## 1 Introduction  
The problem of blind separation in DS-CDMA systems has attracted extensive attention for the past few years [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Research works related to blind separation for DS-CDMA systems are of particular interest in fields such as anti-jamming in military communications (MILCOM) and satellite communications (SATCOM). There are three main problems which need to be solved, including blind user separation [1; 2; 3; 4; 5; 6], blind spreading/chip sequence estimation [7; 8] and blind interference suppression [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. The blind separation problems are denominated \"blind\" to indicate the lack of information concerning the source signals and mixing matrix. The mixing matrix is equivalent to the effect of the channel matrix in DS-CDMA systems. The only prior information utilized is the often soundly justified assumption of statistical independence between the source signals. This technique is called as independent component analysis (ICA), which has important applications for blind separation in wireless communications [1, 19, 20, 21, 22, 23, 24]. So far, some classical ICA algorithms have been proposed to solve blind separation problems in DS-CDMA systems. A second order blind identification (SOBI) algorithm is used to separate the desired signal and interference signal in spread spectrum communication systems [9]. Fast Independent Component Analysis (FastICA) [2, 3, 4] and Joint Approximative Diagonalization of Eigen-matrices (JADE) [1, 10] are used to separate multiuser signals to resist multiple access interference (MAI) or separate the useful signal and jammer signals for interference mitigation in DS-CDMA systems [10, 11, 12, 13, 14, 15, 16]. JADE and FastICA are classical blind separation algorithms. When evaluating these two algorithms for robustness, separation accuracy and reliability, it is found that the JADE algorithm is robust and reliable, and the FastICA algorithm is not stable and sometimes fails. In general, the existing blind separation techniques utilize the second-order statistics (SOS) and higher-order statistics (HOS) of the observations for source separation [1]. For example, SOBI exploits second order moment information, and FastICA and JADE make use of the four order moments/cumulants information to separate the source signal from mixed signals. Classical HOS are powerful tools in the context of multivariate statistical analysis, often entailing valuable statistical information beyond SOS. However, longer observation intervals might be required in order to fully realize the advantages of HOS over SOS. HOS is better than SOS at the expense of increased computational and notational complexity and compromised statistical stability [1, 25]. In this paper, we consider a new generic tool, which offers the structural simplicity and controllable statistical stability of SOS on one hand, yet retains HOS-quality information on the other hand. As is well known, the cumulants are related to higher-order derivatives of the second characteristic function at the origin. However, the new statistical tools are related to lower-order (first and second) derivatives of the second characteristic function away from the origin, at locations called processing-point, and termed charmean and charrelation [25]. The use of charrelation matrices is extremely useful for extracting statistical information in order to establish the optimized cost function for the problem of blind separation work. Due to the structural simplicity and ample statistical information, we consider using the new statistics instead of the HOS statistics used in blind separation problems for DS-CDMA systems. As far as we know there is little literature reporting on the use of charrelation matrix-based blind separation in DS-CDMA systems [6]. Therefore, the main contribution of this paper is to extend blind separation using charrelation matrix and implement blind user separation and blind spreading/chip sequence estimation.