Residual Network-Based Channel Estimation for the Protograph LDPC-Coded OFDM Systems

In orthogonal frequency-division multiplexing (OFDM) systems, channel estimation is required for guaranteeing the transmission quality over rapidly changing wireless channels. At the same time, it is crucial to recover the clipping signal when resolving the high peak average power ratio (PAPR) of the transmitted OFDM signal. In this letter, we apply the deep learning (DL) technique to boost the performance of channel estimation in the protograph low-density parity-check (PLDPC)-coded bit-interleaved coded modulation (BICM)-aided OFDM (BICM-OFDM) systems. Specifically, we propose a channel estimation residual network (CERNet) to estimate the channel state information (CSI). Furthermore, a signal recovery residual network (SRRNet) is designed to recover the clipped signal. Our proposed residual networks can capture the frequency-specific features of the channel matrices and the correlation of the clipping noise over the received data signal adequately without relying on explicit priori channel information to further improve the channel estimation performance. Both analytical and simulation results show that the CERNet achieves superior performance compared with the conventional estimation scheme, and the joint CERNet and SRRNet can further improve the system performance.