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Downlink Beamforming for Dynamic Metasurface Antennas

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第7期22卷 4745-4755页

作者： Kimaryo, Seraphin F. Lee, Kyungchun Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul 01811 South Korea Seoul Natl Univ Sci & Technol Res Ctr Elect & Informat Technol Seoul 01811 South Korea

Dynamic metasurface antennas (DMAs) have great potential to be used in the radiator/receptor elements of future wireless transmitters and receivers, replacing conventional metallic antennas. This can be attributed to their unique properties, such as the ability to be reconfigured in real-time and to reduce the radio frequency chains, resulting in low implementation cost. However, the Lorentzian constraint associated with the DMA elements poses a challenge to real-time configuration and limits the application of the DMA. In this study, we propose a DMA-based wireless network, wherein a DMA-equipped base station (BS) communicates with single and multiple users. For the single-user scenario, we develop an optimal algorithm to maximize the signal-to-noise ratio of the user, which provides the weight of each DMA element in closed form. Furthermore, for multiple users, we formulate the weighted sum rate (WSR) problem and employ techniques from the single-user case to develop an efficient alternating optimization algorithm, which optimizes both the transmit precoders and DMA weights, to enhance the WSR of the system under the transmit power constraint of the BS. The numerical results demonstrate the effectiveness of the proposed algorithms in achieving better performance than that of the benchmark schemes.

关键词： Antenna arrays Massive MIMO Antennas Microstrip Optimization signal processing algorithms Metasurfaces Dynamic metasurface antenna (DMA) manifold optimization (MO) precoding massive MIMO

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Sound Target Detection Under Noisy Environment Using Brain-Computer Interface

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 229-237页

作者： Wang, Ruidong Liu, Ying Shi, Jianting Peng, Bolin Fei, Weijie Bi, Luzheng Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications.

关键词： Electroencephalography signal to noise ratio Object detection Brain modeling Decoding signal processing algorithms Noise measurement Sound target detection BCI auditory ERP ERSP SVM

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Latent Stochastic Differential Equations for Change Point Detection

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IEEE ACCESS 2023年 11卷 104700-104711页

作者： Ryzhikov, Artem Hushchyn, Mikhail Derkach, Denis Natl Res Univ Higher Sch Econ HSE Univ Comp Sci Dept Moscow 10100 Russia

Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.

关键词： Time series analysis signal processing algorithms Deep learning Approximation algorithms Task analysis Monitoring Machine learning algorithms Anomaly detection Machine learning change point detection deep learning machine learning timeseries

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Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 615-630页

作者： Chen, Wei-Kun Liu, Ya-Feng Liu, Fan Dai, Yu-Hong Luo, Zhi-Quan Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACI Beijing 100081 Peoples R China Chinese Acad Sci Inst Computat Math & Sci Engn Comp Acad Math & Syst Sci State Key Lab Sci & Engn Comp Beijing 100190 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration;in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, polynomial worst-case complexity makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.

关键词： signal processing algorithms Network slicing Heuristic algorithms Delays Routing Cloud computing Standards LP relaxation network slicing resource allocation rounding-and-refinement

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Metalearning-Based Alternating Minimization Algorithm for Nonconvex Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5366-5380页

作者： Xia, Jing-Yuan Li, Shengxi Huang, Jun-Jie Yang, Zhixiong Jaimoukha, Imad M. Gunduz, Deniz Natl Univ Def Technol Coll Elect Engn Coll Comp Changsha 410073 Peoples R China Beihang Univ Coll Elect Engn Beijing 100191 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Univ Modena & Reggio Emilia UNIMORE Dept Engn Enzo Ferrari I-41121 Modena Italy

In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization;however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.

关键词： Optimization Minimization Task analysis Deep learning Neural networks signal processing algorithms Iterative algorithms Alternating minimization (AM) deep unfolding gaussian mixture model (GMM) matrix completion meta-learning (ML)

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Partial Discharge Location Algorithm Based on Total Least-Squares With Matern Kernel in Cable Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第3期19卷 2421-2431页

作者： Lu, Lu Zhou, Kai Zhu, Guangya Yang, Xiaomin Chen, Badong Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Sichuan Univ High Voltage Lab Coll Elect Engn Chengdu 610065 Peoples R China Jiaotong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Partial discharge (PD) location techniques are a useful tool for condition monitoring of electrical apparatus in power systems. However, the noisy PD measurements may significantly degrade the performance of location algorithms. This article deals with the PD location problem by using adaptive filtering techniques. Heretofore, scarce literature focuses on addressing the PD location based on such method. A novel adaptive algorithm, termed as total least-squares (TLS)-Matern kernel (TLS-MK), is proposed. Benefiting from the merits of the Matern kernel, the TLS model can effectively suppress the noise from the direct and reflected waves of the PD source. Meanwhile, the TLS-MK algorithm is used to estimate the time difference, which is used in the PD location. Moreover, the convergence behavior of the TLS-MK algorithm is analyzed. Simulations and experiments show that the proposed algorithm can enhance the location accuracy as compared to state-of-the-art methods for various PD signals.

关键词： Partial discharges signal processing algorithms Sensors Kernel Estimation Informatics Noise reduction Adaptive filtering algorithm location matern kernel partial discharge (PD) total least-squares (TLS)

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Adaptive Digital Compensation of Analog Impairments in Frequency Interleaved ADC for Next-Generation High-Speed Communication Receivers

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IEEE ACCESS 2023年 11卷 124855-124868页

作者： Passetti, Leandro Morero, Damian A. Reyes, Benjamin T. Hueda, Mario R. Fdn Fulgor RA-5000 Cordoba Argentina Univ Nacl Cordoba Lab Comunicac Digitales RA-5016 Cordoba Argentina

This paper presents an adaptive background compensation technique designed to address analog impairments in frequency-interleaved analog-to-digital converters. Frequency interleaving has been proposed as a solution to the bandwidth bottleneck of data converters in high-speed digital communication receivers, including those deployed in coherent optical transmission systems. The proposed technique combines an adaptive multiple-input multiple-output equalizer with the well-established backpropagation algorithm commonly utilized in machine learning. Unlike previous proposals, the new algorithm (i) compensates the linear impairments in the analog front-end of the receiver (e.g., mismatches of track-and-hold and trans-impedance amplifier frequency responses, time skews, quadrature imbalance in electrical carriers, etc.), and (ii) maximizes the signal-to-noise ratio at the decision point of the receiver without requiring estimation of the channel parameters. The proposed background compensation scheme is thoroughly investigated in a dual-polarization coherent optical receiver with 16-QAM operating at similar to 200 GBd (i.e., similar to 1.6 Tbps). Numerical results show the excellent performance and high robustness of the new background compensation algorithm. These features, combined with its low implementation complexity, will pave the way for the deployment of commercial transceivers with bandwidths of 100 GHz and beyond.

关键词： Bandwidth signal processing algorithms Calibration signal to noise ratio Optical receivers Equalizers Optical filters Background calibration frequency interleaved ADCs high-speed optical receiver

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Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第3期22卷 1730-1744页

作者： Marata, Leatile Lopez, Onel Luis Alcaraz Djelouat, Hamza Leinonen, Markus Alves, Hirley Juntti, Markku Univ Oulu Ctr Wireless Commun Radio Technol Oulu 90014 Finland

Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC). However, MTC is characterized by unprecedented traffic in cellular networks, thus posing a challenge to its successful incorporation. In this work, we propose a unified framework for amicable coexistence of MTC and HTC. We consider a heterogeneous network where machine-type devices coexist with enhanced mobile broadband (eMBB) devices and propose transceiver techniques that promote efficient signal recovery from these devices. For this, we present an eMBB pilot and MTC data generation strategy that facilitates joint coherent decoding of eMBB data and non-coherent decoding of MTC data. Furthermore, we assess the feasibility of coexistence using receiver operating characteristics, outage probability, and normalized mean square error (NMSE). Our numerical results reveal that a harmonious coexistence of the heterogeneous services can be achieved with properly configured average signal-to-noise ratios and pilot length.

关键词： Decoding Channel estimation signal processing algorithms Coherence Cellular networks Wireless communication Transceivers Coherent and non-coherent decoding detection enhanced mobile broadband grant-free machine-type communication sparse signal recovery

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Distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5310-5321页

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China

This article proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, a large number of samples are allocated to multiple agents in the network. Each agent computes local stochastic gradient and communicates with its neighbors to seek for the global optimum. In this article, we develop a modified variance reduction technique to deal with the variance introduced by stochastic gradients. Combining gradient tracking and variance reduction techniques, this article proposes a distributed stochastic algorithm, gradient tracking algorithm with variance reduction (GT-VR), to solve large-scale non-convex finite-sum optimization over multiagent networks. A complete and rigorous proof shows that the GT-VR algorithm converges to the first-order stationary points with O(1/k) convergence rate. In addition, we provide the complexity analysis of the proposed algorithm. Compared with some existing first-order methods, the proposed algorithm has a lower O(PM epsilon b;(1)) gradient complexity under some mild condition. By comparing state-of-the-art algorithms and GT-VR in numerical simulations, we verify the efficiency of the proposed algorithm.

关键词： signal processing algorithms Optimization Convergence Random variables Distributed algorithms Machine learning Distributed databases Complexity analysis distributed algorithm non-convex finite-sum optimization stochastic gradient variance reduction

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Robust Automatic Modulation Classification in Low signal to Noise Ratio

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IEEE ACCESS 2023年 11卷 7860-7872页

作者： An, To Truong Lee, Byung Moo Sejong Univ Seoul South Korea Sejong Univ Dept Intelligent Mechatron Engn & Convergence Engn Seoul 05006 South Korea

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

关键词： Modulation Feature extraction signal to noise ratio Convolutional neural networks Classification algorithms signal processing algorithms Deep learning Automatic modulation classification autoencoder denoiser denoise signal convolutional neural network automatic modulation classification

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