Clipping Noise Model Based Fast ML Decoding for OSTBC and QOSTBC in Clipped MIMO-OFDM Systems

An efficient way to reduce the peak-to-average power ratio (PAPR) in OFDM systems is clipping. After the clipping in an MIMO-OFDM system, the additive noise may not be white. In this paper, we develop fast ML decoding algorithms for orthogonal space-time block codes (OSTBC) and quasi orthogonal space-time block codes (QOSTBC) in clipped MIMO-OFDM systems by using a clipping noise model with Gaussian approximation. By using the statistics of the clipping distortions, our newly proposed fast ML decoding algorithms improve the performance for clipped MIMO-OFDM systems with OSTBC and QOSTBC without increasing the decoding complexity. Simulation results are presented to illustrate the improvement.