Hybrid optimization algorithm to estimate azimuth angle for millimeter wave massive MIMO system

Rao, Y. Srinivasa; Madhu, R.

doi:10.1007/s10772-021-09798-z

Cited by 1 publication

(2 citation statements)

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“…The proposed "channel estimation in mmWave massive MIMO communication system" was "implemented in MATLAB 2020a using data Deep MIMO. Here, the performance of the proposed model was compared over the conventional models" in terms of several measures like NMSE and spectral efficiency over other heuristic-algorithms like Dragonfly Algorithm (DA), 46 "Deer Hunting Optimization Algorithm (DHOA), 47 Gray Wolf Optimization (GWO)" 48 and HHO-D-LSTM 42 and other channel estimation models like Convolutional Neural Network (CNN), 49 DNN, 39 LSTM 41 and D-LSTM. 39,41 The simulation constraints for designing the mmWave massive MIMO communication system are given in Table 2.…”

Section: Methodsmentioning

confidence: 99%

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Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

Suneetha

Satyanarayana

2023

Int J Communication

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The major goal of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is to get effective channel state information (CSI). Most of the recent works use nuclear norm theory for recovering the low-rank scheme of channels. Some suboptimal solutions to the rank minimization problem can occur while addressing the nuclear norm-based convex problem, which degrades the accuracy of channel estimation. Some works recover the channel with the assumption of the mmWave channel using an over-complete dictionary. On the other hand, the accuracy of available CSI may openly influence the efficiency of mmWave communications. The main intention of this paper is to develop an enhanced channel estimation model with an optimized hybrid deep learning model. Here, the integration of deep neural network (DNN) and long short-term memory (LSTM) form the hybrid deep learning model termed optimized D-LSTM, which is modified by the opposition searched explorationbased Harris hawks optimization (OE-HHO). The input to the proposed hybrid deep learning is taken as the correlation among the received signal vectors and the measurement matrix for predicting the beam space channel amplitude.Finally, the successful channel estimation is observed by deep hybrid learning by the experimental outcomes, which also demonstrate that the proposed channel estimation model overwhelms the conventional models in terms of Normalized Mean-Squared Error (NMSE) and spectral efficiency. The experimental results show that the designed OE-HHO method obtains 9.2%, 8.9%, 8.65%, and 0.47% progressed than DA, DHOA, GWO, and HHO, respectively. Therefore, higher efficiency is observed by OE-HHO based mmWave MIMO communication system.

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Section: Methodsmentioning

confidence: 99%

“…Evaluation of the computational analysis using uplink and downlink The computational complexity of the designed methodAlgorithmTime complexityOE-HHO O [iter+(N*N)] O [iter+(N + N)]-HHO T A B L E 7Evaluation of the statistical analysis using uplink and downlink Measures DA-D-LSTM46 DHOA-D-LSTM47 GWO-D-LSTM48 HHO-D-LSTM42 …”

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confidence: 99%

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

Suneetha

Satyanarayana

2023

Int J Communication

View full text Add to dashboard Cite

show abstract

Hybrid optimization algorithm to estimate azimuth angle for millimeter wave massive MIMO system

Cited by 1 publication

References 39 publications

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

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