Joint Channel Estimation and Impulsive Noise Mitigation Method for OFDM Systems Using Sparse Bayesian Learning
Joint Channel Estimation and Impulsive Noise Mitigation Method for OFDM Systems Using Sparse Bayesian Learning
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DOI：
10.1109/access.2019.2920724
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Xinrong Lv;Youming Li;Yongqing Wu;Xiaoli Wang;Hui Liang
中科院分区：
文献类型：
--
作者：
Xinrong Lv;Youming Li;Yongqing Wu;Xiaoli Wang;Hui Liang
脉冲噪声会急剧恶化正交频分复用（OFDM）系统的性能。在本文中，我们提出了一种基于压缩感知理论的联合信道脉冲响应估计和脉冲噪声抑制算法。在该算法中，信道脉冲响应和脉冲噪声都被视为联合稀疏向量。然后，采用稀疏贝叶斯学习框架来联合估计信道脉冲响应、脉冲噪声和数据符号，其中数据符号被视为未知参数。 Cramér–Rao Bound 是针对基准推导出来的。与之前的脉冲噪声抑制方法不同，该算法利用所有子载波，而无需任何信道和脉冲噪声的先验信息。仿真结果表明，该算法在信道估计和误码率性能上取得了显着的性能提升。
The impulsive noise can deteriorate sharply the performance of orthogonal frequency division multiplexing (OFDM) systems. In this paper, we propose a novel joint channel impulse response estimation and impulsive noise mitigation algorithm based on compressed sensing theory. In this algorithm, both the channel impulse response and the impulsive noise are treated as a joint sparse vector. Then, the sparse Bayesian learning framework is adopted to jointly estimate the channel impulse response, the impulsive noise, and the data symbols, in which the data symbols are regarded as unknown parameters. The Cramér–Rao Bound is derived for the benchmark. Unlike the previous impulsive noise mitigation methods, the proposed algorithm utilizes all subcarriers without any a priori information of the channel and impulsive noise. The simulation results show that the proposed algorithm achieves significant performance improvement on the channel estimation and bit error rate performance.