Machine learning-based forward-shot antenna inclination angle adjustment mechanism

In order to solve the problem that intelligent antenna inclination angle adjustment in a cell network needs to meet the communication real-time requirement, a machine learning (ML)-based proactive antenna inclination angle adjustment (PATAA) mechanism is provided. That is to say, the user position at the next moment is predicted through an echo state network (ESN), and then a base station antennainclination angle group at the next moment is obtained through NN fitting. Due to offline training of the NN, the online solving time can be saved; and in the mechanism, the base station directly performs PATAA with ideal spectral efficiency performance before acquiring the real position of the user, so that the inclination angle adjustment time can be further shortened, and the effect of meetingthe real-time requirement of a communication system is achieved. 针对小区网络中智能天线倾角调整需要满足通信实时性要求的问题，我们提出基于机器学习(ML)的前摄性天线倾斜角度调整(PATAA)机制。即通过回声状态网络(ESN)来预测下一时刻的用户位置，再用NN拟合得到下一时刻的基站天线倾角组。NN的线下训练，能够节省线上求解的时间；而该机制中基站在获取用户真实位置前直接进行谱效性能理想