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SUBSPACE-BASED NOISE VARIANCE AND SNR ESTIMATION FOR MIMO OFDM SYSTEMS

机译:MIMO OFDM系统中基于子空间的噪声方差和SNR估计

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This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR.Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.
机译:提出了一种基于子空间的噪声方差和信噪比(SNR)估计算法,用于多输入多输出(MIMO)无线正交频分复用(OFDM)系统。在导频音中使用具有正交性和相移正交性的特殊训练序列,以获得估计的信道相关矩阵。通过将观测空间划分为延迟子空间和噪声子空间,我们实现了噪声方差和SNR的测量,仿真结果表明,所提出的估计器可以获得各种多径衰落信道上噪声方差和SNR的实时准确测量值,展示了其针对不同渠道的强大鲁棒性。

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