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Approaching Sub-Nyquist Boundary: Optimized Compressed Spectrum Sensing Based on Multicoset Sampler for Multiband signal

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4225-4238页

作者： Song, Zihang Yang, Jian Zhang, Han Gao, Yue Univ Surrey Inst Commun Syst Guildford GU2 7XH Surrey England Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

Compressed spectrum sensing naturally pursues the use of fewer sampling resources to achieve spectrum support reconstruction and signal recovery. The theoretical lower boundary of averaging sampling rate to recover the multiband signal has been proved to be twice the Landau rate. However, it is still unreachable in practice. Based on the multicoset sampling architecture, this paper analyzes the influencing factors on perfect spectrum reconstruction from three aspects: data model, sampling pattern and greedy reconstruction algorithms, for which practical and feasible optimization schemes are proposed. To reduce redundant reconstruction for the multiple measurement vectors (MMV) signal model, a block MMV model is proposed to improve the accuracy in the spectrum support set reconstruction process. A sampling pattern selection algorithm is proposed to ensure a higher success rate of the spectrum support reconstruction to optimize the sensing matrix. We also deduced the representation of the signal and noise energy in the reconstructed spectrum based on the mathematical model of compressed sensing. We thus proposed a non-orthogonal double-threshold matching pursuit algorithm to avoid a high false-alarm rate due to the manually set converging conditions for matching pursuit algorithms. Numerical experiments on real-world wideband signals are carried out to demonstrate the feasibility and advantages of the proposed approaches. With integrated optimization, the required sampling density to ensure perfect reconstruction is approaching the sub-Nyquist sampling boundary.

关键词： Matching pursuit algorithms Sensors Mathematical models signal processing algorithms Delays Reconstruction algorithms Wideband Compressed spectrum sensing multiple measurement vectors matching pursuit algorithms multicoset sampling

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Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2046-2061页

作者： Mohamadi, Niloofar Dong, Min Shahbaz Panahi, Shahram Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction.

关键词： Array signal processing Quality of service Multicast algorithms signal to noise ratio signal processing algorithms Interference Convergence Robust multicast beamforming large-scale systems alternating direction method of multipliers (ADMM) low-complexity convergence

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User Activity Detection for Irregular Repetition Slotted Aloha Based MMTC

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3616-3631页

作者： Srivatsa, Chirag Ramesh Murthy, Chandra R. Indian Inst Sci Robert Bosch Ctr Cyber Phys Syst Bangalore 560012 Karnataka India Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 Karnataka India

Irregular repetition slotted aloha (IRSA) is a grant-free random access protocol for massive machine-type communications, in which users transmit replicas of their packet in randomly selected resource blocks within a frame. In this paper, we first develop a novel Bayesian user activity detection (UAD) algorithm for IRSA, which exploits both the sparsity in user activity as well as the underlying structure of IRSA transmissions. Next, we derive the Cramer-Rao bound (CRB) on the mean squared error in channel estimation. We empirically show that the channel estimates obtained as a by-product of the proposed UAD algorithm achieves the CRB. Then, we analyze the signal to interference plus noise ratio achieved by the users, accounting for UAD, channel estimation errors, and pilot contamination. Finally, we illustrate the impact of these non-idealities on the throughput of IRSA via Monte Carlo simulations. For example, in a system with 1500 users and 10% of the users being active per frame, a pilot length of as low as 20 symbols is sufficient for accurate user activity detection. In contrast, using classical compressed sensing approaches for UAD would require a pilot length of about 346 symbols. Our results reveal crucial insights into dependence of UAD errors and throughput on parameters such as the length of the pilot sequence, the number of antennas at the BS, the number of users, and the signal to noise ratio.

关键词： Channel estimation signal to noise ratio signal processing algorithms Throughput Contamination Decoding Symbols Channel estimation grant-free random access irregular repetition slotted aloha massive machine-type communications user activity detection

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Polarization, Angle, and Delay Estimation for Tri-Polarized Systems in Multipath Environments

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第8期21卷 5828-5841页

作者： He, Jin Wang, Yijing Shu, Ting Truong, Trieu-Kien Shanghai Jiao Tong Univ Shanghai Key Lab Intelligent Sensing & Recognit Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

A new signal processing algorithm proposed here is aimed at estimating five-dimensional (5-D) polarization-space-time (polarization, angle, delay) channel parameters for a tri-polarized system that works in a multipath environment. Unlike most channel estimation algorithms, where spatially spread antenna arrays are deployed, our algorithm is designed for a spatial col-located tripole antenna. A third-order tensor model is firstly established for the multipath polarization-space-time channel. This tensor is subsequently solved by exploiting low-rank decomposition techniques. After that, a closed-form solution of 2-D angles, 2-D polarizations, and multipath delay estimates for each multipath ray are derived. The proposed algorithm requires no computation for iterative searching and parameter pairing, thus offering an advantage in time-sensitive applications. In addition, it is applicable to either narrowband or wideband systems of arbitrary bandwidth and center-frequency.

关键词： signal processing algorithms Estimation Antenna arrays Delay estimation Channel estimation Tensors MIMO communication Wireless communications channel estimation space-time-polarization parameter multipath channel

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Parameter Training Methods for Convolutional Neural Networks With Adaptive Adjustment Method Based on Borges Difference

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 673-685页

作者： Jian, Jing Gao, Zhe Kan, Tao Liaoning Univ Sch Math Shenyang 110036 Peoples R China Liaoning Univ Coll Light Ind Shenyang 110036 Peoples R China

This paper proposes a momentum algorithm based on Borges difference and an adaptive momentum (Adam) algorithm based on Borges difference to update parameters, which can adjust the momentum information more flexibly. The Borges difference is proposed from the definition of Borges derivative to be combined with the gradient algorithm in convolutional neural networks. The proposed momentum algorithm based on Borges difference and Adam algorithm based on Borges difference can be adjusted more flexibly in order to speed up the convergence. The parameter optimization algorithm with the Borges difference presents a better performance compared with the integer-order momentum algorithm and integer-order Adam algorithm, with the proposed nonlinear adjustment method for the parameter tuning of convolutional neural networks. By analyzing experimental results of Fashion-MNIST dataset and CIFAR-10 dataset, the optimization algorithms based on Borges difference proposed in this paper gain better effects on the optimization model compared with the corresponding ones based on the integer-order difference, and can speed up the convergence speed and recognition accuracy of the image recognition.

关键词： signal processing algorithms Optimization Neural networks Fractals Convergence Training Calculus Fractal difference convolution neural networks momentum Adam algorithm nonlinear adaptive tuning method

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Improving Sum-Rate of Cell-Free Massive MIMO With Expanded Compute-and-Forward

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 202-215页

作者： Zhang, Jiayi Zhang, Jing Ng, Derrick Wing Kwan Jin, Shi Ai, Bo Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High Speed Railway Syst Beijing 100044 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Frontiers Sci Ctr Smart High Speed Railway Syst Beijing 100044 Peoples R China Zhengzhou Univ Henan Joint Int Res Lab Intelligent Networking & Zhengzhou 450001 Peoples R China Peng Cheng Lab Res Ctr Networks & Commun Shenzhen 518055 Peoples R China

Cell-free massive multiple-input multiple-output (MIMO) employs a large number of distributed access points (APs) to serve a small number of user equipments (UEs) via the same time/frequency resource. Due to the strong macro diversity gain, cell-free massive MIMO can considerably improve the achievable sum-rate compared to conventional cellular massive MIMO. However, the performance of cell-free massive MIMO is upper limited by inter-user interference (IUI) when employing simple maximum ratio combining (MRC) at receivers. To harness IUI, the expanded compute-and-forward (ECF) framework is adopted. In particular, we propose power control algorithms for the parallel computation and successive computation in the ECF framework, respectively, to exploit the performance gain and then improve the system performance. Furthermore, we propose an AP selection scheme and the application of different decoding orders for the successive computation. Finally, numerical results demonstrate that ECF frameworks outperform the conventional CF and MRC frameworks in terms of achievable sum-rate.

关键词： Massive MIMO Lattices Power control Decoding signal processing algorithms Codes Uplink Cell-free massive MIMO expanded compute-and-forward power control sum rate

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Set-membership affine projection algorithm based on the percentage change of the error signal and variable projection order

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IEEE LATIN AMERICA TRANSACTIONS 2022年第3期20卷 496-502页

作者： Trejo, Carlos Maya, Xochitl Martinez, Rene Sanchez, Gabriel Perez, Hector Avalos, Juan Sanchez, Giovanny Inst Politecn Nacl ESIME Mexico City DF Mexico

Nowadays, the use of adaptive filters plays an important role in multiple signal processing applications, such as active noise control, acoustic echo cancellers, system identifiers, channel equalizer, among others. Until date, many of the existing adaptive algorithms such as affine projection algorithms offer a high convergence speed. However, its computational cost is also high. Currently, several authors make extraordinary efforts to reduce its computational cost to be used in practical applications. In this paper, we propose a new set-membership affine projection algorithm based on the percentage change of the error signal and variable projection order (SMAP-PC-VO). Specifically, we propose two techniques to create this algorithm;1) the new algorithm uses an error bound, which is obtained by calcuting the percentage change of the error signal, to avoid the computation of the variance of additive noise, since in existing approaches this parameter determines the error bound. In practical applications, the computation of the variance of additive noise is infeasible since this signal is not available;2) we propose a new method to dynamically modify the projection order in the new algorithm. As a consequence, its computational cost is reduced. To demonstrate its performance, the proposed algorithm was successfully tested in different environments for system identification and active noise control for headphone applications. The simulation results demonstrate that the proposed algorithm presents good convergence properties. In addition, the proposed algorithm exhibits a low overall computational complexity.

关键词： Projection algorithms signal processing algorithms Internet of Things Computational efficiency IEEE transactions Heuristic algorithms Convergence set-membership algorithm affine projection algorithm data selective algorithms variable projection order

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EEG-Based Drowsiness Detection With Fuzzy Independent Phase-Locking Value Representations Using Lagrangian-Based Deep Neural Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第1期52卷 101-111页

作者： Reddy, Tharun Kumar Arora, Vipul Gupta, Vinay Biswas, Rupam Behera, Laxmidhar Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee 247667 Uttar Pradesh India Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

Passive electroencephalogram (EEG) brain-computer interfaces (BCI) have common usage in the area of Driver Drowsiness Detection. The approach presented herein identifies the cognitive state of the user while no mental action is required. Data recorded in EEG-based BCI experiments are generally noisy, nonstationary, and contaminated with artifacts that can deteriorate any analyzer's performance. Recently, common spatial patterns (CSPs) have been adapted with EEG state-space incorporating spatiospectral optimization using fuzzy time delay (FTD-CSSP). Temporal phase disparity sequence (TPDS) is used to measure synchrony between EEG signals. The output of Linear transforms operating on the TPDS constitute useful features for EEG regression problems. On similar lines, this article proposes spatiospectral optimized fuzzy-independent phase-locking value (SSO-FIPLV) representations (exploiting the spatiospectral information from TPDS) for EEG signals to monitor a user's cognitive states. Specifically, we analyze changes in EEG synchronization for a car driver as she/he drifts between alert and drowsy states. We use neural networks (NNs) for prediction. This article also proposes a cutting-edge method for training NN using the Euler-Lagrangian formulation. A stability proof is provided for the intended training approach alongside, and the performance is corroborated on the EEG reaction time prediction task, both within and across subjects, using a publicly available dataset. The NN trained by the proposed approach performs better than other competitive approaches in terms of minimizing root-mean-squared error and maximizing correlation coefficient. Channelwise feature importance in terms of average relevance values calculated from NN feature representations is visualized in the form of Topoplots using layerwise relevance propagation for regression.

关键词： Electroencephalography Artificial neural networks Optimization Feature extraction signal processing algorithms Fuzzy neural networks Vehicles Brain-computer interface (BCI) electroencephalogram (EEG) fuzzy time-delay common spatiospectral patterns (FTD-CSSP) Lagrangian neural networks (NNs) reaction time (RT)

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A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method

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IEEE ACCESS 2022年 10卷 72850-72865页

作者： Chen, You-Rong Chen, Wei-Ting Liao, Shao-Chieh Chen, Pei-Yin Fang, Hong-Yu Tai, Tzu-You Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Digital IC Design Lab Tainan 70101 Taiwan Novatek Microelect Corp Hsinchu 30076 Taiwan MediaTek Inc Hsinchu 30078 Taiwan

Depth estimation using stereo images can be achieved by calculating the disparity values between the left and the right images captured by two parallel cameras. Reconstructing depth information from 2D images is crucial in many applications, such as self-driving vehicles and robot navigation. Furthermore, most of these applications are employed with resource-constrained devices and have real-time requirements. In this paper, a high-speed, low-cost hardware implementation for disparity estimation is proposed. We adopted the novel disparity fusion method in our architecture, which can significantly reduce the number of calculations in the overall process. A refinement method is also designed to reduce the error rate of the resulting depth map and improve the tolerance to light noise. The proposed algorithm was implemented with the Kintex-7 field-programmable gate array. Its performance was tested by using the Middlebury-Version 2 and -Version 3 datasets. The proposed algorithm provides an operating speed of 118 fps with an error rate of only 6.36%. Compared with other state-of-the-art designs used for similar applications, the proposed method can achieve a 34.6% reduction in the error rate while providing the highest speed with competitive hardware cost.

关键词： Costs Estimation Hardware signal processing algorithms Real-time systems Error analysis Field programmable gate arrays Depth estimation hardware implementation high-speed low-cost stereo matching

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Revisiting Skipped Filter and Development of Robust Localization Method Based on Variational Bayesian Gaussian Mixture Algorithm

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 5639-5651页

作者： Park, Chee-Hyun Chang, Joon-Hyuk Hanyang Univ Sch Elect Engn Seoul 04763 South Korea

This paper presents robust positioning methods that use distance observations to estimate location parameters. The propagation of a non-line-of-sight (NLOS) signal can significantly affect the estimation performance in indoor and densely populated urban areas. Hence, robust localization algorithms are considered for alleviating the adverse effects of the NLOS signal. In particular, the skipped filter is derived theoretically and a robust localization method based on the variational Bayesian Gaussian mixture model (VB GMM) is proposed. The skipped filter has been introduced in the existing literature;however, the derivation of the skipped filter has not been addressed in the previous study. Therefore, we construct the mathematical derivation of the skipped filter in the formulation of the maximum likelihood estimation (MLE). In addition, the VB GMM weighted least squares (WLS) method is presented, wherein the VB GMM WLS method is based on the two-mode GMM. Furthermore, the localization accuracies of the proposed methods are found to be superior to those of the other algorithms. However, it is not guaranteed that NLOS error is necessarily a two-mode GMM. Therefore, we investigate the robustness of the proposed methods against modeling errors using the other state-of-the-art NLOS error distributions, such as the skew-t and Gaussian-uniform mixture models.

关键词： Location awareness signal processing algorithms Prediction algorithms Maximum likelihood estimation Mathematical models Kalman filters Gaussian mixture model Localization skipped filter variational Bayesian gaussian mixture model non-line-of-sight weighted least squares

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