Prairie Dog Optimization Algorithm with Differential Evolution Model for Channel Estimation in MIMO-OFDM

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) integrates MIMO’s spatial multiplexing with OFDM’s effective frequency division which enhance the spectral efficiency and data rates. It increases wireless communication by involving multipath fading and improves signal reliability. However, channel estimation in MIMO-OFDM is challenging because of the complex nature of multipath propagation and channel condition which results in inaccurate estimation. This research proposes Prairie Dog Optimization Algorithm with Differential Evolution Model (PDOA-DEM) to accurately estimate the channel in MIMO-OFDM. PDOA balances exploration which makes it an effective search solution space and avoids the local optima issue. IN PDOA, the DE is applied to provide faster convergence and robustness in optimizing continuous functions. Quadrature Phase Shift Keying (QPSK) provides effective bandwidth utilization by transmitting two bits per symbols. The proposed PDOA-DEM obtains a less Normalized Mean Square Error (NMSE) of 0.005 at SNR 20 dB when compared to the existing method, Distributed Ant Colony Optimization (DACO) respectively.