论文部分内容阅读
Channel reciprocity is a vital component for the key generation from multipath channels. In this paper, we introduce the effect of delay between measures on the channel reciprocity, build an Autoregression(AR)model to approximate time-varying channel measurements and apply a linear prediction method to reduce the measurement errors and to enhance the dependency between measurements of two communication parties. In the following bit conversion, we propose a novel quantization scheme named the Probability distance method(PDM) and compare its performance with other existing methods theoretically. The simulation results show that the optimal solution to the linear prediction problem can effectively increase the channel reciprocity in the assumed channel models.The proposed PDM can achieve a lower disagreement rate with acceptable costs.
Channel reciprocity is a vital component for the key generation from multipath channels. In this paper, we introduce the effect of delay between measures on the channel reciprocity, build an Autoregression (AR) model to approximate time-varying channel measurements and apply a linear prediction method to reduce the measurement errors and to enhance the dependency between measurements of two communication parties. we the a novel quantization scheme named the Probability distance method (PDM) and compare its performance with other existing methods theoretically. The simulation results show that the optimal solution to the linear prediction problem can effectively increase the channel reciprocity in the assumed channel models. proposed PDM can achieve a lower disagreement rate with acceptable costs.