SB-SAGE Based Joint MCFOs and Channel Estimation for DMIMO-OFDM Systems

Distributed multiple-input multiple-output (DMIMO) system with orthogonal frequency division multiplexing (OFDM) modulation is an emerging paradigm for high data rate and cell coverage extension. In order to make the paradigm shift from conventional network to intelligent DMIMO-OFDM systems, one must address the fundamental bottleneck in synchronization and channel estimation for coherent reception. The involvement of multiple cooperative nodes increases the number of corresponding channels and synchronization impairments to be estimated in comparison with collocated multiple input multiple output (MIMO) systems. As a consequence, the traditional pilot-based estimator requires a large pilot overhead to effectively estimate multiple impairments. This paper presents semi-blind space alternating generalized expectation maximization (SB-SAGE) algorithm for jointly estimating the multiple carrier frequency offsets (MCFOs) and frequency-selective channel gains in DMIMO-OFDM systems. SB-SAGE estimator uses soft information of partially received data symbols along with pilot symbols to obtain improved MCFOs and channel estimates with significantly reduced length of pilot overhead. It also increases spectral efficiency of the systems. The proposed estimator converges in almost two iterations and achieves significant improvement over pilot-based methods.