Abstract
A number-theoretic net (NT-net)-based Gaussian particle filter (NT-GPF) is proposed for the joint estimation of frequency offset (FO) and linear phase noise (LPN) in a dynamic and real-time manner. Based on the concept of uniform design, the NT-net can uniformly generate particles on an ellipse for a given bivariate normal distribution. As a result, the NT-GPF can achieve accurate FO and LPN tracking with only a small number of particles, which greatly improves the estimation efficiency and noise tolerance compared with the conventional GPF. We also propose a recursive signal detection algorithm to further enhance the final system bit error rate (BER) performance. Simulations verify the accuracy, robustness and efficiency of the NT-GPF.
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