基于粒子滤波算法毫米波误差综合估计

5G毫米波通信因其高载波频率和速率而备受关注，成为了未来移动通信的研究热点。然而，在毫米波通信测试仪器设计中，通常采用多次倍频的方法为毫米波基波混频器提供毫米波本振，这个过程中附加产生的相位噪声、载波频率偏移和采样时钟偏移等误差，会造成系统解调指标恶化。为了解决这一问题，本文首次提出了一种基于粒子滤波算法的毫米波误差综合估计的方法，利用粒子滤波算法良好的鲁棒性和适应性，可一次性估计出相位噪声、载波频率偏移以及采样时钟偏移。通过仿真与实验分析，本文算法相较于传统PTRS相位噪声估计效果更好，EVM指标更优。并且本文所提算法已应用于国产5G毫米波基站综测仪中，在载波频率为28 GHz,带宽为400 MHz,调制方式为64QAM时，EVM指标可达到2.21%。

Integrated estimation of millimeter wave errors based on particle filtering algorithms

The 5G millimetre wave communication has attracted much attention due to its high carrier frequency and rate. It is a hot research topic for future mobile communications. However, in the design of millimetre-wave communication test instruments, multiple frequency multiplication is usually used to provide millimetre-wave fundamental oscillation for millimetre-wave base wave mixers, and the errors such as phase noise, carrier frequency offset and sampling clock offset additionally generated in this process can cause deterioration of the system demodulation index. To solve this problem, this article presents for the first time a method for comprehensive estimation of millimetre-wave errors based on the particle filtering algorithm, which can estimate the phase noise, carrier frequency offset and sampling clock offset in one go using the good robustness and adaptability of the particle filtering algorithm. Through simulation and experimental analysis, this article shows that the algorithm is more effective than the traditional PTRS phase noise estimation, and the EVM index is better. The proposed algorithm has been applied to the Chinese 5G millimetre wave base station synthesizer, and the EVM index can reach 2. 21% at a carrier frequency of 28 GHz, a bandwidth of 400 MHz and a modulation method of 64QAM.