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Sub-6-GHz Energy-Detection-Based Fast On-Chip Analog Spectrum Sensing With Learning-Driven signal Classification

IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 25033-25046页

作者： Mittal, Ankit Zhang, Milin Gourousis, Thomas Zhang, Ziyue Fei, Yunsi Onabajo, Marvin Restuccia, Francesco Shrivastava, Aatmesh Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

Cognitive communication utilizes transient openings in the spectrum to communicate opportunistically, which is a promising technique to enable more efficient spectrum usage in an increasingly congested spectrum environment. We aim to address two main challenges associated with cognitive communication: 1) spectrum sensing should be fast and energy efficient for processing a large bandwidth in a short time and 2) the spectrum sensing approach should be able to simultaneously recognize multiple signals that are present. In this article, we propose to address these challenges with a novel design framework that consists of a fast on-chip spectrum sensing in conjunction with a novel learning-based spectrum analysis model at the edge to enhance the optimizations for spectrum agility. We first utilize a model of a programmable analog-based high-quality factor (Q) on-chip spectrum sensor that is capable of scanning the sub-6-GHz band to detect the spectrum usage in less than 1 mu s. The proposed spectrum sensor also enhances the energy efficiency of the sensing. To complement the on-chip spectrum sensor, a deep learning (DL) model is deployed for a fine-grained signal detection between channels in the 400 MHz to 6-GHz range, which is intended to be executed on edge devices. Simulation results show that the DL model can detect multiple different modulated signals with a mean Intersection-over-Union (IoU) of 86.8% in highly variable bandwidth and center frequency scenarios. Finally, we present a system-level model of our framework to demonstrate the spectrum sensing and classification in the sub-6-GHz frequency band.

关键词： Sensors Internet of Things Image edge detection Wideband Cognitive radio System-on-chip signal processing algorithms dynamic spectrum access (DSA) integrated sensing and communications interference sensing spectrum sensing

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Event-Triggered Proximal Online Gradient Descent Algorithm for Parameter Estimation

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2594-2606页

作者： Zhou, Yaoyao Chen, Gang Chongqing Univ Coll Automat Chongqing 400044 Peoples R China

The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mechanism is introduced and the novel event-triggered proximal online gradient descent algorithm (EPOGDA) is proposed. The analysis shows that if the event-triggered threshold converges to zero as time tends to infinity and the cumulative difference between consecutive optimal values is sublinear, the dynamic regret of EPOGDA is sublinear. Further, we extend the proposed EPOGDA to the gradient-free scenarios, where the gradients are estimated using the Gaussian smoothed gradient estimator (GSGE). The GSGE-EPOGDA is presented and analyzed, which does not lead to performance degradation as compared to EPOGDA. Finally, the advantages of EPOGDA and GSGE-EPOGDA are verified on a distributed multi-sensor network.

关键词： Heuristic algorithms signal processing algorithms Sensors Estimation Convex functions Parameter estimation Dynamic scheduling Distributed parameter estimation online convex optimization event-triggered mechanism dynamic regret proximal algorithm

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Recognition of Muscular Activity in Complex Noise-Contaminated Myoelectric signals: An Adaptive Two-Step Approach

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zheng, Nan Li, Yurong Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350108 Peoples R China

Surface electromyography (sEMG) signals demonstrate how the muscles react to the control strategies of the nervous system. For stroke patients, the detection of muscle activation is crucial because it allows researchers to better understand their brain control mechanisms. However, extracting the muscular activity precisely from the sEMG signals with various complex noises is a challenge in biomedical data processing. This study presented an adaptive two-step (AdaTS) muscular activity recognition algorithm to handle this problem. The sEMG signal was first pre-extracted using the adaptive threshold approach and annexation method to identify the interval comprising active information. Then, after amplifying the difference of activity and inactivity of the interval signal through the Teager-Kaiser energy operator, the onsets and offsets were determined based on the overall change of the interval signal. The proposed algorithm was tested in semi-synthetic signals, real signals from a public database, and experimentally recorded signals. Compared with other approaches, our method can effectively handle various types of interference and produce the best detection performance. Additionally, the steps of parameter selection and adjustment are removed, which greatly simplifies the practical application.

关键词： Muscles Interference Turning Stroke (medical condition) Electromyography Band-pass filters signal processing algorithms Muscle activity detection signal processing surface electromyography (sEMG) Teager-Kaiser energy operator

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Piecewise-Constant Representation and Sampling of Bandlimited signals on Graphs

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2024年 10卷 332-346页

作者： Yang, Guangrui Zhang, Qing Yang, Lihua South China Agr Univ Coll Math & Informat Dept Math Guangzhou 510642 Peoples R China Jiangsu Univ Sch Math Sci Zhenjiang 212013 Peoples R China Sun Yat Sen Univ Sch Math Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China

signal representations on graphs are at the heart of most graph signal processing techniques, allowing for targeted signal models for tasks such as denoising, compression, sampling, reconstruction and detection. This paper studies the piecewise-constant representation of bandlimited graph signals, thereby establishing the relationship between the bandlimited graph signal and the piecewise-constant graph signal. For this purpose, we first introduce the concept of epsilon-level piecewise-constant representation for a general signal space. Then, using a distance matrix, a single-layer piecewise-constant representation algorithm is proposed to find an epsilon-level piecewise-constant representation for bandlimited graph signals. On this basis, we further propose a multi-layer piecewise-constant representation algorithm, which can find a node partition with as few pieces as possible to represent bandlimited graph signals piecewise within a preset error bound. Finally, as an application, we apply the node partition obtained by the multi-layer algorithm to establish a sampling theory for bandlimited signals, which does not need to compute the eigendecomposition of a variation operator in both sampling and signal reconstruction. Numerical experiments show that the proposed algorithms have good performance.

关键词： Partitioning algorithms Vectors signal processing Information processing signal processing algorithms Approximation algorithms Laplace equations Bandlimited graph signal graph signal processing piecewise-constant graph signal piecewise-constant representation sampling

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Identifiable Solutions to Foreground Signature Extraction From Hyperspectral Images in an Intimate Mixing Scenario

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4081-4097页

作者： Hollis, Jarrod Raich, Raviv Kim, Jinsub Fishbain, Barak Kendler, Shai Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Technion Israel Inst Technol Environm Water & Agr Engn Div IL-32000 Haifa Israel Technion Israeli Inst Technol Fac Civil & Environm Engn Water & Agr Engn Rabin Hall IL-32000 Haifa Israel

Hyperspectral imaging considers the measurement of spectral signatures in near and far field settings. In the far field setting, the interactions of material spectral signatures are typically modeled using linear mixing. In the near field setting, material signatures frequently interact in a nonlinear manner (e.g., intimate mixing). An important task in hyperspectral imaging is to estimate the distribution and spectral signatures of materials present in hyperspectral data, i.e., unmixing. Motivated by forensics, this work considers a specific unmixing task, namely, the problem of foreground material signature extraction in an intimate mixing setting where thin layers of foreground material are deposited on other (background) materials. The unmixing task presents a fundamental challenge of unique (identifiable) recovery of material signatures in this and other settings. We propose a novel model for this intimate mixing setting and explore a framework for the task of foreground material signature extraction with identifiability guarantees under this model. We identify solution criteria and data conditions under which a foreground material signature can be extracted up to scaling and elementwise-inverse variations with theoretical guarantees in a noiseless setting. We present algorithms based on two solution criteria (volume minimization and endpoint member identification) to recover foreground material signatures under these conditions. Numerical experiments on real and synthetic data illustrate the efficacy of the proposed algorithms.

关键词： Data models Task analysis Hyperspectral imaging Lighting Data mining signal processing algorithms Numerical models Endmember extraction hyperspectral imaging identifiability intimate mixing model nonlinear unmixing

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Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 14530-14545页

作者： Zou, Yinan Zhou, Yong Chen, Xu Eldar, Yonina C. ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Weizmann Inst Sci Fac Math & CS IL-7610001 Rehovot Israel

Grant-free random access is an effective technology for enabling low-overhead and low-latency massive access, where joint activity detection and channel estimation (JADCE) is a critical issue. Although existing compressed sensing algorithms can be applied for JADCE, they usually fail to simultaneously harvest the following properties: effective sparsity inducing, fast convergence, robust to different pilot sequences, and adaptive to time-varying networks. To this end, we propose an unfolding framework for JADCE based on the proximal gradient method. Specifically, we formulate the JADCE problem as a group-row-sparse matrix recovery problem and leverage a minimax concave penalty rather than the widely-used l(1) -norm to induce sparsity. We then develop a proximal gradient-based unfolding neural network that parameterizes the algorithmic iterations. To improve convergence rate, we incorporate momentum into the unfolding neural network, and prove the accelerated convergence theoretically. Based on the convergence analysis, we further develop an adaptive-tuning algorithm, which adjusts its parameters to different signal-to-noise ratio settings. Simulations show that the proposed unfolding neural network achieves better recovery performance, convergence rate, and adaptivity than current baselines.

关键词： Internet of Things Convergence signal to noise ratio signal processing algorithms Training Neural networks Gradient methods Massive random access compressed sensing proximal gradient unfolding joint activity detection and channel estimation

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Nested Tensor-Based Integrated Sensing and Communication in RIS-Assisted THz MIMO Systems

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 1141-1157页

作者： Du, Jianhe Cheng, Yuan Jin, Libiao Li, Shufeng Gao, Feifei Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol State Key Lab Intelligent Technol & Syst Inst Artificial IntelligenceDept Automat Beijing 100084 Peoples R China

In this paper, we propose a nested tensor-based algorithm for integrated sensing and communication (ISAC) in reconfigurable intelligent surface (RIS)-assisted downlink terahertz (THz) multiple-input multiple-output (MIMO) systems. By exploiting the multi-group Khatri-Rao space-time (KRST) coding scheme at the base station (BS) and the structure of RIS phase shifts, we formulate the received signal at the vehicle terminal (VT) as a nested tensor that is composed of multiple outer parallel factor (PARAFAC) tensors and an inner PARATUCK tensor. With the outer PARAFAC tensors, a low-complexity least squares Khatri-Rao factorization (LS-KRF) algorithm is developed for joint channel estimation and symbol detection. Then, with the inner PARATUCK tensor, an optimized five-linear alternating least squares (OF-ALS) algorithm is designed to precisely estimate the sensing parameters such as angles of departure (AoDs), angles of arrival (AoAs), and time delays for VT and scatterer positioning. The proposed algorithm can be effectively applied to the scenario where the number of paths is unknown. Simulation results show that the proposed algorithm has more relaxed parameter conditions and provides superior ISAC performance compared with the existing state-of-the-art algorithms.

关键词： Tensors Sensors signal processing algorithms MIMO communication Channel estimation OFDM Symbols Nested tensor RIS THz positioning

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Dual-Stream Reconstruction Network-Aided ESPRIT Algorithm for DoA Estimation in Coherent Scenarios

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 19669-19681页

作者： Lv, Liujie Wu, Sheng Su, Yi Jiang, Chunxiao Kuang, Linling Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Inst Remote Sensing Equipment Beijing 100094 Peoples R China Tsinghua Univ Tsinghua Space Ctr Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

Conventionally, direction of arrival (DoA) estimation of coherent signals relies on pre-processing approaches to eliminate the rank loss of the signal covariance matrix (SCM). However, almost all existing methods utilize the empirical covariance matrix (ECM) as a practical approximation of the SCM, and can not make full use of the information in received signals. In this paper, we propose a deep learning-aided estimation of signal parameters via the rotational invariance technique (ESPRIT) algorithm for DoA estimation in coherent scenarios. Specifically, a dual-stream reconstruction network (DsRNet) is proposed for reconstructing the full-rank SCM directly from received signals, which overcomes the fundamental dependence on the ECM and exploits all available information in received signals. Next, the logarithmic eigenvalue-aided deep residual network (LogDRNet) is proposed for the source number detection, which improves the efficiency of network training and the detection performance, especially with low signal-to-noise ratio (SNR) and limited number of snapshots. Subsequently, we determine the signal subspace using the source number estimated by the LogDRNet and estimate the DoA referring to the ESPRIT algorithm. The proposed deep learning-based framework replaces some crucial aspects of traditional algorithms with customized neural networks, which mitigates the dependence on ideal model assumptions, does not require taking the source number as a prior and improves the estimation performance. Simulation results show that the proposed method not only performs better than existing methods in source number detection and DoA estimation, but also shows superior robustness to various kinds of array imperfections that are widespread in practical systems.

关键词： Estimation Direction-of-arrival estimation signal processing algorithms Eigenvalues and eigenfunctions Detectors Covariance matrices Classification algorithms Direction of arrival (DoA) estimation source number detection coherent sources deep learning transformer

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Strong Convexity of Affine Phase Retrieval

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 1301-1315页

作者： Huang, Meng Xu, Zhiqiang Beihang Univ Sch Math Sci Beijing 100191 Peoples R China Chinese Acad Sci Inst Computat Math Acad Math & Syst Sci LSEC Beijing 100091 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Affine phase retrieval refers to the process of recovering a signal from intensity measurements, with some entries known in advance. In this paper, we demonstrate that a natural least squares formulation for affine phase retrieval is strongly convex on the complete space under certain mild conditions, given that the measurement vectors are complex Gaussian random vectors and that the number of measurements is m >= O(d log d), where d is the dimension of the signals. Based on the result, we prove that the simple gradient descent method for the affine phase retrieval converges linearly to the target solution with high probability from an arbitrary initial point. These results highlight a fundamental difference between affine phase retrieval and classical phase retrieval, where the least squares formulations for classical phase retrieval are non-convex.

关键词： Vectors Extraterrestrial measurements Phase measurement Loss measurement Convergence signal processing algorithms Optimization Phase retrieval strong convexity linear convergence

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EICD: Causal Structure Recovery of Bearing Failure Data Containing Latent Confounding

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IEEE SENSORS JOURNAL 2024年第19期24卷 30560-30574页

作者： Ding, Xu Chen, Jun Chen, Guanhua Xu, Juan Hefei Univ Technol Inst Ind & Equipment Technol Anhui Prov Key Lab Aerosp Struct Parts Forming Tec Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei 230061 Peoples R China

Deep learning is widely used in the field of intelligent fault diagnosis. However, data-driven deep learning models are with unsatisfying interpretability and their performance cannot be maintained when a sample distribution shift occurs. Causal discovery can enhance the interpretability of deep learning models by establishing causal graphs to uncover causal invariance in the data. Nevertheless, causal discovery suffers from the curse of dimensionality due to the large conditional sets used in conditional independence (CI) tests, and the Markov equivalence class (MEC) obtained contains many undirected edges due to some causal structures having the same CI. In response, we incorporate the causal direction criterion (CDC) into iterative causal discovery (ICD) to construct the extended ICD (EICD) algorithm. EICD can obtain a detailed causal graph with lower complexity, enhancing the interpretability of the diagnostic framework. First, EICD reduces the number of required CI tests by limiting the size of the conditioning set and its distance from the target node. Second, based on CDC, EICD uses causal asymmetry to infer the direction of causal edges between pairs of nodes, thus accelerating ICD and adding more directional causal edges compared to MEC. In the experimental section, we compare the number of CI tests and the improvement in directional accuracy with other algorithms and use the EICD algorithm to reconstruct the causal graph of the faulty system. The experiments demonstrate that the EICD algorithm can reduce the required number of CI tests and recover the underlying causal structure more accurately.

关键词： Fault diagnosis Data models Feature extraction Deep learning signal processing algorithms Inference algorithms Convolution Causal discovery casual effect inference fault diagnosis latent confounding

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