首页|An Adaptive Crocodile Optimization Algorithm Based Deep Elman Recurrent Neural Network for Channel Estimation With Hybrid Precoder in MIMO-OFDM System
An Adaptive Crocodile Optimization Algorithm Based Deep Elman Recurrent Neural Network for Channel Estimation With Hybrid Precoder in MIMO-OFDM System
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NETL
NSTL
Wiley
Due to the massive usage of smartphones, frequent usage of the IoT, and wireless visual streaming services, data traffic in thewireless network and data explosion has increased over the next years. System modeling and channel estimation are the twomain challenges while designing the wireless 5G MIMO communication system. A 2 × 2 MIMO-SFBCsystem is proposed toenhance the spectral efficiency and capacity of wireless communication systems by exploiting spatial diversity and frequencydiversity. The SFBC coding technique gives a low bit error rate (BER) and high signal-to-noiseratio (SNR). Channel modelingand channel estimation are very difficult tasks in the complex propagation characteristics of highly dynamic channels. Thispaper proposes an improved ERNN-LSTMnetwork to enhance the accuracy and efficiency of channel modeling and estimationin wireless communication systems. Initially, a least squares estimator is employed to obtain an initial estimate of the historicalchannel responses of a pilot block. These initial estimates are subsequently utilized to train an Elman recurrent neural network(ERNN). The weights of the ERNN's channel parameters are optimized using the Adaptive Crocodile Algorithm. Simulation resultsshow that the proposed ACO-DERNNmethod achieves a BER of 10~(−5) at 30 dB SNR, outperforming conventional methods.
Department of Electronics and Communication Engineering, Rajagiri School of Engineering & Technology, APJ Abdul Kalam Technological University,Kerala, India
NETL
NSTL
Wiley
