Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning - Ying WANG; Jun REN; Ke SHI; Bin LIN (Tongxin xuebao)
There exist intrinsic inter-carrier interference and inter-subsymbol interference in generalized frequency division multiplexing (GFDM) systems.Under condition of time-frequency doubly selective channels, severe effects of pilot contamination would occur and lead to significant performance degradation for the pilot-based channel estimations.To this end, a channel estimation framework for GFDM systems based on deep learning was proposed, which took the low-resolution image constructed with the least squares estimated channel gains of the pilot symbols as input.Consequently, a high-resolution image about the channel time-frequency response was recovered through a deep residual network, and the channel estimation was achieved for GFDM systems.A simulation system for the proposed GFDM time-frequency doubly selective channel estimation algorithm based on deep residual network was developed, and the optimal parameters of the deep residual network were determined through an offline training process.Simulation results show that the proposed algorithm can achieve better performance near to minimum mean square error (MMSE) estimation in terms of estimation error and bit error rate (BER), and has robust Doppler frequency shift generalization capability.