Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach

• URL: http://arxiv.org/abs/2007.09248v5
• Date: Wed, 1 Jun 2022 05:35:27 GMT
• Title: Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach
• Authors: Jun Liu, Kai Mei, Xiaochen Zhang, Des McLernon, Dongtang Ma, Jibo Wei and Syed Ali Raza Zaidi
• Abstract summary: We propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. The proposed method is robust in terms of the choice of channel parameters and also in terms of "generalization ability" from a machine learning standpoint.
• Score: 8.432859469083951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is a key technology component in the evolution towards cognitive radio (CR) in next-generation communication in which the accuracy of timing and frequency synchronization significantly impacts the overall system performance. In this paper, we propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. Specifically, exploiting the preamble signals with synchronization offsets, two ELMs are incorporated into a traditional MIMO-OFDM system to estimate both the residual symbol timing offset (RSTO) and the residual carrier frequency offset (RCFO). The simulation results show that the performance of the proposed ELM-based synchronization scheme is superior to the traditional method under both additive white Gaussian noise (AWGN) and frequency selective fading channels. Furthermore, comparing with the existing machine learning based techniques, the proposed method shows outstanding performance without the requirement of perfect channel state information (CSI) and prohibitive computational complexity. Finally, the proposed method is robust in terms of the choice of channel parameters (e.g., number of paths) and also in terms of "generalization ability" from a machine learning standpoint.

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