Semi-blind precoding aided ML CFO estimation for ICA based MIMO OFDM systems

We propose a semi-blind precoding aided maximum likelihood (ML) carrier frequency offset (CFO) estimation method and a precoding aided equalization based on independent component analysis (ICA) receiver structure for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems. By carefully designing a constant in the precoding process, the power between reference data and source data can be balanced to enable ML CFO estimation and ambiguity elimination for the ICA output signals at the receiver. This proposed semi-blind non-redundant structure is much more bandwidth-and-energy efficient than the pilot aided ML CFO estimation method, as no real-time training or extra transmission power is required. Simulation results show that the proposed scheme provides a bit error rate (BER) performance the same as the performance of the pilot aided ML CFO estimation method, and close to the ideal case with perfect channel state information (CSI) and no CFO.