This paper deals with a Maximum Likelihood receiver for a nonlinearly distorted OFDM signal over a flat channel with AWGN. The nonlinearity destroys the orthogonality between subcarriers, consequently, a per subcarrie...
详细信息
ISBN:
(纸本)9781479959532
This paper deals with a Maximum Likelihood receiver for a nonlinearly distorted OFDM signal over a flat channel with AWGN. The nonlinearity destroys the orthogonality between subcarriers, consequently, a per subcarrier decision, used when the linear PA is considered, is no longer optimal. We propose a sub-optimal receiver based on the Maximum Likelihood (ML) criterion. The ML receiver has to find the minimum Euclidean distance between the received vector and a set of all possible OFDM symbols passed through the same non-linearity. This approach has exponential complexity. To reduce the complexity, we propose a sub-optimal receiver that minimizes the Euclidean distance, seen as a cost function, by the gradient descent algorithm. Unfortunately, due to the nonlinearity, the cost function is non-convex. In order to overcome this obstacle, we propose a method to classify the solution, i.e., to decide if the achieved minimum is local or global. We modify the gradient descent algorithm to avoid convergence to a local minimum. It is shown that the proposed receiver outperforms the simple OFDM and iterative receivers in terms of symbol error rate (SER) performance.
In practical application of neural networks,we often try to change some factors to make the algorithm perform better,but the convergence analysis of the algorithm cannot keep up with it in *** paper studies the conver...
详细信息
In practical application of neural networks,we often try to change some factors to make the algorithm perform better,but the convergence analysis of the algorithm cannot keep up with it in *** paper studies the convergence of interval feedforward neural network(IFNN).First the convergence of the IFNN is derived theoretically based on gradient descent algorithm,where the IFNN possesses the interval input and output but the point weights;Then,by means of numerical analysis,the relationship between the convergence of general IFNN and interval weights is *** the numerical analysis,the coverage of interval output is taken as the objective function,and the particle swarm optimization(PSO) algorithm is used for optimizing to ensure the interval network output approaching the interval width range of the weight and threshold of the point *** the convergence of interval neural networks(INNs) can effectively promote the application range of INNs,making INNs better used in uncertain systems,reducing the impact of fluctuations,noise and measurement errors,and improving the objectivity and robustness of industrial process modeling.
Hyperspectral image contains hundreds of bands so it is spectrally overloaded and contains extent information to differentiate spectrally unique material. Hyperspectral data generally used to identify the presence of ...
详细信息
ISBN:
(纸本)9781509007752
Hyperspectral image contains hundreds of bands so it is spectrally overloaded and contains extent information to differentiate spectrally unique material. Hyperspectral data generally used to identify the presence of material in scene. Almost all the hyperspectral cameras have spatial resolution limit (>5m per pixel) due to that each pixel can be a mixture of several materials. The process ofunmixing is to unmixone of these mixed pixels. There are two models available to approximate mixing, (i) Linear Mixing Model (LMM) (ii) Nonlinear Mixing Model (NMM). Over a time, various approaches have been devised to address LMM and it's unmixing. In LMM, macrospectral mixtures are assumed. Nonlinear model comes under consideration due to microscopic mixing scale. In this paper, Generalized bilinear model is used which is nonlinear parametric model to get mixed data. Its accuracy depends on parametric form and parameter value chosen. It comes under convex optimization problem, so it can be solved using any optimization technique. gradient descent algorithm (GDA) is employed to solve this optimization problem. Advantage of GDA over other unmixing techniques is that it transforms nonlinear model into linear one. To improve unmixing result, it is indeed advisable to consider spatial correlation among abundances. A novel approach has been introduced in this paper which considers 2~(nd)order neighborhood correlation between abundances. Using our approach one can achieve better segmentation.
作者：
Ma DinaCheng HuaTian JianguoChen ShuqiNankai Univ
Key Lab Weak Light Nonlinear Photon Renewable Energy Convers & Storage Ctr Sch PhysMinist EducTEDA Inst Appl Phys Tianjin 300071 Peoples R China Shanxi Univ
Collaborat Innovat Ctr Extreme Opt Taiyuan 030006 Peoples R China Shandong Normal Univ
Collaborat Innovat Ctr Light Manipulat & Applicat Jinan 250358 Peoples R China
In the past three decades,artificial photonics devices have made remarkable achievements in the fields of super-resolution,biosensing and optical communication. The designs of traditional photonics devices are usually...
详细信息
In the past three decades,artificial photonics devices have made remarkable achievements in the fields of super-resolution,biosensing and optical communication. The designs of traditional photonics devices are usually realized by analyzing physical models and establishing numerical simulation methods. However,the structural design based on the numerical simulation method largely depends on the empirical model,and a large number of parameter combinations need to be calculated in the process of structural optimization,so it can only get suboptimal results in a limited parameter space. With the continuous improvement of computer computing capability and the enrichment of computer algorithms,the inverse design of photonics devices can effectively solve the above obstacles. The inverse design method can not only find the optimal structure distribution in a broader parameter space,but also design irregular structures that cannot be directly designed by human brain, which makes the performance of inverse designed photonics devices closer to the limit. This review first introduces the three common methods of photonic device inverse design and then introduces several important applications based on inverse design methods in detail. Common inverse design methods can be divided into gradient descent algorithm and genetic algorithm. gradient descent algorithm uses gradient information to guide the optimization of structure. Topology optimization is a commonly used algorithm in gradient descent algorithm,which can optimize the material distribution in a given design area according to the given objective function and constraint function. The gradient of objective function in topology optimization is usually calculated by adjoint method. Genetic algorithm looks for the global optimal value by simulating the evolution process of"survival of the fittest". The algorithm has four main steps:initial population guess,crossover,mutation and selection. By iterating the above four steps fo
In this work a fuzzy adaptive PI controller is proposed for a class of uncertain nonaffine nonlinear systems. The parameters of the PI controller that can achieve the control objectives are approximated by a set of fu...
详细信息
ISBN:
(纸本)9781479982134
In this work a fuzzy adaptive PI controller is proposed for a class of uncertain nonaffine nonlinear systems. The parameters of the PI controller that can achieve the control objectives are approximated by a set of fuzzy systems. The fuzzy systems' parameters are adjusted by the gradient descent algorithm. The purpose behind the use of this adaptation algorithm is to minimize the error between the unknown ideal PI controller and the fuzzy PI controller. The stability of the closed-loop system is proven analytically based on Lyapunov approach. A simulation example is presented to illustrate the performance of the proposed scheme.
暂无评论