An Automated Sparse Channel Estimation Framework for MU-MIMO-OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

Compressed sensing is used for channel estimation in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, but large-scale networks face challenges regarding the antenna elements and spatial non-stationarities. To enhance spectral efficiency in Multi User-MIMO (MU-MIMO) systems, essential signals are collected by Quadrature Phase Shift Keying (QPSK) in the transformer phase. Further, the modulated signals are provided to the “Pulse Shaping Algorithm (PSA)” for neglecting the inter-symbol interference rate along with inter-carrier interference. Subsequently, in the transmitter phase, the “Inverse Fast Fourier Transforms (IFFT)” technique is performed to map the symbols and then provide the signal to the receiver side. The spare channel is validated using a semi-blind sparse algorithm, with parameters tuned using the Opposition Mud Ring Algorithm (OMRA). Then, the estimated sparse channel values are used for generating the new data, and the Adaptive Extreme Learning Model (AELM) is trained to predict the spare channel outcome. The main objective is to reduce the Minimum Mean Square Error (MMSE) in the channel. Thus, the spare channel outcome is predicted automatically using the AELM. Then, diverse evaluations are executed in the suggested spare channel estimation approach over the different mechanisms to observe their effectualness rate.