Enhancing Data Transmission Reliability in MIMO-OFDM Systems With Intelligent Error Correction Codes.

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems have become a cornerstone of modern wireless communication, offering high data rates and superior spectral efficiency. Despite these advantages, MIMO-OFDM systems are prone to channel impairments such as fading, noise, and interference, which can significantly compromise the reliability of data transmission. This research addresses these challenges by integrating intelligent error correction codes into the MIMO-OFDM framework. Advanced machine learning techniques, including Artificial Neural Networks (ANNs) and Reinforcement Learning (RL), are employed to dynamically optimize encoding and decoding, enabling real-time adaptation to changing channel conditions. Simulation results demonstrate that the proposed intelligent error-correction method yields substantial improvements in bit error rate (BER), signal-to-noise ratio (SNR), and overall system robustness compared to conventional approaches like Turbo Codes and Low-Density Parity-Check (LDPC) codes. Specifically, the integration of intelligent error correction codes reduced the BER from 0.08 to 0.073 bits and improved the SNR from 8.35 dB to 10.02 dB, resulting in a 20% increase in data transmission reliability. These findings highlight the potential of intelligent error correction frameworks as a promising solution for next-generation wireless systems, especially in environments characterized by high mobility and complex propagation conditions.