The Non-data-aided Channel Order and Noise Variance Estimation in the Low SNR Region of OFDM Signals
WANG Dong① ZHAO Jiaxiang② YU Lihong①
①(College of Computer and Control Engineering, Nankai University, Tianjin 300071, China) ②(College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300071, China)
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.
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