System optimization for index modulated OFDM

Abstract

This paper introduces the system optimization scheme of OFDM-IM, an emerging modulation technology that combines OFDM technology and modulation index technology to improve spectral efficiency and system reliability while ensuring high-speed transmission. However, as a new technology, OFDM still has shortcomings. This paper investigates three system optimization schemes for OFDM-IM: he deep learning-based OFDM-IM optimization scheme, MIMO-OFDM-IM, and LDPC coding-assisted subcarrier index protection scheme. It is found that these three optimization schemes are indeed helpful for optimizing the performance of OFDM-IM through system simulation experiments. The IM-OFDM system optimization based on deep learning is very helpful for IM-OFDM in terms of channel estimation, signal detection, and modulation classification. MIMO-OFDM-IM can increase channel capacity and signal transmission reliability. By protecting the subcarrier index, LDPC codes can effectively reduce the subcarrier error rate and improve the reliability and performance of IM-OFDM systems. Due to their different technical characteristics, the three optimization schemes have different advantages. They also have their application scenarios in which they excel. However, considering these technologies are relatively new, there are still certain drawbacks to these three technologies. Researchers need to continue to study them in-depth and gradually overcome them.