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RSS-Based Indoor Localization Using min-max algorithm With Area Partition Strategy

IEEE ACCESS 2021年 9卷 125561-125568页

作者： Yang, Kuo Liang, Zhonghua Liu, Ren Li, Wei Changan Univ Sch Informat Engn Dept Commun Engn Xian 710064 Peoples R China

min-max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original min-max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended min-max (E-min-max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved min-max algorithm with area partition strategy (min-max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is "closest" to the target node. Then the target node's location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed min-max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-min-max algorithm, and is also less than one-seventh of that of the original min-max algorithm. Moreover, for the proposed min-max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-min-max and 1.12 meters for the original min-max, respectively.

关键词： Location awareness Partitioning algorithms Meters Estimation Area measurement Noise measurement Licenses min-max algorithm received signal strength (RSS) area partition indoor localization target node (TN)

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Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With min-max algorithm

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IEEE ACCESS 2022年 10卷 130068-130077页

作者： Wang, Zhenyu Liang, Zhonghua Li, Xunuo Li, Hui Changan Univ Sch Informat Engn Dept Commun Engn Xian 710064 Peoples R China

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the problem of premature convergence and redundant particles of the original PSO used in visible light positioning (VLP) systems. In the proposed IPSO algorithm, an adaptive particle initialization method based on min-max algorithm is used to adjust the number of particles and ensure that there are always particles near the target node (TN). Moreover, a nonlinear decreasing strategy of inertia weight is designed to ensure the stability of particle velocity during the iterative process. Simulation results show that, compared with the original PSO algorithm, the averaged positioning accuracy of the proposed IPSO-min-max algorithm is enhanced significantly at the expense of limited time consumption. What's more, we also find that for the proposed IPSO-min-max algorithm the increase of particle generation spacing will reduce the positioning delay but with the penalty in positioning accuracy. Therefore, it is necessary to select an appropriate particle spacing value according to specific requirements.

关键词： Visible light positioning (VLP) particle swarm optimization (PSO) received signal strength (RSS) min-max algorithm

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THE min-max algorithm AND ISOTONIC REGRESSION

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ANNALS OF STATISTICS 1983年第2期11卷 467-477页

作者： LEE, CIC MEM UNIV NEWFOUNDLAND ST JOHNS A1C 5S7NEWFOUNDLANDCANADA

There is growing interest in statistical inference under order restrictions. A major demand in this subject is to have a fast, direct method to solve the least squares problem of partially ordered isotonic regression. The min-max algorithm is such a method in which the user searches for the global minimum and the local maximum successively. A comparison of algorithms for partially ordered isotonic regression is included. As an application, using this efficient algorithm, it is feasible to approximate critical values of isotonic tests by simulation.

关键词： 62F10 62F03 isotonic regression likelihood ratio tests min-max algorithm

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Relaxed min-max Decoder Architectures for Nonbinary Low-Density Parity-Check Codes

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2013年第11期21卷 2010-2023页

作者： Cai, Fang Zhang, Xinmiao Case Western Reserve Univ Cleveland OH 44106 USA

Compared to binary low-density parity-check (LDPC) codes, nonbinary (NB) LDPC codes can achieve higher coding gain when the codeword length is moderate, but at the cost of higher decoding complexity. One major bottleneck of NB-LDPC decoding is the complicated check node processing. In this paper, a novel relaxed check node processing scheme is proposed for the min-max NB-LDPC decoding algorithm. Each finite field element of GF(2(p)) can be uniquely represented by a linear combination of p independent field elements. Making use of this property, an innovative method is developed in this paper to first find a set of the p most reliable variable-to-check messages with independent field elements, called the minimum basis. Then, the check-to-variable messages are efficiently computed from the minimum basis. With very small performance loss, the complexity of the check node processing can be substantially reduced using the proposed scheme. In addition, efficient VLSI architectures are developed to implement the proposed check node processing and the overall NB-LDPC decoder. Compared to the most efficient prior design, the proposed decoder for a (837, 726) NB-LDPC code over GF(2(5)) can achieve 52% higher efficiency in terms of throughput-over-area ratio.

关键词： Check node processing low-density parity-check codes min-max algorithm nonbinary partial-parallel VLSI design

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min-max VS max-min FLOW-CONTROL algorithmS FOR OPTIMAL COMPUTER NETWORK CAPACITY ASSIGNMENT

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 1984年第2期11卷 209-224页

作者： NIZNIK, CA UNIV ROCHESTER SCH MEDCTR BRAIN RESROCHESTERNY 14627

The theory for optimally assigning capacities to the links of a store forward computer communication network is developed by the minimization of the maximum of two groupings of all link capacities previously assigned to the network. The min-max and max-min algorithms developed have both local and global properties. The basic mathematical structure of the local section of the min-max algorithm is derived from the Lagrange Multiplier technique for minimizing convex functions, or the Kuhn-Tucker method for constrained minimization solutions, and the constraints imposed by the functional structure definitions. Since the min-max algorithm attains the optimal minimized delay assignment by minimization of the min-max assignment, the aspects of its relation to the max-min algorithms, the min-max inequality and the of both algorithms offers an option for considering all possible, allowed link assignment combinations (2 n - 2) of the n capacities available to further minimize delay.

关键词： min-max algorithm max-min algorithm Lagrange multiplier von Neumann minimax theorem capacity topology congestion convex function concave function Kuhn-Tucker method

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Modified Trellis-based min-max Decoder for Non-binary LDPC Codes

Modified Trellis-based Min-max Decoder for Non-binary LDPC C...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Zhang, Xinmiao SanDisk Corp Milpitas CA 95035 USA

ISBN: (纸本)9781479969593

Non-binary low-density parity-check (NB-LDPC) codes over GF(q) (q > 2) have better error-correcting performance than their binary counterparts when the codeword length is moderate. In this paper, a modified trellis-based min-max decoder is proposed for NB-LDPC codes. By relaxing the constraints on which messages can be included, the trellis syndrome computation is simplified without sacrificing the error-correcting performance. In addition, the iterative comparisons needed in computing the check-to-variable messages are replaced by one-step message selection. The decoding complexity of NB-LDPC codes grows substantially with q, and small q is preferred to achieve low complexity and high speed for data storage systems. Making use of the properties of GF(4), the hardware units are further simplified and efficient decoder architectures are developed for codes over GF(4). Compared to prior efforts, the proposed design requires smaller area, consumes much less power, achieves higher throughput, and also has slightly better error-correcting performance.

关键词： Low-density parity-check (LDPC) codes min-max algorithm Non-binary VLSI design

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Biased Constrained Hybrid Kalman Filter for Range-Based Indoor Localization

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IEEE SENSORS JOURNAL 2018年第4期18卷 1647-1655页

作者： Zhao, Yubin Li, Xiaofan Wang, Yang Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shenzhen Inst Radio Testing & Technol Shenzhen 518000 Peoples R China

The range-based localization method is widely used in wireless sensor localization systems. Many existing localization algorithms are unbiased estimators. However, the estimation performance presents biased features in the real localization systems. On the other hand, many biased location estimators show some essential advantages over unbiased estimators, e.g., robust to the noise, more accurate estimation, and low complexity. In this paper, we deeply investigate the performance of biased estimator, min-max, to achieve a new accuracy limit, and propose a hybrid Kalman filtering algorithm, which recursively locates the target based on biased feature. The first contribution is that we formulate the biased Cramer-Rao lower bound of the min-max algorithm to indicate that the biased localization algorithm can outperform the unbiased algorithms, e.g., maximum likelihood, as if the estimation bias were attained. The second contribution is that we propose a hybrid Kalman filtering algorithm while employing the min-max to construct a constrain region and using the dynamic Gaussian model for calculation in non-Gaussian environments. Our algorithm is robust to complicated environments with high accuracy. And, we implement it in an IoT target tracking platform. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods. And our algorithm can control the estimation error in only 1 m.

关键词： Wireless sensor network Cramer-Rao lower bound biased estimation min-max algorithm unscented Kalman filter

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A GA based method for search-space reduction of chess game-tree

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APPLIED INTELLIGENCE 2017年第3期47卷 752-768页

作者： Dehghani, Hootan Babamir, Seyed Morteza Univ Kashan Dept Comp Kashan Iran

In this study, a GA (Genetic algorithm) basesented to reduce the chess game tree space. GA is exploited in some studies and by chess engines in order to: 1) tune the weights of the chess evaluation function or 2) to solve particular problems in chess like finding mate in number of moves. Applying GA for reducing the search space of the chess game tree is a new idea being proposed in this study. A GA-based chess engine is designed and implemented where only the branches of the game tree produced by GA are traversed. Improvements in the basic GA to reduce the problem of GA tactic are evident here. To evaluate the efficiency of this new proposed chess engine, it is matched against an engine where the Alpha-Beta pruning and min-max algorithm are applied.

关键词： Chess game tree Genetic algorithm Alpha-Beta pruning min-max algorithm

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Scalable Pilot Assignment Scheme for Cell-Free Large-Scale Distributed MIMO With Massive Access

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IEEE ACCESS 2021年 9卷 122107-122112页

作者： Li, Jiamin Wu, Zhenggang Zhu, Pengcheng Wang, Dongming You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

With the explosive growth of mobile communication technology, the number of access terminals has increased dramatically, which will make the pilot contamination caused by pilot reuse extremely worse due to limited pilot resources. It is difficult to apply a traditional pilot assignment algorithm to serve the numerous access terminals simultaneously in real time with low complexity, so there is necessity to design a scalable pilot assignment scheme in the case of massive access. In this paper, we propose a scalable deep learning-based pilot assignment algorithm to maximize the sum spectral efficiency (SE) of cell-free large-scale distributed multiple-input multiple-output (MIMO) systems with massive access. The mapping between user locations and pilot assignment schemes is learned by a deep neural network (DNN). The training samples of the DNN are generated by a min-max algorithm, which minimizes the maximum interference to alleviate pilot contamination. The output of the pretrained DNN is used as the initial value of the min-max algorithm to achieve better pilot assignment schemes and reduce the algorithm complexity. The simulation results show that the proposed algorithm has better convergence with massive access and achieves a higher sum SE in near real time.

关键词： Contamination Resource management Interference Complexity theory Channel estimation Scalability Uplink Scalable algorithm deep neural network (DNN) large-scale distributed MIMO pilot assignment min-max algorithm

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Adaptation in the presence of a general nonlinear parameterization: An error model approach

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1999年第9期44卷 1634-1652页

作者： Loh, AP Annaswamy, AM Skantze, FP Natl Univ Singapore Dept Elect Engn Singapore 119260 Singapore MIT Dept Mech Engn Cambridge MA 02139 USA Stanford Univ Grad Sch Business Stanford CA 94305 USA

Parametric uncertainties in adaptive estimation and control have been dealt with, by and large, in the context of linear parameterizations. algorithms based on the gradient descent method either lead to instability or inaccurate performance when the unknown parameters occur nonlinearly, Complex dynamic models are bound to include nonlinear parameterizations which necessitate the need for new adaptation algorithms that behave in a stable and accurate manner. The authors introduce, in this paper, an error model approach to establish these algorithms and their global stability and convergence properties. A number of applications of this error model in adaptive estimation and control are included, in each of which the new algorithm is shown to result in global boundedness. Simulation results are presented which complement the authors' theoretical derivations.

关键词： adaptive control error model global stability min-max algorithm nonlinear parameterization

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