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Robust transmission constrained unit commitment: a column merging method

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第15期14卷 2968-2975页

作者： Li, Xuan Zhai, Qiaozhu Guan, Xiaohong Xi An Jiao Tong Univ Syst Engn Inst MOE KLINNS Lab Xian Peoples R China Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat Beijing Peoples R China

With the rapid integration of power sources with uncertainty, robustness must be carefully considered in the transmission constrained unit commitment (TCUC) problem. The overall computational complexity of the robust TCUC methods is closely related to the vertex number of the uncertainty set. The vertex number is further associated with: (i) the period number in the scheduling horizon, (ii) the number of nodes with uncertain power injections. In this study, a column merging method (CMM) is proposed to reduce the computational burden by merging the uncertain nodes, while still guaranteeing the robustness of the solution. By the CMM, the transmission constraints are modified, with the parameters obtained based on an analytical solution of a uniform approximation problem, so that the computational time for obtaining the modified constraints is negligible. The CMM is applied under a greedy-algorithm-based framework, where the number of merged nodes and the approximation error can be well balanced. The CMM is designed as a preprocessing tool to improve the solution efficiency for robust TCUC problems and is compatible with many solution methods (like two-stage and multi-stage robust optimisation methods). Numerical tests show the method is effective.

关键词： greedy algorithms computational complexity optimisation power generation dispatch approximation theory power generation scheduling robust transmission constrained unit commitment column rapid integration power sources transmission constrained unit commitment problem computational complexity robust TCUC methods vertex number period number scheduling horizon uncertain power injections CMM computational burden uncertain nodes transmission constraints analytical solution uniform approximation problem computational time modified constraints greedy-algorithm-based framework merged nodes solution efficiency robust TCUC problems solution methods two-stage

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An improved block adaptive system for effective feedback cancellation in hearing aids

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DIGITAL SIGNAL PROCESSING 2016年 48卷 216-225页

作者： Vasundhara Panda, Ganapati Puhan, N. B. Indian Inst Technol Sch Elect Sci Bhubaneswar 751013 Orissa India

The hearing aid being a battery operated, portable device requires short processing delay, low computational complexity, with appreciable acoustic feedback cancellation effect. The prediction error method (PEM) and PEM with shadow filter (PEM-SH) based adaptive feedback canceller (AFC) referred as PEMAFC and PEMAFC-SH respectively reduces the amount of bias present in the estimate of feedback path. The available partitioned block frequency domain adaptive filter (PBFAF) based implementation of PEMAFC (PBFAF-P) and PEMAFC-SH (PBFAF-PS), offers a potential option for modelling an adaptive filter with many taps along with short block processing delay. However, the PBFAF suffers from large computational load because of the involvement of computationally expensive gradient constraints in each partition. Though removing or alternately applying the gradient constraint saves some computations but it results in significant performance degradation. With an objective of substantially reducing the computational burden and simultaneously retaining the performance, this paper develops an improved partitioned block Hartley domain adaptive filter (IPBHAF) and then employs it for effective feedback cancellation in hearing aids. Further, the IPBHAF with modified step size (IPBHAF-M) is proposed to achieve both fast convergence and better steady state performance. The simulation based experiments demonstrate the superior performance of IPBHAF-M based implementations of PEMAFC (IPBHAF-MP) and PEMAFC-SH (IPBHAF-MPS) over the PBFAF-P and PBFAF-PS in terms of both computational complexity and feedback cancellation performance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Hartley domain Partitioned block adaptive filter Hearing aids Block frequency domain adaptive filter computational complexity Feedback cancellation

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Physical layer network coding with channel and delay estimation

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IET COMMUNICATIONS 2013年第11期7卷 1109-1116页

作者： Li, Yixin Zheng, Fu-Chun Fitch, Michael Univ Reading Sch Syst Engn Reading RG6 6AY Berks England British Telecommun Plc Res & Innovat Ipswich IP5 3RE Suffolk England

Decode-and-forward physical layer network coding (PLNC) is one of the promising high-performance techniques for wireless relay networks. This study presents a channel and delay estimation algorithm along with a detection scheme for time-domain two-way relay network in Rayleigh block fading channels. Preambles are attached for frame-based synchronisation and channel estimation. Moreover, to achieve low-complexity estimation, the preambles are designed in Alamouti code structure. The Cramer-Rao bound (CRB) of the channel estimation is given, and simulations show that the mean-square error of channel estimation of the proposed scheme could reach the CRB. Compared with the traditional approach, the newly proposed combined scheme can achieve almost the same performance, but with significant improvement in computational complexity. At last, bit-error-rate performances analysis is given. Simulation validates the authors' analysis, and shows the upper bound and lower bound are tight.

关键词： channel estimation delay estimation Rayleigh channels network coding decode and forward communication relay networks (telecommunication) decode and forward physical layer network coding wireless relay networks channel estimation algorithm delay estimation algorithm time domain two way relay network Rayleigh block fading channels frame based synchronisation Alamouti code structure Cramer Rao bound mean square error computational complexity bit error rate performance analysis

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AdaBoost-Based Efficient Channel Estimation and Data Detection in One-Bit Massive MIMO

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 13935-13945页

作者： Esfandiari, Majdoddin Vorobyov, Sergiy A. Heath Jr, Robert W. Aalto Univ Dept Informat & Commun Engn Espoo 00076 Finland Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

The use of one-bit analog-to-digital converter (ADC) has been considered as a viable alternative to high resolution counterparts in realizing and commercializing massive multiple-input multiple-output (MIMO) systems. However, the issue of discarding the amplitude information by one-bit quantizers has to be compensated. Thus, carefully tailored methods need to be developed for one-bit channel estimation and data detection as the conventional ones cannot be used. To address these issues, the problems of one-bit channel estimation and data detection for MIMO orthogonal frequency division multiplexing (OFDM) system that operates over uncorrelated frequency selective channels are investigated here. We first develop channel estimators that exploit Gaussian discriminant analysis (GDA) classifier and approximate versions of it as the so-called weak classifiers in an adaptive boosting (AdaBoost) approach. Particularly, the combination of the approximate GDA classifiers with AdaBoost offers the benefit of scalability with the linear order of computations, which is critical in massive MIMO-OFDM systems. We then take advantage of the same idea for proposing the data detectors. Numerical results validate the efficiency of the proposed channel estimators and data detectors compared to other methods. They show comparable/better performance to that of the state-of-the-art methods, but require dramatically lower computational complexities and run times.

关键词： Channel estimation Detectors OFDM computational complexity Vectors Training Massive MIMO One-bit ADC channel estimation data detection massive MIMO-OFDM frequency selective channel AdaBoost

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NP-completeness results for edge modification problems

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DISCRETE APPLIED MATHEMATICS 2006年第13期154卷 1824-1844页

作者： Burzyn, Pablo Bonomo, Flavia Duran, Guillermo Univ Buenos Aires Fac Ciencias Exactas & Nat Dept Computac Buenos Aires DF Argentina Univ Chile Fac Ciencias Fis & Matemat Dept Ingn Ind Santiago Chile

The aim of edge modification problems is to change the edge set of a given graph as little as possible in order to satisfy a certain property. Edge modification problems in graphs have a lot of applications in different areas, and many polynomial-time algorithms and NP-completeness proofs for this kind of problems are known. In this work we prove new NP-completeness results for these problems in some graph classes, such as interval, circular-arc, permutation, circle, bridged, weakly chordal and clique-Helly graphs. (c) 2006 Elsevier B.V. All rights reserved.

关键词： computational complexity edge modification problems graph classes NP-completeness

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CNN-based ternary tree partition approach for VVC intra-QTMT coding

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第4期18卷 3587-3594页

作者： Belghith, Fatma Abdallah, Bouthaina Ben Jdidia, Sonda Ben Ayed, Mohamed Ali Masmoudi, Nouri Univ Sfax LETI ENIS Sfax Tunisia Univ Sfax NTSCOM ENETCOM Sfax Tunisia

In July 2020, the Joint Video Experts Team has published the versatile video coding (VVC) standard. The VVC encoder enhances the coding efficiency compared with his predecessor high-efficiency video coding encoder, thanks to the improved coding modules and the new proposed techniques such as the new block partitioning structure called quadtree with nested multi-type tree (QTMT). However, QTMT induces a significant increase in encoding time mainly at the rate distortion optimization level (RDO) which causes an enormous computational complexity. Instead of RDO-QTMT partition process, a deep-QTMT partition approach based on a fast convolution neural network-ternary tree (CNN-TT) is proposed to predict the best intra-QTMT decision tree in order to reduce the encoding time. A database is initially established containing CU-based TT partition depths with several video contents. Then, a CNN-TT model is developed under three-levels provided by the TT structure to early determine the QTMT partition at 32x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}32. Different threshold values are fixed for each level according to the CNN-TT predicted probabilities to reach a balance between the encoding complexity and the coding efficiency. The experimental results prove that our deep-QTMT partition approach saves a significant encoder time on average between 23% and 58% with an acceptable RD performance.

关键词： Versatile Video Coding (VVC) Quadtree with nested multi-type tree (QTMT) computational complexity Convolution Neural Network Ternary Tree (CNN-TT)

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A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-10页

作者： Zhou, Xiaozhen Li, Shanping Ye, Zhen Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310012 Zhejiang Peoples R China

Computer systems are becoming extremely complex, while system anomalies dramatically influence the availability and usability of systems. Online anomaly prediction is an important approach to manage imminent anomalies, and the high accuracy relies on precise system monitoring data. However, precise monitoring data is not easily achievable because of widespread noise. In this paper, we present a method which integrates an improved Evidential Markov model and ensemble classification to predict anomaly for systems with noise. Traditional Markov models use explicit state boundaries to build the Markov chain and then make prediction of different measurement metrics. A Problem arises when data comes with noise because even slight oscillation around the true value will lead to very different predictions. Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario. The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream. This study further applies ensemble classification to identify system anomaly based on the predicted metrics. Extensive experiments on anomaly data collected from 66 metrics in PlanetLab have confirmed that our approach can achieve high prediction accuracy and time efficiency.

关键词： SYSTEMS theory PREDICTION models MARKOV processes COMPUTER systems computational complexity ACQUISITION of data

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Low-complexity sphere decoding for MIMO-SCMA systems

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IET COMMUNICATIONS 2021年第4期15卷 537-545页

作者： Pan, Zhipeng Lei, Jing Wen, Lei Tang, Chaojing Wang, Zhongfeng Natl Univ Def Technol Sch Elect Sci Changsha Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

Multiple-input multiple-output-sparse code multiple access, a non-trivial integration of sparse code multiple access and multiple-input multiple-output techniques, is able to achieve high spectrum efficiency and massive user connections. However, this integration also increases the complexity of signal detection. Here, the signal detection problem of multiple-input multiple-output sparse code multiple access is transformed into a tree search problem and use sphere decoding to detect the signal. By setting the initial radius to positive infinity, sphere decoding can achieve optimal maximum likelihood performance while the complexity is high. In order to further reduce the complexity of sphere decoding, a block-wise sorted QR decomposition algorithm is proposed. Based on block-wise sorted QR decomposition, the improved sphere decoding, namely block-wise sorted QR decomposition-sphere decoding, is able to make the tree search more efficient. Since only the detection order of each user's signal has been changed, block-wise sorted QR decomposition-sphere decoding can maintain the optimal maximum likelihood performance. Simulation results and complexity analysis show that block-wise sorted QR decomposition-sphere decoding can achieve optimal performance and both hard-output and soft-output block-wise sorted QR decomposition-sphere decoding have much lower complexity than joint message passing algorithm. Furthermore, given the same signal-to-noise ratio, the complexity of block-wise sorted QR decomposition-sphere decoding decreases with the increase of receiving antennas, while the complexity of joint message passing algorithm increases linearly.

关键词： Signal detection hard-output computational complexity Radio links and equipment low-complexity sphere decoding improved sphere decoding QR codes message passing maximum likelihood decoding multiple-input multiple-output techniques multiple-input multiple-output sparse code multiple access Combinatorial mathematics block-wise sorted QR decomposition-sphere decoding decreases tree searching signal detection MIMO communication block-wise sorted QR decomposition algorithm Codes

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On the use of particle swarm optimization for adaptive resource allocation in orthogonal frequency division multiple access systems with proportional rate constraints

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INFORMATION SCIENCES 2012年第1期182卷 115-124页

作者： Sharma, Nitin Tarcar, Anand Kamat Antony, Varghese Anupama, Thomas K. R. BITS Pilani Zuarinagar 403726 Goa India

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling algorithm. In this paper we have proposed the use of a customized particle swarm optimization (PSO) aided algorithm to allocate the subchannels. The PSO algorithm is population-based: a set of potential solutions evolves to approach a near-optimal solution for the problem under study. The customized algorithm works for discrete particle positions unlike the classical PSO algorithm which is valid for only continuous particle positions. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Orthogonal frequency division multiple access Particle swarm optimization Resource allocation Sum capacity computational complexity

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On the complexity and approximation of the maximum expected value all-or-nothing subset

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DISCRETE APPLIED MATHEMATICS 2020年 283卷 1-10页

作者： Goldberg, Noam Rudolf, Gabor Bar Ilan Univ Ramat Gan Israel Koc Univ Istanbul Turkey

An unconstrained nonlinear binary optimization problem of selecting a maximum expected value subset of items is considered. Each item is associated with a profit and probability. Each of the items succeeds or fails independently with the given probabilities, and the profit is obtained in the event that all selected items succeed. The objective is to select a subset that maximizes the total value times the product of probabilities of the chosen items. The problem is proven NP-hard by a nontrivial reduction from subset sum. Then, after proving bounds on the probabilities of items selected in an optimal solution, we develop a fully polynomial time approximation scheme (FPTAS) for this problem. Next we consider an extension to maximizing the same objective over a more general downward closed set system. Here the problem is reduced to the solution of a sequence of constrained linear problems. This method is then used to propose a polynomial time approximation scheme (PTAS) for maximum expected value all-or-nothing matching in a graph and all-or-nothing matroid intersection using results for the corresponding constrained linear problem variants. (C) 2019 Elsevier B.V. All rights reserved.

关键词： computational complexity Approximation schemes Pseudo-Boolean optimization

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