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Two complexity reducing schemes are proposed in this letter for the recently presented Kolmogorov-Smirnov (K-S) test based signal-to-noise ratio (SNR) estimator. The K-S test based SNR estimator can work properly over an extended SNR range for various multilevel constellations with limited signal samples, but involves considerably more add operations as a result for the huge amount of reference signals needed for matching operations. The proposed two complexity reducing schemes explore the order characteristic of the SNR matching pool to accelerate the searching procedure. For the situation under consideration, the computational complexities (numbers of add operation) of the two proposed schemes are about 1/5 and 1/20 of the original one respectively. Simulation results have verified these schemes' effectiveness.

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This paper focuses on the modulation classification problem based on approximate entropy(ApEn). The original ApEn-based classification procedure is extended to a more comprehensive one. In the new procedure, preprocessing method of complex squaring is proposed to cancel out the negative effect introduced by noise. Simulation results demonstrate the effectiveness of our method to alleviate the demanding for extreme high signal-to-noise ratio(SNR). Specifically,almost 20dB gain can be obtained to achieve a reasonable high classification accuracy(≥ 90%) under the special occasion being studied. Furthermore,hybrid-c value scheme is proposed to ensure relatively good performance over the whole SNR range. In this scheme,two different c values are adopted simultaneously to form four-dimensional ApEn feature vectors for classification step,which combines each c value's good performance over the specific SNR range.

In this paper, we propose a modified superposition modulation (M8M) and a new demodulation method based on decorrelation. Compared to the traditional superposition modulation (8M) signal, the constellation of M8M signal is asymmetric, which makes it hard to be demodulated by the un cooperative receiver. Traditionally, iterative processing is adopted for demodulation of 8M signal, and the computational complexity is amazing. Aiming at M8M signal, we proposed a new demodu lation method based on decorrelation, the calculation complexity can be reduced remarkably.