# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
## 1 Introduction  
In the 1970s, OFDM was introduced and implemented by researchers such as [PERSON] and [PERSON]. Its principle involves dividing the wireless channel into multiple orthogonal subchannels in the frequency domain and transmitting data in parallel over these subchannels. The main steps include modulation, inverse discrete Fourier transform (IDFT), cyclic prefix (CP) insertion, parallel-to-serial conversion and transmission, reception and serial-to-parallel conversion, cyclic prefix removal, discrete Fourier transform (DFT), demodulation, and data recovery [1, 2, 3, 4, 5]. Currently, OFDM technology is the primary modulation scheme in high-speed wireless communication systems due to its advantages in mitigating inter-symbol interference [6, 7], improving spectral efficiency [8, 9], enhancing system anti-interference capability, and offering flexible access methods [10, 11]. With the development of 5G communication technology, OFDM has been widely applied in various high-speed wireless communication systems, becoming the mainstream modulation technique. However, due to the complexity and variability of wireless channels, the signals received at the receiver often suffer from noise, interference, and multipath effects, leading to signal quality degradation. Therefore, obtaining accurate channel state information (CSI) is crucial for ensuring system reliability and performance [12, 13]. Currently, channel estimation can be categorized into blind channel estimation, semi-blind channel estimation, and non-blind channel estimation (pilot-based channel estimation) based on prior knowledge. Blind channel estimation relies solely on the statistical characteristics of the received signals without any known pilot signals or training sequences. It saves spectral resources but has high algorithm complexity and slow convergence speed, making it suitable for scenarios with tight spectral resources. Common techniques include high-order statistics [14; 15; 16], cyclostationary characteristics [17; 18; 19], and subspace methods [20; 21; 22; 23]. Non-blind channel estimation involves inserting known pilot signals at the transmitter and estimating the channel response at the receiver using these known signals. Common non-blind estimation methods include the LS method and the MMSE method [24; 25; 26]. The LS method estimates the channel matrix by minimizing the squared error between the pilot and received signals and is widely applied due to its simplicity and ease of implementation. However, the LS method performs poorly under low SNR conditions, being highly susceptible to noise. The MMSE method, on the other hand, takes into account the statistical characteristics of the noise, optimizing channel estimation by minimizing the mean square error. While MMSE offers better performance under low SNR conditions compared to LS, it requires prior knowledge of the channel and noise statistics, which is challenging to obtain in practical applications, leading to higher computational complexity [27; 28]. Semi-blind channel estimation combines partial known pilot signals with received data for channel estimation. Compared to blind estimation, it improves accuracy and convergence speed by introducing a small number of pilot signals; compared to non-blind estimation, it reduces the number of pilot signals, enhancing spectral efficiency. Common semi-blind estimation methods include Eigenvalue Decomposition (EVD) [29; 30; 31] and Independent Component Analysis (ICA) [32; 33; 34; 35]. Recently, deep learning-based channel estimation methods have gained extensive attention due to their potential to achieve near-MMSE performance without prior channel statistical information [36; 37]. However, these methods suffer from high computational complexity, lack of interpretability, limited adapt-ability to channel variations, and strong data dependency. Meanwhile, the Parrot Optimizer (PO) has demonstrated outstanding optimization performance, sparking research into its application across various fields.