Advanced Signal Detection and Channel Estimation in OFDM Systems via LSTM Network

Recently in Wi-Fi broadband systems, orthogonal frequency-division multiplexing (OFDM) has emerged as a popular modulation approach for preventing frequency-selective fading in wireless channels. For coherent detection and interpretation in OFDM systems, channel state information (CSI) is essential. Pilots are usually used in order to estimate CSI prior to information detection, which allows transmitted symbols to be retrieved at the receiver based on the estimated CSI. This chapter presents the utilization of deep learning method for joint channel estimation and signal identification within an OFDM structure to address shortcomings associated with conventional strategies. Long short-term memory (LSTM) model is designed to learn appropriate parameters for predicting transmitted information across diverse channels. Performance evaluation is done by analyzing bit error rate (BER) under various signal-to-noise ratios (SNRs), comparing results with traditional strategies.