A Tensor-Based Approach to Blind Despreading of Long-Code Multiuser DSSS Signals

Abstract

In this paper, a tensor-based approach to blind despreading of long-code multiuser DSSS signals is proposed. We aim to generalize the tensor-based methods originally developed for blind separation of short-code multiuser DSSS signals to long-code cases. Firstly, we model the intercepted long-code multiuser DSSS signals with an antenna-array receiver as a three-order tensor with missing values, and then, the blind separation problem can be formulated as a canonical or parallel factor (CANDECOMP/PARAFAC) decomposition problem of the missing-data tensor, which can be solved using optimum methods. Secondly, a constrained Cramér–Rao Bound (CRB) is also derived to provide a performance benchmark for the proposed approach. Simulation results verify the feasibility of our proposed approach in the case of low signal-to-noise (SNR) conditions.