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A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems

Qasaymeh Mahmoud M., Alqatawneh Ali, Khodeir Mahmoud A., Aljaafreh Ahmad F.

Journal of Telecommunications and Information Technology

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2024

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nr 3

57--64

EN

This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.

2

Applicability of artificial intelligence to reservoir induced earthquakes

Samui P., Kim D.

Acta Geophysica

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2014

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Vol. 62, no. 3

608--619

EN

This paper proposes to use least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of the magnitude (M) of induced earthquakes based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are used as input variables of the LSSVM and RVM. The output of the LSSVM and RVM is M. Equations have been presented based on the developed LSSVM and RVM. The developed RVM also gives variance of the predicted M. A comparative study has been carried out between the developed LSSVM, RVM, artificial neural network (ANN), and linear regression models. Finally, the results demonstrate the effectiveness and efficiency of the LSSVM and RVM models.

3

Least square image matting

Kun W., Nanning Z., Huilai Q.

Machine Graphics and Vision

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2007

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Vol. 16, No. 1/2

139-152

EN

This paper addresses the well-known problem of natural image matting. Most of the previous matting algorithms require the user to define the tri-map, which is an inconvenient work and sometimes a burden, especially in a complex situation. This paper uses ceratain user defined foreground and background strokes to estimate the image matte. First we use a Gauss Markov random field to model the matting problem. Then we use the least square optimization approach to solve it. Experimental results show that our approach could properly handle confused boundaries. It also could deal with semi-transparent conditions such as fire etc.

4

A randomized algorithm for detecting multiple ellipses based on least square approach

Li L., Feng Z., He K.

Opto-Electronics Review

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2005

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Vol. 13, No. 1

61--67

EN

In this paper, a randomized method for detecting multiple ellipses based on the least square approach is presented. The main concept used is that we first randomly select three edge pixels in the image, which are the centre of three windows with the same size. In order to determine a possible ellipse, we use the least square method to fit all the edge points in these three window, and to solve the ellipse parameters through Lagrange multiplier method. Then we randomly select the fourth edge pixel in the image and define a distance criterion to determine whether there is a possible ellipse in the image. After finding a possible ellipse, we apply a further verification process to determine whether the possible ellipse is a true ellipse or not. Some artificial images with different levels of noises and some natural grey images containing circular objects with some occluded ellipses and missing edges have been taken to test the performance. Experimental results demonstrate that the proposed algorithm is faster and more accurate than other methods.