A Novel Application of Pattern Search Algorithm for Efficient Estimation of Channel State Information in MIMO Network

Ali, Wasiq; Li, Yaan; Ahmed, Nauman; Ali, Wasif; Kashif, Muhammad

doi:10.1007/s11277-020-07717-0

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…In 2021, Kashif et al 22 implemented a global optimizer pattern search algorithm for the estimation of CSI in the MIMO system. The accurate CSI was found according to the channel matrix coefficients.…”

Section: Literature Surveymentioning

confidence: 99%

“…Therefore, it requires high power for the channel estimation. DML 22 method is used to resolve the problems in the blind joint channel estimation based on the channel impulse response, which helps to enhance the accuracy. Moreover, the unknown parameters for input signals in the joint channel estimation have become a challenging task 23 .…”

Section: Literature Surveymentioning

confidence: 99%

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Efficient Channel Estimation in Multiple Input Multiple Output Communication Systems using Hybrid Intelligence‐based Deep Learning Model using Horse Dingo Optimizer

Manasa

Venugopal

2023

Trans Emerging Tel Tech

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The estimation of the channel in multiple input multiple output (MIMO) systems over wireless communication has faced many challenges, such as high bit rate transmission with less time complexity, low error rate, and high convergence rate. And also, there is some potential for enhancing the system performance regarding channel efficiency diversity. MIMO system uses a huge count of antennas on the receiver and the transmitter side. This increases the cost requirements and power consumption. For achieving coherent detection in the MIMO system, optimization such as water filling and beam forming are required to find the proper channel. Hence, multiple transceivers used in both the transmitter side and receiver side create a channel estimation issue that is complicated when analyzed to a single input, single output system. Therefore, several solutions have been recommended to decrease the computational cost and power consumption in channel estimation of MIMO systems. This work intends to propose a novel deep structured architecture to handle the channel estimation problem in a MIMO system. The major objective of the developed method is to validate the channel coefficients of MIMO at the transmitter according to the obtained SNR feedback data packet from an acceptor with the help of the hybrid intelligence‐based deep learning model (HI‐DLM). For both scenarios, the HI‐DLM is introduced, in which the convolution neural network (CNN) is integrated with other deep‐structured architecture. Especially, the fully connected layers of CNN are restored by the long short‐term memory. In the HI‐DLM‐aided channel estimation, the hyperparameter optimization is accomplished by the novel dingo‐horse herd optimization for reducing the mean square error and bit error rate to acquire the estimated channel. The corresponding algorithm is validated on the communication of the MIMO system by experimentations. Thus, it is revealed that the offered algorithm provides better convergence and less computational cost.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%