pwc-archive/papers-with-abstracts huggingface dataset:
Impact of Estimation Errors of a Matrix of Transfer Functions onto Its Analytic Singular Values and Their Potential Algorithmic Extraction
Authors: Mohammed Bakhit, Faizan A. Khattak, Ian K. Proudler, Stephan Weiss
Date: 2024-09-30 00:00:00

A matrix of analytic functions A(z), such as the matrix of transfer functions in a multiple-input multiple-output (MIMO) system, generally admits an analytic singular value decomposition (SVD), where the singular values themselves are functions. When evaluated on the unit circle, for the sake of analyticity, these singular values must be permitted of become negative. In this paper, we address how the estimation of such a matrix, causing a stochastic perturbation of A}(z), results in fundamental changes to the analytic singular values: for the perturbed system, we show that their analytic singular values lose any algebraic multiplicities and are strictly non-negative with probability one. We present examples and highlight the impact that this has on algorithmic solutions to extracting an analytic or approximate analytic SVD.
A Framework for Holistic KLD-based Waveform Design for Multi-User-Multi-Target ISAC Systems
Authors: Yousef Kloob, Mohammad Al-jarrah, Emad Alsusa
Date: 2024-09-30 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper introduces a novel framework aimed at designing integrated waveforms for robust integrated sensing and communication systems. The system model consists of a multiple-input multiple-output (MIMO) base station that simultaneously serves communication user equipments and detects multiple targets using a shared-antenna deployment scenario. By leveraging Kullback-Leibler divergence to holistically characterise both communication and sensing subsystems, three optimisation problems are formulated: (i) radar waveform KLD maximisation under communication constraints, (ii) communication waveform KLD maximisation subject to radar KLD requirements, and (iii) an integrated waveform KLD-based optimisation for ISAC that jointly balances both subsystems. The first two problems are solved using a projected gradient method with adaptive penalties for the radar waveforms and a gradient assisted interior point method for the communication waveforms. The third, integrated waveform optimisation approach adopts an alternating direction method of multipliers framework to unify radar and communication waveform designs into a single integrated optimisation, thereby strengthening the trade-off between sensing and communication objectives and achieving higher overall performance than the individual radar- or communication-only techniques. Unlike most existing ISAC waveform designs that regard communication signals solely as interference for sensing, the proposed framework utilises the holistic ISAC waveform-that is, the superimposed communication and sensing signals to boost detection performance in the radar subsystem. Simulation results show significant improvements in both radar detection and communication reliability compared with conventional zero-forcing beamforming and identity covariance radar baselines, demonstrating the promise of KLD-based waveform designs for next-generation ISAC networks.
Trajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing
Authors: Chao Yu, Bojie Lv, Haoyu Qiu, Rui Wang
Date: 2024-09-30 00:00:00

In this paper, a cooperative passive sensing framework for millimeter wave (mmWave) communication systems is proposed and demonstrated in a scenario with one mobile signal blocker. Specifically, in the uplink communication with at least two transmitters, a cooperative detection method is proposed for the receiver to track the blocker's trajectory, localize the transmitters and detect the potential link blockage jointly. To facilitate detection, the receiver collects the signal of each transmitter along a line-of-sight (LoS) path and a non-line-of-sight (NLoS) path separately via two narrow-beam phased arrays. The latter path should scatter at the mobile blocker, and hence it can be identified by the Doppler frequency. Comparing the received signals of both paths, the Doppler frequency and angle-of-arrival (AoA) of the NLoS path can be estimated. To resolve the blocker's trajectory and the transmitters' locations, the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations. Finally, a gradient-descent-based algorithm is proposed for joint detection. With the reconstructed trajectory, the potential link blockage can be predicted. It is demonstrated that the system can achieve decimeter-level localization and trajectory estimation, and predict the blockage time with an error less than 0.1 s.
Double-Side Polarization and Beamforming Alignment in Polarization Reconfigurable MISO System with Deep Neural Networks
Authors: Seungcheol Oh, Han Han, Joongheon Kim, Sean Kwon
Date: 2024-09-30 00:00:00

Polarization reconfigurable (PR) antennas enhance spectrum and energy efficiency between next-generation node B(gNB) and user equipment (UE). This is achieved by tuning the polarization vectors for each antenna element based on channel state information (CSI). On the other hand, degree of freedom increased by PR antennas yields a challenge in channel estimation with pilot training overhead. This paper pursues the reduction of pilot overhead, and proposes to employ deep neural networks (DNNs) on both transceiver ends to directly optimize the polarization and beamforming vectors based on the received pilots without the explicit channel estimation. Numerical experiments show that the proposed method significantly outperforms the conventional first-estimate-then-optimize scheme by maximum of 20% in beamforming gain.
Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach
Authors: Yajie Sheng, Bin Chen, Yi Lei, Jingxin Deng, Jiwei Xu, Mengfan Fu, Qunbi Zhuge, Shen Li
Date: 2024-09-30 00:00:00

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM with CCDM blocklength of 200.
The EAVI EMG/EEG Board: Hybrid physiological sensing
Authors: Atau Tanaka, David Fierro, Francesco Di Maggio, Martin Klang, Stephen Whitmarsh
Date: 2024-09-30 00:00:00
Tasks: EEG, Physiological Computing

We present an update on the EAVI physiological interface, a wireless, microcontroller based hardware design for the acquisition of bioelectrical signals. The system has been updated to process electroencephalogram brain signals in addition to muscle electromyogram. The hardware/firmware system interfaces with host software carrying out feature extraction and signal processing. Recent advances in electronics have made physiological computing applications practical and feasible. However, there is a gap between high end biomedical equipment and consumer DIY solutions. The hardware design we present here bridges this gap, and combines a specialized biosignal acquisition chip mated with a general-purpose microcontroller. It is based on the Texas Instruments ADS129x family a single chip integrated solution for high quality biosignal amplification and digitization. It serves as analogue front end via programmable gain amplifiers to a 24bit delta-sigma analog-digital converter. The microcontroller is the STMicroelectronics STM32F427, a Cortex-M4 family microcontroller with floating point unit . In addition to EMG acquisition, the board includes a Kionix KX122 three-axis accelerometer. The TI and Kionix sensing chipts communicate with the ST microcontroller over an I2C digital serial bus. The board communicates with the host computer or rest of the music system wirelessly over Bluetooth LE 4.2 using an ST SPBTLE-1S transceiver. The board can also communicate over USB where it registers with the host as a class compliant audio and MIDI device. Audio and physiological signals are treated in the same signal processing chain using the OWL framework. The demo will show multichannel EMG, and single channel EEG. We call this hybridization ''ExG''. We will present documentation of the EAVI board used in the lab and on stage, in user studies with neuro-diverse musicians and trained instrumentalists, as well as in performance with the experimental all-female band, Chicks on Speed.
A database of upper limb surface electromyogram signals from demographically diverse individuals
Authors: Harshavardhana T. Gowda, Neha Kaul, Carlos Carrasco, Marcus A. Battraw, Safa Amer, Saniya Kotwal, Selena Lam, Zachary McNaughton, Ferdous Rahimi, Sana Shehabi, Jonathon S. Schofield, Lee M. Miller
Date: 2024-09-30 00:00:00

Upper limb based neuromuscular interfaces aim to provide a seamless way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (EMG) signals hold significant promise. However, their sensitivity to physiological and anatomical factors remains poorly understood, raising questions about how these factors influence gesture decoding across individuals or groups. To facilitate the study of signal distribution shifts across individuals or groups of individuals, we present a dataset of upper limb EMG signals and physiological measures from 91 demographically diverse adults. Participants were selected to represent a range of ages (18 to 92 years) and body mass indices (healthy, overweight, and obese). The dataset also includes measures such as skin hydration and elasticity, which may affect EMG signals. This dataset provides a basis to study demographic confounds in EMG signals and serves as a benchmark to test the development of fair and unbiased algorithms that enable accurate hand gesture decoding across demographically diverse subjects. Additionally, we validate the quality of the collected data using state-of-the-art gesture decoding techniques.
An Overview of Multi-Object Estimation via Labeled Random Finite Set
Authors: Ba-Ngu Vo, Ba-Tuong Vo, Tran Thien Dat Nguyen, Changbeom Shim
Date: 2024-09-27 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking

This article presents the Labeled Random Finite Set (LRFS) framework for multi-object systems-systems in which the number of objects and their states are unknown and vary randomly with time. In particular, we focus on state and trajectory estimation via a multi-object State Space Model (SSM) that admits principled tractable multi-object tracking filters/smoothers. Unlike the single-object counterpart, a time sequence of states does not necessarily represent the trajectory of a multi-object system. The LRFS formulation enables a time sequence of multi-object states to represent the multi-object trajectory that accommodates trajectory crossings and fragmentations. We present the basics of LRFS, covering a suite of commonly used models and mathematical apparatus (including the latest results not published elsewhere). Building on this, we outline the fundamentals of multi-object state space modeling and estimation using LRFS, which formally address object identities/trajectories, ancestries for spawning objects, and characterization of the uncertainty on the ensemble of objects (and their trajectories). Numerical solutions to multi-object SSM problems are inherently far more challenging than those in standard SSM. To bridge the gap between theory and practice, we discuss state-of-the-art implementations that address key computational bottlenecks in the number of objects, measurements, sensors, and scans.
Unleashing the Potential of Beamspace Modulation in Near-Field MIMO: Opportunities and Challenges
Authors: Shuaishuai Guo, Kaiqian Qu, Shuping Dang
Date: 2024-09-29 00:00:00
Tasks: Navigate

The principal distinction in transitioning from far-field multiple-input multiple-output (MIMO) systems to near-field MIMO configurations lies in the notable augmentation of spatial degrees of freedom (DoF). This increase is not static;rather, it dynamically fluctuates in response to user mobility. A critical challenge emerges in effectively leveraging this significantly enhanced and continuously evolving spatial DoF, particularly when constrained by a limited and energy-intensive array of radio frequency (RF) chains. This article presents an exhaustive review of the current methodologies for exploring spatial DoF in MIMO systems, with a particular emphasis on the near-field context. Central to this review is the exploration of beamspace modulation, a technique that ingeniously capitalizes on the increased and dynamic spatial DoFs inherent in near-field MIMO systems. This strategic exploitation is demonstrated to yield significant enhancements in both spectral efficiency and system reliability. Furthermore, the article delves into a detailed analysis of the multifaceted challenges associated with implementing this technology. Through this comprehensive evaluation, the work provides crucial insights into ongoing efforts to navigate these challenges and suggests potential pathways for future research in this rapidly evolving field.
Anti-jamming Transmission of Downlink Cell Free Millimeter-Wave MIMO System
Authors: Zilong Wang, Cheng Zhang, Changwei Zhang, Yongming Huang
Date: 2024-09-20 00:00:00
Tasks: Fairness

In this letter, the maximization of resistible jamming power is studied for multi-user downlink millimeter-wave cell-free multiple-input-multiple-output (CF-MIMO) systems. We propose an alternate optimization-based anti-jamming hybrid beamforming (AO-AJHBF) scheme. For receiving beamforming, more practical prior about the jamming channel, i.e., second-order statistics rather than instantaneous information, is exploited via maximizing the generalized Rayleigh quotient. For transmitting beamforming, we use the max-min fairness principle and propose a low-complexity projected gradient ascent-based method to circumvent the excessive computation of semi-definite relaxation (SDR). Simulations verify the performance advantage of proposed AO-AJHBF over schemes based on weighted minimum mean square error and SDR methods.
How connected cars could capture cloud dynamics -- first evidence from two simulation scenarios
Authors: Tobias Veihelmann, Philipp Reitz, Maximilian Lübke, Norman Franchi
Date: 2024-09-28 00:00:00

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image-based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal-based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40 %, the root mean square errors for both sensor networks are still below 5 m/s. In conclusion, the spatio-temporal characteristics of a vehicle network offer some potential for estimating cloud movements.
Channel Estimation for Movable Antenna Aided Wideband Communication Systems
Authors: Zhenyu Xiao, Songqi Cao, Lipeng Zhu, Boyu Ning, Xiang-Gen Xia, Rui Zhang
Date: 2024-09-28 00:00:00
Tasks: compressed sensing, Position

Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers, is a crucial issue. In this paper, we study the channel estimation problem for wideband MA systems. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at selected position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem with multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Moreover, we also devise an alternating refinement approach to further improve the accuracy of the estimated FRVs, DRV, and PRT. This is achieved by minimizing the discrepancy between the received pilots and those constructed by the estimated CSI, which can be efficiently carried out by using the gradient descent algorithm. Finally, simulation results demonstrate that both the SOMP-based channel estimation method and alternating refinement method can reconstruct the complete wideband CSI with high accuracy, where the alternating refinement method performs better despite a higher complexity.
Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface
Authors: Jianhua Zhang, Yichen Cai, Li Yu, Zhen Zhang, Yuxiang Zhang, Jialin Wang, Tao Jiang, Liang Xia, Ping Zhang
Date: 2024-09-28 00:00:00
Tasks: Dimensionality Reduction

The air interface technology plays a crucial role in optimizing the communication quality for users. To address the challenges brought by the radio channel variations to air interface design, this article proposes a framework of wireless environment information-aided 6G AI-enabled air interface (WEI-6G AI$^{2}$), which actively acquires real-time environment details to facilitate channel fading prediction and communication technology optimization. Specifically, we first outline the role of WEI in supporting the 6G AI$^{2}$ in scenario adaptability, real-time inference, and proactive action. Then, WEI is delineated into four progressive steps: raw sensing data, features obtained by data dimensionality reduction, semantics tailored to tasks, and knowledge that quantifies the environmental impact on the channel. To validate the availability and compare the effect of different types of WEI, a path loss prediction use case is designed. The results demonstrate that leveraging environment knowledge requires only 2.2 ms of model inference time, which can effectively support real-time design for future 6G AI$^{2}$. Additionally, WEI can reduce the pilot overhead by 25\%. Finally, several open issues are pointed out, including multi-modal sensing data synchronization and information extraction method construction.
Towards ubiquitous radio access using nanodiamond based quantum receivers
Authors: Qunsong Zeng, Jiahua Zhang, Madhav Gupta, Zhiqin Chu, Kaibin Huang
Date: 2024-09-28 00:00:00

The development of sixth-generation (6G) wireless communication systems demands innovative solutions to address challenges in the deployment of a large number of base stations and the detection of multi-band signals. Quantum technology, specifically nitrogen vacancy (NV) centers in diamonds, offers promising potential for the development of compact, robust receivers capable of supporting multiple users. For the first time, we propose a multiple access scheme using fluorescent nanodiamonds (FNDs) containing NV centers as nano-antennas. The unique response of each FND to applied microwaves allows for distinguishable patterns of fluorescence intensities, enabling multi-user signal demodulation. We demonstrate the effectiveness of our FNDs-implemented receiver by simultaneously transmitting two uncoded digitally modulated information bit streams from two separate transmitters, achieving a low bit error ratio. Moreover, our design supports tunable frequency band communication and reference-free signal decoupling, reducing communication overhead. Furthermore, we implement a miniaturized device comprising all essential components, highlighting its practicality as a receiver serving multiple users simultaneously. This approach paves the way for the integration of quantum sensing technologies in future 6G wireless communication networks.
Detection of Sleep Apnea-Hypopnea Events Using Millimeter-wave Radar and Pulse Oximeter
Authors: Wei Wang, Chenyang Li, Zhaoxi Chen, Wenyu Zhang, Zetao Wang, Xi Guo, Jian Guan, Gang Li
Date: 2024-09-28 00:00:00
Tasks: Temporal Localization

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep-related breathing disorder associated with significant morbidity and mortality worldwide. The gold standard for OSAHS diagnosis, polysomnography (PSG), faces challenges in popularization due to its high cost and complexity. Recently, radar has shown potential in detecting sleep apnea-hypopnea events (SAE) with the advantages of low cost and non-contact monitoring. However, existing studies, especially those using deep learning, employ segment-based classification approach for SAE detection, making the task of event quantity estimation difficult. Additionally, radar-based SAE detection is susceptible to interference from body movements and the environment. Oxygen saturation (SpO2) can offer valuable information about OSAHS, but it also has certain limitations and cannot be used alone for diagnosis. In this study, we propose a method using millimeterwave radar and pulse oximeter to detect SAE, called ROSA. It fuses information from both sensors, and directly predicts the temporal localization of SAE. Experimental results demonstrate a high degree of consistency (ICC=0.9864) between AHI from ROSA and PSG. This study presents an effective method with lowload device for the diagnosis of OSAHS.
Dynamic Adaptive Resource Scheduling for Phased Array Radar: Enhancing Efficiency through Synthesis Priorities and Pulse Interleaving
Authors: Mingguang Han
Date: 2024-09-28 00:00:00
Tasks: Scheduling

To enhance the resource scheduling performance of phased array radar, we propose a dynamic adaptive resource scheduling algorithm based on synthesis priorities and pulse interleaving. This approach addresses the challenges of low efficiency, high loss ratios, and significant subjectivity in task assignment within phased array radar systems. We introduce a task synthesis priority design method that considers the working mode priority, deadlines, and time shift ratios. By implementing this method, we can increase the flexibility of task scheduling and improve the efficiency of radar processing tasks. Additionally, our proposed pulse interleaving method effectively utilizes the waiting periods between receiving and transmitting pulses to process other beams, thereby enhancing resource utilization. Simulation results demonstrate that the proposed scheduling algorithm significantly reduces time deviation ratios and scheduling failure rates while improving scheduling yield and time utilization ratios.