pwc-archive/papers-with-abstracts huggingface dataset:
Angle estimation using mmWave RSS measurements with enhanced multipath information
Authors: Neharika Valecha, Jesus Omar Lacruz, Michael Lentmaier, Joerg Widmer, Fredrik Tufvesson
Date: 2024-03-14 00:00:00

mmWave communication has come up as the unexplored spectrum for 5G services. With new standards for 5G NR positioning, more off-the-shelf platforms and algorithms are needed to perform indoor positioning. An object can be accurately positioned in a room either by using an angle and a delay estimate or two angle estimates or three delay estimates. We propose an algorithm to jointly estimate the angle of arrival (AoA) and angle of departure (AoD), based only on the received signal strength (RSS). We use mm-FLEX, an experimentation platform developed by IMDEA Networks Institute that can perform real-time signal processing for experimental validation of our proposed algorithm. Codebook-based beampatterns are used with a uniquely placed multi-antenna array setup to enhance the reception of multipath components and we obtain an AoA estimate per receiver thereby overcoming the line-of-sight (LoS) limitation of RSS-based localization systems. We further validate the results from measurements by emulating the setup with a simple ray-tracing approach.
Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging
Authors: Han Wang, Yhonatan Kvich, Eduardo Pérez, Florian Römer, Yonina C. Eldar
Date: 2024-03-14 00:00:00
Tasks: Computational Efficiency

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing Sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient Convolutional Sparse Coding (CSC) algorithms. Additionally, we explore the integration of Convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block Convolutional ISTA (BC-LISTA).
Delay Dispersion in IRS-assisted FSO Links
Authors: Hedieh Ajam, Vahid Jamali, Bernhard Schmauss, Robert Schober
Date: 2024-03-14 00:00:00
Tasks: Position

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical intelligent reflecting surfaces (IRSs). In this paper, we model the impact of the IRS-induced delay dispersion and derive the channel impulse response (CIR) of IRS-assisted FSO links. The proposed model takes into account the characteristics of the incident and reflected beams' wavefronts, the position of transmitter and receiver, the size of the IRS, and the incident beamwidth on the IRS. Our simulation results reveal that a maximum effective delay spread of 0.7 ns is expected for a square IRS with area 1 $\mathrm{m}^2$, which induces inter-symbol interference for bit rates larger than 10 Gbps. We show that the IRS-induced delay dispersion can be mitigated via equalization at the receiver.
Intelligent Reflecting Surfaces vs. Full-Duplex Relays: A Comparison in the Air
Authors: Qian Ding, Jie Yang, Yang Luo, Chunbo Luo
Date: 2024-03-14 00:00:00

This letter aims to provide a fundamental analytical comparison for the two major types of relaying methods: intelligent reflecting surfaces and full-duplex relays, particularly focusing on unmanned aerial vehicle communication scenarios. Both amplify-and-forward and decode-and-forward relaying schemes are included in the comparison. In addition, optimal 3D UAV deployment and minimum transmit power under the quality of service constraint are derived. Our numerical results show that IRSs of medium size exhibit comparable performance to AF relays, meanwhile outperforming DF relays under extremely large surface size and high data rates.
Radar Rainbow Beams For Wideband mmWave Communication: Beam Training And Tracking
Authors: Gui Zhou, Moritz Garkisch, Zhendong Peng, Cunhua Pan, Robert Schober
Date: 2024-03-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

We propose a novel integrated sensing and communication (ISAC) system that leverages sensing to assist communication, ensuring fast initial access, seamless user tracking, and uninterrupted communication for millimeter wave (mmWave) wideband systems. True-time-delayers (TTDs) are utilized to generate frequency-dependent radar rainbow beams by controlling the beam squint effect. These beams cover users across the entire angular space simultaneously for fast beam training using just one orthogonal frequency-division multiplexing (OFDM) symbol. Three detection and estimation schemes are proposed based on radar rainbow beams for estimation of the users' angles, distances, and velocities, which are then exploited for communication beamformer design. The first proposed scheme utilizes a single-antenna radar receiver and one set of rainbow beams, but may cause a Doppler ambiguity. To tackle this limitation, two additional schemes are introduced, utilizing two sets of rainbow beams and a multi-antenna receiver, respectively. Furthermore, the proposed detection and estimation schemes are extended to realize user tracking by choosing different subsets of OFDM subcarriers. This approach eliminates the need to switch phase shifters and TTDs, which are typically necessary in existing tracking technologies, thereby reducing the demands on the control circurity. Simulation results reveal the effectiveness of the proposed rainbow beam-based training and tracking methods for mobile users. Notably, the scheme employing a multi-antenna radar receiver can accurately estimate the channel parameters and can support communication rates comparable to those achieved with perfect channel information.
CRB Analysis for Mixed-ADC Based DOA Estimation
Authors: Xinnan Zhang, Yuanbo Cheng, Xiaolei Shang, Jun Liu
Date: 2024-03-14 00:00:00
Tasks: Quantization, Sparse Learning

We consider a mixed analog-to-digital converter (ADC) based architecture consisting of high-precision and one-bit ADCs with the antenna-varying threshold for direction of arrival (DOA) estimation using a uniform linear array (ULA), which utilizes fixed but different thresholds for one-bit ADCs across different receive antennas. The Cram{\'e}r-Rao bound (CRB) with the antenna-varying threshold is obtained. Then based on the lower bound of the CRB, we derive the asymptotic CRB of the DOA, which depends on the placement of mixed-ADC. Our analysis shows that distributing high-precision ADCs evenly around the two edges of the ULA yields improved performance. This result can be extended to a more general case where the ULA is equipped with two types of ADCs with different quantization precisions. To efficiently obtain the maximum likelihood DOA estimates, we propose a two-step algorithm. Firstly, we formulate the model as a sparse signal representation problem, and modify the sparse learning via iterative minimization (SLIM) approach to the mixed-ADC based DOA estimation. In the second step, we use the relaxation-based approach to cyclically refine the estimates of SLIM, further enhancing the DOA estimation performance. Numerical examples are presented to demonstrate the validity of the CRB analysis and the effectiveness of our methods.
Near-Field EM-Based Multistatic Radar Range Estimation
Authors: François De Saint Moulin, Guillaume Thiran, Christophe Craeye, Luc Vandendorpe, Claude Oestges
Date: 2024-03-14 00:00:00

Radar targets are traditionally modelled as point target reflectors, even in the near-field region. Yet, for radar systems operating at high carrier frequencies and small distances, traditional radar propagation models do not accurately model the scatterer responses. In this paper, a novel electromagnetic-based model is thus developed for the multistatic radar detection of a rectangular plate reflector in the near-field region. This model is applied to an automotive scenario, in which a linear antenna array is spread out at the front of a vehicle, and performs a radar measurement of the distance to the back of the vehicle ahead. Based on the developed received signal model, the maximum likelihood estimator of the range is designed. By exploiting the near-field target model, this estimator is shown to provide a significant gain with respect to traditional range estimators. The impact of the system and scenario parameters, i.e. the carrier frequency, bandwidth and distance to the target, is furthermore evaluated. This analysis shows that the radar resolution in the near-field regime is improved at high carrier frequencies, while saturating to the traditional bandwidth-dependent resolution in the far-field region.
A Robust Semantic Communication System for Image
Authors: Xiang Peng, Zhijin Qin, Xiaoming Tao, Jianhua Lu, Khaled B. Letaief
Date: 2024-03-14 00:00:00
Tasks: Semantic Communication

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in image, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity.
Asymptotically Near-Optimal Hybrid Beamforming for mmWave IRS-Aided MIMO Systems
Authors: Jeongjae Lee, SongNam Hong
Date: 2024-03-14 00:00:00

Hybrid beamforming is an emerging technology for massive multiple-input multiple-output (MIMO) systems due to the advantages of lower complexity, cost, and power consumption. Recently, intelligent reflection surface (IRS) has been proposed as the cost-effective technique for robust millimeter-wave (mmWave) MIMO systems. Thus, it is required to jointly optimize a reflection vector and hybrid beamforming matrices for IRS-aided mmWave MIMO systems. Due to the lack of RF chain in the IRS, it is unavailable to acquire the TX-IRS and IRS-RX channels separately. Instead, there are efficient methods to estimate the so-called effective (or cascaded) channel in literature. We for the first time derive the near-optimal solution of the aforementioned joint optimization only using the effective channel. Based on our theoretical analysis, we develop the practical reflection vector and hybrid beamforming matrices by projecting the asymptotic solution into the modulus constraint. Via simulations, it is demonstrated that the proposed construction can outperform the state-of-the-art (SOTA) method, where the latter even requires the knowledge of the TX-IRS and IRS-RX channels separately. Furthermore, our construction can provide robustness for channel estimation errors, which is inevitable for practical massive MIMO systems.
A Mean-Field Game of Market Entry: Portfolio Liquidation with Trading Constraints
Authors: Guanxing Fu, Paul P. Hager, Ulrich Horst
Date: 2024-03-15 00:00:00
Tasks: Position

We consider both $N$-player and mean-field games of optimal portfolio liquidation in which the players are not allowed to change the direction of trading. Players with an initially short position of stocks are only allowed to buy while players with an initially long position are only allowed to sell the stock. Under suitable conditions on the model parameters we show that the games are equivalent to games of timing where the players need to determine the optimal times of market entry and exit. We identify the equilibrium entry and exit times and prove that equilibrium mean-trading rates can be characterized in terms of the solutions to a highly non-linear higher-order integral equation with endogenous terminal condition. We prove the existence of a unique solution to the integral equation from which we obtain the existence of a unique equilibrium both in the mean-field and the $N$-player game.
Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup
Authors: Touchchai Chotisorayuth, Andreas Tiffeau-Mayer
Date: 2024-03-14 00:00:00
Tasks: Diversity, Specificity

An individual's adaptive immune receptor (AIR) repertoire records immune history due to the exquisite antigen specificity of AIRs. Reading this record requires computational approaches for inferring receptor function from sequence, as the diversity of possible receptor-antigen pairs vastly outstrips experimental knowledge. Identification of AIRs with similar sequence and thus putatively similar function is a common performance bottleneck in these approaches. Here, we benchmark the time complexity of five different algorithmic approaches to radius-based search for Levenshtein neighbors. We show that a symmetric deletion lookup approach, originally proposed for spell-checking, is particularly scalable. We then introduce XTNeighbor, a variant of this algorithm that can be massively parallelized on GPUs. For one million input sequences, XTNeighbor identifies all sequence neighbors that differ by up to two edits in seconds on commodity hardware, orders of magnitude faster than existing approaches. We also demonstrate how symmetric deletion lookup can speed up search with more complex sequence-similarity metrics such as TCRdist. Our contribution is poised to greatly speed up existing analysis pipelines and enable processing of large-scale immunosequencing data without downsampling.
Perceptual learning in contour detection transfer across changes in contour path and orientation
Authors: Yue Ding, Hongqiao Shi, Shuang Song, Yonghui Wang, Ya Li
Date: 2024-03-18 00:00:00
Tasks: Contour Detection, Sensitivity, Specificity

The integration of local elements into shape contours is critical for target detection and identification in cluttered scenes. Previous studies have shown that observers can learn to use image regularities for contour integration and target identification. However, we still know little about the generalization of perceptual learning in contour integration. Specifically, whether training in contour detection task could transfer to untrained contour type, path or orientation is still unclear. In a series of four experiments, human perceptual learning in contour detection was studied using psychophysical methods. We trained participants to detect contours in cluttered scenes over several days, which resulted in a significant improvement in sensitivity to trained contour type. This improved sensitivity was highly specific to contour type, but transfer across changes in contour path and contour orientation. These results suggest that short-term training improves the ability to integrate specific types of contours by optimizing the ability of the visual system to extract specific image regularities. The differential specificity and generalization across different stimulus features may support the involvement of both low-level and higher-level visual areas in perceptual learning in contour detection. These findings provide further insights into understanding the nature and the brain plasticity mechanism of contour integration learning.
Inference of Monosynaptic Connections from Parallel Spike Trains: A Review
Authors: Ryota Kobayashi, Shigeru Shinomoto
Date: 2024-03-16 00:00:00
Tasks: Functional Connectivity

This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of ``neuronal connectivity'' in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity. Among these, we focus on the methods used to infer the monosynaptic connectivity from spike data. We then summarize the inference methods based on two main approaches, i.e., correlation-based and model-based approaches. Finally, we describe available source codes for connectivity inference and future challenges. Although inference will never be perfect, the accuracy of identifying the monosynaptic connections has improved dramatically in recent years due to continuous efforts.
Thalamocortical interactions shape hierarchical neural variability during stimulus perception
Authors: Adrià Tauste Campo, Antonio Zainos, Yuriria Vázquez, Raul Adell Segarra, Manuel Álvarez, Gustavo Deco, Héctor Díaz, Sergio Parra, Ranulfo Romo, Román Rossi-Pool
Date: 2024-03-15 00:00:00
Tasks: Functional Connectivity

The brain is hierarchically organized to process sensory signals. But, to what extent do functional connections within and across areas shape this hierarchical order? We addressed this problem in the thalamocortical network, while monkeys judged the presence or absence of a vibrotactile stimulus. We quantified the variability by means of intrinsic timescales and Fano factor, and functional connectivity by means of a directionality measure in simultaneously recorded neurons sharing the same cutaneous receptive field from the somatosensory thalamus (VPL) and areas 3b and 1 from the somatosensory cortex. During the pre-stimulus periods, VPL and area 3b exhibited similarly fast dynamics while area 1 showed much slower timescales. Furthermore, during the stimulus presence, the Fano factor increased along the network VPL-3b-1. In parallel, VPL established two separate main feedforward pathways with areas 3b and 1 to process stimulus information. While feedforward interactions from VPL and area 3b were favored by neurons within specific Fano factor ranges, neural variability in area 1 was invariant to the incoming pathways. In contrast to VPL and area 3b, during the stimulus arrival, area 1 showed significant intra-area interactions, which mainly pointed to neurons with slow intrinsic timescales. Overall, our results suggest that the lower variability of VPL and area 3b regulates feedforward thalamocortical communication, while the higher variability of area 1 supports intra-cortical interactions during sensory processing. These results provide evidence of a hierarchical order along the thalamocortical network.
Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition
Authors: Michele Mastella, Tesse Tiemens, Elisabetta Chicca
Date: 2024-03-12 00:00:00

In this paper, we present a novel spiking neural network model designed to perform frequency decomposition of spike trains. Our model emulates neural microcircuits theorized in the somatosensory cortex, rendering it a biologically plausible candidate for decoding the spike trains observed in tactile peripheral nerves. We demonstrate the capacity of simple neurons and synapses to replicate the phase-locked loop (PLL) and explore the emergent properties when considering multiple spiking phase-locked loops (sPLLs) with diverse oscillations. We illustrate how these sPLLs can decode textures using the spectral features elicited in peripheral nerves. Leveraging our model's frequency decomposition abilities, we improve state-of-the-art performances on a Multifrequency Spike Train (MST) dataset. This work offers valuable insights into neural processing and presents a practical framework for enhancing artificial neural network capabilities in complex pattern recognition tasks.
Understanding data analysis aspects of TMS-EEG in clinical study: a mini review and a case study with open dataset
Authors: Hua Cheng
Date: 2024-03-09 00:00:00
Tasks: EEG

Concurrency of transcranial magnetic stimulation with electroencephalography (TMS-EEG) technique is a powerful and challenging methodology for basic research and clinical applications. Aspects considered in experiments for effective TMS-EEG recordings and analysis, including artifact management, data analysis and interpretation and protocols. mini review offers an extensive insight of TMS-EEG methodology in experimental and computational procedures. Case study aims to leverage an openly available, high-quality EEG dataset to delve into the alterations in cortical activity. By applying Intermittent theta-burst stimulation (iTBS) and continuous theta-burst stimulation (cTBS) to the left dorsolateral prefrontal cortex (DLPFC) in healthy individuals, we observe changes in oscillatory patterns within the EEG data. The dataset includes meticulously extracted resting-state EEG recordings, TMS-evoked potential data, and MRI scans. To process these data, we utilized Brainstorm, an open-source Matlab application, which facilitated noise reduction through independent component analysis and signal-space projection techniques. It allowed us to identify, visualize, and analyze TMS-evoked potentials (TEPs) and TMS-induced oscillations (TIOs). In addition, the study presents detailed plots of resting-state EEG power, local mean field power (LMFP), TMS-related spectral perturbation (TSRP), and inter-trial phase clustering (ITPC). Paired t-tests and cluster-based permutation tests have been performed for statistical analysis. The wealth and quality of this dataset make it ideal for examining the neuromodulatory impact of TBS on the prefrontal cortex. Brainstorm's extensive feature set greatly supports the exploration of such neurological data. Future research directions could concentrate on conducting source localization analyses and comparative group studies.
A Quasi-Stationary Approach to Metastability in a System of Spiking Neurons with Synaptic Plasticity
Authors: Christophe Pouzat, Morgan André
Date: 2024-02-07 00:00:00

After reviewing the behavioral studies of working memory and of the cellular substrate of the latter, we argue that metastable states constitute candidates for the type of transient information storage required by working memory. We then present a simple neural network model made of stochastic units whose synapses exhibit short-term facilitation. The Markov process dynamics of this model was specifically designed to be analytically tractable, simple to simulate numerically and to exhibit a quasi-stationary distribution (QSD). Since the state space is finite this QSD is also a Yaglom limit, which allows us to bridge the gap between quasi-stationarity and metastability by considering the relative orders of magnitude of the relaxation and absorption times. We present first analytical results: characterization of the absorbing region of the Markov process, irreducibility outside this absorbing region and consequently existence and uniqueness of a QSD. We then apply Perron-Frobenius spectral analysis to obtain any specific QSD, and design an approximate method for the first moments of this QSD when the exact method is intractable. Finally we use these methods to study the relaxation time toward the QSD and establish numerically the memorylessness of the time of extinction.