Modified Probabilistic Data Association algorithms

Probabilistic Data Association (PDA) algorithm has shown promising performance in symbol detection and interference cancellation in different communication schemes. This paper proposes new algorithms that build on PDA and introduce modifications in the way the symbol being detected is treated. While PDA models this symbol as a discrete sample from a constellation, PDA with symbol uncertainty (SU-PDA) views it as a sum of a deterministic symbol and random noise, while the Gaussian PDA (G-PDA) models it as a random variable with either a single Gaussian or Gaussian mixture distribution. The proposed algorithms are tested via computer simulations on both simulated and experimentally measured channels. The performance study reveals that the SU-PDA and G-PDA outperform the conventional PDA with the performance gain ranging from few dBs on measured channel with block fading up to and exceeding 10 dB on the simulated channel with fast fading.