Cell-Straddling Robust-Fast Cross-Correlation Mitigation via MMSE Filter for DSSS Signals

Abstract

Existing cross-correlation mitigation algorithms based on the minimum mean square error (MMSE) criterion can effectively suppress multiple access interference (MAI) but suffer from high complexity and modeling grid mismatch. In this work, we propose a cell-straddling robust-fast cross-correlation mitigation (CSR-FCCM) algorithm with two improvements. First, CSRFCCM combines the two-dimensional joint iterative adaptive filtering with interference cancellation, which significantly reduces the number and computational cost of complex amplitude MMSE filters. Second, two discriminators are designed to estimate the straddling offset of delay and frequency for the direct sequence spread spectrum (DSSS) signal. The mismatch problem can be ameliorated by substituting estimated straddling offsets into the signal model, which further improves the MAI mitigation effect. The effectiveness of the CSR-FCCM is verified by simulations using 1023- and 63- length Gold codes. Simulation results show that CSR-FCCM has a better MAI mitigation performance and a lower complexity than the open-loop MAI mitigation algorithms, including 2D-JIAF, and RSR-APC.