Deep Learning-Based Wireless Channel Estimation for MIMO Uncoded Space-Time Labeling Diversity

Mthethwa, Bhekisizwe; Xu, Hongjun

doi:10.1109/access.2020.3044097

Cited by 10 publications

(6 citation statements)

References 30 publications

(54 reference statements)

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“…From the system model, the variable √ AΓ is replaced by G reducing the computational complexity of the method. The aforementioned Equation ( 5) is deeply explained in Equation (6).…”

Section: Developed Channel Estimation Modelmentioning

confidence: 99%

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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“…From the system model, the variable √ AΓ is replaced by G reducing the computational complexity of the method. The aforementioned Equation ( 5) is deeply explained in Equation (6).…”

Section: Developed Channel Estimation Modelmentioning

confidence: 99%

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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“…DNN is used on the deep image prior system to denoise the received signal first and conventional least-squares estimation is done. A blind wireless channel and bandwidth-efficient estimator for the uncoded space-time labeling diversity system is designed in [ 147 ]. An NN-ML channel estimator with transmitting power-sharing is used to perform blind channel estimation for the given system and to reduce the bandwidth utilization.…”

Section: Rl and DL Application In Mimomentioning

confidence: 99%

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Naeem

Pietro

Coronato

2021

Sensors

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The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

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“…Toward enhancing the link reliability, a neural network model for a wireless channel estimator is proposed in [ 11 ] to be used with uncoded space-time diversity procedure in Multi Input Multi Output (MIMO) system. Based on the neural network ML structure, a channel estimator is suggested, and a mathematical scheme is presented to derive an optimum power transmission factors that can assist in lessening the channel prediction bandwidth utilization.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

Gaballa

Abbod

Aldallal

2023

Sensors

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In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.

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Deep Learning-Based Wireless Channel Estimation for MIMO Uncoded Space-Time Labeling Diversity

Cited by 10 publications

References 30 publications

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

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

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

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