has been cited by the following article(s):
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[1]
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PCMA 信号幅度的联合估计算法
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2019 |
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[2]
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PCMA 信号幅度的联合估计算法.
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Systems Engineering & …,
2019 |
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[3]
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单通道混合信号的深度联合分离译码算法
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2018 |
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[4]
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Single‐channel blind source separation for paired carrier multiple access signals
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IET Signal Processing,
2018 |
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[5]
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Single-channel blind source separation for paired carrier multiple access signals
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IET Signal Processing,
2017 |
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[6]
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Markov Chain Monte Carlo-Based Separation of Paired Carrier Multiple Access Signals
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2016 |
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[7]
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单通道邻频数字调制混合信号的载波初相估计
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通信学报,
2016 |
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[8]
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Carrier initial phases estimation for single-channel adjacent-frequency mixture of digitally modulated signals
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Journal on Communications,
2016 |
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[9]
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Tensor based source separation for single and multichannel signals
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2015 |
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[10]
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Frequency offset estimation of the linear mixture of two co‐frequency 8 phase‐shift keying modulated signals
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IET Signal Processing,
2015 |
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[11]
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Tensor based source separation for single and multichannel signals.
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2015 |
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[12]
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Frequency offset estimation of the linear mixture of two co-frequency 8 phase-shift keying modulated signals
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IET Signal Processing,
2015 |
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[13]
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利用 Gibbs 采样的同频混合信号单通道盲分离
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通信学报,
2015 |
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[14]
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Single-channel blind separation of co-frequency modulated signals based on Gibbs sampler
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2015 |
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[15]
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Sparsity promoted non-negative matrix factorization for source separation and detection
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Digital Signal Processing (DSP), 2014 19th International Conference on. IEEE,
2014 |
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[1]
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Markov Chain Monte Carlo-Based Separation of Paired Carrier Multiple Access Signals
IEEE Communications Letters,
2016
DOI:10.1109/LCOMM.2016.2599874
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[2]
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Sparsity promoted non-negative matrix factorization for source separation and detection
2014 19th International Conference on Digital Signal Processing,
2014
DOI:10.1109/ICDSP.2014.6900744
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