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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
minimum free distance for any convolutional code, it is the minimum Hamming distance between the all-zero path and all the paths that diverge from and merge with the all-zero path at a given node of the trellis diagram. minimum mean square error (MMSE) a common estimation criterion which seeks to minimize the mean (or expected) squared error, E = E eT e where e represents the error. minimum mean square estimator (MMSE) a broad class of estimators based on minimizing the expected squared error criterion. Both the Linear least squares estimator and the Bayesian least squares estimator are special cases. See minimum variance unbiased estimator, linear least squares estimator, Bayesian least squares estimator, maximum a posteriori estimation, maximum likelihood estimation. minimum noise factor for an active circuit or device, occurs when the input terminal is terminated with an impedance which produces the minimum noise factor. minimum phase system a system that has all poles and zeroes inside the unit circle. It is called minimum phase because the poles and zeroes inside the unit circle cause the group delay which is the derivative of the phase of the signal to be minimized. minimum polynomial for a given element of a field, and for a subfield F, the polynomial of smallest degree, with coefficients in F having as a root. The set of roots of a minimum polynomial form a conjugacy class that is defined by the polynomial. minimum time-to-clear minimum time-to-melt See clearing time. See melting time.
MIMO Techniques for 5G Systems
Published in Athanasios G. Kanatas, Konstantina S. Nikita, Panagiotis Mathiopoulos, New Directions in Wireless Communications Systems, 2017
Athanasios G. Kanatas, Konstantinos Maliatsos
Minimum mean square error (MMSE) is an alternative receiver that exploits the knowledge of noise variance to maximize the SINR. The detection is performed using the matrix () WMMSE=(HHH+σn2INt)−1HH
Machine Learning in IoT-Based Ambient Backscatter Communication System
Published in Bhawana Rudra, Anshul Verma, Shekhar Verma, Bhanu Shrestha, Futuristic Research Trends and Applications of Internet of Things, 2022
Shivani Chouksey, Tushar S. Muratkar, Ankit Bhurane, Prabhat Sharma, Ashwin Kothari
As we note here the optimal criteria for MMSE is the average value of random vector y. A MMSE estimation is a method to estimate the reduced mean square error (MSE) of the dependent variable, which is a typical a measure of quality of the estimator. The MMSE estimation is determined by averaging the latest known parameters. Designing of such estimator is confined in a small space because calculating the posterior average is difficult task.
A real 3D scene rendering optimization method based on region of interest and viewing frustum prediction in virtual reality
Published in International Journal of Digital Earth, 2022
Pei Dang, Jun Zhu, Jianlin Wu, Weilian Li, Jigang You, Lin Fu, Yiqun Shi, Yuhang Gong
The problem of predicting the VR viewing frustum can be simplified to predict the state of the next time according to the past state through extrapolation. Extrapolation methods mainly include Lagrange interpolation, the Hermite interpolation method, and a Kalman filter. The Lagrange method is a polynomial-based method, which has less computation and is suitable for smooth motion trajectory prediction. However, when the interpolation times are high, the Runge phenomenon will appear, resulting in a large deviation of the interpolation results. Hermite interpolation is an optimization of the Lagrange interpolation method, but it requires the same derivative value at the viewpoint and has great restrictions on use, so it is not suitable for viewing frustum prediction. A Kalman filter is a linear optimal filtering algorithm (Meinhold and Singpurwalla 1983) that uses the mean square error and the criterion of the minimum mean square error to predict the rotation of the VR helmet. It has high accuracy and no requirements for the movement of the viewing frustum (Gómez and Maravall 1994). In VR, to prevent the occurrence of dizziness, teleportation movement mode is mostly used. This movement mode is discontinuous and cannot realize position prediction through extrapolation. Therefore, it is more reasonable to extrapolate and predict the rotation of the VR helmet using a Kalman filter (Zhang and Zhang 2010).
Joint state and fault estimation for nonlinear complex networks with mixed time-delays and uncertain inner coupling: non-fragile recursive method
Published in Systems Science & Control Engineering, 2022
Shuyang Feng, Hui Yu, Chaoqing Jia, Pingping Gao
In view of the previous analyses, the purpose of this paper is devoted to solving joint state and fault estimation problem for nonlinear time-varying CNs (NTVCNs) with mixed time-delays and uncertain inner coupling. The main three difficulties and challenges encountered are emphasized as: (1) How to deal with the mixed time-delays, uncertain coupling and gain perturbation by means of recursive estimation scheme? (2) How to design the desired estimator gain in the sense of minimum mean-square error at each sampling instant? (3) How to evaluate the algorithm performance based on some certain assumption conditions? Compared with the existing literature, the contributions of this paper can be listed as follows: (i) A novel joint state and fault estimator is constructed in the simultaneous presence of uncertain coupling and gain perturbation; (ii) the estimator gain is parameterized for the purpose of minimizing the trace of the upper bound of SEECM; and (iii) a sufficient condition is given to ensure the uniform boundedness of the developed recursive joint estimation strategy.
Deep Learning Techniques for OFDM Systems
Published in IETE Journal of Research, 2021
M. Meenalakshmi, Saurabh Chaturvedi, Vivek K. Dwivedi
Recently, DL techniques have been implemented in OFDM system to achieve better performance, reduce computational complexity, and address the PAPR problem. In [7], the DL technique was applied in the OFDM receiver to calculate the CSI implicitly and to recover the transmitted symbols directly. In this case, the DL model was trained offline using channel statistic data. The results were obtained for fewer training pilots and nonlinear clipping noise with cyclic prefix (CP) was omitted. Then the performance was compared with the minimum mean square error (MMSE) estimator. A novel DL detector called DeepIM was proposed in [8] with fully connected (FC) layers of deep neural network (DNN) with index modulation (IM) system. The proposed DeepIM was trained offline and deployed online for the detection of OFDM IM signal, which minimizes the detection complexity and bit error rate (BER) of the OFDM-IM system. ComNet receiver of [9] combined the DL technique with expert knowledge to replace the FC DNN OFDM receiver. The receiver comprised two subnets, one for channel estimation and the other for signal detection (SD). ComNet receiver recovered the transmitted data in the OFDM system with nonlinear and linear distortions.