scholarly journals Unimodular-Upper polynomial matrix decomposition for MIMO spatial multiplexing

Author(s):  
Moustapha Mbaye ◽  
Moussa Diallo ◽  
Mamadou Mboup
Author(s):  
Moustapha Mbaye ◽  
Moussa Diallo ◽  
Mamadou Mboup

Abstract This paper considers time-domain spatial multiplexing in MIMO wideband system, using an LU-based polynomial matrix decomposition. Because the corresponding pre- and post-filters are not paraunitary, the noise output power is amplified and the performance of the system is degraded, compared to QR-based spatial multiplexing approach. Degradations are important as the post-filter polynomial matrix is ill-conditioned. In this paper, we introduce simple transformations on the decomposition that solve the ill-conditioning problem. We show that this results in a MIMO spatial multiplexing scheme that is robust to noise and channel estimation errors. In the latter context, the proposed LU-based beamforming compares favorably to the QR-based counterpart in terms of complexity and bit error rate.


2020 ◽  
Author(s):  
Moustapha Mbaye ◽  
Moussa Diallo ◽  
Mamadou Mboup

Abstract This paper considers time-domain spatial multiplexing in MIMO wideband system, using an LU-based polynomial matrix decomposition. Because the corresponding pre- and post-filters are not paraunitary, the noise output power is amplified and the performance of the system is degraded, compared to QR-based spatial multiplexing approach. Degradations are important as the post-filter polynomial matrix is ill-conditioned. In this paper, we introduce simple transformations on the decomposition that solve the ill-conditioning problem. We show that this results in a MIMO spatial multiplexing scheme that is robust to noise and channel estimation errors. In the latter context, the proposed LU-based beamforming compares favorably to the QR-based counterpart in terms of complexity and bit error rate.


2016 ◽  
Vol Volume 25 - 2016 - Special... ◽  
Author(s):  
Moustapha Mbaye ◽  
Moussa Diallo ◽  
Bamba Gueye

This paper presents a MIMO-OFDM " Beamforming " approach in a IEEE 802.11ac context. This technique of " Beamforming " has the same performance as the conventional technique while allowing to perform the precoding and postcoding at one time and whatever the number of OFDM subcarriers. Dans ce papier nous présentons une nouvelle approche de " Beamforming " en MIMO OFDM dans un contexte IEEE 802.11ac. La nouvelle technique de " Beamforming " présente les mêmes performances que la technique conventionnelle tout en permettant de réaliser le pré-codage et le post-codage en une seule fois et ce quelque soit le nombre de sous-porteuses OFDM.


Author(s):  
Alexander Voevoda ◽  
◽  
Vladislav Filiushov ◽  

The application of advanced synthesis methods is due to the increasing complexity of control objects. Relatively simple objects are represented as a single-channel system or as a combination of such systems and are calculated separately. More complex systems must be viewed as multi-input and multi-output systems. There are several approaches to this. Within the framework of this paper we will consider the synthesis of a system presented in the form of a polynomial matrix decomposition. It allows us to write a closed loop system in such a way that, by analogy with single-channel systems, it is possible to single out the "numerator" and "denominator" not only of the object and the controller, but of the entire system. For multichannel objects, they will be written in a matrix form allowing you to select the characteristic matrix whose determinant is the characteristic polynomial. In this paper, an emphasis is placed on the derivation of four variants of the polynomial matrix description (PMD) of a closed system. Such a variety of representation of a closed-loop system follows from the equivalent writing of the transfer matrix in the form of left and right PMD of an object or controller. Of the four options for recording the system, two options – left and right – for the characteristic matrix are distinguished. When they are reduced to a diagonal form, the elements on the main diagonal contain the poles of a closed system along the corresponding channel. From the example given at the end of the paper, it can be seen that it is more convenient to use the left characteristic matrix because it has a lower dimension for a non-square object (the number of input and output quantities is not equal), with the number of input actions exceeding the number of output quantities, The right characteristic matrix can also be used to synthesize such a control object, but the resulting solution is more complicated and not obvious. The situation is reversed if we consider an object with fewer inputs than outputs. In this case, the right characteristic matrix will be smaller and more suitable for synthesis. It follows from this that the procedure for synthesizing a control system for non-square objects differs depending on the number of inputs and outputs.


Author(s):  
Alexsander Voevoda ◽  
◽  
Victor Shipagin ◽  

In this article, we consider a method for selecting a structure of a neural network used to regulate an "inverted pendulum on a cart" object taking into account its additional features of a mathematical description, namely, nonlinear parameters. The algorithm is illustrated by the example of control synthesis which includes two neuroregulators. One of them is responsible for bringing the cart to the specified position, and the second is responsible for holding the pendulum in a vertical position. The structure transformations will be performed for the controller responsible for bringing the cart to the specified position. The architecture of a neural network controller is based on a discrete controller synthesized using polynomial matrix decomposition. For the original controller, we define the limits of its possible control of a nonlinear system. To increase the range of control of a nonlinear object, we perform transformations of the neural network structure of the original controller. We will make some complications in the structure of the neural network of the regulator, namely, increase the number of neurons and replace some activation functions with nonlinear ones (hyperbolic tangent). Next, we suggest one of the ways to select initial values of weight coefficients. Then we train the neural network and check the performance of the resulting controller on a nonlinear object. At the next stage, we compare the obtained performance of a controller having a complicated neural network structure with the performance of a classical controller. Thus, the purpose of this study is to formalize the synthesis procedure for a neural network controller for controlling a nonlinear object using a calculated classical controller for a linearized object model. The proposed method of generating the architecture of a neural network of controllers makes it possible to increase the range of control by a nonlinear object in comparison with the controller obtained by the method of polynomial matrix decomposition for a linear object. Compared to the typical ones, the proposed neural network structure is not redundant and therefore does not require additional computing resources to configure it.


2011 ◽  
Vol 30 (1) ◽  
pp. 81-85
Author(s):  
Er-lin Zeng ◽  
Shi-hua Zhu ◽  
Xue-wen Liao ◽  
Jun Wang

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