Estimating Spreading Waveform of DS-SS signals at low SNR

摘要

This paper proposes a computationally efficient spreading-waveform-estimation method for the non-symbol-periodic Direct Sequence Spread Spectrum (DS-SS) signals. The method is a low SNR unconditional maximum likelihood (UML) estimation algorithm. It works under the assumption of uniformly distributed transmission delay, which may cause mismatch in the real-world model. Equivalence exists between the proposed estimator and the dominant mode despreading estimator. Advantages of the proposed estimator are less computational complexity and simple expression that allows for comprehensive performance analysis. Asymptotic analysis shows that the UML estimator acts as a weighted version of the spreading waveform, although it may be biased by model mismatch. Using the Perron-Frobenius Theorem, the validity of the estimator for the blind applications is discussed. Simulation results demonstrate the merits of the proposed UML estimator.