Joint Frequency Offset and Channel Estimation Using Rao-Blackwellized Particle Filter for Uplink MIMO-OFDMA Systems

The Rao-Blackwellized particle filter (RBPF) is proposed to estimate the carrier frequency offset (CFO) and channel state information (CSI) for uplink multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) systems. In the proposed scheme, the channel response and the frequency offset are described as auto- regressive (AR) model and generalized AR model, respectively. The carrier frequency offset can be estimated using the particle filter, and the distribution of the fading channel is updated analytically using the Kalman filter, which is associated with each particle. Furthermore, the expectation-maximization (EM) algorithm is evolved to learn model parameters recursively. Simulation results show that the proposed RBPF algorithm has lower block error rate (BLER) than the conventional particle filter and the Rao-Blackwellized Gauss-Hermite filter (RB-GHF), while the processing complexity is rather reasonable.