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A Robust Cooperative Sensing Approach for Incomplete and Contaminated Data

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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Full-Duplex Beyond Self-Interference: The Unlimited Sensing Way

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 165-169页

作者： Liu, Ziang Bhandari, Ayush Clerckx, Bruno Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC). Towards this end, in this letter, we consider unlimited sensing framework (USF) enabled full-duplex system. We show that by injecting folding non-linearities in the sensing pipeline, one can not only suppress self-interference but also recover the signal of interest (SoI). This approach leads to novel design of the receiver architecture that is complemented by a modulo-domain channel estimation method. We then demonstrate the advantages of M-lambda -ADC by analyzing the relationship between quantization noise, quantization bits, and dynamic range. Numerical experiments show that the USF enabled receiver structure can achieve up to 40 dB digital SIC by using as few as 4-bits per sample. Our method outperforms the previous approach based on adaptive filters when it comes to digital SIC performance, SoI reconstruction, and detection.

关键词： Interference cancellation Channel estimation Quantization (signal) Receivers Noise Hardware Uplink signal processing algorithms Full-duplex system signal resolution Digital self-interference cancellation full duplex unlimited sampling quantization noise

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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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Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5644-5658页

作者： Akiyama, Yuki Vu, Minh Slavakis, Konstantinos Inst Sci Tokyo Dept Informat & Commun Engn Yokohama 2268502 Japan

This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL). The proposed mappings benefit from the rich approximating properties of RKHSs, adopt no assumptions on the statistics of the data owing to their nonparametric nature, require no knowledge on transition probabilities of Markov decision processes, and may operate without any training data. Moreover, they allow for sampling on-the-fly via the design of trajectory samples, re-use past test data via experience replay, effect dimensionality reduction by random Fourier features, and enable computationally lightweight operations to fit into efficient online or time-adaptive learning. The paper offers also a variational framework to design the free parameters of the proposed Bellman mappings, and shows that appropriate choices of those parameters yield several popular Bellman-mapping designs. As an application, the proposed mappings are employed to offer a novel solution to the problem of countering outliers in adaptive filtering. More specifically, with no prior information on the statistics of the outliers and no training data, a policy-iteration algorithm is introduced to select online, per time instance, the "optimal" coefficient p in the least-mean-p-power-error method. Numerical tests on synthetic data showcase, in most of the cases, the superior performance of the proposed solution over several RL and non-RL schemes.

关键词： signal processing algorithms Kernel Vectors Trajectory Synthetic data Reinforcement learning Performance analysis Indexes Hilbert space Heuristic algorithms Bellman mappings reinforcement learning nonparametric adaptive filtering outliers

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A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第5期8卷 3214-3227页

作者： Zhou, Yu Chen, Yu Zhang, Xiao Lai, Pan Huang, Lei Jiang, Jianmin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China South Cent Minzu Univ Dept Comp Sci Wuhan 430074 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

Recently, deep learning-based compressed sensing (CS) has achieved great success in reducing the sampling and computational cost of sensing systems and improving the reconstruction quality. These approaches, however, largely overlook the issue of the computational cost;they rely on complex structures and task-specific operator designs, resulting in extensive storage and high energy consumption in CS imaging systems. In this article, we propose a lightweight but effective deep neural network based on recurrent learning to achieve a sustainable CS system;it requires a smaller number of parameters but obtains high-quality reconstructions. Specifically, our proposed network consists of an initial reconstruction sub-network and a residual reconstruction sub-network. While the initial reconstruction sub-network has a hierarchical structure to progressively recover the image, reducing the number of parameters, the residual reconstruction sub-network facilitates recurrent residual feature extraction via recurrent learning to perform both feature fusion and deep reconstructions across different scales. In addition, we also demonstrate that, after the initial reconstruction, feature maps with reduced sizes are sufficient to recover the residual information, and thus we achieved a significant reduction in the amount of memory required. Extensive experiments illustrate that our proposed model can achieve a better reconstruction quality than existing state-of-the-art CS algorithms, and it also has a smaller number of network parameters than these algorithms. Our source codes are available at: https://***/C66YU/CSRN.

关键词： Image reconstruction Sensors Convolution Image resolution Compressed sensing signal processing algorithms Task analysis Compressed sensing lightweight neural network energy efficient recurrent learning

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Low-Power FPGA-Based Spiking Neural Networks for Real-Time Decoding of Intracortical Neural Activity

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IEEE SENSORS JOURNAL 2024年第24期24卷 42448-42459页

作者： Martis, Luca Leone, Gianluca Raffo, Luigi Meloni, Paolo Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy

Brain-machine interfaces (BMIs) are systems designed to decode neural signals and translate them into commands for external devices. Intracortical microelectrode arrays (MEAs) represent a significant advancement in this field, offering unprecedented spatial and temporal resolutions for monitoring brain activity. However, processing data from MEAs presents challenges due to high data rates and computing power requirements. To address these challenges, we propose a novel solution leveraging spiking neural networks (SNNs) that, due to their similarity to biological neural networks and their event-based nature, promise high compatibility with neural signals and low energy consumption. In this study, we introduce a real-time neural decoding system based on an SNN, deployed on a Lattice iCE40UP5k FPGA. This system is capable of reconstructing multiple target variables, related to the kinematics and kinetics of hand motion, from iEEG signals recorded by a 96-channel MEA. We evaluated the system using two different public datasets, achieving results similar to state-of-the-art neural decoders that use more complex deep learning models. This was obtained while maintaining an average power consumption of 13.9 mW and an average energy consumption per inference of 13.9 uJ.

关键词： Decoding Field programmable gate arrays Accuracy Spiking neural networks Real-time systems signal processing algorithms Microelectrodes Hardware Biological neural networks Training FPGA low power neural decoding real time spike detection spiking neural network (SNN)

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Novel Multi-Stage Feedback Technique for Time Skew Calibration in Time-Interleaved ADCs

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IEEE ACCESS 2024年 12卷 188940-188956页

作者： Hu, Min Yi, Pengxing Hao, Zhengxu Zhou, Zhitong Huazhong Univ Sci & Technol Dept Mech Sci & Engn Wuhan 430074 Peoples R China

This paper introduces a novel low-complexity algorithm for time skew calibration, which is crucial in ensuring undistorted signal acquisition in a time-interleaved analog-to-digital conversion system. By approximating the absolute value of the difference during linear approximation, the algorithm decouples derivatives from the step size factor, significantly reducing computational complexity. An enhanced multi-stage feedback approach is employed to minimize Taylor series approximation errors without increasing the number of derivative calculations. Simulation results demonstrate notable improvements, with an SNDR above 66 dB and an SFDR above 69 dB achieved for single-tone sinusoidal signals after calibration. The algorithm remains effective for multi-tone and frequency modulation signals, demonstrating its versatility. Real-world experimental measurements validate the algorithm's efficacy, highlighting its potential for practical applications with reasonable resource utilization. The proposed multi-stage feedback calibration technique consumes 1509 look-up tables (LUTs), 1409 flip flops (FFs), 33 Inputs/Outputs (I/Os), 1 buffer-global-clock (BUFG), 33 digital signal processors (DSPs), and 16.5 block random access memories (BRAMs) in the implementation, making it a practical solution for various applications.

关键词： Calibration Mathematical models Iterative methods Taylor series signal processing algorithms Estimation Approximation algorithms Interleaved codes Time-interleaved digital calibration time skew multi-stage feedback approach fewer derivatives

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Low-Complexity Fiber Nonlinearity Compensation Based on Operator Learning Over 12 075 km Single Model Fiber

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第23期42卷 8117-8126页

作者： He, Xingchen Yan, Lianshan Jiang, Lin Yi, Anlin Yu, Youren Pu, Zhengyu Pan, Wei Luo, Bin Southwest Jiaotong Univ Ctr Informat Photon & Commun Sch Informat Sci & Technol Chengdu 610031 Peoples R China Southwest Jiaotong Univ Ctr Informat Photon & Commun Sch Informat Sci & Technol Chengdu 610031 Peoples R China Peng Cheng Lab Shenzhen 518052 Peoples R China

Fiber nonlinearity is a significant constraint on the maximum achievable capacity in long-distance fiber optic transmission systems. The fiber nonlinearity compensation can be done in digital signal processing through the inversion of the Manakov model using the split-step Fourier method (SSFM) with appropriate inverted channel parameters, which are implemented via digital backpropagation (DBP) algorithm. Nevertheless, the practical deployment of this algorithm and its variants require a significant amount of latency and signal processing resources. Currently, the data-driven approximation of partial differential equations (PDEs) is rapidly emerging as a powerful paradigm, wherein the operator learning framework has been demonstrated to effectively approximate the solution space of PDEs. Here, we construct an inverse parameterized Manakov model based on the neural operator, utilizing it to compensate the fiber nonlinearity impairments at low computational complexity. The model proposed has been validated in a 12,075 km wavelength division multiplexing (WDM) system. The received signal waveform compensated by this model produces results that are similar to single-channel DBP (optimal step size), with an MSE of 0.0008. The analysis of the computational complexity shows that the proposed algorithm achieves a performance loss of 0.024 dB compared to single-channel DBP under the optimal step size, but its highly parallel nature significantly reduces computational latency.

关键词： Optical fibers Finite impulse response filters Standards Wavelength division multiplexing signal processing algorithms Optical fiber dispersion Mathematical models And digital backpropagation deep learning fiber nonlinearity compensation operator learning

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Efficient Beacon-Aided AUV Localization: A Reinforcement Learning Based Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 7799-7811页

作者： Liu, Chuhuan Lv, Zefang Xiao, Liang Su, Wei Ye, Liqing Yang, Helin You, Xudong Han, Shuai Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

Beacon-aided autonomous underwater vehicle (AUV) localization supporting maritime surveillance applications in underwater acoustic sensor networks selects a fixed number of beacons with constant transmit power, and thus has degradation of localization accuracy with severe channel fading and position fluctuation of beacons. In this paper, we propose a reinforcement learning based AUV localization scheme to choose the beacons and their transmit power to improve the localization accuracy and energy efficiency based on the AUV depth, the received signal strength, the number of selected beacons and the beacon energy consumption. According to the least squares method, the AUV position is calculated based on the isogradient sound speed model and the round-trip time of the localization signals. The localization error averaged over different beacon sets is evaluated to formulate the localization policy distribution. Deep neural network is designed to estimate the expected long-term discounted utility with higher feature extraction efficiency for the underwater networks with a large number of beacons. The Cramer-Rao lower bounds of the proposed localization schemes are derived to analyze the effect of the position fluctuation of beacons on the localization accuracy. Simulation results verify the performance gain in terms of the localization accuracy and the beacon energy consumption over the benchmark.

关键词： Location awareness Estimation Energy consumption Simulation signal processing algorithms Position measurement Underwater acoustics Underwater acoustic sensor networks localization autonomous underwater vehicles Cramer-Rao lower bound reinforcement learning

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Angular Parameter Estimation for Incoherently Distributed Sources With Single RF Chain

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5244-5257页

作者： Guo, Ziyu Yang, Tao Chen, Peng Han, Jun Zeng, Xiaoyang Hu, Bo Fudan Univ Sch Informat Sci & Technol Shanghai 200433 Peoples R China Fudan Univ State Key Lab Integrated Chips & Syst Shanghai 200433 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

In this paper, we consider the problem of estimating the angular parameters, i.e., the nominal angle-of-arrivals (AoAs) and angular spreads, of incoherently distributed sources using the phased-array equipped with a single RF chain. We first derive the approximate Fourier series of the received power. The coefficients can be expressed in closed form with the angular parameters. In the case of single source, this finding directly suggests the design of the low-complexity algorithm that performs spatial sampling and discrete Fourier transform to estimate the Fourier series coefficients, from which the nominal AoA and angular spread can be obtained successively. In the case of multiple sources, we focus on one source at one time, and the multiples sources are handled one by one. Based on the Fourier series expression, the power fitting approach is proposed to build the nonlinear least-squares problem. Then, the semi-exhaustive search algorithm is developed to find the solution, which gives the angular parameters of the target source. Additionally, the approximate Cramer-Rao bound is derived as benchmark. The numerical results demonstrate that in certain cases, the proposed methods can even outperform the existing method that uses fully-digital array.

关键词： Radio frequency Fourier series Antenna arrays signal processing algorithms Parameter estimation Receiving antennas Discrete Fourier transforms Array processing angular parameter estimation incoherently distributed sources phased-array single RF chain power fitting

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