A low-complexity near-optimal algorithm for blind estimation of pseudo-noise sequences in DSSS communication systems
Saeed Mehboodi, Ali Jamshidi, Mahmoud Farhang · 2016
A novel algorithm is proposed for blind estimation of the spreading sequence in direct-sequence spread-spectrum (DSSS) communication systems. This algorithm is based on the maximum likelihood decision rule. Due to high computational complexity, the maximum likelihood method is feasible only for spreading sequence with very short periods. The novel algorithm has two steps for estimation. At first, a prior estimation of the spreading sequence is obtained via a simple algorithm which has a very low computational complexity. In the second step, using the prior estimation and the maximum likelihood decision rule, the estimation accuracy increases greatly. The proposed algorithm can reach the performance of the eigen value decomposition (EVD) method with low computational complexity.