Deep Convolutional Spiking Neural Network Optimized with Coyote Chimp Optimization Algorithm for Imperfect Channel Estimation in MIMO-f-OFDM/FQAM Based 5G Network
International journal of intelligent engineering and systems · 2023
multiple-input multi-output (MIMO) models need orthogonal frequency division multiplexing (OFDM) to employ in multipath communication efficiently.In channel constraints, the channel estimation (CE) is utilized where time changing features are needed.The previous implemented CE algorithms are highly complex.So, there is a requirement for efficient CE technique to estimate the correctness of received signal.To resolve this difficulty in CE approaches, an innovative CE algorithm called deep convolutional spiking neural network with coyote and chimp optimization algorithm (DCSNN-HCCOA) with the assistance of deep learning (DL) has been proposed.Throughout the estimation period, the proposed MIMO/f-OFDM/FQAM requires the channel consistent, for this purpose the introduced DCSNN is integrated with the hybrid Coyote and Chimp optimization.It provides maximum estimation precision and minimum mean square error (MMSE) and bit error rate (BER).In terms of NMSE, BER, ISI and ICI the obtained results of proposed architecture are better than the other channel estimation systems.The achieved performance measure values are: 0.02, 0.02, 1% and 4% respectively.