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

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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Byzantine-Robust Distributed Learning With Compression

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2023年 9卷 280-294页

作者： Zhu, Heng Ling, Qing Univ Calif San Diego San Diego CA 92093 USA Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China Sun Yat Sen Univ Pazhou Lab Guangzhou 510275 Peoples R China

Communication between workers and the master node to collect local stochastic gradients is a key bottleneck in a large-scale distributed learning system. Various recent works have proposed to compress the local stochastic gradients to mitigate the communication overhead. However, robustness to malicious attacks is rarely considered in such a setting. In this work, we investigate the problem of Byzantine-robust compressed distributed learning, where the attacks from Byzantine workers can be arbitrarily malicious. We theoretically point out that different to the attacks-free compressed stochastic gradient descent (SGD), its vanilla combination with geometric median-based robust aggregation seriously suffers from the compression noise in the presence of Byzantine attacks. In light of this observation, we propose to reduce the compression noise with gradient difference compression so as to improve the Byzantine-robustness. We also observe the impact of the intrinsic stochastic noise caused by selecting random samples, and adopt the stochastic average gradient algorithm (SAGA) to gradually eliminate the inner variations of regular workers. We theoretically prove that the proposed algorithm reaches a neighborhood of the optimal solution at a linear convergence rate, and the asymptotic learning error is in the same order as that of the state-of-the-art uncompressed method. Finally, numerical experiments demonstrate the effectiveness of the proposed method.

关键词： Stochastic processes Distance learning Computer aided instruction Compressors Convergence Robustness signal processing algorithms Distributed learning communication efficiency Byzantine-robustness gradient compression

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Tensor-Based Sparse Bayesian Learning With Intra-Dimension Correlation

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 31-46页

作者： Song, Yuhui Gong, Zijun Chen, Yuanzhu Li, Cheng Mem Univ Dept Comp Sci St John NL A1B 3X5 Canada HKUST IoT Thrust Guangzhou 999077 Peoples R China HKUST Dept ECE Hongkong Peoples R China Queens Univ Sch Comp Kingston ON K7L 2N8 Canada Mem Univ Dept Elect & Comp Engn St John NL A1B 3X5 Canada

Unlike the flat-view matrix, tensor provides an elegant representation to preserve the structure of data in the multidimensional nature. The wide use of tensor-based approaches highlights the limitation of matrix-based methods and has generated increasing needs in multidimensional data analytics. Driven by such demands, tensor decomposition with automatic rank determination has recently emerged as a promising tool for image processing, wireless communications and neural network compression. However, unlike the success in matrix-based schemes of exploring the correlation for performance improvement, existing tensor-based methods fail to consider the correlation structure in the tensor. In this paper, we propose to exploit the tensor intra-dimension correlation within the framework of sparse Bayesian learning. We provide the sparsity-inducing probabilistic models under both canonical polyadic decomposition and Tucker decomposition to capture the correlation structure effectively. We derive an efficient model inference method with a fast convergence rate based on the expectation-maximization algorithm. Furthermore, a complexity reduction method is proposed to speed up the computation by exploiting several properties of the Kronecker product. We further extend the proposed framework for the incomplete tensor corrupted by sparse outliers to achieve effective tensor completion and outlier mitigation. Simulation results demonstrate the superior performance of the proposed framework over those ignoring the intra-dimension correlation in terms of accuracy and sparsity.

关键词： Tensors Correlation Bayes methods Matrix decomposition Sparse matrices Computational modeling signal processing algorithms Tensor canonical polyadic decomposition Tucker decomposition intra-dimension correlation sparse Bayesian learning expectation-maximization

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Real-Time Polarization Demultiplexing by Multi-Thread Constant Modulus Algorithm for Fully Softwarized Access Networks

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第5期41卷 1346-1356页

作者： Suzuki, Takahiro Kim, Sang-Yuep Kani, Jun-ichi Yoshida, Tomoaki NTT Corp NTT Access Network Serv Syst Labs Yokosuka Kanagawa 2390847 Japan

Optical access networks are replacing dedicated hardware-based architectures with architectures centered on general-purpose servers based on network function virtualization (NFV) and the software-defined network (SDN) in order to efficiently support various new services. To maximize the advantage of this new approach, studies are now targeting the fully softwarized access network which requires softwarization of digital signal processing (DSP). However, polarization demultiplexing includes sequential processing, and it is difficult to softwarize high-speed filter updates in a constant modulus algorithm (CMA) that supports real-world state of polarization (SOP) change. This paper proposes a novel multi-thread CMA that can track fast SOP changes by using multiple threads to implement filter convolution. It tracks faster SOP changes with the same filter update cycle as the conventional method by calculating multiple filters on multiple threads and averaging them. Its graphics processing unit (GPU) implementation adopts parallel reduction for averaging the filters and parallelizes the update of filters for tributaries X and Y in order to take advantage of many GPU threads. We evaluate the real-time bit error rate (BER) performance achieved by implementing the DSP functions with the proposal in a 5-Gb/s dual-polarization-quadrature phase-shift keying (DP-QPSK) system. The demonstration results show that the proposed algorithm supports 2,000 rd/s real-time performance for optical systems and offers high feasibility for application to the fully softwarized access network.

关键词： Instruction sets Equalizers Demultiplexing Software Graphics processing units signal processing algorithms Optical network units NFV PON SDN Softwarization and polarization demultiplexing

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Reconfigurable-Intelligent-Surface-Aided OTFS: Transmission Scheme and Channel Estimation

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IEEE INTERNET OF THINGS JOURNAL 2023年第22期10卷 19518-19532页

作者： Li, Zhongjie Yuan, Weijie Li, Buyi Wu, Jun You, Changsheng Meng, Fanke Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Tianjin Yinyuan Informat Technol Co Ltd Tianjin 300457 Peoples R China

In this article, we study the uplink transmission scheme and channel estimation design for reconfigurable intelligent surfaces (RIS)-aided orthogonal time-frequency space (OTFS) systems in high-mobility scenarios. To this end, we first propose an efficient and reliable transmission scheme that utilizes the delay-Doppler (DD) information in OTFS to facilitate the configuration of RIS. Specifically, the proposed scheme exploits the estimated delay and Doppler shifts of the cascaded channel to sense the channel parameters, and the sensing parameters are then used for RIS passive beamforming. It is noteworthy that we estimate the channel state information (CSI) by employing only one OTFS frame and configure the RIS based on the predicted channel parameters, leading to substantially reduced channel training overhead and more real-time RIS configuration. To obtain the essential information for channel information sensing, we then propose a low-complexity algorithm which determines the Doppler and delay shifts of the channel between the user and RIS based on linear systems and the mapping relationship of the DD pairs, respectively. With the DD information in hand, a user localization algorithm constructed by the least square (LS) and a channel tracking method relying on extended Kalman filter (EKF) are then presented to obtain the spatial angle information. By making use of the channel parameters acquired at the base station (BS), the RIS reflection vector is designed to maximize the achievable rate. The results obtained from the simulation experiments affirm the efficacy of the proposed scheme, thereby confirming its capability to attain efficient communications under high Doppler channels.

关键词： Channel estimation Delays Doppler shift Training OFDM signal processing algorithms Sensors orthogonal time-frequency space (OTFS) passive beamforming reconfigurable intelligent surfaces (RIS)

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De-Noising of Sparse signals Using Mixture Model Shrinkage Function

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IEEE ACCESS 2023年 11卷 7551-7563页

作者： Ullah, Hayat Amir, Muhammad Iqbal, Muhammad Malik, Suheel Abdullah Jadoon, Muhammad Mohsin Khan NYU Tandon Sch Engn Dept Elect & Comp Engn New York NY 11201 USA Int Islamic Univ Islamabad Dept Elect Engn Islamabad 44000 Pakistan Tampere Univ Dept Automat & Mech Engn Tampere 33720 Finland Pak Austria Fachhsch Inst Fac Elect Com IT & Design Haripur 22653 Pakistan

In this work a new thresholding function referred to as 'mixture model shrinkage' (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.

关键词： Noise reduction Transforms Thresholding (Imaging) Mixture models signal processing algorithms Noise measurement Wavelet coefficients Convex optimization EM algorithm maximum a posteriori estimator (MAP) MM algorithm PSNR RMSE sparse signal processing

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VISION-ASSISTED MILLIMETER-WAVE BEAM MANAGEMENT FOR NEXT-GENERATION WIRELESS SYSTEMS

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IEEE VEHICULAR TECHNOLOGY MAGAZINE 2023年第3期18卷 58-68页

作者： Zheng, Kan Yang, Haojun Ying, Ziqiang Wang, Pengshuo Hanzo, Lajos Ningbo Univ Ningbo 315211 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Informat & Commun Engn Beijing 100876 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton England

Beamforming techniques have been widely used in the millimeter-wave (mm-wave) bands to mitigate the path loss of mm-wave radio links as narrow straight beams by directionally concentrating the signal energy. However, traditional mm-wave beam management algorithms usually require excessive channel state information (CSI) overhead, leading to extremely high computational and communication costs. This hinders the widespread deployment of mm-wave communications. By contrast, the revolutionary vision-assisted beam management system concept employed at base stations (BSs) can select the optimal beam for the target user equipment (UE), based on location information determined by machine learning (ML) algorithms applied to visual data, without requiring channel information. In this article, we present a comprehensive framework for a vision-assisted mm-wave beam management system, its typical deployment scenarios, and the specifics of the framework. Then, some of the challenges faced by this system and their efficient solutions are discussed from the perspective of ML. Next, a new simulation platform is conceived to provide both visual and wireless data for model validation and performance evaluation. Our simulation results indicate that vision-assisted beam management is indeed attractive for next-generation wireless systems.

关键词： Wireless communication Detectors Computational modeling Cameras Predictive models Visualization signal processing algorithms

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Joint Generalized Inner Product Method for Main Lobe Jamming Suppression in Distributed Array Radar

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6940-6953页

作者： Pu, Weiming Liang, Zhennan Wu, Jianxin Liu, Quanhua Beijing Inst Technol Minist Educ Key Lab Elect & Informat Technol Satellite Nav Beijing 100081 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401135 Peoples R China

Modern radar detection faces an increasingly complex electromagnetic environment, and strong clutter and strong jamming have severely restricted radar performance. This article proposes a robust jamming suppression method that is based on the joint generalized inner product (JGIP) method in a distributed array radar, which utilizes the characteristics of the extremely high angular resolution of the distributed array radar to effectively suppress main lobe jamming. Thus, the advantages of flexible aperture synthesis of distributed array radar are investigated to design a JGIP to detect targets in a cluttered environment. This method is not sensitive to the various nonideal factors that exist in distributed array radar engineering practice and is highly robust. Simulation and actual measured data verify the correctness and effectiveness of the proposed method.

关键词： Jamming Radar Radar antennas Clutter Radar clutter Spaceborne radar signal processing algorithms Distributed array radar joint generalized inner product (JGIP) main lobe jamming measured data

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