Joint Superimposed Pilot-aided Channel Estimation and Data Detection for FTN Signaling over Doubly-Selective Channels

Abstract: Faster-than-Nyquist (FTN) signaling and superimposed pilot (SP) techniques are effective solutions for significantly enhancing the spectral efficiency (SE) in next-generation wireless communication systems. This paper proposes an innovative SP-aided channel estimation method for FTN signaling enhancing the SE over doubly-selective (i.e., time- and frequency-selective) channels. To avoid complex channel tracking, we utilize a basis expansion model (BEM) to characterize doubly-selective channel variations. We propose a frame structure that superimposes a known periodic pilot sequence onto the information sequence, avoiding SE loss by eliminating the additional overhead of multiplexed pilot (MP). Additionally, we find the optimal FTN signaling SP sequence that minimizes the mean square error (MSE) of the channel estimation. Expanding on our proposed SP-aided channel estimation method, we propose two detection methods: (1) an SP-aided separate channel estimation and data detection (SCEDD) method performing a single channel estimation followed by iterative data detection via a turbo equalizer, serving as a baseline for evaluating the SP-aided channel estimation method, and (2) an SP-aided joint channel estimation and data detection (JCEDD) method, which extends the SCEDD by updating the channel estimate in each turbo equalization iteration, becoming our primary focus for its superior performance. At equivalent SE and a higher fading rate on the order of $10^{-3}$, our simulations show that SP-aided SCEDD method outperforms MP-aided techniques in both MSE and BER, while the SP-aided JCEDD method delivers remarkable performance, where reference approaches fail to track rapid channel variations.