A novel channel estimation of MIMO-OFDM using hybrid bionic binary spotted hyena optimization

Abstract

A promising generalized inverse discrete Fourier transform non-orthogonal frequency division multiplexing (GIDFT-OFDM) system can satisfy the requirement of supporting higher data rates in fifth-generation (5G) technology. However, this system has a high peak-to-average power ratio (PAPR) because many subcarrier signals are transmitted. The inverse discrete Fourier transform (IDFT) is used in an orthogonal frequency-division multiplexing (OFDM) modulator to convert symbols from the frequency domain to the time domain and add a cyclic prefix before sending them through the channel. In pilot-based channel estimation, pilots are inserted into the transmitter and detected at the receiver, along with the OFDM symbols. In this study, we searched for local and global optimal solutions of the Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm with position coordinate vectors (PCVs) of social behavior. It also introduces the BBSHO algorithm to improve the local search capability within the search space. Optimized pilots provided better performance than orthogonal and randomly placed pilots. The stochastic, quadrature, and whale swarm algorithms detect the position of the pilot. To improve the data quality and reduce the BER, MSE, and SER, we introduced several optimization algorithms on the channels of MIMO-OFDM devices. The performance of the two optimization algorithms proposed above contrasts with that of the current simple algorithms and shows improved results in MIMO-OFDM networks. The proposed optimization algorithm was implemented using the MATLAB 2021(a) software. For channel optimization, metaheuristic algorithms such as the Whale Swarm Algorithm (WSA) and the Hybrid Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm are used.