Frequency-Domain Joint Channel Estimation and Decoding for Faster-Than-Nyquist Signaling

Abstract

Faster-than-Nyquist (FTN) signaling has attracted a lot of attentions for the fifth-generation (5G) cellular communication systems. However, low-complexity receiver design for FTN signaling becomes challenging. In this paper, we develop frequency-domain joint channel estimation and decoding methods for FTN signaling transmitting systems over frequency-selective fading channels. To deal with the colored noise inherent in FTN signaling, we propose to approximate the corresponding autocorrelation matrix by a circulant matrix, the special eigenvalue decomposition of which facilitates an efficient fast Fourier transform operation and decoupling the noise in frequency domain. Through a specific partition of the received symbols, many independent estimates are obtained and combined to further improve the accuracy of the channel estimation and data detection. Moreover, instead of assuming the data symbols to be Gaussian random variables, a generalized approximated message passing-based equalization is developed and embedded in the turbo iterations between the channel estimation and the soft-in soft-out decoder. Simulation results show that the proposed algorithm outperforms the cyclic prefix-based and overlap-based frequency-domain equalization methods. With the proposed algorithms, FTN signaling reaches up to 67% higher transmission rate compared to the Nyquist counterpart without substantially consuming more transmitter energy per bit, and the overall complexities grow logarithmically with the length of the observations.