低信噪比下非数据辅助的OFDM系统信道阶数和噪声方差的估计

在低信噪比情况下，该文提出一种新的针对正交频分复用(OFDM)系统信道阶数和噪声方差的非数据辅助(NDA)估计算法。算法中应用了一种新的基于联合极大几何均值(MGM)的代价函数。新的代价函数不仅利用了循环前缀(CP)冗余性，同时也利用了信道记忆性。对比只利用了CP的方法，该算法可以在低信噪比情况下更准确地估计信道阶数和噪声方差。仿真结果表明，在低信噪比情况下，该算法针对信道阶数的估计得到约10 dB的信噪比增益；同时，对噪声方差的估计，该算法显著提高了估计精度，抑制了信噪比20 dB以下估计性能恶化的现象。

The Non-data-aided Channel Order and Noise Variance Estimation in the Low SNR Region of OFDM Signals

This paper proposes a new Non-Data-Aided (NDA) scheme to estimate the channel order and noise variance in the low Signal to Noise Ratio (SNR) region of Orthogonal Frequency Division Multiplexing (OFDM) signals. In this scheme, a new cost function is derived based on the joint Maximum Geometric Mean (MGM) which relies on both the Cyclic Prefix (CP) redundancy and channel memory. Compared with the schemes which only rely on the CP, more accurate estimations of channel order and noise variance can be obtained from this joint MGM cost function. Simulation results show that the proposed channel order estimator gets approximately 10 dB SNR gain in the low SNR region. Meanwhile, the proposed noise variance estimator outperforms significantly the other existing NDA algorithms, and suppresses the performance deterioration when SNR below 20 dB.