Dropout and Constellation Deletion Parametric Bilinear Generalized Approximate Message Passing-Based Equalization in Hybrid-MIMO

This paper proposes a novel equalizer for a hybrid-MIMO system combining both linear and nonlinear magnitude-only radio-frequency chains. A dropout-and-constellation-deletion parametric bilinear generalized approximate message passing (DCDP-BIG-AMP) algorithm is developed, and then applied for joint channel and data estimation (JCDE) in the hybrid-MIMO. Compared to parametric bilinear generalized approximate message passing (P-Bi-GAMP), DCDP-BIG-AMP randomly selects only a portion of variables to be updated and deletes constellation points which are changed slightly during the DCDP-BIG-AMP iterations. Consequently, the computational complexity is decreased significantly with the cost of minor performance degradation based on our experimental results. Its core operation is cyclic convolution, which is accelerated by fast Fourier transform (FFT). Subsequently, it is suitable for parallel hardware implementation. Moreover, the expectation maximization (EM) framework is combined to learn the unknown prior distribution parameters of channel. Simulation results show that DCDP-BIG-AMP successfully exploits the nonlinear magnitude measurements, and enables hybrid-MIMO to outperform the traditional MIMO on communication performance and energy-efficiency. DCDP-BIG-AMP based JCDE alternatively enhances the channel estimation (CE) and multiuser detection (MUD) performances, and converges quickly after only about 5 iterations. The dropout and constellation deletion mechanisms well balance between computational complexity and performance degradation.