A Novel Algorithm of Soft Fast Correlation Attack and Applications

The main researches on improving fast correlation attacks (FCA) are focused on adapting the usual decoding algorithms and the best involved parameters to the practical applications. In this paper a novel soft fast correlation attack (SFCA) is presented for sequences obtained from a highly noisy BPSK channel, and a feasible strategy is provided to adapt the involved parameters in the techniques to fit in with the concrete applications in different channel situations. Fast Walsh transformation is used to realize decoding procedure instead of exhaust search used by conventional attacks. A theorem is derived, which exploits that log-likelihood ratio of a correct state estimate is just the value of corresponding Walsh transformation. The simulation results show that the proposed SFCA algorithm for sequences from BPSK channel has a gain that exceeds 2 dB compared with FCA algorithm for sequences from BSC channel. As a practical application, an efficient acquisition based on SFCA of m-sequence in spread spectrum communication system is given. Compared with the recent RSSE acquisition scheme proposed by Yang, this scheme has a significant improvement in acquisition performance as well as acquisition delay. Furthermore, the number of chips required by this scheme increases linearly as signal noise rate decreases, which results in much better performance in real-time communication.