Low Complexity MIMO-FBMC Sparse Channel Parameter Estimation for Industrial Big Data Communications

Industrial applications can produce significant amounts of data that require low delay and high data rate communications. Multiple input multiple output filter bank multicarrier (MIMO-FBMC) communications employing offset quadrature amplitude modulation has been proposed for industrial big data (IBD) due to its reliability and high spectrum efficiency. One of the difficulties in implementing a MIMO-FBMC system is accurate channel estimation (CE). The main factor affecting CE performance is intrinsic imaginary interference, and conventional preamble-based CE is not effective in this case. Thus, in this paper, a low-complexity sparse adaptive CE scheme is proposed which is based on a dynamic threshold. This reduces the number of inner product calculations by considering only the columns of the measurement matrix greater than the threshold. Simulation results are presented which show that the proposed scheme is better than other well-known methods in terms of computational complexity and CE accuracy.