pwc-archive/papers-with-abstracts huggingface dataset:
Full-Duplex V2X Integrated Sensing and Communication Scenario: Stochastic geometry, Monte-Carlo, and Ray-Tracing Comparison
Authors: François De Saint Moulin, Simon Demey, Charles Wiame, Luc Vandendorpe, Claude Oestges
Date: 2024-07-09 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, performance of an Integrated Sensing and Communication (ISAC) Vehicle-to-Everything (V2X) scenario is evaluated, in which a vehicle simultaneously detects the next vehicle ahead while receiving a communication signal from a RoadSide Unit (RSU) of the infrastructure. Univariate and joint radar and communication performance metrics are evaluated within three different frameworks, namely the Stochastic Geometry (SG), Monte-Carlo (MC), and Ray-Tracing (RT) frameworks. The parameters of the system model are extracted from the RT simulations, and the metrics are compared to assess the accuracy of the SG framework. It is shown that the SG and MC system models are relevant w.r.t. RT simulations for the evaluation of univariate communication and sensing metrics, but larger discrepancies are observed for the joint metrics.
Experimental Demonstration of 16D Voronoi Constellation with Two-Level Coding over 50km Four-Core Fiber
Authors: Can Zhao, Bin Chen, Jiaqi Cai, Zhiwei Liang, Yi Lei, Junjie Xiong, Lin Ma, Daohui Hu, Lin Sun, Gangxiang Shen
Date: 2024-07-09 00:00:00

A 16-dimensional Voronoi constellation concatenated with multilevel coding is experimentally demonstrated over a 50km four-core fiber transmission system. The proposed scheme reduces the required launch power by 6dB and provides a 17dB larger operating range than 16QAM with BICM at the outer HD-FEC BER threshold.
UAV Formation and Resource Allocation Optimization for Communication-Assisted 3D InSAR Sensing
Authors: Mohamed-Amine Lahmeri, Víctor Mustieles-Pérez, Martin Vossiek, Gerhard Krieger, Robert Schober
Date: 2024-07-09 00:00:00
Tasks: Stochastic Optimization

In this paper, we investigate joint unmanned aerial vehicle (UAV) formation and resource allocation optimization for communication-assisted three-dimensional (3D) synthetic aperture radar (SAR) sensing. We consider a system consisting of two UAVs that perform bistatic interferometric SAR (InSAR) sensing for generation of a digital elevation model (DEM) and transmit the radar raw data to a ground station (GS) in real time. To account for practical 3D sensing requirements, we use non-conventional sensing performance metrics, such as the SAR interferometric coherence, i.e., the local cross-correlation between the two co-registered UAV SAR images, the point-to-point InSAR relative height error, and the height of ambiguity, which together characterize the accuracy with which the InSAR system can determine the height of ground targets. Our objective is to jointly optimize the UAV formation, speed, and communication power allocation for maximization of the InSAR coverage while satisfying energy, communication, and InSAR-specific sensing constraints. To solve the formulated non-smooth and non-convex optimization problem, we divide it into three sub-problems and propose a novel alternating optimization (AO) framework that is based on classical, monotonic, and stochastic optimization techniques. The effectiveness of the proposed algorithm is validated through extensive simulations and compared to several benchmark schemes. Furthermore, our simulation results highlight the impact of the UAV-GS communication link on the flying formation and sensing performance and show that the DEM of a large area of interest can be mapped and offloaded to ground successfully, while the ground topography can be estimated with centimeter-scale precision. Lastly, we demonstrate that a low UAV velocity is preferable for InSAR applications as it leads to better sensing accuracy.
Off-grid Channel Estimation for Orthogonal Delay-Doppler Division Multiplexing Using Grid Refinement and Adjustment
Authors: Yaru Shan, Akram Shafie, Jinhong Yuan, Fanggang Wang
Date: 2024-07-09 00:00:00
Tasks: Bayesian Inference

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) has been recently proposed as a promising multicarrier modulation scheme to tackle Doppler spread in high-mobility environments. Accurate channel estimation is of paramount importance to guarantee reliable communication for the ODDM, especially when the delays and Dopplers of the propagation paths are off-grid. In this paper, we propose a novel grid refinement and adjustment-based sparse Bayesian inference (GRASBI) scheme for DD domain channel estimation. The GRASBI involves first formulating the channel estimation problem as a sparse signal recovery through the introduction of a virtual DD grid. Then, an iterative process is proposed that involves (i) sparse Bayesian learning to estimate the channel parameters and (ii) a novel grid refinement and adjustment process to adjust the virtual grid points. The grid adjustment in GRASBI relies on the maximum likelihood principle to attain the adjustment and utilizes refined grids that have much higher resolution than the virtual grid. Moreover, a low-complexity grid refinement and adjustment-based channel estimation scheme is proposed, that can provides a good tradeoff between the estimation accuracy and the complexity. Finally, numerical results are provided to demonstrate the accuracy and efficiency of the proposed channel estimation schemes.
Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives
Authors: Ark Ifeanyi, Jamie Coble
Date: 2024-07-09 00:00:00
Tasks: Anomaly Detection, Decoder, Fault Detection

This paper explores the optimization of fault detection and diagnostics (FDD) in the Control Rod Drive System (CRDS) of GE-Hitachi's BWRX-300 small modular reactor (SMR), focusing on the electrically powered fine motion control rod drive (FMCRD) servomotors. Leveraging the coordinated motion of multiple FMCRDs for control rod adjustments, the study proposes a deep learning approach, utilizing one-dimensional convolutional neural network (1D CNN)-based autoencoders for anomaly detection and encoder-decoder structured 1D CNN classifiers for fault classification. Simulink models simulate normal and fault operations, monitoring electric current and electromagnetic torque. The training of the fault isolation and fault classification models is optimized. Various optimizers, including Adaptive Moment Estimation (Adam), Nesterov Adam (Nadam), Stochastic Gradient Descent (SGD), and Root Mean Square Propagation (RMSProp), are evaluated, with Nadam demonstrating a relatively superior performance across the isolation and classification tasks due to its adaptive gradient and Nesterov components. The research underscores the importance of considering the number of runs (each run has a different set of initial model parameters) as a hyperparameter during empirical optimizer comparisons and contributes insights crucial for enhancing FDD in SMR control systems and for the application of 1D CNN to FDD.
RS-BNN: A Deep Learning Framework for the Optimal Beamforming Design of Rate-Splitting Multiple Access
Authors: Yiwen Wang, Yijie Mao, Sijie Ji
Date: 2024-07-09 00:00:00

Rate splitting multiple access (RSMA) relies on beamforming design for attaining spectral efficiency and energy efficiency gains over traditional multiple access schemes. While conventional optimization approaches such as weighted minimum mean square error (WMMSE) achieve suboptimal solutions for RSMA beamforming optimization, they are computationally demanding. A novel approach based on fractional programming (FP) has unveiled the optimal beamforming structure (OBS) for RSMA. This method, combined with a hyperplane fixed point iteration (HFPI) approach, named FP-HFPI, provides suboptimal beamforming solutions with identical sum rate performance but much lower computational complexity compared to WMMSE. Inspired by such an approach, in this work, a novel deep unfolding framework based on FP-HFPI, named rate-splitting-beamforming neural network (RS-BNN), is proposed to unfold the FP-HFPI algorithm. Numerical results indicate that the proposed RS-BNN attains a level of performance closely matching that of WMMSE and FP-HFPI, while dramatically reducing the computational complexity.
Fast Signal Interpolation Through Zero-padding and FFT/IFFT
Authors: Zijun Gong
Date: 2024-07-09 00:00:00

Based on the sampling theorem, interpolation should be conducted by employing the sinc functions as the kernels. Inspired by the fact that the discrete Fourier transform (DFT) is sampled from the discrete time Fourier transform, a fast signal interpolation algorithm based on zero-padding and fast Fourier transform (FFT) and inverse FFT (IFFT) is presented. This algorithm gives a good approximate of the ideal interpolation, in spite of the windowing effect. The fundamental difference of this algorithm and the ideal sinc interpolation is unveiled, and shown to be deeply rooted in the connection of the sinc function and the Dirichlet function.
Soli-enabled Noncontact Heart Rate Detection for Sleep and Meditation Tracking
Authors: Luzhou Xu, Jaime Lien, Haiguang Li, Nicholas Gillian, Rajeev Nongpiur, Jihan Li, Qian Zhang, Jian Cui, David Jorgensen, Adam Bernstein, Lauren Bedal, Eiji Hayashi, Jin Yamanaka, Alex Lee, Jian Wang, D Shin, Ivan Poupyrev, Trausti Thormundsson, Anupam Pathak, Shwetak Patel
Date: 2024-07-08 00:00:00

Heart rate (HR) is a crucial physiological signal that can be used to monitor health and fitness. Traditional methods for measuring HR require wearable devices, which can be inconvenient or uncomfortable, especially during sleep and meditation. Noncontact HR detection methods employing microwave radar can be a promising alternative. However, the existing approaches in the literature usually use high-gain antennas and require the sensor to face the user's chest or back, making them difficult to integrate into a portable device and unsuitable for sleep and meditation tracking applications. This study presents a novel approach for noncontact HR detection using a miniaturized Soli radar chip embedded in a portable device (Google Nest Hub). The chip has a $6.5 \mbox{ mm} \times 5 \mbox{ mm} \times 0.9 \mbox{ mm}$ dimension and can be easily integrated into various devices. The proposed approach utilizes advanced signal processing and machine learning techniques to extract HRs from radar signals. The approach is validated on a sleep dataset (62 users, 498 hours) and a meditation dataset (114 users, 1131 minutes). The approach achieves a mean absolute error (MAE) of $1.69$ bpm and a mean absolute percentage error (MAPE) of $2.67\%$ on the sleep dataset. On the meditation dataset, the approach achieves an MAE of $1.05$ bpm and a MAPE of $1.56\%$. The recall rates for the two datasets are $88.53\%$ and $98.16\%$, respectively. This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.
Earable and Wrist-worn Setup for Accurate Step Counting Utilizing Body-Area Electrostatic Sensing
Authors: Sizhen Bian, Rakita Strahinja, Philipp Schilk, Clénin Marc-André, Silvano Cortesi, Elio Reinschmidt, Kanika Dheman, Michele Magno
Date: 2024-07-08 00:00:00

Step-counting has been widely implemented in wrist-worn devices and is accepted by end users as a quantitative indicator of everyday exercise. However, existing counting approach (mostly on wrist-worn setup) lacks robustness and thus introduces inaccuracy issues in certain scenarios like brief intermittent walking bouts and random arm motions or static arm status while walking (no clear correlation of motion pattern between arm and leg). This paper proposes a low-power step-counting solution utilizing the body area electric field acquired by a novel electrostatic sensing unit, consuming only 87.3 $\mu$W of power, hoping to strengthen the robustness of current dominant solution. We designed two wearable devices for on-the-wrist and in-the-ear deployment and collected body-area electric field-derived motion signals from ten volunteers. Four walking scenarios are considered: in the parking lot/shopping center with/without pushing the shopping trolley. The step-counting accuracy from the prototypes shows better accuracy than the commercial wrist-worn devices (e.g.,96% of the wrist- and ear-worn prototype vs. 66% of the Fitbit when walking in the shopping center while pushing a shopping trolley). We finally discussed the potential and limitations of sensing body-area electric fields for wrist-worn and ear-worn step-counting and beyond.
A Scalable Architecture for Future Regenerative Satellite Payloads
Authors: Olfa Ben Yahia, Zineb Garroussi, Brunilde Sansò, Jean-François Frigon, Stéphane Martel, Antoine Lesage-Landry, Gunes Karabulut Kurt
Date: 2024-07-08 00:00:00

This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexibility to adapt to increasing data demands and uneven traffic. To overcome these challenges, we present a novel architecture for future regenerative and programmable satellite payloads and utilize interconnected modem banks to promote higher scalability and flexibility. We formulate an optimization problem to efficiently manage traffic among these modem banks and balance the load. Additionally, we provide comparative numerical simulation results, considering end-to-end delay and packet loss analysis. The results illustrate that our proposed architecture maintains lower delays and packet loss even with higher traffic demands and smaller buffer sizes.
CA-FedRC: Codebook Adaptation via Federated Reservoir Computing in 5G NR
Authors: Ziqiang Ye, Sikai Liao, Yulan Gao, Shu Fang, Yue Xiao, Ming Xiao, Saviour Zammit
Date: 2024-07-08 00:00:00

With the burgeon deployment of the fifth-generation new radio (5G NR) networks, the codebook plays a crucial role in enabling the base station (BS) to acquire the channel state information (CSI). Different 5G NR codebooks incur varying overheads and exhibit performance disparities under diverse channel conditions, necessitating codebook adaptation based on channel conditions to reduce feedback overhead while enhancing performance. However, existing methods of 5G NR codebooks adaptation require significant overhead for model training and feedback or fall short in performance. To address these limitations, this letter introduces a federated reservoir computing framework designed for efficient codebook adaptation in computationally and feedback resource-constrained mobile devices. This framework utilizes a novel series of indicators as input training data, striking an effective balance between performance and feedback overhead. Compared to conventional models, the proposed codebook adaptation via federated reservoir computing (CA-FedRC), achieves rapid convergence and significant loss reduction in both speed and accuracy. Extensive simulations under various channel conditions demonstrate that our algorithm not only reduces resource consumption of users but also accurately identifies channel types, thereby optimizing the trade-off between spectrum efficiency, computational complexity, and feedback overhead.
Deep Learning-based CSI Feedback in Wi-Fi Systems
Authors: Fan Qi, Jiajia Guo, Yiming Cui, Xiangyi Li, Chao-Kai Wen, Shi Jin
Date: 2024-07-08 00:00:00
Tasks: Decoder, Deep Learning

In Wi-Fi systems, channel state information (CSI) plays a crucial role in enabling access points to execute beamforming operations. However, the feedback overhead associated with CSI significantly hampers the throughput improvements. Recent advancements in deep learning (DL) have transformed the approach to CSI feedback in cellular systems. Drawing inspiration from the successes witnessed in the realm of mobile communications, this paper introduces a DL-based CSI feedback framework, named EFNet, tailored for Wi-Fi systems. The proposed framework leverages an autoencoder to achieve precise feedback with minimal overhead. The process involves the station utilizing the encoder to compress and quantize a series of matrices into codeword bit streams, which are then fed back to the access point. Subsequently, the decoder installed at the AP reconstructs beamforming matrices from these bit streams. We implement the EFNet system using standard Wi-Fi equipment operating in the 2.4 GHz band. Experimental findings in an office environment reveal a remarkable 80.77% reduction in feedback overhead compared to the 802.11ac standard, alongside a significant boost in net throughput of up to 30.72%.
Joint Beamforming and Antenna Design for Near-Field Fluid Antenna System
Authors: Yixuan Chen, Mingzhe Chen, Hao Xu, Zhaohui Yang, Kai-Kit Wong, Zhaoyang Zhang
Date: 2024-07-08 00:00:00
Tasks: Position

In this letter, we study the energy efficiency maximization problem for a fluid antenna system (FAS) in near field communications. Specifically, we consider a point-to-point near-field system where the base station (BS) transmitter has multiple fixed-position antennas and the user receives the signals with multiple fluid antennas. Our objective is to jointly optimize the transmit beamforming of the BS and the fluid antenna positions at the user for maximizing the energy efficiency. Our scheme is based on an alternating optimization algorithm that iteratively solves the beamforming and antenna position subproblems. Our simulation results validate the performance improvement of the proposed algorithm and confirm the effectiveness of FAS.
Orthogonal Time Frequency Space with Delay-Doppler Alignment Modulation
Authors: Xianda Liu, Zhiwen Zhou, Zhiqiang Xiao, Yong Zeng
Date: 2024-07-08 00:00:00

Delay-Doppler alignment modulation (DDAM) is a novel technique to mitigate time-frequency doubly selective channels by leveraging the high spatial resolution offered by large antenna arrays and multi-path sparsity of millimeter wave (mmWave) and TeraHertz (THz) channels. By introducing per-path delay and Doppler compensations, followed by path-based beamforming, it is possible to reshape the channel features with significantly reduced channel delay and Doppler spreads. This offers new degrees-of-freedom for waveform designs such as orthogonal time frequency space (OTFS), since the reshaped channel can significantly relax the constraints on OTFS parameter selection and reduce the complexity of signal detection at the receiver. Therefore, in this paper, by combing DDAM with OTFS, we propose a novel technique termed DDAM-OTFS. Two implementation schemes are introduced for DDAM-OTFS, namely path-based alignment and bin-based alignment. Simulation results are provided to demonstrate the superior performance of the proposed DDAM-OTFS in terms of spectral efficiency (SE) and peak-to-average power ratio (PAPR) compared to the conventional OTFS.
A Reinforcement Learning Approach for Wildfire Tracking with UAV Swarms
Authors: Carles Diaz-Vilor, Angel Lozano, Hamid Jafarkhani
Date: 2024-07-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Suitably equipped with cameras and sensors, uncrewed aerial vehicles (UAVs) can be instrumental for wildfire prediction, tracking, and monitoring, provided that uninterrupted connectivity can be guaranteed even if some of the ground access points (APs) are damaged by the fire itself. A cell-free network structure, with UAVs connecting to a multiplicity of APs, is therefore ideal in terms of resilience. This work proposes a trajectory optimization framework for a UAV swarm tracking a wildfire while maintaining cell-free connectivity with ground APs. Such optimization entails a constant repositioning of the multiplicity of UAVs as the fire evolves to ensure that the best possible view is acquired and transmitted reliably, while respecting altitude limits, avoiding collisions, and proceeding to recharge batteries as needed. Given the complexity and time-varying nature of this multi-UAV trajectory optimization, reinforcement learning is leveraged, specifically the twin-delayed deep deterministic policy gradient algorithm. The approach is shown to be highly effective for wildfire tracking and coverage and could be likewise applicable to survey other natural and man-made phenomena, including weather events, earthquakes, or chemical spills.
Interference Management in MIMO-ISAC Systems: A Transceiver Design Approach
Authors: Yangyang Niu, Zhiqing Wei, Dingyou Ma, Xiaoyu Yang, Huici Wu, Zhiyong Feng, Jianhua Yuan
Date: 2024-07-07 00:00:00
Tasks: Integrated sensing and communication, ISAC, Management

The integrated sensing and communication (ISAC) system under multi-input multi-output (MIMO) architecture achieves dual functionalities of sensing and communication on the same platform by utilizing spatial gain, which provides a feasible paradigm facing spectrum congestion. However, the dual functionalities of sensing and communication operating simultaneously in the same platform bring severe interference in the ISAC systems. Facing this challenge, we propose a joint optimization framework for transmit beamforming and receive filter design for ISAC systems with MIMO architecture. We aim to maximize the signal-to-clutter-plus-noise ratio (SCNR) at the receiver while considering various constraints such as waveform similarity, power budget, and communication performance requirements to ensure the integration of the dual functionalities. In particular, the overall transmit beamforming is refined into sensing beamforming and communication beamforming, and a quadratic transformation (QT) is introduced to relax and convert the complex non-convex optimization objective. An efficient algorithm based on covariance matrix tapers (CMT) is proposed to restructure the clutter covariance matrix considering the mismatched steering vector, thereby improving the robustness of the ISAC transceiver design. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.
Fluid-Antenna Enhanced Integrated Sensing and Communication: Joint Antenna Positioning and Beamforming Design
Authors: Tian Hao, Changxin Shi, Yinghong Guo, Bin Xia, Feng Yang
Date: 2024-07-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper investigates a fluid antenna (FA) enhanced integrated sensing and communication (ISAC) system consisting of a base station (BS), multiple single-antenna communication users, and one point target, where the BS is equipped with FAs to enhance both the communication and sensing performance. First, we formulate a problem that maximizes the radar signal-to-noise ratio (SNR) by jointly optimizing the FAs' positions and transmit beamforming matrix. Then, to tackle this highly non-convex problem, we present efficient algorithms by using alternating optimization (AO), successive convex approximation (SCA), and semi-definite relaxation (SDR). Numerical results demonstrate the convergence behavior and effectiveness of the proposed algorithm.