Characterization of GFDM Signal with Timing Offset, CFO, Non-Linearity, and PN

In this paper, we study the joint effects of timing offset (TO), carrier frequency offset (CFO), nonlinear power amplifier distortion, and phase noise (PN) on generalized frequency division multiplexing (GFDM) system. Closed form expressions for signal-to-interference ratio (SIR) at GFDM receiver with synchronization errors and PN using a nonlinear power amplifier is derived. Then, we have been conducted simulation studies to compare the performance of GFDM systems with orthogonal frequency division multiplexing (OFDM) systems using matched filter (MF) and zero forcing (ZF), in presence of these impairments. The results show that GFDM systems are more robust against TO and PN while they are more sensitive to CFO and nonlinear distortion compared to OFDM systems.

Deep Learning-Based Timing Offset Estimation for Deep-Sea Vertical Underwater Acoustic Communications

This study proposes a novel receiver structure for underwater vertical acoustic communication in which the bias in the correlation-based estimation for the timing offset is learned and then estimated by a deep neural network (DNN) to an accuracy that renders subsequent use of equalizers unnecessary. For a duration of 7 s, 15 timing offsets of the linear frequency modulation (LFM) signals obtained by the correlation were fed into the DNN. The model was based on the Pierson–Moskowitz (PM) random surface height model with a moderate wind speed and was further verified under various wind speeds and experimental waveforms. This receiver, embedded with the DNN model, demonstrated lower complexity and better performance than the adaptive equalizer-based receiver. The 5000 m depth deep-sea experimental data show the superiority of the proposed combination of DNN-based synchronization and the time-invariant equalizer.