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Pilot Symbol Arrangement in Compressed-Sensing-Based Channel Estimation for MIMO-OFDM

机译：MIMO-OFDM的基于压缩感知的信道估计中的导频符号排列

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Abstract: Compressed Sensing (CS) algorithms can achieve highly accurate OFDM channel estimation with a small number of pilots than conventional interpolation methods. However, the main problem of CS is its high computational complexity due to the large measurement matrix, especially in multiple-input multiple-output (MIMO) deployments where there are many wireless links need to be estimated. In this paper, assuming that the dominant paths of all individual links have almost the same delays, we apply the same measurement matrix for all the links and randomly select a small number of pilots to reduce the computational burden. In addition, we also average the estimated CIRs of the links to increase the possibility for correct detection of the delays which is essential for CS. As a consequence, simulation results show that our proposed method provides a good BER performance compared to the conventional method while drastically reducing computational complexity to 75%. Performance comparison with the existing OMP algorithm such as CoSaMP and SAMP algorithms is also provided.
机译：摘要：压缩传感（CS）算法与传统的插值方法相比，能够以较少的导频实现高精度的OFDM信道估计。但是，CS的主要问题是由于庞大的测量矩阵而带来的高计算复杂性，尤其是在需要估计许多无线链路的多输入多输出（MIMO）部署中。在本文中，假设所有单个链路的主导路径具有几乎相同的延迟，我们对所有链路应用相同的测量矩阵，并随机选择少量的导频以减轻计算负担。此外，我们还对链路的估计CIR进行平均，以增加正确检测延迟的可能性，这对于CS是必不可少的。结果，仿真结果表明，与传统方法相比，我们提出的方法提供了良好的BER性能，同时将计算复杂度大大降低到了75％。还提供了与现有OMP算法（例如CoSaMP和SAMP算法）的性能比较。

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