摘要
近年来,密码的快速相关攻击(FCA)是一个研究热点.由于采用了一系列纠错编码的译码新技术,使得该问题的研究取得了很大的进展.但如何使这些新技术充分地适应具体的密钥序列的攻击依然是一个公开的研究课题.首先把模2域上的FCA算法推广成软判决快速相关攻击(SFCA)算法,并给出了如何根据信道信噪比情况配置最佳的参数的方法.实验表明,在BPSK调制下,SFCA算法比FCA算法有2dB以上的增益.作为SFCA算法的应用,给出了实现扩频通信中的m序列的快速同步捕获的新方法.与最近Yang(2004)提出的RSSE方法比较,新方法无论是捕获性能还是捕获速度都有了大幅度地提升,而且在低信噪比通信情况下,同步捕获所需的码片数只是RSSE方法所需的码片数的1/20.
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.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2007年第4期581-588,共8页
Journal of Computer Research and Development
基金
国家自然科学基金重大研究计划项目(90204013)
国家自然科学基金项目(60673082)
上海市科技发展基金项目(035115019)
教育部全国优秀博士学位论文作者专项基金项目(200084)
关键词
快速相关攻击
同步捕获
软判决置信度函数
WALSH变换
fast correlation attack
synchronization acquisition
soft reliability function
Walsh transform
