Performance Evaluation of Turbo Equalization

In this paper, we provide a detailed discussion on the performance evaluation of Turbo equalization. First, it is essential to clarify that in modern communication systems, the performance of the Turbo equalization algorithm is critical to the final communication quality. Therefore, we conduct a series of tests and computations to evaluate the algorithm's performance. Specifically, we employ the calculation of extrinsic information and integrate it with the BCJR (Bahl, Cocke, Jelinek, and Raviv) algorithm for soft-decision decoding to achieve more accurate results. The BCJR algorithm, widely used for maximum a posteriori probability (MAP) decoding, efficiently computes log-likelihood ratios (LLRs) by processing the received sequence through forward and backward recursions. Additionally, we compare and analyze the performance of the Turbo equalization algorithm under various parameters, such as signal-to-noise ratios (SNR) and iteration counts, to better understand its characteristics and advantages. By leveraging iterative processing between the equalizer and decoder, Turbo equalization enhances performance through extrinsic information exchange, modeled in simulations using feedback loops. Through this research, we aim to provide a comprehensive and in-depth understanding of the performance evaluation of Turbo equalization algorithms, useful for implementation in software-defined radio (SDR) platforms like GNU Radio or MATLAB.