pwc-archive/papers-with-abstracts huggingface dataset:
Scatterer Recognition for Multi-Modal Intelligent Vehicular Channel Modeling via Synesthesia of Machines
Authors: Ziwei Huang, Lu Bai, Zengrui Han, Xiang Cheng
Date: 2024-06-27 00:00:00

In this paper, a novel multi-modal intelligent vehicular channel model is proposed by scatterer recognition from light detection and ranging (LiDAR) point clouds via Synesthesia of Machines (SoM). The proposed model can support the design of intelligent transportation systems (ITSs). To provide a robust data foundation, a new intelligent sensing-communication integration dataset in vehicular urban scenarios is constructed. Based on the constructed dataset, the complex SoM mechanism, i.e., mapping relationship between scatterers in electromagnetic space and LiDAR point clouds in physical environment, is explored via multilayer perceptron (MLP) in consideration of electromagnetic propagation mechanism. By using LiDAR point clouds to implement scatterer recognition, channel non-stationarity and consistency are captured closely coupled with the environment. Using ray-tracing (RT)-based results as the ground truth, the scatterer recognition accuracy exceeds 90%. The accuracy of the proposed model is further verified by the close fit between simulation results and RT results.
Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy
Authors: Silvia Mura, Dario Tagliaferri, Marouan Mizmizi, Umberto Spagnolini, Athina Petropulu
Date: 2024-06-27 00:00:00
Tasks: Integrated sensing and communication, ISAC, Matrix Completion, Scheduling

The sixth generation (6G) of wireless networks introduces integrated sensing and communication (ISAC), a technology in which communication and sensing functionalities are inextricably linked, sharing resources across time, frequency, space, and energy. Despite its popularity in communication, the orthogonal frequency division multiplexing (OFDM) waveform, while advantageous for communication, has limitations in sensing performance within an ISAC network. This paper delves into OFDM waveform design through optimal resource allocation over time, frequency, and energy, maximizing sensing performance while preserving communication quality. During quasi-normal operation, the Base Station (BS) does not utilize all available time-frequency resources, resulting in high sidelobes in the OFDM waveform's ambiguity function, as well as decreased sensing accuracy. To address these latter issues, the paper proposes a novel interpolation technique using matrix completion through the Schatten p quasi-normal approximation, which requires fewer samples than the traditional nuclear norm for effective matrix completion and interpolation. This approach effectively suppresses the sidelobes, enhancing the sensing performance. Numerical simulations confirm that the proposed method outperforms state-of-the-art frameworks, such as standard complaint resource scheduling and interpolation, particularly in scenarios with limited resource occupancy.
Interference Cancellation Based Neural Receiver for Superimposed Pilot in Multi-Layer Transmission
Authors: Han Xiao, Wenqiang Tian, Shi Jin, Wendong Liu, Jia Shen, Zhihua Shi, Zhi Zhang
Date: 2024-06-27 00:00:00

In this paper, an interference cancellation based neural receiver for superimposed pilot (SIP) in multi-layer transmission is proposed, where the data and pilot are non-orthogonally superimposed in the same time-frequency resource. Specifically, to deal with the intra-layer and inter-layer interference of SIP under multi-layer transmission, the interference cancellation with superimposed symbol aided channel estimation is leveraged in the neural receiver, accompanied by the pre-design of pilot code-division orthogonal mechanism at transmitter. In addition, to address the complexity issue for inter-vendor collaboration and the generalization problem in practical deployments, respectively, this paper also provides a fixed SIP (F-SIP) design based on constant pilot power ratio and scalable mechanisms for different modulation and coding schemes (MCSs) and transmission layers. Simulation results demonstrate the superiority of the proposed schemes on the performance of block error rate and throughput compared with existing counterparts.
Constant Modulus Waveform Design with Space-Time Sidelobe Reduction for DFRC Systems
Authors: Byunghyun Lee, Anindya Bijoy Das, David Love, Christopher Brinton, James Krogmeier
Date: 2024-06-27 00:00:00

Dual-function radar-communication (DFRC) is a key enabler of location-based services for next-generation communication systems. In this paper, we investigate the problem of designing constant modulus waveforms for DFRC systems. We consider the joint optimization of the spatial beam pattern and space-time correlation levels for better separating multiple targets in different angle and delay bins. In particular, we use the space-time correlation function to quantify the correlations between different angle and delay bins and formulate integrated sidelobe levels (ISLs). To serve communication users, we employ constructive interference (CI)-based precoding to modulate information symbols, which leverages distortion induced by multiuser multiple-input multiple-output (MU-MIMO) and radar transmission. We propose two solution algorithms based on the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) principles, which are effective for small and large block sizes, respectively. The proposed ADMM-based solution decomposes the nonconvex formulated problem into multiple tractable subproblems, each of which admits a closed-form solution. To accelerate convergence of the MM-based solution, we propose a novel majorizing function for complex quadratic functions. After majorization, we decompose the approximated problem into independent subproblems for parallelization, mitigating the complexity that increases with block size. We evaluate the performance of the proposed algorithms in comparison to the existing DFRC algorithm. Simulation results demonstrate that the proposed methods can substantially enhance the detection and estimation performance due to reduced space-time correlation levels.
L-Sort: An Efficient Hardware for Real-time Multi-channel Spike Sorting with Localization
Authors: Yuntao Han, Shiwei Wang, Alister Hamilton
Date: 2024-06-26 00:00:00
Tasks: Brain Computer Interface, Clustering, Spike Sorting

Spike sorting is essential for extracting neuronal information from neural signals and understanding brain function. With the advent of high-density microelectrode arrays (HDMEAs), the challenges and opportunities in multi-channel spike sorting have intensified. Real-time spike sorting is particularly crucial for closed-loop brain computer interface (BCI) applications, demanding efficient hardware implementations. This paper introduces L-Sort, an hardware design for real-time multi-channel spike sorting. Leveraging spike localization techniques, L-Sort achieves efficient spike detection and clustering without the need to store raw signals during detection. By incorporating median thresholding and geometric features, L-Sort demonstrates promising results in terms of accuracy and hardware efficiency. We assessed the detection and clustering accuracy of our design with publicly available datasets recorded using high-density neural probes (Neuropixel). We implemented our design on an FPGA and compared the results with state of the art. Results show that our designs consume less hardware resource comparing with other FPGA-based spike sorting hardware.
CmWave and Sub-THz: Key Radio Enablers and Complementary Spectrum for 6G
Authors: Mayur V. Katwe, Aryan Kaushik, Keshav Singh, Marco Di Renzo, Shu Sun, Doohwan Lee, Ana G. Armada, Yonina C. Eldar, Octavia A. Dobre, Theodore S. Rappaport
Date: 2024-06-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

Sixth-generation (6G) networks are poised to revolutionize communication by exploring alternative spectrum options, aiming to capitalize on strengths while mitigating limitations in current fifth-generation (5G) spectrum. This paper explores the potential opportunities and emerging trends for cmWave and sub-THz spectra as key radio enablers. This paper poses and answers three key questions regarding motivation of additional spectrum to explore the strategic implementation and benefits of cmWave and sub-THz spectra. Also, we show using case studies how these complementary spectrum bands will enable new applications in 6G, such as integrated sensing and communication (ISAC), re-configurable intelligent surfaces (RIS) and non-terrestrial networks (NTN). Numerical simulations reveal that the ISAC performance of cmWave and sub-THz spectra outperforms that of existing 5G spectrum, including sub-6 GHz and mmWave. Additionally, we illustrate the effective interplay between RIS and NTN to counteract the effects of high attenuation at sub-THz frequencies. Finally, ongoing standardization endeavors, challenges and promising directions are elucidated for these complementary spectrum bands.
Neural Network-Based Intelligent Reflecting Surface Assisted Direction of Arrival Estimation under Non-Line-of-Sight Scenario
Authors: Yasin Azhdari, Mahmoud Farhang
Date: 2024-06-26 00:00:00
Tasks: Direction of Arrival Estimation

Direction-of-Arrival (DoA) estimation assisted with an Intelligent Reflecting Surface (IRS) is crucial for various wireless applications, especially in challenging Non-Line-of-Sight (NLoS) environments. This paper presents a novel neural network-based architecture to address this challenge. The key innovation is the introduction of a dedicated, learnable IRS layer integrated integrated within a carefully designed network structure established upon the physical and geometrical basis of the problem. Unlike conventional neural network layers, this specific layer incorporates block diagonal sinusoidal weight constraints, where the phase arguments of these sinusoids are learned during training to directly emulate the phase shifts of the IRS elements. This allows the network to optimize the IRS configuration for enhanced DoA estimation, eliminating the need for separate IRS optimization algorithms. Moreover, various trainable DoA regressors, including a proposed structure, are presented and examined. Numerical simulations, conducted under various conditions and noise levels, where controlled coherent multi-path components are introduced due to the presence of the IRS, demonstrate the superior performance of the proposed approach compared to others and highlight its potential to significantly improve the accuracy of DoA estimation in complex IRS-assisted wireless systems.
High Resolution Millimeter Wave Imaging Based on FMCW Radar Systems at W-Band
Authors: Shahrokh Hamidi, M. R. Nezhad-Ahmadi
Date: 2024-06-26 00:00:00

In this paper, we present a unique $\text {2D}$ high resolution, compact, low-cost, low-weight, and highly accurate millimeter wave imagery system capable of operating in all weather conditions. We describe millimeter wave imaging process in detail and present several novel signal processing methods with their applications. To create the array, we utilize the Synthetic Aperture Radar (SAR) concept. The imagery system presented in this paper, can strongly compete with Lidar systems as the resolution limit is at the same level. Furthermore, in contrast to the Lidar systems, our imagery system can operate in heavy rain and dense fog and produce high quality images. Finally, we utilize our wide-band custom-made Frequency Modulated Continuous Wave (FMCW) radar, which operates at W-band with $\text {33 GHz}$ bandwidth, for data collection and present the results.
V2X Sidelink Positioning in FR1: From Ray-Tracing and Channel Estimation to Bayesian Tracking
Authors: Yu Ge, Maximilian Stark, Musa Furkan Keskin, Hui Chen, Guillaume Jornod, Thomas Hansen, Frank Hofmann, Henk Wymeersch
Date: 2024-06-25 00:00:00
Tasks: parameter estimation, Super-Resolution

Sidelink positioning research predominantly focuses on the snapshot positioning problem, often within the mmWave band. Only a limited number of studies have delved into vehicle-to-anything (V2X) tracking within sub-6 GHz bands. In this paper, we investigate the V2X sidelink tracking challenges over sub-6 GHz frequencies. We propose a Kalman-filter-based tracking approach that leverages the estimated error covariance lower bounds (EECLBs) as measurement covariance, alongside a gating method to augment tracking performance. Through simulations employing ray-tracing data and super-resolution channel parameter estimation, we validate the feasibility of sidelink tracking using our proposed tracking filter with two novel EECLBs. Additionally, we demonstrate the efficacy of the gating method in identifying line-of-sight paths and enhancing tracking performance.
Environmental Variation or Instrumental Drift? A Probabilistic Approach to Gas Sensor Drift Modeling and Evaluation
Authors: Cheng Yang, Gustav Bohlin, Tobias Oechtering
Date: 2024-06-25 00:00:00

Drift is a significant issue that undermines the reliability of gas sensors. This paper introduces a probabilistic model to distinguish between environmental variation and instrumental drift, using low-cost non-dispersive infrared (NDIR) CO2 sensors as a case study. Data from a long-term field experiment is analyzed to evaluate both sensor performance and environmental changes over time. Our approach employs importance sampling to isolate instrumental drift from environmental variation, providing a more accurate assessment of sensor performance. The results show that failing to account for environmental variation can significantly affect the evaluation of sensor drift, leading to improper calibration processes.
Relaxed Multi-Tx DDM Online Calibration
Authors: Mayeul Jeannin, Oliver Lang, Farhan Bin Khalid, Dian Tresna Nugraha, Mario Huemer
Date: 2024-06-25 00:00:00

In multiple-input and multiple-output (MIMO) radar systems based on Doppler-division multiplexing (DDM), phase shifters are employed in the transmit paths and require calibration strategies to maintain optimal performance all along the radar system's life cycle. In this paper, we propose a novel family of DDM codes that enable an online calibration of the phase shifters that scale realistically to any number of simultaneously activated transmit (Tx)-channels during the calibration frames. To achieve this goal we employ the previously developed odd-DDM (ODDM) sequences to design calibration DDM codes with reduced inter-Tx leakage. The proposed calibration sequence is applied to an automotive radar data set modulated with erroneous phase shifters.
Consistent Spectrogram Separation from Nonstationary Mixture
Authors: Adrien Meynard, Ama Marina Kreme
Date: 2024-06-25 00:00:00

We present a spectrogram separation method tailored for mixtures comprising two nonstationary components. By exploiting the unique characteristics of their time-frequency representations, we propose an inverse problem formulation to estimate the spectrograms of the components. We then introduce an alternating optimization algorithm that ensures the consistency of the estimated spectrograms. The efficacy of the algorithm is evaluated through testing on synthetic mixtures and is applied to a bioacoustic signal.
MIMO-OFDM ISAC Waveform Design for Range-Doppler Sidelobe Suppression
Authors: Peishi Li, Ming Li, Rang Liu, Qian Liu, A. Lee Swindlehurst
Date: 2024-06-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a key enabling technique for future wireless networks owing to its efficient hardware and spectrum utilization. In this paper, we focus on dual-functional waveform design for a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system, which is considered to be a promising solution for practical deployment. Since the dual-functional waveform carries communication information, its random nature leads to high range-Doppler sidelobes in the ambiguity function, which in turn degrades radar sensing performance. To suppress range-Doppler sidelobes, we propose a novel symbol-level precoding (SLP) based waveform design for MIMO-OFDM ISAC systems by fully exploiting the temporal degrees of freedom (DoFs). Our goal is to minimize the range-Doppler integrated sidelobe level (ISL) while satisfying the constraints of target illumination power, multi-user communication quality of service (QoS), and constant-modulus transmission. To solve the resulting non-convex waveform design problem, we develop an efficient algorithm using the majorization-minimization (MM) and alternative direction method of multipliers (ADMM) methods. Simulation results show that the proposed waveform has significantly reduced range-Doppler sidelobes compared with signals designed only for communications and other baselines. In addition, the proposed waveform design achieves target detection and estimation performance close to that achievable by waveforms designed only for radar, which demonstrates the superiority of the proposed SLP-based ISAC approach.
Improving Rehabilitative Assessment with Statistical and Shape Preserving Surrogate Data and Singular Spectrum Analysis
Authors: T. K. M. Lee, H. W. Chan, K. H. Leo, E. Chew, Ling Zhao, S. Sanei
Date: 2024-06-22 00:00:00
Tasks: Time Series

Time series data are collected in temporal order and are widely used to train systems for prediction, modeling and classification to name a few. These systems require large amounts of data to improve generalization and prevent over-fitting. However there is a comparative lack of time series data due to operational constraints. This situation is alleviated by synthesizing data which have a suitable spread of features yet retain the distinctive features of the original data. These would be its basic statistical properties and overall shape which are important for short time series such as in rehabilitative applications or in quickly changing portions of lengthy data. In our earlier work synthesized surrogate time series were used to augment rehabilitative data. This gave good results in classification but the resulting waveforms did not preserve the original signal shape. To remedy this, we use singular spectrum analysis (SSA) to separate a signal into trends and cycles to describe the shape of the signal and low level components. In a novel way we subject the low level component to randomizing processes then recombine this with the original trend and cycle components to form a synthetic time series. We compare our approach with other methods, using statistical and shape measures and demonstrate its effectiveness in classification.
Towards a data-driven and scalable approach for window operation detection in multi-family residential buildings
Authors: Juliet Nwagwu Ume-Ezeoke, Kopal Nihar, Catherine Gorle, Rishee Jain
Date: 2024-06-20 00:00:00

Natural cooling, utilizing non-mechanical cooling, presents a low-carbon and low-cost way to provide thermal comfort in residential buildings. However, designing naturally cooled buildings requires a clear understanding of how opening and closing windows affect occupants' comfort. Predicting when and why occupants open windows is a challenging task, often relying on specialized sensors and building-specific training data. This limits the scalability of natural cooling solutions. Here, we, propose a novel unsupervised method that utilizes easily deployable off-the-shelf temperature and humidity sensors to detect window operations. The effectiveness of our approach is evaluated using an empirical dataset and compared with a state-of-the-art support vector machine (SVM) model. The results demonstrate that our proposed method outperforms the SVM on key indicators, except when indoor and outdoor temperatures have small differences. Unlike the SVM's sensitivity to time series characteristics, our proposed method relies solely on indoor temperature and exhibits robust performance in pilot studies, making it a promising candidate for developing a highly scalable and generalizable window operation detection model This work demonstrates the potential of unsupervised data-driven methods for understanding window operations in residential buildings. By enabling more accurate modeling of naturally cooled buildings, our work aims to facilitate the widespread adoption of this low-cost and low-carbon technology.
Validation of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity: transversal study
Authors: Justine Frija, Juliette Millet, Emilie Bequignon, Ala Covali, Guillaume Cathelain, Josselin Houenou, Helene Benzaquen, Pierre Alexis Geoffroy, Emmanuel Bacry, Mathieu Grajoszex, Marie-Pia d Ortho
Date: 2024-06-20 00:00:00
Tasks: Sensitivity

Obstructive sleep apnea (OSA) is frequent and responsible for cardiovascular complications and excessive daytime sleepiness. It is underdiagnosed due to the difficulty to access the gold standard for diagnosis, polysomnography (PSG). Alternative methods using smartphone sensors could be useful to increase diagnosis. The objective is to assess the performances of Apneal, an application that records the sound using a smartphone's microphone and movements thanks to a smartphone's accelerometer and gyroscope, to estimate patients' AHI. In this article, we perform a monocentric proof-of-concept study with a first manual scoring step, and then an automatic detection of respiratory events from the recorded signals using a sequential deep-learning model which was released internally at Apneal at the end of 2022 (version 0.1 of Apneal automatic scoring of respiratory events), in adult patients during in-hospital polysomnography.46 patients (women 34 per cent, mean BMI 28.7 kg per m2) were included. For AHI superior to 15, sensitivity of manual scoring was 0.91, and positive predictive value (PPV) 0.89. For AHI superior to 30, sensitivity was 0.85, PPV 0.94. We obtained an AUC-ROC of 0.85 and an AUC-PR of 0.94 for the identification of AHI superior to 15, and AUC-ROC of 0.95 and AUC-PR of 0.93 for AHI superior to 30. Promising results are obtained for the automatic annotations of events.This article shows that manual scoring of smartphone-based signals is possible and accurate compared to PSG-based scorings. Automatic scoring method based on a deep learning model provides promising results. A larger multicentric validation study, involving subjects with different SAHS severity is required to confirm these results.
Energy-Efficient Seizure Detection Suitable for low-power Applications
Authors: Julia Werner, Bhavya Kohli, Paul Palomero Bernardo, Christoph Gerum, Oliver Bringmann
Date: 2024-06-19 00:00:00
Tasks: EEG, Seizure Detection, Time Series Analysis

Epilepsy is the most common, chronic, neurological disease worldwide and is typically accompanied by reoccurring seizures. Neuro implants can be used for effective treatment by suppressing an upcoming seizure upon detection. Due to the restricted size and limited battery lifetime of those medical devices, the employed approach also needs to be limited in size and have low energy requirements. We present an energy-efficient seizure detection approach involving a TC-ResNet and time-series analysis which is suitable for low-power edge devices. The presented approach allows for accurate seizure detection without preceding feature extraction while considering the stringent hardware requirements of neural implants. The approach is validated using the CHB-MIT Scalp EEG Database with a 32-bit floating point model and a hardware suitable 4-bit fixed point model. The presented method achieves an accuracy of 95.28%, a sensitivity of 92.34% and an AUC score of 0.9384 on this dataset with 4-bit fixed point representation. Furthermore, the power consumption of the model is measured with the low-power AI accelerator UltraTrail, which only requires 495 nW on average. Due to this low-power consumption this classification approach is suitable for real-time seizure detection on low-power wearable devices such as neural implants.