network, namely U-net++, to separate the mixed noise from the received single-channel signal. Compared with existing blind source separation based methods, simulation results show that our proposed method can obtain rather better performance in terms of estimation accuracy and robustness under various scenarios.
Estimating uterine activity from electrohysterogram measurements via statistical tensor decomposition
Authors: Uri Goldsztejn, Arye Nehorai
Date: 2022-09-06 00:00:00
Tasks: Tensor Decomposition

Complications during pregnancy and labor are common and can be especially detrimental in populations with limited access to healthcare. A promising technology to address these complications is the electrohysterogram (EHG), which measures abdominal electric potentials. Since EHG recordings are measured noninvasively, they record uterine electrical activity together with activity from other interfering sources, making their analysis more challenging. To facilitate the analysis of EHGs, we separate these measurements into uterine activity that is more variable across different electrodes and over time, which we term localized activity, and activity that is more evenly distributed in space and time. We represent multi-electrode EHGs as tensors and develop a Bayesian tensor decomposition for estimating localized and distributed electrical activities. To demonstrate that our method can estimate localized and distributed activities more accurately than existing methods, we simulate EHG measurements based on previous characterizations of the activities comprising these measurements. Furthermore, we demonstrate the effectiveness of our method in separating EHG bursts from other interfering activities recorded in EHGs using real measurements from two public datasets. Our results show that our method denoises EHG bursts with higher signal-to-noise ratios, defined as power ratios between EHG bursts and segments with only baseline EHG activity, than alternative methods. Such accurate estimation of uterine and other activities from EHG measurements could be useful for more precisely characterizing patterns of activity in EHGs, which is a step forward towards developing reliable, portable devices to reduce risks during pregnancy and labor.
Full-Duplex Massive MIMO Cellular Networks with Low Resolution ADC/DAC
Authors: Elyes Balti, Brian L. Evans
Date: 2021-11-20 00:00:00
Tasks: Quantization

In this paper, we provide an analytical framework for full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks with low resolution analog-to-digital and digital-to-analog converters (ADCs and DACs). Matched filters are employed at the FD base stations (BSs) at the transmit and receive sides. For both reverse and forward links, our contributions are (1) derivations of the signal-to-quantization-plus-interference-and-noise ratio (SQINR) for general and special cases; (2) derivations of spectral efficiency for asymptotic cases as well as for power scaling laws; and (3) quantifying effects of quantization error, loopback self-interference, and inter-user interference for hexagonal cells and Poisson Point Process (PPP) tessellations on outage probability and spectral efficiency.
Optimization for Reflection and Transmission Dual-Functional Active RIS-Assisted Systems
Authors: Yanan Ma, Ming Li, Yang Liu, Qingqing Wu, Qian Liu
Date: 2022-09-05 00:00:00

Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to the severe double path loss, the traditional passive RIS can provide sufficient gain only when receivers are very close to the RIS. Moreover, RIS cannot provide signal coverage for the receivers at the back side of it. To address these drawbacks in practical implementation, we introduce a novel reflection and transmission dual-functional active RIS architecture in this paper, which can simultaneously realize reflection and transmission functionalities with active signal amplification to significantly extend signal coverage and enhance the quality-of-service (QoS) of all users. The problem of joint transmit beamforming and dual-functional active RIS design is investigated in RIS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both sum-rate maximization and power minimization problems are considered. To address their non-convexity, we develop efficient iterative algorithms to decompose them into separate several design problems, which are efficiently solved by exploiting fractional programming (FP) and Riemannian-manifold optimization techniques. Simulation results demonstrate the superiority of the proposed dual-functional active RIS architecture and the effectiveness of our proposed algorithms over various benchmark schemes.
Hardware Architecture for Inplace Compute of Burrows-Wheeler Transform in a Single Iteration
Authors: Kleber Stangherlin, Andrew Kennings
Date: 2022-09-05 00:00:00

The Burrows-Wheeler transform (BWT) is used by the bzip2 family of compressors. In this paper, we present a hardware architecture that implements an inplace algorithm to compute the BWT. Our design does not have explicit storage for the suffix array, or output array. The performance of our implementation is fixed, and does not depend on the input string content. We use a register based character buffer in a scanchain configuration, such that the BWT is computed from right to left, as characters are loaded. Loading new characters is done every six cycles, producing a new output character from the previously computed block at the same rate. Our FGPA implementation does not use block ram instances, and achieves throughput of 66, 35, 18, and 15 MB/s for block sizes of 128 B, 1 kB, 4 kB, and 8 kB. We also report results for an ASIC implementation in 65 nm CMOS that achieves 161 MB/s when using block size of 128 B.
Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task
Authors: Anant Jain, Lalan Kumar
Date: 2022-09-05 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Trajectory Prediction

Brain-computer interface (BCI) systems can be utilized for kinematics decoding from scalp brain activation to control rehabilitation or power-augmenting devices. In this study, the hand kinematics decoding for grasp and lift task is performed in three-dimensional (3D) space using scalp electroencephalogram (EEG) signals. Twelve subjects from the publicly available database WAY-EEG-GAL, has been utilized in this study. In particular, multi-layer perceptron (MLP) and convolutional neural network-long short-term memory (CNN-LSTM) based deep learning frameworks are proposed that utilize the motor-neural information encoded in the pre-movement EEG data. Spectral features are analyzed for hand kinematics decoding using EEG data filtered in seven frequency ranges. The best performing frequency band spectral features has been considered for further analysis with different EEG window sizes and lag windows. Appropriate lag windows from movement onset, make the approach pre-movement in true sense. Additionally, inter-subject hand trajectory decoding analysis is performed using leave-one-subject-out (LOSO) approach. The Pearson correlation coefficient and hand trajectory are considered as performance metric to evaluate decoding performance for the neural decoders. This study explores the feasibility of inter-subject 3-D hand trajectory decoding using EEG signals only during reach and grasp task, probably for the first time. The results may provide the viable information to decode 3D hand kinematics using pre-movement EEG signals for practical BCI applications such as exoskeleton/exosuit and prosthesis.
Auxiliary Factor Method to Remove ISI of Nyquist Filters
Authors: Zijian Zhou, Lifeng Lin, Bingli Jiao
Date: 2022-09-05 00:00:00

As has been known, the Nyquist first condition promises no intersymbol interference (ISI) as derived in the frequency domain. However, the practical implementation using the FIR filter truncates the Fourier transform by its window and prevents the mathematical calculation from reaching the ideal solution at zero-ISI. For obtaining better results, an increase in the window's length is required in general. To address this problem, a new approach is presented by using auxiliary factors (AFs) to compensate shortcomings of the truncated Fourier transform and remove the ISI completely, regardless of the window's length. In addition, the performance in the presence of the timing jitter is also improved significantly. The closed-form solution of the AFs is derived and the effectiveness is confirmed by the simulation results. Finally, the problems of the transmission delay and additional calculation complexity are analysed.
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G
Authors: Wonjun Kim, Yongjun Ahn, jinhong Kim, Byonghyo Shim
Date: 2022-09-05 00:00:00
Tasks: image-classification, Image Classification, Model Selection, speech-recognition, Speech Recognition

Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.
An Iterative 5G Positioning and Synchronization Algorithm in NLOS Environments with Multi-Bounce Paths
Authors: Zhixing Li, Fan Jiang, Henk Wymeersch, Fuxi Wen
Date: 2022-09-04 00:00:00

5G positioning is a very promising area that presents many opportunities and challenges. Many existing techniques rely on multiple anchor nodes and line-of-sight (LOS) paths, or single reference node and single-bounce non-LOS (NLOS) paths. However, in dense multipath environments, identifying the LOS or single-bounce assumptions is challenging. The multi-bounce paths will make the positioning accuracy deteriorate significantly. We propose a robust 5G positioning algorithm in NLOS multipath environments. The corresponding positioning problem is formulated as an iterative and weighted least squares problem, and different weights are utilized to mitigate the effects of multi-bounce paths. Numerical simulations are carried out to evaluate the performance of the proposed algorithm. Compared with the benchmark positioning algorithms only using the single-bounce paths, similar positioning accuracy is achieved for the proposed algorithm.
Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples
Authors: Zhongchang Sun, Shaofeng Zou
Date: 2022-09-04 00:00:00
Tasks: Anomaly Detection

The problem of quickest anomaly detection in networks with unlabeled samples is studied. At some unknown time, an anomaly emerges in the network and changes the data-generating distribution of some unknown sensor. The data vector received by the fusion center at each time step undergoes some unknown and arbitrary permutation of its entries (unlabeled samples). The goal of the fusion center is to detect the anomaly with minimal detection delay subject to false alarm constraints. With unlabeled samples, existing approaches that combines local cumulative sum (CuSum) statistics cannot be used anymore. Several major questions include whether detection is still possible without the label information, if so, what is the fundamental limit and how to achieve that. Two cases with static and dynamic anomaly are investigated, where the sensor affected by the anomaly may or may not change with time. For the two cases, practical algorithms based on the ideas of mixture likelihood ratio and/or maximum likelihood estimate are constructed. Their average detection delays and false alarm rates are theoretically characterized. Universal lower bounds on the average detection delay for a given false alarm rate are also derived, which further demonstrate the asymptotic optimality of the two algorithms.
Model Selection in High-Dimensional Block-Sparse Linear Regression
Authors: Prakash B. Gohain, Magnus Jansson
Date: 2022-09-03 00:00:00
Tasks: Model Selection, regression, Vocal Bursts Intensity Prediction

Model selection is an indispensable part of data analysis dealing very frequently with fitting and prediction purposes. In this paper, we tackle the problem of model selection in a general linear regression where the parameter matrix possesses a block-sparse structure, i.e., the non-zero entries occur in clusters or blocks and the number of such non-zero blocks is very small compared to the parameter dimension. Furthermore, a high-dimensional setting is considered where the parameter dimension is quite large compared to the number of available measurements. To perform model selection in this setting, we present an information criterion that is a generalization of the Extended Bayesian Information Criterion-Robust (EBIC-R) and it takes into account both the block structure and the high-dimensionality scenario. The analytical steps for deriving the EBIC-R for this setting are provided. Simulation results show that the proposed method performs considerably better than the existing state-of-the-art methods and achieves empirical consistency at large sample sizes and/or at high-SNR.
Active Reconfigurable Intelligent Surface for Mobile Edge Computing
Authors: Zhangjie Peng, Ruisong Weng, Zhenkun Zhang, Cunhua Pan, Jiangzhou Wang
Date: 2022-09-03 00:00:00
Tasks: Edge-computing

This paper investigates an active reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system. Compared with passive RIS, the active RIS is equipped with active reflective amplifier, which can effectively circumvent the "double path loss" attenuation. We propose a joint computing and communication design to minimize the maximum computational latency (MCL), subject to both the phase shift constraints and the edge computing capability constraints. Specifically, the original problem is decoupled into four subproblems, and then the block coordinate descent (BCD) method and the successive convex approximation (SCA) method are applied to alternately optimize the subproblems. The simulation results show that with the same power budget, the performance gain achieved by the active RIS is much larger than that by the passive RIS.
Dynamic Write-Voltage Design and Read-Voltage Optimization for MLC NAND Flash Memory
Authors: Runbin Cai, Yi Fang, Zhifang Shi, Lin Dai, Guojun Han
Date: 2022-09-03 00:00:00

To mitigate the impact of noise and interference on multi-level-cell (MLC) flash memory with the use of low-density parity-check (LDPC) codes, we propose a dynamic write-voltage design scheme considering the asymmetric property of raw bit error rate (RBER), which can obtain the optimal write voltage by minimizing a cost function. In order to further improve the decoding performance of flash memory, we put forward a low-complexity entropy-based read-voltage optimization scheme, which derives the read voltages by searching for the optimal entropy value via a log-likelihood ratio (LLR)-aware cost function. Simulation results demonstrate the superiority of our proposed dynamic write-voltage design scheme and read-voltage optimization scheme with respect to the existing counterparts.
Neuromorphic implementation of ECG anomaly detection using delay chains
Authors: Stefan Gerber, Marc Steiner, Maryada, Giacomo Indiveri, Elisa Donati
Date: 2022-09-02 00:00:00
Tasks: Anomaly Detection

Real-time analysis and classification of bio-signals measured using wearable devices is computationally costly and requires dedicated low-power hardware. One promising approach is to use spiking neural networks implemented using in-memory computing architectures and neuromorphic electronic circuits. However, as these circuits process data in streaming mode without the possibility of storing it in external buffers, a major challenge lies in the processing of spatio-temporal signals that last longer than the time constants present in the network synapses and neurons. Here we propose to extend the memory capacity of a spiking neural network by using parallel delay chains. We show that it is possible to map temporal signals of multiple seconds into spiking activity distributed across multiple neurons which have time constants of few milliseconds. We validate this approach on an ECG anomaly detection task and present experimental results that demonstrate how temporal information is properly preserved in the network activity.
Decentralized Eigendecomposition for Online Learning over Graphs with Applications
Authors: Yufan Fan, Minh Trinh-Hoang, Cemil Emre Ardic, Marius Pesavento
Date: 2022-09-02 00:00:00

In this paper, the problem of decentralized eigenvalue decomposition of a general symmetric matrix that is important, e.g., in Principal Component Analysis, is studied, and a decentralized online learning algorithm is proposed. Instead of collecting all information in a fusion center, the proposed algorithm involves only local interactions among adjacent agents. It benefits from the representation of the matrix as a sum of rank-one components which makes the algorithm attractive for online eigenvalue and eigenvector tracking applications. We examine the performance of the proposed algorithm in two types of important application examples: First, we consider the online eigendecomposition of a sample covariance matrix over the network, with application in decentralized Direction-of-Arrival (DoA) estimation and DoA tracking applications. Then, we investigate the online computation of the spectra of the graph Laplacian that is important in, e.g., Graph Fourier Analysis and graph dependent filter design. We apply our proposed algorithm to track the spectra of the graph Laplacian in static and dynamic networks. Simulation results reveal that the proposed algorithm outperforms existing decentralized algorithms both in terms of estimation accuracy as well as communication cost.
The impact of dormancy on evolutionary branching
Authors: Jochen Blath, Tobias Paul, András Tobiás, Maite Wilke Berenguer
Date: 2022-09-05 00:00:00
Tasks: Diversity

In this paper, we investigate the consequences of dormancy in the `rare mutation' and `large population' regime of stochastic adaptive dynamics. Starting from an individual-based micro-model, we first derive the polymorphic evolution sequence of the population, based on previous work by Baar and Bovier (2018). After passing to a second `small mutations' limit, we arrive at the canonical equation of adaptive dynamics, and state a corresponding criterion for evolutionary branching, extending a previous result of Champagnat and M\'el\'eard (2011). The criterion allows a quantitative and qualitative analysis of the effects of dormancy in the well-known model of Dieckmann and Doebeli (1999) for sympatric speciation. In fact, a quite intuitive picture merges: Dormancy enlarges the parameter range for evolutionary branching, increases the carrying capacity and niche width of the post-branching sub-populations, and, depending on the model parameters, can either increase or decrease the `speed of adaptation' of populations. Finally, dormancy increases diversity by increasing the genetic distance between subpopulations.