2018
DOI: 10.1109/access.2018.2871060
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Robust Joint Synchronization and Channel Estimation Approach for Frequency-Selective Environments

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Cited by 13 publications
(38 citation statements)
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“…• Firstly, an ELM-based FS method by using superimposed training is proposed. In contrast to the ELMbased time-division FS scheme in [9], not only the occupation of bandwidth resources is avoided in the proposed FS method, but also the smaller error probability of FS is achieved with the same energy cost. • Secondly, the superimposed training-based FS is investigated in the scenarios of nonlinear distortion.…”
Section: B Contributionsmentioning
confidence: 99%
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“…• Firstly, an ELM-based FS method by using superimposed training is proposed. In contrast to the ELMbased time-division FS scheme in [9], not only the occupation of bandwidth resources is avoided in the proposed FS method, but also the smaller error probability of FS is achieved with the same energy cost. • Secondly, the superimposed training-based FS is investigated in the scenarios of nonlinear distortion.…”
Section: B Contributionsmentioning
confidence: 99%
“…The modulated data symbol c is formed according to the symbol of quadrature-phaseshift-keying (QPSK) modulation. For the channel model, the multi-path Rayleigh fading channel with an exponentiallydecayed power coefficient η = 0.2 is considered, where each of the following L − 1 paths is set as zero-valued with a probability of 0.5 beside the first path to keep the same situation as [9] and [10]. For the sake of fair comparison, we assume the superimposed FS and the time-division FS consume the same energy for transmitting symbols.…”
Section: A Parameter Settingmentioning
confidence: 99%
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“…• Firstly, an ELM-based FS method by using superimposed training is proposed. In contrast to the ELMbased time-division FS scheme in [9], not only the occupation of bandwidth resources is avoided in the proposed FS method, but also the smaller error probability of FS is achieved with the same energy cost. Compared with the classical correlation method in [8] and the time division method in [10], both the FS's error probability and the symbol detection (SD)'s bit error rate (BER) are reduced with the same energy consumption.…”
Section: B Contributionsmentioning
confidence: 99%
“…Then, an ELM network is employed to alleviate system's nonlinear distortion and improve SMs. Compared with the correlation-based FS [5] and recent FS method in [15], the proposed method can effectively reduce the error probability of FS for the cases with nonlinear distortion. Furthermore, with the parameter impacts, the proposed method shows a stable improvement given the change of system parameters.…”
Section: Introductionmentioning
confidence: 99%