# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 1 Introduction  
In communication systems, a long 0-run or 1-run often appears in transmitted binary sequences [1], and this affects the extraction of timing information in signals. To improve the balance of 0 and 1 symbols in transmitted signals, transmitted signals are scrambled to make it nearly completely random digital sequences [2]. Scrambling processing enhances the reliability and security of signal transmission, so it is widely used in satellite communications, spread spectrum communications, and cryptography [3, 4, 5]. In non-cooperative communication, the extraction of scrambled information must be descrambled first, so the blind identification technology of scrambling code parameters is of great significance to the extraction of non-cooperative information and the deciphering of ciphers [6, 7]. A scrambling code is divided into a synchronous scrambling code and a self-synchronous scrambling code [6, 7]. Among these, the parameter identification of the synchronous scrambling code is the identification of a generator polynomial and the initial state, and the identification of the self-synchronous scrambling code is the identification of a generator polynomial. In recent years, scrambling code parameter identification technology has been widely studied [6, 7]. The methods of scrambling parameter identification mainly focus on three types of situations: source unbalance [8, 9, 10, 11, 12, 13, 14, 15, 16], source balance (coding scrambling) [17, 18, 19, 20, 21], and the estimation of scrambled sequences in a direct-sequence spread spectrum [22, 23, 24, 25]. In an actual communication system, signal transmission is often channel-coded, so interecepted scrambled data are usually a source-unbalanced sequence that has been channel-coded and then scrambled. A method for reconstructing the synchronous scrambling code generator polynomial based on a dual code and double search was proposed by [PERSON] [17],in which one must know the scrambling type; in addition, the detection performance index always meeting the requirements cannot be maintained. In [18], a more robust sparse double-search algorithm, which improved the recognition performance of the algorithm under a low signal-to-noise ratio, was put forward. However, knowledge of the constraint length of the encoding is required. In [19], a polynomial identification based on run-length statistics was proposed to solve the self-synchronous scrambling of linear block codes. This method does not require prior information, and the influence of bit errors and channel noise is also not considered. The cost function of a generator polynomial based on the known check vector of the dual code was constructed in [20], and the reconstruction of the generator polynomial under the noisy condition of the convolutional code was completed. The third-order correlation of a synchronously scrambled m-sequence was used in [21] to complete the reconstruction of a generator polynomial under error conditions. This method does not require the information of an a priori vector, nor does it consider the influence of channel noise, but it requires the known coding of the constraint length. From the above analyses, all the known scrambling code parameter identification methods are based on the known channel coding and scrambling types, but the scrambling code data intercepted by non-cooperative communication parties are unknown, so it is necessary to analyze whether the outgoing signal is scrambled and what scrambling method is adopted that can further identify the parameters. Based on this, a multitype spectrum classification method, which solved the binary classification of linear block codes and linear block code self-synchronous scrambling under error conditions, was proposed in [26]. However, since the biased difference between linear self-synchronous scrambling and linear synchronous scrambling is very small, the distinction between the two leads to poor classification performance.