Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals
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DOI：
10.1109/twc.2023.3247887
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发表时间：
2023-02
影响因子：
10.4
通讯作者：
Alexandra Gallyas-Sanhueza;Christoph Studer
中科院分区：
文献类型：
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作者：
Alexandra Gallyas-Sanhueza;Christoph Studer
基带处理算法通常需要噪声功率、信号功率或信噪比（SNR）的知识。在实践中，这些参数通常是未知的，必须估计。此外，均方误差（MSE）是在各种估计和信号恢复算法中要最小化的期望度量。然而，不能直接使用MSE，因为它取决于估计器通常未知的真实信号。在本文中，我们提出了新的盲估计的平均噪声功率，平均接收信号功率，信噪比，和MSE。所提出的估计可以计算在低复杂度，仅依赖于处理数据的高维和稀疏的性质。我们的估计器可用于（i）快速跟踪一些关键系统参数，同时避免额外的导频开销，（ii）设计低复杂度的非参数算法，需要这样的数量，以及（iii）加速更复杂的估计或恢复算法。我们进行了理论分析，提出了一个伯努利复高斯（BCG）先验估计，我们通过合成实验证明其有效性。我们还提供了三个应用程序的例子，偏离BCG之前在毫米波多天线和无蜂窝无线系统，我们开发的非参数去噪算法，提高信道估计精度与性能相当的去噪，假设完美的知识系统参数。
Baseband processing algorithms often require knowledge of the noise power, signal power, or signal-to-noise ratio (SNR). In practice, these parameters are typically unknown and must be estimated. Furthermore, the mean-square error (MSE) is a desirable metric to be minimized in a variety of estimation and signal recovery algorithms. However, the MSE cannot directly be used as it depends on the true signal that is generally unknown to the estimator. In this paper, we propose novel blind estimators for the average noise power, average receive signal power, SNR, and MSE. The proposed estimators can be computed at low complexity and solely rely on the large-dimensional and sparse nature of the processed data. Our estimators can be used (i) to quickly track some of the key system parameters while avoiding additional pilot overhead, (ii) to design low-complexity nonparametric algorithms that require such quantities, and (iii) to accelerate more sophisticated estimation or recovery algorithms. We conduct a theoretical analysis of the proposed estimators for a Bernoulli complex Gaussian (BCG) prior, and we demonstrate their efficacy via synthetic experiments. We also provide three application examples that deviate from the BCG prior in millimeter-wave multi-antenna and cell-free wireless systems for which we develop nonparametric denoising algorithms that improve channel-estimation accuracy with a performance comparable to denoisers that assume perfect knowledge of the system parameters.