Channel Estimation In Mimo-ofdm System

The fast development of wireless communication technology brings about great facility to people around the world. For it's convenient to communicate with others anytime, anywhere, by any means. Meanwhile, it raises even larger demand on capacity and quality of communication systems. Then MIMO-OFDM technology comes with hope on increasing capacity and spectrum efficiency. A MIMO system takes advantage of the spatial diversity that is obtained by spatially separated antennas in a dense multi-path scattering environment, and can increase channel capacity without the band-width increasing. On the other hand, OFDM can resist the frequency-selective fading in wireless channel. So it is a challenge work to combining MIMO and OFDM to improving the performance and increasing the system capacity.Not only space-time coding in the MIMO but also demodulation in the OFDM, the rather exact CSI is essential parameter for dealing with data in the MIMO-OFDM. So the channel estimation is the very important technology, its veracity can influence the whole performance of the MIMO-OFDM directly. Recently with the development of the 4G, many domestic and overseas scholars put forward many methods about the channel estimation, but these methods have localization. Generally speaking, the channel estimation in high speed motion state is researching; the newer and the more effective method about the channel estimation is urgent.This paper introduces the basic character of the wireless fading channel, and gives the mathematic model and the familiar MIMO-OFDM systems, and researches the channel estimation technology in the MIMO-OFDM system briefly.The author briefly introduces the channel estimation methods and decision criterions of both SISO-OFDM system and MIMO-OFDM system, and gets the compared figures about performance of mean square error and bit error ratio. On the base of investigating a channel parameter estimation approach with LS criterion, which are based training serial and the author improves it in order to decreasing the computing complexity. Finally, the algorithm is evaluated by computer simulation, and the simulation results are compared underdifferent number of sub-tamer or number of antennae. The simulation results indicate that the improving approach can decrease the computing complexity without the deteriorating of estimation accuracy.