ABSTRACT
Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless transmission systems. But if the peak-to-average power ratio (PAPR) is too high, OFDM symbols can be distorted at the MIMO OFDM transmitter. It will make it harder for the MIMO OFDM receiver in the channel estimation and signal detection phase. To explore the possibilities of Deep Learning (DL) in particular and machine learning in general in the MIMO OFDM system and to serve as a foundation for future research, we develop a DL-based MIMO OFDM receiver using DL in this work. From there, DL models can help filter out the noise caused by the high PAPR problem and change some parts at the receiver to improve the receiver’s performance in the point-to-point MIMO OFDM system. The simulations show that the suggested DL-based receivers have a lower bit error rate (BER) than conventional receivers.
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Index Terms
- Robust Deep Learning Approaches for Wireless Communication Systems
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