摘要
Pilot-assisted channel estimation has been investigated to improve the performance of OFDM based LTE systems. LS and MMSE method do not perform excellently because they do not consider the inherent sparse feature of wireless channel. The sparse feature of channel impulse response satisfies the requirement of using compressive sensing (CS) theory, which has recently gained much attention in signal processing. Result in the application of using compressive sensing to estimate fading channel. And it achieves a much better performance than that with traditional methods. In this paper, we propose heuristic channel estimation based on CS in LTE Downlink channel. According to the feature of recovery algorithm in CS, we design a modified pilot placement method. CS recovery algorithms for channel estimation don’t consider the statistics character of channel. So we proposed an optimization method which combines the CS and noise reduction. First we get initial channel statistics obtained by LS. Let the channel statistics as the heuristic information input of CS recovery algorithm. Then we perform CS recovery algorithm to estimate channel. Simulation results show this approach significantly reduces the complexity of channel estimation and get a better mean square error (MSE) performance.
Pilot-assisted channel estimation has been investigated to improve the performance of OFDM based LTE systems. LS and MMSE method do not perform excellently because they do not consider the inherent sparse feature of wireless channel. The sparse feature of channel impulse response satisfies the requirement of using compressive sensing (CS) theory, which has recently gained much attention in signal processing. Result in the application of using compressive sensing to estimate fading channel. And it achieves a much better performance than that with traditional methods. In this paper, we propose heuristic channel estimation based on CS in LTE Downlink channel. According to the feature of recovery algorithm in CS, we design a modified pilot placement method. CS recovery algorithms for channel estimation don’t consider the statistics character of channel. So we proposed an optimization method which combines the CS and noise reduction. First we get initial channel statistics obtained by LS. Let the channel statistics as the heuristic information input of CS recovery algorithm. Then we perform CS recovery algorithm to estimate channel. Simulation results show this approach significantly reduces the complexity of channel estimation and get a better mean square error (MSE) performance.
