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LDPC code reconstruction without a candidate set is one of the tough problems in channel code reconstruction.First,theoretical analysis was provided for the number of received code-vectors needed for the reconstruction,and a lower bound was derived.Then,according to the lower bound,and based on an algorithm for finding low weight code-words,a new reconstruction method was proposed.It looked for low weight vectors one by one from the dual space of the received code-vector space and used them to reconstruct the sparse parity-check matrices.Number of iterations and the computational complexity of the method were analyzed based on exponential distribution theory.Under noise-free conditions,drawbacks of the existing method,including limited applicable range and large quantity of required data,have been overcame.Under noisy conditions,the proposed method has higher robustness against noise and relatively low complexity,compared to existing methods.For QC-LDPC codes,the reconstruction performance can be further improved using the quasi-cyclic property of their sparse parity-check matrices.

LDPC code reconstruction without a candidate set is one of the tough problems in channel code reconstruction.First,theoretical analysis was provided for the number of received code-vectors needed for the reconstruction,and a lower bound was derived.Then,according to the lower bound,and based on an algorithm for finding low weight code-words,a new reconstruction method was proposed.It looked for low weight vectors one by one from the dual space of the received code-vector space and used them to reconstruct the sparse parity-check matrices.Number of iterations and the computational complexity of the method were analyzed based on exponential distribution theory.Under noise-free conditions,drawbacks of the existing method,including limited applicable range and large quantity of required data,have been overcame.Under noisy conditions,the proposed method has higher robustness against noise and relatively low complexity,compared to existing methods.For QC-LDPC codes,the reconstruction performance can be further improved using the quasi-cyclic property of their sparse parity-check matrices.

Two scrambler blind recognition methods based on soft information were proposed for received signal in non-cooperative ways.The first method established a cost function of the scrambler coefficients by using the soft information,and adopted the optimization theory of real number field for positive solution.So it didn’t need to traverse the closed set of test polynomial any more.The second method built conformity degree concept with the soft information,and used the size of conformity of each test scrambler polynomial as the discriminant criteria.So it made more full use of the received information compared to the hard sentence recognition algorithm.Simulation results show that the first method can shorten the recognition time of a synchronous scrambler polynomial from 5 min 18 s to 8 s compared with the traversal method put forward by Cluzeau,and the second method has 2 dB SNR gain when to achieve the relatively high accuracy compared with the hard sentence recognition algorithm.

Two scrambler blind recognition methods based on soft information were proposed for received signal in non-cooperative ways.The first method established a cost function of the scrambler coefficients by using the soft information,and adopted the optimization theory of real number field for positive solution.So it didn’t need to traverse the closed set of test polynomial any more.The second method built conformity degree concept with the soft information,and used the size of conformity of each test scrambler polynomial as the discriminant criteria.So it made more full use of the received information compared to the hard sentence recognition algorithm.Simulation results show that the first method can shorten the recognition time of a synchronous scrambler polynomial from 5 min 18 s to 8 s compared with the traversal method put forward by Cluzeau,and the second method has 2 dB SNR gain when to achieve the relatively high accuracy compared with the hard sentence recognition algorithm.

Channel coding parameter analysis meant reversely analyzing and estimating the coding parameters,and was one of the key techniques for intelligent communication,information interception and so on.Since Turbo codes were widely used in communication systems,parameter estimation for their interleavers was of great importance.Focusing on the random interleaver of Turbo codes,an iterative estimation algorithm based on correlation decision between the soft outputs of the Turbo decoder was presented.The algorithm used soft-decisions of the demodulator output sequence.It overcame the shortcomings of existing methods which often aimed at certain interleaving structures only,and suffered from weak robustness since they used hard-decisions.Simulation results show that the proposed algorithm has better performance and relatively lower complexity,especially in low signal-to-noise ratio cases.

Channel coding parameter analysis meant reversely analyzing and estimating the coding parameters,and was one of the key techniques for intelligent communication,information interception and so on.Since Turbo codes were widely used in communication systems,parameter estimation for their interleavers was of great importance.Focusing on the random interleaver of Turbo codes,an iterative estimation algorithm based on correlation decision between the soft outputs of the Turbo decoder was presented.The algorithm used soft-decisions of the demodulator output sequence.It overcame the shortcomings of existing methods which often aimed at certain interleaving structures only,and suffered from weak robustness since they used hard-decisions.Simulation results show that the proposed algorithm has better performance and relatively lower complexity,especially in low signal-to-noise ratio cases.