Hideki Furudate

Modified Matching Pursuit based channel estimation for ISDB-T

This paper demonstrates a new approach in reliable channel estimation for Integrated Services Digital Broadcasting Terrestrial (ISDB-T) using Modified Matching Pursuit Algorithm (MMP). The assumption is that the channel characteristic of the succeeding symbols does have correlation with the adjacent symbols. The Matching Pursuit (MP) is used first for solving the channel estimation of the first symbol, and then a proposed MMP is used for succeeding symbol based on the last estimate of the MP. The result of the simulation shows that the MMP was able to validate decent channel estimation with reduced computational cost.

Low-complexity channel estimation for ISDB-T over doubly-selective fading channels

Orthogonal Frequency Division Multiplexing (OFDM) technique that has been adopted by ISDB-T system is an effective multi-carrier modulation technique due to its robustness for multi-path fading channels. Recently, in the case of the doubly selective fading channel that is usually caused by high-speed moving reception, a new method named Compressed Sensing (CS) has been proposed to estimate the channel state information (CSI) for OFDM system, which can improve the accuracy of estimate with less pilots. However, high computational complexity of CS makes it difficult to implement for practical applications. Although orthogonal matching pursuit has been proposed that can reduce the computational complexity of CS, the improvement is still not enough. In this paper, therefore, a new scheme of channel estimation by using modified orthogonal matching pursuit (MOMP) is proposed that can further reduce the computational complexity as well as keep accurate estimation performance, comparing with the conventional OMP scheme. The computer simulation demonstrates its effect on channel estimation for ISDB-T system.

ICI cancellation method for OFDM systems using compressed sensing based channel estimation

The time-varying multipath fading causes the intercarrier interference (ICI) that destroys the orthogonality between the subcarriers for orthogonal frequency division multiplexing (OFDM) systems. In this paper, we propose an iterative equalization method using channel estimation based on compressed sensing (CS) that can be used to optimize and reconstruct the channel state information (CSI) for sparse channel. Simulation results demonstrate that the proposed method significantly improves the system performance, comparing with the conventional Least Square (LS) methods.