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According to the invariance property of the BCH code and Euclidean algorithm to calculate the greatest common factor (GCF) between code group and its cyclic shift code group. There is a method by using the degree distribution probability sum function of the greatest common factor to recognize the code length of BCH code. On this basis, the GCF can be extracted of the greatest degree distribution probability from the sum function, with the generator polynomial obtained by decomposing the GCF. The simulation results show that this method can achieve the recognition probability above 95% with BER of 4×10-3，and the high BER is much better to recognize the short codes.

For the disadvantage of degrade PAPR property of the existing methods using m sequences in traditional way, which is too simple in the aspects of the conversion from m sequences to rotation factors, an improved algorithm of PTS is proposed. This method can obtained the matrix, depending on the rotation factors including four phases (1,−1,j,-j) by using m sequences, to reserch, so the scope of choice is expanded, and the PAPR of OFDM system is greater reducted. The results of the simulation show that compared to the methods using m sequences in traditional way, the modified method basically doesn't increase the complexity of the system, while PAPR has been in a situation of better control.

This paper proposes a union algorithm of SLM and PTS to reduce PAPR in OFDM system. The simulation results show that the union algorithm can better reduce the complexity of OFDM than traditional SLM or PTS algorithm, and equally effective in restraining the growth of the PAPR.

A new algorithm based on high order cumulants(HOC) and support vector machines(SVM) for modulation and recognition of digital communication signals is proposed. The new method can identify six digital modulation signals: 2ASK, 4ASK, 8ASK, 4PSK, 8PSK and 16QAM digital signals using fourth and sixth order cumulants of the signals as vectors and SVM based on binary tree as classifiers. The method uses new class distance as rules of constructing binary tree, which separates the furthest class from others first, so that the method can maintain high generalization ability. The computer simulation results justify the method's validity.