ELM‐based timing synchronization for OFDM systems by exploiting computer‐aided training strategy

Abstract

Due to the implementation bottleneck of training data collection in realistic wireless communications systems, supervised learning‐based timing synchronization (TS) is challenged by the incompleteness of training data. To tackle this bottleneck, the computer‐aided approach is extended, with which the local device can generate the training data instead of generating learning labels from the received samples collected in realistic systems, and then construct an extreme learning machine (ELM)‐based TS network in orthogonal frequency division multiplexing (OFDM) systems. Specifically, by leveraging the rough information of channel impulse responses (CIRs), i.e. root‐mean‐square (r.m.s) delay, the loose constraint‐based and flexible constraint‐based training strategies are proposed for the learning‐label design against the maximum multi‐path delay. The underlying mechanism is to improve the completeness of multi‐path delays that may appear in the realistic wireless channels and thus increase the statistical efficiency of the designed TS learner. By this means, the proposed ELM‐based TS network can alleviate the degradation of generalization performance. Numerical results reveal the robustness and generalization of the proposed scheme against varying parameters.

Timing‐deviation and frequency‐offset estimations for multicarrier transmission in high mobility environments using deep neural network

“…The extreme learning machine (ELM) network is also used for timing synchronization in the OFDM system with nonlinear distortion, 32 but the specific preamble structure limits its application. Also, Mintao et al 33 construct an ELM‐based timing synchronization network for the OFDM systems. In addition, the lightweight convolutional neural network (CNN) is utilized for the STO estimation in OFDM systems 34,35 .…”