Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems

Accurate and efficient channel estimation (CE) is critical in the context of autonomous driving. This paper addresses the issue of orthogonal frequency-division multiplexing (OFDM) channel estimation in 5G communication systems by proposing a channel estimation model based on the Parrot Optimizer (PO). The model optimizes for the minimum bit error rate (BER) and the minimum mean square error (MMSE) using the Parrot Optimizer to estimate the optimal channel characteristics. Simulation experiments compared the performance of PO-CE with the Least Squares (LS) method and the MMSE method under various signal-to-noise ratios (SNR) and modulation schemes. The results demonstrate that PO-CE's performance approximates that of MMSE under high SNR conditions and significantly outperforms LS in the absence of prior information. The experiments specifically included scenarios with different modulation schemes (QPSK, 16QAM, 64QAM, and 256QAM) and pilot densities (1/3, 1/6, 1/9, and 1/12). The findings indicate that PO-CE has substantial potential for application in 5G channel estimation, offering an effective method for optimizing wireless communication systems.