In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channe...In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channel. Therefore,methods for the calculation of equivalent SINR of a random fading channel are very necessary.In this paper,an enhanced algorithm on the exponential effective SINR mapping( EESM) model for random fading channels was proposed. First, the optimal adjustment parameters of typical channel fading models including extended pedestrian A( EPA)model,extended vehicular A( EVA) model and extended typical urban( ETU) model were obtained by simulation. Then the proposed solution was used to actualize channel classification according to the maximum multipath delay and the average power of the random channel. The solution can determine the typical channel closest to random channel for obtaining the optimal adjustment value of EESM. The evaluation results indicate that the proposed one can improve the whole system throughput significantly and meanwhile the accuracy of the link prediction algorithm is also guaranteed.展开更多
基金Institute of Nonlinear Science of Donghua University,China
文摘In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channel. Therefore,methods for the calculation of equivalent SINR of a random fading channel are very necessary.In this paper,an enhanced algorithm on the exponential effective SINR mapping( EESM) model for random fading channels was proposed. First, the optimal adjustment parameters of typical channel fading models including extended pedestrian A( EPA)model,extended vehicular A( EVA) model and extended typical urban( ETU) model were obtained by simulation. Then the proposed solution was used to actualize channel classification according to the maximum multipath delay and the average power of the random channel. The solution can determine the typical channel closest to random channel for obtaining the optimal adjustment value of EESM. The evaluation results indicate that the proposed one can improve the whole system throughput significantly and meanwhile the accuracy of the link prediction algorithm is also guaranteed.
