A MUSIC-based algorithm for blind user identification in multiuser DS-CDMA
EURASIP Journal on Applied Signal Processing
Architecture and FPGA design of dichotomous coordinate descent algorithms
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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The level of multiple access interference (MAI) in code division multiple access (CDMA) communication systems is a time-varying parameter related to the number of active users. Almost all existing multiuser detection schemes were designed based on a priori information of the active users. In many situations, however, the multiuser receiver does not know the number of active users, and the receiver designed for the detection of all users may lead to poor performance. To develop a more efficient detection scheme in practical applications, we propose a two-stage detection structure consisting of preprocessing (identification) and postprocessing (detection). In the preprocessing, we apply the subspace concept and a method based on the multiple signal classification (MUSIC) algorithm to identify the active users while requiring only a priori knowledge of all of the users' signature sequences. The proposed preprocessor is shown to be asymptotically near-far resistant, and to have the ability to identify the active users in a simple and reliable way. While in the detection process, as we efficiently use the active users' information in every observation interval, the performance is clearly improved compared to the conventional structure without identification. Moreover, the effect of imperfect identification on the decorrelating detector is also extensively analyzed. Though the decorrelating detector's inherent near-far resistant characteristic is impaired by imperfect identification, the proposed structure still outperforms the conventional structure in the general near-far environment