Performance comparison of deep learning model-based channel estimation and signal detection for OFDM systems

다국어 초록 (Multilingual Abstract)

Channel estimation and signal detection play key roles in ensuring the quality of end-to-end communication in orthogonal frequency-division multiplexing (OFDM) systems. Recently, many deep learning (DL) model-based estimation and detection approaches are being researched. These models have their advantages and disadvantages in channel estimation and signal detection for OFDM systems. To further open systematic research for real world applicability of DL in this area, this paper provides quantitative results of various DL models to compare both performance and reliability of these models to handle OFDM channels. Furthermore, simulation results show that DL scheme outperforms existing conventional schemes in terms of improving channel estimation and signal detection performance.