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Non-data-aided SNR estimation for QPSK modulation in AWGN channel

机译:AWGN信道中用于QPSK调制的非数据辅助SNR估计

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摘要

Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signal-to-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms.
机译:信噪比(SNR)估计是任何接收器或通信系统中所需的重要参数。它可以通过导频信号数据辅助方法(其中接收器将知道发射信号)来计算,也可以通过不知道发射信号的任何方法来计算,这是一种非数据辅助(NDA)估计方法。提出了一种QPSK信号的NDA SNR估计算法。所提出的算法修改了现有的信噪比(SVR)SNR估计算法,目的是在少量样本的SNR值为负的情况下减少其偏差和均方误差。我们首先介绍现有的SVR算法,然后说明新的NDA算法的数学推导。此外,我们使用不同的测试用例将我们的算法与两种基线估计方法(即M2M4和SVR算法)进行了比较。这些测试案例包括低SNR值,极高SNR值和少量样本。结果表明,相对于第二和第四矩估计(M2M4)和原始SVR算法,在归一化均方误差(NMSE)和偏差估计方面,我们的算法具有更好的性能,同时保持与原始算法几乎相同的复杂性。

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