Gaussian Approximation Based Mixture Reduction for Joint Channel Estimation and Detection in MIMO Systems

  • Authors:

  • Jia Yugang;C. Andrieu;R. J. Piechocki;M. Sandell

  • Affiliations:

  • Philips Res. East Asia, Shanghai;-;-;-

  • Venue:
  • IEEE Transactions on Wireless Communications
  • Year:
  • 2007

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Abstract

A novel Gaussian approximation based mixture reduction algorithm is proposed for semi-blind joint channel tracking and symbol detection for spatial multiplexing multiple-input multiple-output (MIMO) systems with frequency-flat time-selective channels. The proposed algorithm is based on a modified sequential Gaussian approximation detector (SGA) which takes into account channel uncertainty, and the first order generalized pseudo-Bayesian (GPB1) channel estimator. Simulation results show that the proposed algorithm performs better than the conventional and computationally expensive decision-directed method with Kalman filter based channel estimation and a posteriori probability (APP) symbol detection.