Filter bank

Algorithm

Channel (broadcasting)

Engineering

Spectral efficiency

Computational complexity theory

MIMO

Electronic engineering

Preamble

Interference (communication)

Quadrature amplitude modulation

Computer science

Bit error rate

Telecommunications

IEEE Transactions on Industrial Informatics

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 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 the CE performance is intrinsic imaginary interference, and the conventional preamble-based CE is not effective in this case. Thus, in this article, a low-complexity sparse adaptive CE scheme is proposed that 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 that show that the proposed scheme is better than other well-known methods in terms of computational complexity and CE accuracy.

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