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Sensing Within Ultra-Short Duration: Extended Subspace algorithms With Insufficient Snapshots

IEEE signal processing LETTERS 2025年 32卷 1490-1494页

作者： Ma, Teng Feng, Yuxuan Xiao, Yue Lei, Xia Poulkov, Vladimir Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Tech Univ Sofia Fac Telecommun Sofia 1000 Bulgaria

In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as multiple signal classification (MUSIC) are hindered by their need for an extensive number of snapshots to accumulate the rank of the spatial covariance matrix (SCM), resulting in poor real-time performance. Moreover, advanced techniques like compressed sensing and machine learning are constrained by requirements for high signal sparsity or suffer from limited generality. To handle these challenges, this paper proposes an innovative extension of subspace theory tailored to insufficient-snapshot scenarios, leveraging the concept of spatio-temporal exchangeability. Based on the defined spatio-temporal correlation predicated on the space translation invariance characteristic of uniform linear arrays, we engineer a pseudo SCM that inherently possesses sufficient rank. This methodology not only resolves the rank-deficiency issue but also fully exploits the array aperture and significantly reduces the noise level. Simulation results are presented, substantiating the feasibility and enhanced performance of the proposed algorithms, marking a significant advancement over existing methodologies.

关键词： Estimation Multiple signal classification Sensors signal processing algorithms Real-time systems Training Matrix converters Eigenvalues and eigenfunctions Classification algorithms Translation Angle estimation spatio-temporal exchangeability multiple signal classification (MUSIC) insufficient snapshots

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YOLO-RLDW: An Algorithm for Object Detection in Aerial Images Under Complex Backgrounds

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IEEE ACCESS 2024年 12卷 128677-128693页

作者： Zhao, Liangjun Liang, Gang Hu, Yueming Xi, Yubin Ning, Feng He, Zhongliang Sichuan Univ Sci & Engn Sch Comp Sci & Engn Yibin 644000 Peoples R China Hainan Univ Sch Trop Agr & Forestry Haikou 570228 Hainan Peoples R China Sichuan Univ Sci & Engn Sch Automat & Informat Engn Yibin 644000 Peoples R China

Aiming at the challenges of low detection accuracy, susceptibility to complex background interference, difficulty in detecting small objects, and multi-scale object issues in aerial images, our proposed an improved YOLOv8-based object detection algorithm, named YOLO-RLDW. Leveraging the advantages of Receptive Field Attention Convolution (RFAConv), we designed a feature extraction module named C2f-RFA to enhance the feature extraction capability for small objects in aerial images. Inspired by the concept of Large Separable Kernel Attention (LSKA), we developed the SPPF-LSKA module, which effectively reduces the interference of aerial backgrounds in object detection. We replaced the YOLOv8 detection head with a Dynamic Head (DyHead), further enhancing the model's generalization and adaptability. Finally, we employed as boundary box regression loss based on a dynamic focusing mechanism, WIoU, as the loss function, which accelerates model convergence while improving the localization capability for multi-scale objects. Experimental results demonstrate that on the VisDrone2021 dataset, the proposed algorithm achieves improvements of 5.5%, 3.9%, 5.4%, and 3.7% in precision (P), recall (R), mean average precision (mAP50), and mAP95, respectively, compared to the original algorithm. On our self-built remote sensing image dataset RSI, the accuracy, recall, and mean average precision reach 94.2%, 91.0%, and 95.4%, respectively, demonstrating good performance in detecting objects in aerial images. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed method.

关键词： Feature extraction Accuracy Task analysis YOLO signal processing algorithms Satellite images Deep learning Aerial image object detection deep learning complex background YOLO-RLDW

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A Communication-Efficient Adaptive Algorithm for Federated Learning Under Cumulative Regret

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 735-743页

作者： Salgia, Sudeep Gabay, Tamir Zhao, Qing Cohen, Kobi Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA Ben Gurion Univ Negev Sch Elect & Comp Engn Beer Sheva 8410501 Israel

We consider the problem of online stochastic optimization in a distributed setting with $M$ clients connected through a central server. We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon. This is in contrast to existing studies which focus on the offline measure of simple regret for learning efficiency. The holistic measure for communication cost also departs from the prevailing approach that separately tackles the communication frequency and the number of bits in each communication round.

关键词： Costs signal processing algorithms Servers Federated learning Optimization Noise measurement Estimation communication efficiency cumulative regret

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Joint Design of Doppler Resilient Unimodular Discrete Phase Sequence Waveform and Receiving Filter for Multichannel Radar

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4207-4221页

作者： Chen, Yun Chen, Zixuan Zhang, Yunhua Yang, Jiefang Li, Dong Chinese Acad Sci Natl Space Sci Ctr CAS Key Lab Microwave Remote Sensing Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Guangdong Shenglu Telecommun Tech Co Ltd Foshan 528100 Peoples R China

Design of discrete phase sequence waveform (DPSW) with desirable co- and cross-ambiguity function (AF) properties has been a longstanding and critical challenge in the field of high-performance multichannel electronic systems, e.g. radar systems. This paper focuses on the joint design of Doppler-resilient DPSW and receiving filter with low weighted integrated sidelobe level (WISL) for multichannel radar system. This design aims to construct DPSWs of "thumbtack" shape and all-zero AFs within the desired Range-Doppler region for both co-channels and cross-channels, respectively. A peak constraint function, i.e. the penalty function, is incorporated into the objective function to control the signal-to-noise ratio loss (SNRL) due to mismatched filtering. In the design, unimodular and discrete phase constraints are imposed on each element of the sequences, while the receiving filters are subject to the energy constraint and the mismatch constraint of SNRL. Different constraints on transmitted sequences and receiving filters make the optimization problem difficult to solve. Here, an alternatively iterative algorithm based on the majorization-minimization (MM) and the coordinate descent (CD) frameworks is proposed to handle the differently constrained optimization problem. Moreover, by incorporating a general acceleration scheme and the fast Fourier transform (FFT), the computational efficiency of the proposed algorithm can be further improved. Simulation and practical experiments are conducted to validate the designed DPSWs showing superior performance when compared to that by the latest and representative methods.

关键词： Radar Filtering algorithms signal processing algorithms Radar imaging Filter banks Filtering theory Radar polarimetry Multichannel radar discrete phase sequences receiving filters Doppler tolerance majorization-minimization coordinate descent alternatively iterative

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Cognitive learning enabled agile optical network

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JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING 2024年第11期16卷 1170-1178页

作者： Cheng, Yijun Chen, Zejun Hu, Zihe Xiang, Meng Yan, Zhijun Qin, Yuwen Fu, Songnian Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan 430074 Peoples R China Guangdong Univ Technol Inst Adv Photon Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Informat Photon Technol Guangzhou 510006 Peoples R China Guangdong Univ Technol Key Lab Photon Technol Integrated Sensing & Commun Minist Educ China Guangzhou 510006 Peoples R China

Nonlinear equalization (NLE) is essential for guaranteeing the performance of an optical network (ON). Effective NLE implementation relies on key parameters of the transmission link, including the modulation format (MF) and the launch power. As ONs become more agile, the parameters of fiber optical transmission need to be adaptive and relevant to the routing condition. Therefore, successful NLE implementation relies on the realization of transmission awareness (TA). Although machine learning-enabled optical performance monitoring (OPM) has been extensively investigated in the past few years, current NLE algorithms cannot autonomously perceive transmission parameters. Furthermore, current TA implementation still needs human intervention to guide the NLE. In addition, existing ML-based OPM and NLE cannot be trained autonomously, leading to the incapability of environmental change and mislabeling. Here, we propose cognitive learning (CL) for TA-guided NLE in agile ONs. We perform an experiment involving 32 Gbaud polarization-division-multiplexed (PDM)-quadrature phase shift keying (QPSK)/16-quadrature amplitude modulation (QAM) transmission over 1500 km of standard single-mode fiber (SSMF) with a variable launch power from 0 to 3 dBm. When a deep neural network (DNN) with amplitude histograms (AHs) as inputs and one step per span-learned digital back-propagation (1stps-LDBP) are developed, the CL simultaneously enables both TA and NLE, with the capability of self-learning, mislabeling resistance, and dynamic adaptation. The proof-of-concept experimental results indicate that both the accuracy of TA and the Q-factor of PDM-16QAM can be improved by 34.8% and 0.84 dB, respectively, when the launch power is 3 dBm. Moreover, the accuracy of TA is enhanced by 35.3%, even when the used data has 30% mislabeling. Therefore, the CL framework can be customized to satisfy various NLE implementations, thereby supporting the adaptive transmission of agile ONs.

关键词： Training Nonlinear optics Heuristic algorithms Monitoring Optical receivers signal processing algorithms Optical polarization Optical modulation Optical computing Adaptation models

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Co-PARAFAC: A Novel Cost-Efficient Scalable Tensor Decomposition Algorithm

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IEEE ACCESS 2024年 12卷 135764-135778页

作者： Shams, Farshad Lottici, Vincenzo Tian, Zhi Univ Pisa Dept Informat Engn I-56126 Pisa Italy George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

This paper proposes a novel tensor decomposition method, cooperative parallel factor (Co-PARAFAC), that is devised to achieve higher accuracy with lower computational complexity and memory requirements than the conventional PARAFAC. The rationale relies on dividing a given tensor, even with a large size, into smaller disjoint sub-tensors, which are independently and parallelly decomposed using the conventional PARAFAC. The intermediate results are then properly merged to obtain the decomposition of the original tensor. As case study, we apply Co-PARAFAC to estimate the uplink channels of RIS-assisted wireless communications. Simulation results corroborate the efficiency of the Co-PARAFAC in achieving significantly lower computational complexity and higher channel estimation accuracy than the conventional PARAFAC. Broadly, the proposed algorithm is advantageous in various fields requiring efficient and high accurate tensor decomposition.

关键词： Tensors Channel estimation Matrix decomposition Wireless communication Accuracy signal processing algorithms Estimation PARAFAC RIS tensor decomposition wireless communications

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A Robust Cooperative Sensing Approach for Incomplete and Contaminated Data

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3945-3957页

作者： Zhou, Rui Pu, Wenqiang You, Ming-Yi Shi, Qingjiang Chinese Univ Hong Kong Shenzhen Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Natl Key Lab Electromagnet Space Secur Jiaxing Peoples R China Tongji Univ Sch Software Engn Shanghai Peoples R China

Cooperative sensing utilizes multiple receivers dispersed across different locations, capitalizing on the advantages of multiple antennas and spatial diversity gain. This mechanism is crucial for monitoring the availability of licensed spectrum for secondary use when free from primary users. However, the efficacy of cooperative sensing relies heavily on the flawless transmission of raw data from cooperating receivers to a fusion center, a condition that may not always be fulfilled in real-world scenarios. This study investigates cooperative sensing in the context where the raw data is compromised by errors introduced during transmission, attributable to a relatively high bit error rate (BER). Consequently, the data received at the fusion center becomes incomplete and contaminated. Conventional multiantenna detectors are not adequately designed to handle such situations. To overcome this, we introduce the missing-data t-distribution generalized likelihood ratio test (mtGLRT) detectors to manage such problematic data at the fusion center. The structured covariance matrices are estimated from this problematic data. Efficient optimization algorithms using the generalized expectation-maximization (GEM) method are developed accordingly. Numerical experiments corroborate the robustness of the proposed cooperative sensing methods.

关键词： Sensors Detectors Imputation Covariance matrices Receivers Bit error rate signal processing algorithms Cooperative sensing transmission error incomplete data contaminated data robust detector

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Accelerating Quadratic Transform and WMMSE

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第11期42卷 3110-3124页

作者： Shen, Kaiming Zhao, Ziping Chen, Yannan Zhang, Zepeng Cheng, Hei Victor Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Swiss Fed Inst Technol Lausanne Lab Intelligent Maintenance & Operat Syst CH-1015 Lausanne Switzerland Aarhus Univ Dept Elect & Comp Engn DK-8200 Aarhus Denmark

Fractional programming (FP) arises in various communications and signal processing problems because several key quantities in these fields are fractionally structured, e.g., the Cram & eacute;r-Rao bound, the Fisher information, and the signal-to-interference-plus-noise ratio (SINR). A recently proposed method called the quadratic transform has been applied to the FP problems extensively. The main contributions of the present paper are two-fold. First, we investigate how fast the quadratic transform converges. To the best of our knowledge, this is the first work that analyzes the convergence rate for the quadratic transform as well as its special case the weighted minimum mean square error (WMMSE) algorithm. Second, we accelerate the existing quadratic transform via a novel use of Nesterov's extrapolation scheme. Specifically, by generalizing the minorization-maximization (MM) approach, we establish a subtle connection between the quadratic transform and the gradient projection, thereby further incorporating the gradient extrapolation into the quadratic transform to make it converge more rapidly. Moreover, the paper showcases the practical use of the accelerated quadratic transform with two frontier wireless applications: integrated sensing and communications (ISAC) and massive multiple-input multiple-output (MIMO).

关键词： Transforms Convergence Extrapolation Vectors Symmetric matrices signal processing algorithms Array signal processing Fractional programming (FP) convergence rate acceleration weighted minimum mean square error (WMMSE)

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NsigNet: A Neural Network Design for Detecting the Number of signals Under Sparse Observations

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IEEE INTERNET OF THINGS JOURNAL 2024年第11期11卷 19355-19367页

作者： Lee, Woong-Hee Kim, Minhoe Dongguk Univ Seoul Div Elect & Elect Engn Seoul 04620 South Korea Korea Univ Dept Comp Convergence Software Sejong Si 30019 South Korea

Many estimation and reconstruction algorithms in signal processing fields can be improved themselves if the number of signals is known. However, this assumption of preknowledge is challenging in real environments. Additionally, it is often necessary to obtain the information of physical parameters of the signal through a short data acquisition time, i.e., a small number of samples, in systems requiring the low latency. Accordingly, an algorithm to effectively detect the number of signals through a small number of samples can be of great help to various estimation and reconstruction algorithms as the preprocessor for them. In this article, we introduce a new algorithm which detects the number of signals with the efficiently designed neural network (NN), referred to as NsigNet. The proposed method is based on optimizing the NN by inputting the singular values of the reshaped informative matrix from the sampled signal and outputting the one-hot encoding vectors indicating the number of signals. Simulation results show that NsigNet outperforms the conventional schemes in the various environments. Notably, the proposed scheme requires extremely small number of training data set and network size. Finally, we provide two applications, i.e., (i) sparse signal recovery with compressive sensing and (ii) signal denoising with the iterative K -truncated singular value decomposition (SVD), to validate the benefit of NsigNet in the practical on-/off-grid problems, respectively.

关键词： signal processing algorithms Artificial neural networks Training signal processing Internet of Things Wireless communication Compressed sensing Detection of the number of signals neural networks signal denoising singular values sparse signal recovery

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Enhanced Batch Adaptive Filter Based on Fractional-Order Generalized Cauchy Kernel Loss

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IEEE signal processing LETTERS 2025年 32卷 1201-1205页

作者： Cui, Mingjing Jiang, Yunxiang Lin, Dongyuan Wang, Shiyuan He, Fuliang Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Changsha Jinwei Integrated Circuit Co Ltd Changsha 410000 Peoples R China

Adaptive filters utilizing the low-order moments hidden in robust loss functions have achieved desirable performance under Gaussian input and impulsive noises. However, when the input cannot be modeled by Gaussian process and is simultaneously contaminated by outliers, these filters may suffer from misalignment. To this end, applying fractional-order calculus in stochastic gradient descent method, this letter proposes a fractional-order generalized Cauchy kernel loss (FoGCKL) algorithm to model complex alpha-stable process input. The mean square deviation (MSD) is calculated to evaluate the steady-state performance of FoGCKL. To further avoid steady-state jitters and improve filtering accuracy, an enhanced batch method is constructed in FoGCKL using optimized weighted term, generating another enhanced batch FoGCKL (EBFoGCKL) algorithm. Simulations on system identification verify the correctness of theoretical analysis and demonstrate the superiorities of FoGCKL and EB-FoGCKL.

关键词： Steady-state Noise signal processing algorithms Accuracy Kernel Filters Gaussian processes Convergence Adaptation models Training Fractional-order filter symmetric alpha-stable process enhanced batch method steady-state analysis

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