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Estimating and Tracking Wireless Channels Under Carrier and Sampling Frequency Offsets

IEEE TRANSACTIONS ON signal processing 2023年 71卷 1053-1066页

作者： Parlin, Karel Riihonen, Taneli Le Nir, Vincent Adrat, Marc Tampere Univ Fac Informat Technol & Commun Sci Tampere 33720 Finland Royal Mil Acad Signal & Image Ctr B-1000 Brussels Belgium Fraunhofer Inst Commun Informat Proc & Ergon FKIE D-53343 Wachtberg Germany

This article addresses the challenge of estimating and tracking wireless channels under carrier and sampling frequency offsets, which also incorporate phase noise and sampling time jitter. We propose a novel adaptive filter that explicitly estimates the channel impulse response, carrier frequency offset, and sampling frequency offset by minimizing the mean-square error (MSE) and, when the estimated parameters are time-varying, inherently performs tracking. The proposed filter does not have any requirements for the structure of the waveform, but the digital transmitted waveform must be known to the receiver in advance. To aid practical implementation, we derive upper bounds for the filter's step sizes. We also derive expressions for the filter's steady-state MSE performance, by extending the well-known energy conservation relation method to account for the self-induced nonstationarity and coupling of update equations that are inherent in the proposed filter. Theoretical findings are verified by comparison to simulated results. Proof-of-concept measurement results are also provided, which demonstrate that the proposed filter is able to estimate and track a practical wireless channel under carrier and sampling frequency offsets.

关键词： Frequency estimation Receivers OFDM Time-frequency analysis Channel estimation Wireless communication signal processing algorithms Adaptive filtering frequency offset mean-square error steady-state analysis

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Comparative Analysis of High Impedance Fault Detection Techniques on Distribution Networks

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IEEE ACCESS 2023年 11卷 25817-25834页

作者： Hamatwi, Ester Imoru, Odunayo Kanime, Matheus M. M. Kanelombe, Hitila S. A. Univ Namibia Dept Elect & Comp Engn Oshakati 9000 Namibia Univ Johannesburg Dept Elect & Elect Engn ZA-2006 Johannesburg South Africa HSK Architects & Engn cc Outapi 9000 Namibia

This study focused on comparing the performance of three high impedance fault detection techniques for the case of a distribution network to select the technique that is best suited for detecting faults in this network. The three most common techniques;discrete Fourier transform, discrete wavelet transform, and the power spectrum among other techniques were selected for the research work based on literature. The distribution network was modelled in MATLAB/Simulink, along with the High Impedance Fault condition. These fault detection techniques were modelled and applied separately to the distribution network under different operating conditions: high impedance fault, load switching, and normal operation. The suitability of these techniques for the distribution network is evaluated by comparing them in terms of accuracy, processing time, and fault detection margin. The power spectrum technique was found to be the most suitable technique for detecting high impedance faults on a distribution network, with a 100% accuracy, a shorter processing time, and a larger fault detection margin.

关键词： Distribution networks Conductors Impedance Surface impedance Fault currents Discrete wavelet transforms Fault detection Discrete Fourier transforms discrete wavelet transforms fault currents fault detection fault diagnosis power distribution faults power distribution networks power system faults power system protection signal processing algorithms

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Possibility and Impossibility of Propagation Safety and Reliability: A 1-Safe and Reliable Snap-Stabilizing Broadcast Algorithm

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年第3期20卷 2174-2187页

作者： Al Jady, Zahraa Jawad Karaata, Mehmet Hakan Kuwait Univ Dept Comp Engn Safat Kuwait 13060 Kuwait

Propagation safety and propagation reliability, are important building blocks for secure network protocols, defense and safety mechanisms, and command and control systems. Propagation safety refers to the broadcasting of only those messages that have actually originated from a legitimate source. Propagation reliability, on the other hand, refers to the propagation of a message to every node in the system exactly once. In this article, we first prove that it is impossible to devise a safe self- or snap-stabilizing broadcast algorithm. We then propose the first self- and snap-stabilizing broadcast algorithm with the propagation 1-safety and the propagation reliability properties. Starting in an arbitrary initial configuration or after a fault, our proposed algorithm implements propagation 1-safety ensuring that a spurious message, a message that did not originate from the legitimate root process, can be sent by a process to its neighbors, however, it cannot be forwarded further. In addition, the propagation reliability property of the proposed algorithm ensures that a legitimate message reaches all system processes exactly once.

关键词： Safety Reliability signal processing algorithms Protocols Delays Transient analysis Fault tolerant systems Echo algorithms distributed systems fault-tolerance stabilization safety 1-safe broadcast wave algorithm self-stabilization snap-stabilization propagation

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Customer-Centered Pricing Strategy Based on Privacy-Preserving Load Disaggregation

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IEEE TRANSACTIONS ON SMART GRID 2023年第5期14卷 3401-3412页

作者： Tao, Yuechuan Qiu, Jing Lai, Shuying Sun, Xianzhuo Ma, Yuan Zhao, Junhua Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518100 Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518100 Peoples R China

Demand response (DR) is a demand reduction or shift of electricity use by customers to make electricity systems flexible and reliable, which is beneficial under increasing shares of intermittent renewable energy. For residential loads, thermostatically controlled loads (TCLs) are considered as major DR resources. In a price-based DR program, an aggregation agent, such as a retailer, formulates price signals to stimulate the customers to change electricity usage patterns. The conventional DR management methods fully rely on mathematical models to describe the customer ’s price responsiveness. However, it is difficult to fully master the customers ’ detailed demand elasticities, cost functions, and utility functions in practice. Hence, in this paper, we proposed a data-driven non-intrusive load monitoring (NILM) approach to study the customers ’ power consumption behaviors and usage characteristics. Based on NILM, the DR potential of the TCLs can be properly estimated, which assists the retailer in formulating a proper pricing strategy. To realize privacy protection, a privacy-preserving NILM algorithm is proposed. The proposed methodologies are verified in case studies. It is concluded that the proposed NILM algorithm not only reaches a better prediction performance than state-of-art works but also can protect privacy by slightly sacrificing accuracy. The DR pricing strategy with NILM integrated brings more profit and lower risks to the retailer, whose results are close to the fully model-based method with strong assumptions. Furthermore, a NILM algorithm with higher performance can help the retailer earn more benefits and help the grids better realize DR requirements.

关键词： signal processing algorithms Pricing Prediction algorithms Power demand Elasticity Behavioral sciences Smart meters Demand response potential pricing strategy thermostatically controlled loads non-intrusive load monitoring privacy protection

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Self-Calibration of a Network of Radar Sensors for Autonomous Robots

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6771-6781页

作者： Grebner, Timo Janoudi, Vinzenz Schoeder, Pirmin Waldschmidt, Christian Univ Ulm Inst Microwave Engn D-89081 Ulm Germany

Radar sensor networks are today widely used in the field of autonomous driving and for generating high-precision images of the environment. The accuracy of the environmental representation depends to a large extent on the accurate knowledge of the sensor's mounting orientation. Both the relative orientation of the sensors to each other and the relative sensor orientation in relation to the vehicle coordinate system are determining factors. For the first time, the orientation estimation of the radar sensors of a network is possible exclusively on the basis of radar target lists without additional localization and orientation devices, such as an inertial measurement unit or global navigation satellite systems. In this work, two algorithms for determining the orientation of incoherently networked radar sensors with respect to the vehicle coordinate system and with respect to each other are derived and characterized. With the presented algorithms, orientation accuracies up to 0.25 degrees are achieved. Furthermore, the algorithms do not impose any requirements on the positioning or the orientation of the radar sensors, such as overlapping field of views or the detection of identical targets. The presented algorithms are applicable to arbitrary driving trajectories as well as for point targets and extended targets which enables the use in regular road traffic.

关键词： Radar Estimation Spaceborne radar Robot sensing systems signal processing algorithms Radar imaging Mathematical models Ego-motion estimation orientation estimation radar imaging sensors radar sensor networks self-calibration chirp-sequence radar sensors

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Link Analysis for Solving Multiple-Access MDPs With Large State Spaces

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 947-962页

作者： Bozkus, Talha Mitra, Urbashi Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90007 USA

Wireless communication networks can be well-modeled by Markov Decision Processes (MDPs). While traditional dynamic programming algorithms such as value and policy iteration have lower complexity than brute force strategies, they still suffer from complexity issues for large state spaces. In this paper, the development of moderate complexity algorithms with high performance is sought for wireless network control. To this end, the approximate value function can be computed by projecting the original value function into a lower-dimensional subspace with the careful choice of basis vectors using tools from graph signal processing (GSP). Although GSP theory mainly focuses on undirected graphs, the transition graphs of MDPs for wireless networks are generally directed. For this reason, graph symmetrization based on the co-link method is considered due to its ability to preserve multi-hop dependencies in the graph. Given the multiple-access model under consideration, key properties of the transition probability matrix are exploited to determine the optimal subspace for the approximate value function with low complexity. The numerical results for a multiple-access channel show that the proposed algorithm can find the optimal basis vectors with high accuracy, and furthermore, the approach is robust to changes in system parameters. It is also shown that the projected equation method outperforms the state aggregation technique in producing higher accuracy with a lower runtime complexity.

关键词： Complexity theory signal processing Directed graphs Wireless networks signal processing algorithms Costs Approximation algorithms Markov decision process (MDP) value function approximation wireless networks multiple-access link analysis graph signal processing graph symmetrization

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An Iterative Implementation-Based Approach for Joint Source Localization and Association Under Multipath Propagation Environments

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 121-135页

作者： Liu, Yuan Tan, Zhi-Wei Khong, Andy W. H. Liu, Hongwei Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Xidian Univ Collaborat Innovat Ctr Informat Sensing & Understa Natl Lab Radar Signal Proc Xian 710071 Peoples R China

We address the problem of joint source localization and association (JSLA) under a multipath propagation environment for sensor arrays. Taking into account inaccurate prior information in practical applications, we propose a JSLA algorithm based on the iterative implementation of the minimum mean-square error (MMSE) framework with semi-unitary and sparsity constraints and the subspace technique. The proposed algorithm exploits benefits of both spatial signals' sparse characteristic and coherence structure when localizing unknown sources in a mixed interference environment. In contrast with previous works, the proposed algorithm can associate the incident paths to each source from the complex propagation environment with improved association performance even under low signal-to-noise ratio conditions. Neither additional decorrelation preprocessing nor prior information pertaining to the multipath propagation is required. Both simulations and real data experiments demonstrate the effectiveness and robustness of the proposed algorithm.

关键词： Location awareness Direction-of-arrival estimation signal processing algorithms Sensor arrays Estimation Indexes Sparse matrices Complex Stiefel manifold convex optimization direction-of-arrival (DOA) estimation mixed interference multipath propagation source localization and association

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A Joint Model Extraction and Data Detection Framework for IRS-NOMA System

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 164-177页

作者： Yang, Zikun Li, Feng Zhang, Dou Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Xian 710049 Peoples R China

This paper addresses the problem of data detection for intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. Also, the phase noise, analog-to-digital converter (ADC) quantization error and imperfect channel estimation are taken into consideration. At present, IRS is designed to be deployed in a wide range of scenarios. However, the quantization error model and IRS phase error model developed for limited scenarios may not be well adapted to the complex and changing environment, which brings a new challenge for the application of IRS. Therefore, this paper investigates a new hybrid method for joint noise model extraction, device activity detection and data detection in the case of unknown noise models, to solve the aforementioned IRS problem. Firstly, the Gaussian mixture model (GMM) is used to describe the model of quantization noise and phase noise. Thus the problem of model extraction is transformed into the calculation of the key parameters of GMM. Secondly, an improved expectation maximization algorithm is used to calculate the quantization noise model parameters. Finally, utilizing the property of the structure of the problem, a message-passing based algorithm is investigated. Simulation results show the merits of the proposed hybrid algorithm.

关键词： Quantization (signal) Data models signal processing algorithms Phase noise Wireless communication NOMA Channel estimation Intelligent reflecting surface non-orthogonal multiple access model-extraction data detection meassage passing

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Sparse Symmetric Format for Tucker Decomposition

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第6期34卷 1743-1756页

作者： Shivakumar, Shruti Li, Jiajia Kannan, Ramakrishnan Aluru, Srinivas Georgia Inst Technol Atlanta GA 30332 USA North Carolina State Univ Raleigh NC 27695 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA

Tensor-based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making them an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTMC) in general sparse tensors, the Sparse Symmetric Tensor Times Same Matrix chain (S-3 TTMC) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. We present the novel Compressed Sparse Symmetric (CSS) format for sparse symmetric tensors, along with an efficient parallel algorithm for the S-3 TTMC operation. We theoretically establish that S-3 TTMC on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations, and visualize the variation of the performance gap over the parameter space. We demonstrate experimental findings that confirm these results and achieve up to 2.72x speedup on synthetic and real datasets. The scaling of the algorithm on different test architectures is also show-cased to highlight the effect of machine characteristics on algorithm performance.

关键词： Tensors Symmetric matrices Sparse matrices Indexes signal processing algorithms Matrix decomposition Parallel algorithms Compressed storage sparse tensors symmetric tensors tensor times matrix chain

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Efficient Channel Estimation for RIS-Aided MIMO Communications With Unitary Approximate Message Passing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第2期22卷 1403-1416页

作者： Guo, Yabo Sun, Peng Yuan, Zhengdao Huang, Chongwen Guo, Qinghua Wang, Zhongyong Yuen, Chau Zhengzhou Univ Sch Informat Engn Zhengzhou 450000 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China Zhejiang Singapore Innovat & AI Joint Res Lab Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Info Proc Commun & Netw IPCA Hangzhou 310027 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia Singapore Univ Technol & Design Engn Prod Dev EPD Pillar Singapore 487372 Singapore

Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc. To unleash the full potentials of RIS-aided communications, acquiring accurate channel state information is crucial, which however is very challenging. For RIS-aided multiple-input and multiple-output (MIMO) communications, the existing channel estimation methods have computational complexity growing rapidly with the number of RIS units N (e.g., in the order of N-2 or N-3) and/or have special requirements on the matrices involved (e.g., the matrices need to be sparse for algorithm convergence to achieve satisfactory performance), which hinder their applications. In this work, instead of using the conventional signal model in the literature, we derive a new signal model obtained through proper vectorization and reduction operations. Then, leveraging the unitary approximate message passing (UAMP), we develop a more efficient channel estimator that has complexity linear with N and does not have special requirements on the relevant matrices, thanks to the robustness of UAMP. These facilitate the applications of the proposed algorithm to a general RIS-aided MIMO system with a larger N. Moreover, extensive numerical results show that the proposed estimator delivers much better performance and/or requires significantly less number of training symbols, thereby leading to notable reductions in both training overhead and latency.

关键词： Channel estimation Sparse matrices signal processing algorithms Message passing MIMO communication Electronic mail Estimation Reconfigurable intelligent surface (RIS) channel estimation approximate message passing (AMP)

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