Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system.

Multi-user detection techniques are crucial in minimizing the impact of Multi-Access Interference in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems. It is suggested to combine channel estimation with multi-user identification to get rid of different interferences and lower the Bit Error Rate. In this manuscript, Channel Estimation and Data Detection for 5G MIMO Communication in Fading Channels based on Recalling Enhanced Recurrent Neural Network are proposed. The suggested method prioritizes fast communication and strives to provide high reliability even when there is just a small amount of user equipment per base station. The study of the multipath Rayleigh fading channel in MIMO-OFDM systems has a difficulty with frequency selective channel estimate. The primary air interface for 4G and 5G broadband wireless communications is MIMO-OFDM. With its emphasis on the network's active users and its ability to reduce interference, MIMO-OFDM offers further advantages in multi-user identification. When the activity factor is unknown, the deep learning technique is used to identify the active users in the multi-access system. With a high data rate, the RERNN classifier is utilized to evaluate the unknown activity component. The BS receiver scans for active users and deciphers their transmissions. Both pilot and data symbols are used in activity detection. A unique pilot allocation strategy that reduces the common relation of the measurement matrix is proposed for optimum pilot placement. The sparse channel assessment is eventually verified, and the computational cost is decreased, by selecting the best pilot patterns with the aid of improved Mexican axolotl optimization algorithm. The CE and decoder techniques both regulate the impact of sounds and MAI. It accurately decodes the received signal without channel issues using Python implementation. Here the performance metrics like of Symbol Error Rate, Bit Error Rate, Uplink Sum Rate, Downlink Sum Rate, Normalized CE Error and Mean Square Error. The suggested approach demonstrates a reduction in BER by 23.34%, 17.02%, and 32.21%, as well as a reduction in SER by 21.09%, 12.32%, and 19.08%, when compared to conventional methodologies. [ABSTRACT FROM AUTHOR]