Channel Coding Parameter Analysis Based On Soft-decision Sequences

Owing to lack of the prior knowledge for coding parameters, the receivers usually employ channel coding analysis for obtaining the information sequences by decoding with the identified parameters. Such technology is very essential in applications like signal interception and cognitive radio. The object of the traditional coding analysis is hard-decision sequences. With the development of communication technology and encoding system, the hard-decision based methods have been suffering weak error-tolerating ability and narrow applicability. Unlike the hard one, soft-decision sequences can offer more prior information. In order to overcome the existing problems, this paper will study on soft-decision based channel coding analysis methods.Solving the error-containing equations is a common way to finding the constraint relationship. The hard-decision based Walsh-Hadamard Transform(WHT) method has poor error-tolerating capability for solving the equations. Thus, this paper proposes a novel method to weight the coefficient vectors by combining the hard-decision WHT and candidate codes set inspired by Chase2, defines the meaning of WHT spectral coefficients under soft-decision, and deduce a useful threshold for block codes. The simulation results show that the new method has better performance than the hard one, without much more additional computation, especially when the signal-to-noise ratio(SNR) is low.For the low density parity check(LDPC) codes with medium or short code length, the existing analysis methods will fail for the severe error propagation caused by the complicated matrix transforms. To solve this problem, the thesis presents a new approach for the pre-decision relied on credible parity check relationship and deduces the relative parameters, which enhances the anti-error capacity during the parity vector searching. Based on that, the generalized likelihood ratio test(GLRT) is applied to the parity vector detection. Finally, an iterative strategy for the parity vector is raised, using multi-data and soft-in and soft-out decoding. The experiment results prove that the new algorithm can efficiently deal with the open-set LDPC codes identification with medium or short code length.The existing soft-based closed-set identification algorithm for LDPC codes is unsatisfied for high code ratio situation. For that problem, the thesis firstly makes analysis on the distribution characteristics of the parity relation log-likelihood ratio(PLLR), pointing out the reasons for performance loss. Then, the deviation ratio is put forward, which is a new statistics that can reflect both mean value and variance for a random variable. Furthermore, a recognized algorithm is proposed based on maximum deviation ratio(MDR). At last, the MDR-based algorithm can effectively solve the closed-set LDPC codes identify problem with low SNR, especially for the high code ratio, the new algorithm brings the improvement of performances and the error-tolerating capability.