pwc-archive/papers-with-abstracts huggingface dataset:
Revealing the Trade-off in ISAC Systems: The KL Divergence Perspective
Authors: Zesong Fei, Shuntian Tang, Xinyi Wang, Fanghao Xia, Fan Liu, J. Andrew Zhang
Date: 2024-05-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is regarded as a promising technique for 6G communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler (KL) divergence performance metric. We firstly present the relationship between KL divergence and explicit ISAC performance metric, i.e., demodulation error and probability of detection. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the trade-off between sensing and communication performance in terms of bit error rate (BER) and probability of detection under different parameter set-ups.
Radar Positioning for Accurate Sensing of Pulse Waves at Multiple Sites Using a 3D Human Model
Authors: Takehito Koshisaka, Takuya Sakamoto
Date: 2024-05-17 00:00:00

This study proposes a sensing method using a millimeter-wave array radar and a depth camera to measure pulse waves at multiple sites on the human body. Using a three-dimensional shape model of the target human body measured by the depth camera, the method identifies reflection sites on the body through electromagnetic scattering simulation. On the basis of the simulation, the radar system can be positioned at a suitable location for measuring pulse waves depending on the posture of the target person. Through measurements using radar and depth camera systems, we demonstrate that the proposed method can estimate the body displacement waveform caused by pulse waves accurately, improving the accuracy by 14% compared with a conventional approach without a depth camera. The proposed method can be a key to realizing an accurate and noncontact sensor for monitoring blood pressure.
Dual-Robust Integrated Sensing and Communication: Beamforming under CSI Imperfection and Location Uncertainty
Authors: Wanting Lyu, Songjie Yang, Yue Xiu, Xinyi Chen, Zhongpei Zhang, Chadi Assi, Chau Yuan
Date: 2024-05-17 00:00:00
Tasks: Integrated sensing and communication, ISAC, Robust Design

A dual-robust design of beamforming is investigated in an integrated sensing and communication (ISAC) system.Existing research on robust ISAC waveform design, while proposing solutions to imperfect channel state information (CSI), generally depends on prior knowledge of the target's approximate location to design waveforms. This approach, however, limits the precision in sensing the target's exact location. In this paper, considering both CSI imperfection and target location uncertainty, a novel framework of joint robust optimization is proposed by maximizing the weighted sum of worst-case data rate and beampattern gain. To address this challenging problem, we propose an efficient two-layer iteration algorithm based on S-Procedure and convex hull. Finally, numerical results verify the effectiveness and performance improvement of our dual-robust algorithm, as well as the trade-off between communication and sensing performance.
Flexible Beamforming for Movable Antenna-Enabled Integrated Sensing and Communication
Authors: Wanting Lyu, Songjie Yang, Yue Xiu, Zhongpei Zhang, Chadi Assi, Chau Yuen
Date: 2024-05-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper investigates flexible beamforming design in an integrated sensing and communication (ISAC) network with movable antennas (MAs). A bistatic radar system is integrated into a multi-user multiple-input-single-output (MU-MISO) system, with the base station (BS) equipped with MAs. This enables array response reconfiguration by adjusting the positions of antennas. Thus, a joint beamforming and antenna position optimization problem, namely flexible beamforming, is proposed to maximize communication rate and sensing mutual information (MI). The fractional programming (FP) method is adopted to transform the non-convex objective function, and we alternatively update the beamforming matrix and antenna positions. Karush-Kuhn-Tucker (KKT) conditions are employed to derive the close-form solution of the beamforming matrix, while we propose an efficient search-based projected gradient ascent (SPGA) method to update the antenna positions. Simulation results demonstrate that MAs significantly enhance the ISAC performance when employing our proposed algorithm, achieving a 59.8% performance gain compared to fixed uniform arrays.
On Detecting Low-pass Graph Signals under Partial Observations
Authors: Hoang-Son Nguyen, Hoi-To Wai
Date: 2024-05-16 00:00:00

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.
End-to-end Optimization of Optical Communication Systems based on Directly Modulated Lasers
Authors: Sergio Hernandez F., Christophe Peucheret, Francesco Da Ros, Darko Zibar
Date: 2024-05-16 00:00:00

The use of directly modulated lasers (DMLs) is attractive in low-power, cost-constrained short-reach optical links. However, their limited modulation bandwidth can induce waveform distortion, undermining their data throughput. Traditional distortion mitigation techniques have relied mainly on the separate training of transmitter-side pre-distortion and receiver-side equalization. This approach overlooks the potential gains obtained by simultaneous optimization of transmitter (constellation and pulse shaping) and receiver (equalization and symbol demapping). Moreover, in the context of DML operation, the choice of laser-driving configuration parameters such as the bias current and peak-to-peak modulation current has a significant impact on system performance. We propose a novel end-to-end optimization approach for DML systems, incorporating the learning of bias and peak-to-peak modulation current to the optimization of constellation points, pulse shaping and equalization. The simulation of the DML dynamics is based on the use of the laser rate equations at symbol rates between 15 and 25 Gbaud. The resulting output sequences from the rate equations are used to build a differentiable data-driven model, simplifying the calculation of gradients needed for end-to-end optimization. The proposed end-to-end approach is compared to 3 additional benchmark approaches: the uncompensated system without equalization, a receiver-side finite impulse response equalization approach and an end-to-end approach with learnable pulse shape and nonlinear Volterra equalization but fixed bias and peak-to-peak modulation current. The numerical simulations on the four approaches show that the joint optimization of bias, peak-to-peak current, constellation points, pulse shaping and equalization outperforms all other approaches throughout the tested symbol rates.
Beam Pattern Modulation Embedded Hybrid Transceiver Optimization for Integrated Sensing and Communication
Authors: Boxun Liu, Shijian Gao, Zonghui Yang, Xiang Cheng, Liuqing Yang
Date: 2024-05-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widely utilized hybrid architecture in mmWave systems compromises multiplexing gain due to the constraints of limited radio frequency chains. Moreover, additional sensing functionalities exacerbate the impairment of spectrum efficiency (SE). In this paper, we present an optimized beam pattern modulation-embedded ISAC (BPM-ISAC) transceiver design, which spares one RF chain for sensing and the others for communication. To compensate for the reduced SE, index modulation across communication beams is applied. We formulate an optimization problem aimed at minimizing the mean squared error (MSE) of the sensing beampattern, subject to a symbol MSE constraint. This problem is then solved by sequentially optimizing the analog and digital parts. Both the multi-aperture structure (MAS) and the multi-beam structure (MBS) are considered for the design of the analog part. We conduct theoretical analysis on the asymptotic pairwise error probability (APEP) and the Cram\'er-Rao bound (CRB) of direction of arrival (DoA) estimation. Numerical simulations validate the overall enhanced ISAC performance over existing alternatives.
Optimizing Curved EM Skins for Opportunistic Relaying in Vehicular Networks
Authors: Reza Aghazadeh Ayoubi, Silvia Mura, Dario Tagliaferri, Marouan Mizmizi, Umberto Spagnolini
Date: 2024-05-15 00:00:00

Electromagnetic skins (EMSs) are recognized for enhancing communication performance, spanning from coverage to capacity. While much of the scientific literature focuses on reconfigurable intelligent surfaces that dynamically adjust phase configurations over time, this study takes a different approach by considering low-cost static passive curved EMS (CEMS)s. These are pre-configured during manufacturing to conform to the shape of irregular surfaces, e.g., car doors, effectively transforming them into anomalous mirrors. This design allows vehicles to serve as opportunistic passive relays, mitigating blockage issues in vehicular networks. This paper delves into a novel design method for the phase profile of CEMS based on coarse a-priori distributions of incident and reflection angles onto the surface, influenced by vehicular traffic patterns. A penalty-based method is employed to optimize both the average spectral efficiency (SE) and average coverage probability, and it is compared against a lower-complexity and physically intuitive modular architecture, utilizing a codebook-based discrete optimization technique. Numerical results demonstrate that properly designed CEMS lead to a remarkable improvements in average SE and coverage probability, namely when the direct path is blocked.
Attention-aided Outdoor Localization in Commercial 5G NR Systems
Authors: Guoda Tian, Dino Pjanić, Xuesong Cai, Bo Bernhardsson, Fredrik Tufvesson
Date: 2024-05-15 00:00:00
Tasks: Outdoor Localization

The integration of high-precision cellular localization and machine learning (ML) is considered a cornerstone technique in future cellular navigation systems, offering unparalleled accuracy and functionality. This study focuses on localization based on uplink channel measurements in a fifth-generation (5G) new radio (NR) system. An attention-aided ML-based single-snapshot localization pipeline is presented, which consists of several cascaded blocks, namely a signal processing block, an attention-aided block, and an uncertainty estimation block. Specifically, the signal processing block generates an impulse response beam matrix for all beams. The attention-aided block trains on the channel impulse responses using an attention-aided network, which captures the correlation between impulse responses for different beams. The uncertainty estimation block predicts the probability density function of the UE position, thereby also indicating the confidence level of the localization result. Two representative uncertainty estimation techniques, the negative log-likelihood and the regression-by-classification techniques, are applied and compared. Furthermore, for dynamic measurements with multiple snapshots available, we combine the proposed pipeline with a Kalman filter to enhance localization accuracy. To evaluate our approach, we extract channel impulse responses for different beams from a commercial base station. The outdoor measurement campaign covers Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and a mix of LoS and NLoS scenarios. The results show that sub-meter localization accuracy can be achieved.
Deep Learning-Enabled One-Bit DoA Estimation
Authors: Farhang Yeganegi, Arian Eamaz, Tara Esmaeilbeig, Mojtaba Soltanalian
Date: 2024-05-15 00:00:00
Tasks: compressed sensing, Deep Learning, Quantization

Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We present a compressed sensing formulation for DoA estimation from one-bit data in which estimating target DoAs requires recovering a sparse signal from a limited number of severely quantized linear measurements. In particular, we exploit covariance recovery from one-bit dither samples. To recover the covariance of transmitted signal, the learned iterative shrinkage and thresholding algorithm (LISTA) is employed fed by one-bit data. We demonstrate that the upper bound of estimation performance is governed by the recovery error of the transmitted signal covariance matrix. Through numerical experiments, we demonstrate the proposed LISTA-based algorithm's capability in estimating target locations. The code employed in this study is available online.
A Deep Joint Source-Channel Coding Scheme for Hybrid Mobile Multi-hop Networks
Authors: Chenghong Bian, Yulin Shao, Deniz Gündüz
Date: 2024-05-15 00:00:00

Efficient data transmission across mobile multi-hop networks that connect edge devices to core servers presents significant challenges, particularly due to the variability in link qualities between wireless and wired segments. This variability necessitates a robust transmission scheme that transcends the limitations of existing deep joint source-channel coding (DeepJSCC) strategies, which often struggle at the intersection of analog and digital methods. Addressing this need, this paper introduces a novel hybrid DeepJSCC framework, h-DJSCC, tailored for effective image transmission from edge devices through a network architecture that includes initial wireless transmission followed by multiple wired hops. Our approach harnesses the strengths of DeepJSCC for the initial, variable-quality wireless link to avoid the cliff effect inherent in purely digital schemes. For the subsequent wired hops, which feature more stable and high-capacity connections, we implement digital compression and forwarding techniques to prevent noise accumulation. This dual-mode strategy is adaptable even in scenarios with limited knowledge of the image distribution, enhancing the framework's robustness and utility. Extensive numerical simulations demonstrate that our hybrid solution outperforms traditional fully digital approaches by effectively managing transitions between different network segments and optimizing for variable signal-to-noise ratios (SNRs). We also introduce a fully adaptive h-DJSCC architecture {with both SNR-adaptive (SA) and rate-adaptive (RA) modules} capable of adjusting to different network conditions and achieving diverse rate-distortion objectives, thereby reducing the memory requirements on network nodes.
Distributed PMCW Radar Network in Presence of Phase Noise
Authors: Jialun Kou, Marc Bauduin, André Bourdoux, Sofie Pollin
Date: 2024-05-15 00:00:00

In Frequency Modulated Continuous Waveform (FMCW) radar systems, the phase noise from the Phase-Locked Loop (PLL) can increase the noise floor in the Range-Doppler map. The adverse effects of phase noise on close targets can be mitigated if the transmitter (Tx) and receiver (Rx) employ the same chirp, a phenomenon known as the range correlation effect. In the context of a multi-static radar network, sharing the chirp between distant radars becomes challenging. Each radar generates its own chirp, leading to uncorrelated phase noise. Consequently, the system performance cannot benefit from the range correlation effect. Previous studies show that selecting a suitable code sequence for a Phase Modulated Continuous Waveform (PMCW) radar can reduce the impact of uncorrelated phase noise in the range dimension. In this paper, we demonstrate how to leverage this property to exploit both the mono- and multi-static signals of each radar in the network without having to share any signal at the carrier frequency. The paper introduces a detailed signal model for PMCW radar networks, analyzing both correlated and uncorrelated phase noise effects in the Doppler dimension. Additionally, a solution for compensating uncorrelated phase noise in Doppler is presented and supported by numerical results.
Design and Implementation of mmWave Surface Wave Enabled Fluid Antennas and Experimental Results for Fluid Antenna Multiple Access
Authors: Yuanjun Shen, Boyi Tang, Shuai Gao, Kin-Fai Tong, Hang Wong, Kai-Kit Wong, Yangyang Zhang
Date: 2024-05-15 00:00:00
Tasks: Position

While multiple-input multiple-output (MIMO) technologies continue to advance, concerns arise as to how MIMO can remain scalable if more users are to be accommodated with an increasing number of antennas at the base station (BS) in the upcoming sixth generation (6G). Recently, the concept of fluid antenna system (FAS) has emerged, which promotes position flexibility to enable transmitter channel state information (CSI) free spatial multiple access on one radio frequency (RF) chain. On the theoretical side, the fluid antenna multiple access (FAMA) approach offers a scalable alternative to massive MIMO spatial multiplexing. However, FAMA lacks experimental validation and the hardware implementation of FAS remains a mysterious approach. The aim of this paper is to provide a novel hardware design for FAS and evaluate the performance of FAMA using experimental data. Our FAS design is based on a dynamically reconfigurable "fluid" radiator which is capable of adjusting its position within a predefined space. One single-channel fluid antenna (SCFA) and one double-channel fluid antenna (DCFA) are designed, electromagnetically simulated, fabricated, and measured. The measured radiation patterns of prototypes are imported into channel and network models for evaluating their performance in FAMA. The experimental results demonstrate that in the 5G millimeter-wave (mmWave) bands (24-30 GHz), the FAS prototypes can vary their gain up to an averaged value of 11 dBi. In the case of 4-user FAMA, the double-channel FAS can significantly reduce outage probability by 57% and increases the multiplexing gain to 2.27 when compared to a static omnidirectional antenna.
JNEEG shield for Jetson Nano for real-time EEG signal processing with deep learning
Authors: Ildar Rakhmatulin
Date: 2024-05-14 00:00:00
Tasks: Brain Computer Interface, EEG

The article presents an accessible route into the field of neuroscience through the JNEEG device. This device allows converting the Jetson Nano board into a brain-computer interface, making it easy to measure EEG, EMG, and ECG signals with 8 channels. With Jetson Nano is possible use deep learning for real-time signal processing and feature extraction from EEG in real-time without any data transmission. Over the past decade, the proliferation of artificial intelligence has significantly impacted various industries, including neurobiology. The integration of machine learning techniques has opened avenues for practical applications of EEG signals across technology sectors. This surge in interest has led to the widespread popularity of low-cost brain-computer interface devices capable of recording EEG signals using non-invasive electrodes. JNEEG device demonstrates satisfactory noise levels and accuracy for use in applied tasks with machine learning.
A Mathematical Reconstruction of Endothelial Cell Networks
Authors: Okezue Bell, Anthony Bell
Date: 2024-05-16 00:00:00

Endothelial cells form the linchpin of vascular and lymphatic systems, creating intricate networks that are pivotal for angiogenesis, controlling vessel permeability, and maintaining tissue homeostasis. Despite their critical roles, there is no rigorous mathematical framework to represent the connectivity structure of endothelial networks. Here, we develop a pioneering mathematical formalism called $\pi$-graphs to model the multi-type junction connectivity of endothelial networks. We define $\pi$-graphs as abstract objects consisting of endothelial cells and their junction sets, and introduce the key notion of $\pi$-isomorphism that captures when two $\pi$-graphs have the same connectivity structure. We prove several propositions relating the $\pi$-graph representation to traditional graph-theoretic representations, showing that $\pi$-isomorphism implies isomorphism of the corresponding unnested endothelial graphs, but not vice versa. We also introduce a temporal dimension to the $\pi$-graph formalism and explore the evolution of topological invariants in spatial embeddings of $\pi$-graphs. Finally, we outline a topological framework to represent the spatial embedding of $\pi$-graphs into geometric spaces. The $\pi$-graph formalism provides a novel tool for quantitative analysis of endothelial network connectivity and its relation to function, with the potential to yield new insights into vascular physiology and pathophysiology.
Stochastic two-patch epidemic model with nonlinear recidivism
Authors: Juan G. Calvo, Mario I. Simoy, Juan P. Aparicio, José E. Chacón, Fabio Sanchez
Date: 2024-05-16 00:00:00

We develop a stochastic two-patch epidemic model with nonlinear recidivism to investigate infectious disease dynamics in heterogeneous populations. Extending a deterministic framework, we introduce stochasticity to account for random transmission, recovery, and inter-patch movement fluctuations. We showcase the interplay between local dynamics and migration effects on disease persistence using Monte Carlo simulations and three stochastic approximations-discrete-time Markov chain (DTMC), Poisson, and stochastic differential equations (SDE). Our analysis shows that stochastic effects can cause extinction events and oscillations near critical thresholds like the basic reproduction number, R0, phenomena absent in deterministic models. Numerical simulations highlight source-sink dynamics, where one patch is a persistent infection source while the other experiences intermittent outbreaks.