阅读说明：本技术 一种业务数据的处理方法和装置 (service data processing method and device ) 是由 周丹 王杰丽 张永丽 于 2018-06-11 设计创作，主要内容包括：本发明实施例提供了一种业务数据的处理方法和装置,应用于数据预失真系统中；所述方法包括：所述数据预失真系统接收所述业务数据；从所述业务数据中提取出目标数据；判断所述目标数据是否满足数据预失真系数训练的条件；若是,则基于所述目标数据进行数据预失真系数训练,得到数据预失真系数；采用所述数据预失真系数对所述业务数据进行数据预失真处理。本发明实施例中的数据预失真系统可以实时地、循环不间断地进行目标数据提取,相对于现有技术,大大提高了目标数据提取的速度。(The embodiment of the invention provides a method and a device for processing service data, which are applied to a data predistortion system; the method comprises the following steps: the data predistortion system receives the service data; extracting target data from the service data; judging whether the target data meets the condition of data predistortion coefficient training or not; if so, performing data predistortion coefficient training based on the target data to obtain a data predistortion coefficient; and carrying out data predistortion treatment on the service data by adopting the data predistortion coefficient. The data predistortion system in the embodiment of the invention can extract the target data in real time and circularly uninterruptedly, and greatly improves the speed of extracting the target data compared with the prior art.)

1. a service data processing method applied to a data predistortion system is characterized by comprising the following steps:

the data predistortion system receives the service data;

Extracting target data from the service data;

Judging whether the target data meets the condition of data predistortion coefficient training or not;

if so, performing data predistortion coefficient training based on the target data to obtain a data predistortion coefficient;

carrying out data predistortion treatment on the service data by adopting the data predistortion coefficient;

the step of judging whether the target data meets the condition of data predistortion coefficient training comprises the following steps:

judging whether the power value of the service data is larger than the lowest power value configured by the current cell under the condition of no service and is not larger than a cell calibration power value, and judging whether the peak value of the service data is larger than the minimum peak value;

and if so, judging that the target data meets the condition of pre-distortion coefficient training.

2. The method of claim 1, wherein the target data comprises training sequence data and feedback data.

3. The method of claim 1 or 2, wherein the step of determining whether the target data meets the condition of data pre-distortion coefficient training comprises:

judging whether the power value of the service data is larger than the lowest power value configured by the current cell under the condition of no service and is not larger than a cell calibration power value, and judging whether the peak value of the service data is larger than the minimum peak value;

and if so, judging that the target data meets the condition of pre-distortion coefficient training.

4. the method of claim 2, wherein the step of performing data pre-distortion coefficient training based on the target data comprises:

Extracting an adjacent channel power ratio index of a feedback signal from the feedback data;

if the adjacent channel power ratio index is larger than or equal to the adjacent channel power ratio index threshold, performing data predistortion coefficient training on the target data by adopting a direct learning mode;

And if the adjacent channel power ratio index is smaller than the adjacent channel power ratio index threshold, performing data predistortion coefficient training on the target data by adopting an indirect learning mode.

5. The method of claim 1 or 2, wherein the data predistortion system comprises a preset look-up table;

The step of performing data predistortion processing on the service data by using the data predistortion coefficient includes:

generating a lookup table based on the data predistortion coefficients;

Updating the preset lookup table by adopting the lookup table to generate an updated lookup table;

and carrying out data pre-distortion processing on the service data according to the updated lookup table.

6. the method of claim 1 or 2, further comprising:

And if the target data does not meet the condition of data predistortion coefficient training, discarding the target data.

7. The device for processing the service data is characterized in that the device is applied to a data predistortion system; the device comprises:

the receiving module is positioned in the data predistortion system and used for receiving the service data;

The extraction module is positioned in the data predistortion system and used for extracting target data from the service data;

the judging module is positioned in the data predistortion system and used for judging whether the target data meets the condition of data predistortion coefficient training or not;

The training module is positioned in the data predistortion system and used for carrying out data predistortion coefficient training based on the target data to obtain a data predistortion coefficient;

And the predistortion processing module is positioned in the data predistortion system and used for carrying out data predistortion processing on the service data by adopting the data predistortion coefficient.

8. The apparatus of claim 7, wherein the target data comprises training sequence data and feedback data.

9. the apparatus according to claim 7 or 8, wherein the determining module at the data pre-distortion system comprises:

the first judging submodule is used for judging whether the power value of the service data is larger than the lowest power value configured by the current cell under the condition of no service and is not larger than a cell calibration power value, and judging whether the peak value of the service data is larger than the minimum peak value;

and the judging submodule is used for judging that the target data meets the condition of pre-distortion coefficient training.

10. the apparatus of claim 8, wherein the training module at the data predistortion system comprises:

an adjacent channel power ratio index extraction submodule, configured to extract an adjacent channel power ratio index of a feedback signal from the feedback data;

the direct learning submodule is used for performing data predistortion coefficient training on the target data in a direct learning mode;

and the indirect learning submodule is used for performing data predistortion coefficient training on the target data in an indirect learning mode.

11. the apparatus of claim 7 or 8, wherein the data predistortion system comprises a preset look-up table;

the predistortion processing module located in the data predistortion system comprises:

The generating submodule is used for generating a lookup table based on the data predistortion coefficient;

The updating submodule is used for updating the preset lookup table by adopting the lookup table to generate an updated lookup table;

And the processing submodule is used for carrying out data predistortion processing on the service data according to the updated lookup table.

12. the apparatus of claim 7 or 8, wherein if the target data does not satisfy the condition of data pre-distortion coefficient training, the apparatus further comprises:

and the filtering module is used for discarding the target data.