I. Introduction
The reduction of the Frame Error Rate (FER) is a major concern for wireless communication systems, as it affects the overall efficiency of the system. The traditional methods of reducing FER have relied on static signal processing and error correction algorithms, such as Cyclic Redundancy Check (CRC) and Forward Error Correction (FEC). However, these methods can only reduce the FER up to a certain limit and cannot eliminate it completely [1]. A new technique to reduce the FER is channel estimation, which involves a process of tracking and estimating the effects of the channel on the transmitted data. With this method, errors can be detected and corrected more quickly and accurately than with static signal processing algorithms, leading to a decreased FER. The channel estimation process usually involves a two-step procedure. The first step is channel estimation, where a base station transmits a pilot signal to the transmitter and receiver to check the channel conditions. Based on this information, an estimate of the channel is created and used to calculate the transmission parameters for the data signals [2]. The second step is channel error detection, where the channel condition is monitored and possible errors in the data transmission are detected and corrected. Channel estimation is a relatively new technique and has proven to be very successful in reducing the FER. It is also useful in reducing the time required for data transfer, as fewer corrections are required. In addition, it does not require any additional power or equipment, making it a very cost-effective solution for wireless communication systems. Overall, the use of channel estimation has been very successful in reducing the FER of wireless communication systems [3]. By using this technique, faster data transfer speeds, improved accuracy, and reduced FER are achieved, making it an essential tool for modern wireless communication networks. Reduction of FOR (FSO Receiver Extinction Ratio) with channel estimation methods is an optimization tool for FSO (Free Space Optical) communications. FSO communications provide a reliable, high-data rate solution for point-to-point communication links. This technology has gained in popularity due to increased security, immunity to EM interference, and scalability [4]. However, the poor precision of the phase noise of the FSO receivers degrades the FSO link quality. The phase noise has a direct impact on the FOR of the FSO receiver, which is one of the most important performance indicators for the system. The FOR of the FSO receivers can be improved by channel estimation techniques. By measuring the channel's impulse response, channel estimation techniques allow the receiver to adapt to the changing channel conditions and reduce its sensitivity to phase noise [5]. With accurate channel estimation, the FSO receiver can reduce its estimation error and also effectively pre-process the signal by applying equalization filters. This eliminates the need to use very high bitwidths in the receiver, which is critical to reduce energy consumption. Additionally, improved channel estimation reduces the rate of symbol errors in the receiver, thereby increasing the data rate and improving the overall reception quality in the system. In conclusion, reducing the FER of FSO receivers with channel estimation methods is an essential optimization tool for FSO communication [6]. With improved channel estimation, the system is able to adapt to the changing channel conditions and reduce its sensitivity to phase noise. This increased accuracy of information allows the FSO receiver to reduce its estimation error and to apply equalization filters. Additionally, improved channel estimation increases the reception quality and data rate and reduces energy consumption in the system. The advancement of technology has allowed for the significant reduction of FER (Frame Error Rate) when using channel estimation methods. FER is used to estimate the amount of errors present in a data frame when transmitted over a wireless channel. Through the implementation of a state-of-the-art channel estimation technique, the FER rate can be dramatically reduced [7]. This can provide an improved quality of service for many applications. This technique utilizes mathematical algorithms that allow for the estimation of channel characteristics such as multipath, fading, and noise. The channel estimates are then combined with an error correcting code to reduce the FER rate. By doing this, the required signal to noise ratio at the receiver can be reduced while simultaneously achieving a higher frame throughput [8]. The method can provide remarkable performance improvements in multipath fading channels, especially in the 5G ecosystem where the fading effects are of higher severity. This technique can also offer significant gains when used in combination with advanced modulation and coding techniques, enabling the transmission of reliable data over much longer distances [9]. Furthermore, the reduction of FER with channel estimation methods can improve the transmission of applications that require a low latency. This is beneficial in applications such as remote surgical operations, autonomous vehicle decisions, and 4K/8K video streaming. By making the required SNR at the receiver much lower, the data frames that need to be retransmitted in a time sensitive manner, can now reach the receiver with much less retransmissions [10]. The advances made in the reduction of FER with channel estimation methods have allowed for the discovery of new and exciting applications. This achieves a higher throughput, better spectral efficiency, and most importantly, a much-improved quality of service.