一种改进的SBR射线跟踪信道仿真方法

An improved SBR ray-tracing channel simulation method

针对传统入射及反弹射线跟踪算法(Shooting and Bouncing Ray tracing, SBR)中接收球半径通常难以准确确定导致信道仿真出现"射线泄露"或"射线重叠"的问题,提出了一种基于场景认知的改进方法.利用人工神经网络动态调整接收球半径,进而提升射线跟踪算法的信道仿真精度.针对收发端为大规模阵列天线时,SBR仿真时间较长的问题,提出了基于天线方向图的发射方法,用以提高使用大规模天线时的信道仿真效率.将改进的SBR信道仿真与基于传统SBR信道仿真和实测结果进行对比,结果表明:针对单天线,使用本文方法仿真获取的单天线路径损耗均方根误差为2.6 dB,误差小于传统的SBR信道仿真;针对大规模阵列天线仿真,使用本文方法进行接收功率单次仿真的平均时间比传统SBR仿真节省了97%.

To address the “ray leakage” or “ray overlapping” problem frequently encountered in the traditional Shooting and Bouncing Ray tracing(SBR)-based channel simulation with inaccurate receiving radius, a scenario cognition-based improved method is proposed in this paper. This method uses the artificial neural network to dynamically adjust the receiving radius, thereby improving the channel simulation accuracy of the ray-tracing algorithm. In addition, to reduce the SBR simulation time for massive Multiple-Input Multiple-Output(MIMO) scenarios, a antenna pattern-based ray launching method is proposed to improve the channel simulation efficiency when using MIMO antennas. The performance of the improved SBR-based channel simulation is compared with that of the traditional SBR-based channel simulation, and the measured results in the lab are also compared. The results show that for a single transmitting/receiving antenna, the Root Mean Square Error(RMSE) of path loss obtained using the proposed method is 2.6 dB, which is smaller than that when using the traditional SBR channel simulation;for the MIMO channel simulation, the average simulation time for receiving power when using the proposed method is reduced by 97% compared to the traditional SBR algorithm.