| In wireless communication, the wireless radio will encounter diffraction, reflection and dispersion, which could cause path loss and multi path. Path loss will degrade the transmitted signal power, and multi path will cause inter-symbol-interference and frequency selective fading. In order to overcome these problems, wireless communication systems must use some technologies like adaptive modulation and coding (AMC), equalization and so on. But some technologies need to know channel information first, i.e. maximum likelihood sequence estimation, AMC. So channel estimation plays a very important role in wireless communication. In this paper, we introduce radio wave propagation and multi path channel models like ray tracing model and statistical model. Then we introduce traditional methods for channel estimation.Wireless propagation may pass only a few number of paths, which is encountered in ultra wide band (UWB) systems, under-water acoustic channel, high-definition television (HDTV). That means channel impulse response is"long"and consists of a large number of zero taps. Conventional channel estimation methods such as Minimum Mean Square Error (MMSE) and Least Square (LS) do not exploit the sparse a priori knowledge, so the accuracy of this estimate is no longer satisfactory since estimation effort is directed towards estimating all the channel taps. In this paper, we introduced some sparse algorithm i.e. Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), Lp norm constraint, and Focal Undetermined System Solver (FOCUSS), all of which explored the sparse a prior. Simulation shows that sparse algorithm have better results than traditional methods.OFDM system is sensitive to frequency selective fading channel. OFDM systems also encounter sparse multi path i.e. under water OFDM signals, UWB OFDM signals and so on. Finally, we introduce sparse algorithm to OFDM system, which could get smaller MSE of channel parameters. |
