Pilot-Assisted Methods for Channel Estimation in MIMO-V-OFDM Systems

Multiple-input multiple-output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) technology has both the advantages of MIMO and OFDM. Vector Orthogonal Frequency Division Multiplexing (V-OFDM) is an extension of OFDM, which makes data transmission flexible. In MIMO systems using V-OFDM technology, different novel schemes are proposed to improve channel estimation performance for different channel sparsity. The 2-D Kriging interpolation scheme is proposed for the non-sparse channels, which can significantly improve the performance of conventional Least Square (LS) and Minimum Mean Square Error (MMSE) algorithms. When the channel is sparse, the estimation process can be modeled as a sparse recovery problem using compressed sensing (CS) theory. In this paper, the measurement matrix is determined by pilot locations, and a pilot search algorithm based on random genetic algorithm (RGA) is proposed to minimize the cross-correlation value of the measurement matrix. Besides, a variable threshold sparsity adaptive matching pursuit (VTSAMP) algorithm is designed to obtain more accurate estimates, which achieves better Normalized Mean Square Error (NMSE) performance, higher calculation speed, and lower implementation complexity.