A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system

Abstract

In this paper, we propose a novel pilot-aided channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems where the wireless channel is assumed to be both sparse and time-varying. In the proposed method, we firstly model the time-varying sparse channel as an autoregressive (AR) process. Then, utilizing the time-domain convergence property of Kalman filter, we formulate the channel estimation as an iteration problem. During the iteration, the path delays are estimated through a simple reconstruction algorithm. After the path delays are estimated, the Kalman filter is performed on the path delays to obtain the minimum mean squared error (MMSE) estimation of the channel impulse response (CIR). Simulation results demonstrate the effectiveness of the proposed channel estimation method. Compared with the conventional compressed sensing (CS) based channel estimators which perform CS at each time separately, the method proposed here enjoys superior performance in terms of bit error rate (BER) and mean square error (MSE).

Joint Kalman Channel Estimation and Turbo Equalization for MIMO OFDM Systems over Fast Fading Channels

“…In the simulation, we choose the parameter Q=9 in the complex-exponential basis expansion model. Q must be bounded Q ≥ 2� � [3]- [7]. The parameter that is used for the AR-1 channel model is selected as 0.996.…”

“…However, the proposed scheme only employs Kalman estimation based on the previous hard decision which is produced by the minimum mean-square-error decision feedback equalization. In [7], the multipath fading environment is modeled as an AR system model and employs the modified KF channel estimation to predict the AR channel coefficients. In [8], several blind channel impulse response predictor of the multicarrier system is employed for the multi-input multi-output multipath fading channels.…”

A Novel Successive Interference Cancellation Arithmetic Based on NOMA System

“…Because the reductive accuracy of each symbol and the channel state of each time slot will all directly influence on the subsequent signal detecting. So, this paper also simulates the channel which is linear time-varying [9] and proposes a scalar Kalman filter algorithm [10]- [12] to estimate the channel in the direction of time. Restored every users' signal through the successive interference cancellation technology with the exact state of channel at every time slot and the structural signals after sampling.…”