pwc-archive/papers-with-abstracts huggingface dataset:
DIFNet: SAR RFI suppression based on domain invariant features
Authors: Fuping Fang, Wenhao Lv, Dahai Dai
Date: 2024-03-05 00:00:00

Synthetic aperture radar is a high-resolution two-dimensional imaging radar, however, during the imaging process, SAR is susceptible to intentional and unintentional interference, with radio frequency interference (RFI) being the most common type, leading to a severe degradation in image quality. Although inpainting networks have achieved excellent results, their generalization is unclear, and whether they still work effectively in cross-sensor experiments needs further verification. Through time-frequency analysis of interference signals, we find that interference holds domain invariant features between different sensors. Therefore, this paper reconstructs the loss function and extracts the domain invariant features to improve the generalization. Ultimately, this paper proposes a SAR RFI suppression method based on domain invariant features, and embeds the RFI suppression into SAR imaging process. Compared to traditional notch filtering methods, the proposed approach not only removes interference but also effectively preserves strong scattering targets. Compared to PISNet, our method can extract domain invariant features and holds better generalization ability, and even in the cross-sensor experiment, our method can still achieve excellent results.
STAR-RIS Assisted Wireless-Powered and Backscattering Mobile Edge Computing Networks
Authors: Bin Lyu, Yining Zhang, Pengcheng Chen, Ziwei Liu, Feng Tian
Date: 2024-03-05 00:00:00
Tasks: Edge-computing

Wireless powered and backscattering mobile edge computing (WPB-MEC) network is a novel network paradigm to supply energy supplies and computing resource to wireless sensors (WSs). However, its performance is seriously affected by severe attenuations and inappropriate assumptions of infinite computing capability at the hybrid access point (HAP). To address the above issues, in this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided scheme for boosting the performance of WPB-MEC network under the constraint of finite computing capability. Specifically, energy-constrained WSs are able to offload tasks actively or passively from them to the HAP. In this process, the STAR-RIS is utilized to improve the quantity of harvested energy and strengthen the offloading efficiency by adapting its operating protocols. We then maximize the sum computational bits (SCBs) under the finite computing capability constraint. To handle the solving challenges, we first present interesting results in closed-form and then design a block coordinate descent (BCD) based algorithm, ensuring a near-optimal solution. Finally, simulation results are provided to confirm that our proposed scheme can improve the SCBs by 9.9 times compared to the local computing only scheme.
An Optimal Baseband Delay-Based Beam Squint Removal Scheme across a Range of Steering Angles for Digital Wideband Beamformers in Radars
Authors: Neeraja P. K., Bindiya T. S., Raghu C. V
Date: 2024-03-05 00:00:00

This paper is an attempt to mitigate the beam squint happening due to frequency-dependent phase shifts in the wideband beamforming scenario, specifically in radar applications. The estimation of the direction of arrival is significant for precise target detection in radars. The undesirable beam squint effect due to the phase shift-only mechanism in conventional phased array systems, which becomes exacerbated when dealing with wide bandwidth signals, is analyzed for a large set of steering angles in this paper. An optimum baseband delay combined with the phase shift technique is proposed for wideband radar beamforming to mitigate beam squint effectively. This technique has been demonstrated to function properly with 1-GHz carrier frequency for signals with wide bandwidths of up to +/-250MHz and for steering angles ranging from 0 to 90 degrees.
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment
Authors: Cunyi Yin, Xiren Miao, Jing Chen, Hao Jiang, Deying Chen, Yixuan Tong, Shaocong Zheng
Date: 2024-03-05 00:00:00
Tasks: Activity Recognition, Domain Adaptation, Human Activity Recognition

Low-resolution infrared-based human activity recognition (HAR) attracted enormous interests due to its low-cost and private. In this paper, a novel semi-supervised crossdomain neural network (SCDNN) based on 8 $\times$ 8 low-resolution infrared sensor is proposed for accurately identifying human activity despite changes in the environment at a low-cost. The SCDNN consists of feature extractor, domain discriminator and label classifier. In the feature extractor, the unlabeled and minimal labeled target domain data are trained for domain adaptation to achieve a mapping of the source domain and target domain data. The domain discriminator employs the unsupervised learning to migrate data from the source domain to the target domain. The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data. Experimental results show that the proposed method achieves 92.12\% accuracy for recognition of activities in the target domain by migrating the source and target domains. The proposed approach adapts superior to cross-domain scenarios compared to the existing deep learning methods, and it provides a low-cost yet highly adaptable solution for cross-domain scenarios.
Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives
Authors: Zhiqing Wei, Haotian Liu, Zhiyong Feng, Huici Wu, Fan Liu, Qixun Zhang, Yucong Du
Date: 2024-03-05 00:00:00
Tasks: Integrated sensing and communication, ISAC

With the emerging Integrated Sensing and Communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach satisfying the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep cooperation in ISAC system including three perspectives is investigated. In the microscopic perspective, namely, within a single node, the sensing information carried by time-frequency-space-code domain resources is processed, such as phase compensation, coherent accumulation and other operations, thereby improving the sensing accuracy. In the mesoscopic perspective, the sensing accuracy could be improved through the cooperation of multiple nodes. We explore various multi-node cooperative sensing scenarios and present the corresponding challenges and future research trends. In the macroscopic perspective, the massive number of infrastructures from the same operator or different operators could perform cooperative sensing to extend the sensing coverage and improve the sensing continuity. We investigate network architecture, target tracking methods, and the large-scale sensing assisted digital twin construction. Simulation results demonstrate the superiority of multi-nodes and multi-resources cooperative sensing over single resource or node sensing. This article may provide a deep and comprehensive view on the cooperative sensing in ISAC system to enhance the performance of sensing, supporting the applications of IoE.
A Tunable Reflection Surface with Independently Variable Phase and Slope
Authors: Omran Abbas, Anas Chaaban, Loic Markley
Date: 2024-03-04 00:00:00

A reconfigurable intelligent surface (RIS) is an essential component in the architecture of the next generation of wireless communication systems. An RIS is deployed to provide a controllability to the multi-path environment between the transmitter and the receiver, which becomes critical when the line-of-sight signal between them is blocked. In this work, we design an electrically tunable linearly polarized RIS at 2.5 GHz that yields a controllable reflection phase and phase-frequency slope; in other words, we add tunability of the phase-frequency slope to the tunability of the resonance center frequency. The proposed design consists of two layers of unit cells placed over a ground plane, with dog-bone-shaped elements in the top layer and patch elements in the bottom layer. Each patch and dog-bone element is loaded with a varactor, whose reverse bias voltage is controlled to provide a phase-frequency profile with a slope value of 9 degrees/MHz or 0.95 degrees/MHz, and a phase shift range of 320 degrees.
Target Localization in Cooperative ISAC Systems: A Scheme Based on 5G NR OFDM Signals
Authors: Zhenkun Zhang, Hong Ren, Cunhua Pan, Sheng Hong, Dongming Wang, Jiangzhou Wang, Xiaohu You
Date: 2024-03-04 00:00:00
Tasks: CPU, Integrated sensing and communication, ISAC

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-bearing orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we propose a twodimensional fast Fourier transform (2D-FFT)-based algorithm to measure the ranges of scattered paths induced by targets, through the extraction of delay and Doppler information from the sensing channels between APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering the potential occurrence of ill-conditioned measurements with large error during the extraction of time-frequency information, we propose an efficient algorithm to match the range measurements with the targets while eliminating ill-onditioned measurements, achieving high-accuracy target localization. In addition, based on the transmission configurations defined in the fifth generation (5G) standards, we elucidate the performance trade-offs in both communication and sensing, and extend the proposed sensing scheme for general scenarios. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.
Continuous emission ultrasound: a new paradigm to ultrafast ultrasound imaging
Authors: A. Adam, B. Nicolas, A. Basarab, H. Liebgott
Date: 2024-03-04 00:00:00

Current imaging techniques in echography rely on the pulse-echo (PE) paradigm which provides a straight-forward access to the in-depth structure of tissues. They inherently face two major challenges: the limitation of the pulse repetition frequency, directly linked to the imaging framerate, and, due to the emission scheme, their blindness to the phenomena that happen in the medium during the majority of the acquisition time. To overcome these limitations, we propose a new paradigm for ultrasound imaging, denoted by continuous emission ultrasound imaging (CUEI) \cite{CEUIpatent2023}, for a single input single output (SISO) device. A continuous insonification of the medium is done by the probe using a coded waveform inspired from the radar and sonar literature. A framework coupling a sliding window approach (SWA) and pulse compression methods processes the recorded echoes to rebuild a motion-mode (M-mode) image from the medium with a high temporal resolution compared to state-of-the-art ultrafast imaging methods. A study on realistic simulated data, with regards to the motion of the medium, has been carried out and, achieved results assess an unequivocal improvement of the slow time frequency up to, at least, two orders of magnitude compared to ultrafast US imaging methods. This enhancement leads, therefore, to a ten times improvement in the temporal separability of the imaging system. In addition, it demonstrates the capability of CEUI to catch relatively short and quick events, in comparison to the imaging period of PE methods, at any instant of the acquisition.
Pseudo-inverse reconstruction of bandlimited signals from nonuniform generalized samples with orthogonal kernels
Authors: Nguyen T. Thao, Dominik Rzepka, Marek Miskowicz
Date: 2024-03-04 00:00:00

Contrary to the traditional pursuit of research on nonuniform sampling of bandlimited signals, the objective of the present paper is not to find sampling conditions that permit perfect reconstruction, but to perform the best possible signal recovery from any given set of nonuniform samples, whether it is finite as in practice, or infinite to achieve the possibility of unique reconstruction in $L^2({\mathbb R})$. This leads us to consider the pseudo-inverse of the whole sampling map as a linear operator of Hilbert spaces. We propose in this paper an iterative algorithm that systematically performs this pseudo-inversion under the following conditions: (i) the input lies in some closed space $\mathscr A$ (such as a space of bandlimited functions); (ii) the samples are formed by inner product of the input with given kernel functions; (iii) these functions are orthogonal at least in a Hilbert space $\mathscr H$ that contains $\mathscr A$. This situation turns out to appear in certain time encoders that are part of the increasingly important area of event-based sampling. As a result of pseudo-inversion, we systematically achieve perfect reconstruction whenever the samples uniquely characterize the input, we obtain minimal-norm estimates when the sampling is insufficient, and the reconstruction errors are controlled in the case of noisy sampling. The algorithm consists in alternating two projections according to the general method of projections onto convex sets (POCS) and can be implemented by iterating time-varying discrete-time filtering. We finally show that our signal and sampling assumptions appear in a nontrivial manner in other existing problems of data acquisition. This includes multi-channel time encoding where $\mathscr H$ is of the type $L^2({\mathbb R})^M$, and traditional point sampling with the adoption of a Sobolev space $\mathscr H$.
Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems
Authors: Yan Yang, Shuping Dang, Miaowen Wen, Bo Ai, Rose Qingyang Hu
Date: 2024-03-02 00:00:00

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.
Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI
Authors: Zhiyuan He, Ke Deng, Jiangchao Gong, Yi Zhou, DeSheng Wang
Date: 2024-03-02 00:00:00
Tasks: Diversity, Indoor Localization, Transfer Learning

Passive indoor localization, integral to smart buildings, emergency response, and indoor navigation, has traditionally been limited by a focus on single-target localization and reliance on multi-packet CSI. We introduce a novel Multi-target loss, notably enhancing multi-person localization. Utilizing this loss function, our instantaneous CSI-ResNet achieves an impressive 99.21% accuracy at 0.6m precision with single-timestamp CSI. A preprocessing algorithm is implemented to counteract WiFi-induced variability, thereby augmenting robustness. Furthermore, we incorporate Nuclear Norm-Based Transfer Pre-Training, ensuring adaptability in diverse environments, which provides a new paradigm for indoor multi-person localization. Additionally, we have developed an extensive dataset, surpassing existing ones in scope and diversity, to underscore the efficacy of our method and facilitate future fingerprint-based localization research.
Multi-Source Interactive Resilient Fusion Algorithm Based on RIEKF
Authors: Ye Xiaoyu, Song Fujun, Zhu Xiaohu, Zeng Qinghua
Date: 2024-03-02 00:00:00
Tasks: Fault Diagnosis, State Estimation

As the number of heterogeneous redundant sensors on unmanned aerial vehicle (UAV) increases, onboard sensors require a more rational and efficient credibility evaluation system and a resilient fusion framework to achieve the essence of seamless sensor group switching. A simple and efficient sensor credibility evaluation system is proposed to guide the selection of the optimal multi-source sensor submodel combination, thereby providing key model prior knowledge for multi-source resilient fusion. Furthermore, a multi-model interactive resilient fusion framework based on RIEKF is proposed, utilizing the defined sensor credibility indexes to guide the design of the model transition probability matrix, thereby reducing the sensitivity of submodel weights to fusion stability and solving the problem of the model transition matrix lacking a basis for adjustment. Model weights are updated in real time through credibility prior information and submodel posterior probabilities, thus leveraging the adaptive resilience advantage between models to achieve seamless switching between submodels in complex environments. Experimental results show that the algorithm presented in this paper, without using any sensor fault diagnosis and isolation logic, without setting any complex detection timing and thresholds, demonstrates a resilience advantage, thereby enhancing the adaptability of the state estimation system in complex environments.
Variational Bayesian Learning Based Localization and Channel Reconstruction in RIS-aided Systems
Authors: Yunfei Li, Yiting Luo, Xianda Wu, Zheng Shi, Shaodan Ma, Guanghua Yang
Date: 2024-03-02 00:00:00

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparser angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms.
Degradation-Invariant Music Indexing
Authors: Rémi Mignot, Geoffroy Peeters
Date: 2024-03-01 00:00:00

For music indexing robust to sound degradations and scalable for big music catalogs, this scientific report presents an approach based on audio descriptors relevant to the music content and invariant to sound transformations (noise addition, distortion, lossy coding, pitch/time transformations, or filtering e.g.). To achieve this task, one of the key point of the proposed method is the definition of high-dimensional audio prints, which are intrinsically (by design) robust to some sound degradations. The high dimensionality of this first representation is then used to learn a linear projection to a sub-space significantly smaller, which reduces again the sensibility to sound degradations using a series of discriminant analyses. Finally, anchoring the analysis times on local maxima of a selected onset function, an approximative hashing is done to provide a better tolerance to bit corruptions, and in the same time to make easier the scaling of the method.
Channel Measurements and Modeling for Dynamic Vehicular ISAC Scenarios at 28 GHz
Authors: Zhengyu Zhang, Ruisi He, Bo Ai, Mi Yang, Xuejian Zhang, Ziyi Qi, Yuan Yuan
Date: 2024-03-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a promising technology for 6G, with the goal of providing end-to-end information processing and inherent perception capabilities for future communication systems. Within ISAC emerging application scenarios, vehicular ISAC technologies have the potential to enhance traffic efficiency and safety through integration of communication and synchronized perception abilities. To establish a foundational theoretical support for vehicular ISAC system design and standardization, it is necessary to conduct channel measurements, and modeling to obtain a deep understanding of the radio propagation. In this paper, a dynamic statistical channel model is proposed for vehicular ISAC scenarios, incorporating Sensing Multipath Components (S-MPCs) and Clutter Multipath Components (C-MPCs), which are identified by the proposed tracking algorithm. Based on actual vehicular ISAC channel measurements at 28 GHz, time-varying sensing characteristics in front, left, and right directions are investigated. To model the dynamic evolution process of channel, number of new S-MPCs, lifetimes, initial power and delay positions, dynamic variations within their lifetimes, clustering, power decay, and fading of C-MPCs are statistically characterized. Finally, the paper provides implementation of dynamic vehicular ISAC model and validates it by comparing key simulation statistics between measurements and simulations.