The Improved Detection Algorithms of Parallel Combinatory Spread Spectrum Signal

To reduce the influence of imperfect correlation characteristic between the spread sequences and noise in the channel on the performance of detection algorithms, two improved algorithms used to detect parallel combinatory spread spectrum (PCSS) signal are proposed. The improved detection algorithms based on cyclic correlation apply the cyclic correlation characteristic of PCSS signals to suppress inner-interference and noise as well as jamming in the channel. And the other ones based on multi-user detection (MUD) use the configuration characteristic of PCSS signal to improve the performance of the detection. The mathematics models of improved detection algorithms are provided and the performances are analyzed. Theoretic analysis and experimental results indicate that the improved algorithms based on cyclic correlation have the better performance to suppress noise and jamming in the channel and the improved algorithms based on MUD have the better performance to suppress inner-interference as the result of imperfect correlation characteristic between the spread sequences. At the same time, the complexities of the algorithms are increased that limit their applied occasion except for abrupt communication systems.