An Improved Semi-blind Joint Data Detection and Channel Estimation Algorithm for MIMO-OFDM System

In this paper, a new Semi-blind joint Grover’s Quantum Search (GS) based data detection and Space-Alternating Generalized Expectation-maximization (SAGE) channel estimation algorithm for Multiple-Input Multiple-Output (MIMO)-Orthogonal frequency division multiplexing (OFDM) system is proposed. According to the training symbols inserted in the head of sub-frame, we get the initial estimation of joint algorithm with the Maximum a posteriori (MAP) estimator. Then, an iterative optimization loop which joints the SAGE’s E&M steps and GS based data detection is employed. In the iteration process, we apply channel estimation of the previous transmitted symbols to initial estimation in the current transmitted symbols with GS based data detection, update channel estimate and data detection until converge. All transmitted symbols can be updated in turn. The simulation results show that our proposed joint algorithm for MIMO-OFDM system have low Bit Error Rate (BER), and also have lower complexity than joint Maximum-Likelihood (ML) based data detection and SAGE based channel estimation algorithm.