基于马尔可夫过程的卫星移动信道模型及长期预测方法

Markov Process Based Satellite Mobile Channel Model and Long Term Prediction Method

卫星移动信道可被描述为基于有限状态马尔可夫过程的衰落模型,该文分析了卫星信道的可预测性,然后基于加权预测思想提出了一种卫星移动信道长期预测方法,该方法在当前信道采样的基础上进行二次采样,采样频率大于马尔可夫状态转移速率的2倍,利用信道状态的相关性和信道状态转移概率信息来加权预测未来长期内的信道状态,并依据自回归预测模型给出信道预测输出值,仿真结果表明,采用此方法对卫星信道未来的信道状态进行预测,在信噪比较高时均方误差能够达到10 2量级,在自适应传输过程中可以降低系统平均误比特率,且能够提高系统吞吐量性能,这对卫星移动通信系统的自适应传输和自适应资源分配都具有一定的指导意义。

Satellite mobile channel can be described as a fading channel model based on Markov process. Firstly, the predictability of satellite mobile channel is analyzed in this paper. Then a long term prediction method is proposed based on weighting prediction. The method resamples the channel sample values with a sample rate 2 times of the Markov state transferring speed, and uses the correlation of the channel states and Markov state transfer probability to predict the future channel state and outputs the channel prediction value of the long term future according to autoregression prediction model. Simulation results show that by using the proposed method the mean square error can reach about 10^-2 when signal noise ratio is relatively high, moreover, the system average bit error rate can be reduced and the system throughput can be increased. This method can be used to instruct the adaptive transmission and adaptive resource assignment of satellite mobile communications.