Ofdm Channel Estimation Algorithm

Orthogonal frequency division multiplexing (OFDM) has the following advantages: high spectrum efficiency and a good ability to combat muti-path interference. Thus, it is considered as one of the key techniques of future wireless communications.Channel estimation is one of the hot research topics of OFDM. According to normalized frame Doppler spread (NFDS=frame time length/coherent time), wireless channels are divided into three categories: slow time-varying channel(NFDS≤0.1), medium time-varying channel(0.11). The main works are as follows:a) In slow time-varying typical Urban (TU) channel, we focus mainly on investigating linear interpolation (LI), Sinc interpolation (SI), Gaussian interpolation (GI) and linear minimum mean square error (LMMSE). The problem of the LMMSE algorithm is statistics mismatch, in order to solve it, an adaptive LMMSE algorithm based on threshold is proposed. Simulation shows: in the case of a dense pilot grid, the performance of SI and GI is close to that of LMMSE, considering their low complexity, they are very attractive; this adaptive algorithm achieves a 4dB signal-to-noise ratio (SNR) gain on mean square error (MSE) performance over the original LMMSE when there is mismatch between statistical correlation and exact correlation.b) In three time-varying TU channels, we focus mainly on the study of two-dimensional LMMSE (2D LMMSE), one-dimensional time-frequency LMMSE (1D LMMSE), sinc interpolation (SI), and polynomial basis expansion based model algorithm. To reduce complexity of channel estimation, a weighted second-order spline (WSOS) basis algorithm is proposed. Simulation results show: due to its exploiting the statistical property of time-varying channels, the 2D LMMSE perform best in three time-varying channels, however, its computational amount is very high compared with polynomial, WSOS and 1D LMMSE; due to low complexity and good performance, polynomial and WSOS are attractive for time-varying channel estimation; particularly, the proposed WSOS can strike a good balance between complexity and performance for 0.3≤NFDS≤0.8.