Journal
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
Volume 8, Issue 2, Pages 720-732Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCCN.2021.3118465
Keywords
Extreme learning machine; timing; frequency synchronization; MIMO-OFDM; frequency selective fading
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61931020, 61372099, 62101569, 61601480]
- School of Electronic and Electrical Engineering, University of Leeds, U.K
- China Scholarship Council (CSC)
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This paper proposes a novel synchronization scheme based on extreme learning machine (ELM) for achieving high-precision synchronization in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Simulation results demonstrate that the proposed ELM-based scheme outperforms the traditional method under both additive white Gaussian noise and frequency selective fading channels, without requiring perfect channel state information (CSI) and excessive computational complexity.
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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