Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems

Abstract

Universal filtered multicarrier (UFMC) has emerged as a potential waveform contender of orthogonal frequency division multiplexing (OFDM) for the fifth generation (5G) and beyond wireless systems. In this paper, we propose a bidirectional long short-term memory (Bi-LSTM)-based detector for the UFMC system. The proposed detector directly detects the transmitted symbols using the deep learning (DL)-based training data. The system is first trained with the aid of training data and pilot symbols. The training tunes the DL-based network parameters. During the testing phase, the signal is detected using the trained network. The performance of the proposed scheme is compared with that of the DL-aided OFDM system, and with the signal detection strategies using the conventional channel estimation techniques. Our simulations show that the proposed Bi-LSTM-based DL can flexibly and effectively detect UFMC signals.

Keywords

• Universal filtered multicarrier
• Deep learning
• Long short-term memory (LSTM)
• Bidirectional LSTM
• Rayleigh fading channel