WO2012174842A1 - Distortion correction apparatus and method for non-linear system - Google Patents
Distortion correction apparatus and method for non-linear system Download PDFInfo
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- WO2012174842A1 WO2012174842A1 PCT/CN2011/084583 CN2011084583W WO2012174842A1 WO 2012174842 A1 WO2012174842 A1 WO 2012174842A1 CN 2011084583 W CN2011084583 W CN 2011084583W WO 2012174842 A1 WO2012174842 A1 WO 2012174842A1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
- H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
- H04L27/36—Modulator circuits; Transmitter circuits
- H04L27/366—Arrangements for compensating undesirable properties of the transmission path between the modulator and the demodulator
- H04L27/367—Arrangements for compensating undesirable properties of the transmission path between the modulator and the demodulator using predistortion
- H04L27/368—Arrangements for compensating undesirable properties of the transmission path between the modulator and the demodulator using predistortion adaptive predistortion
Abstract
Disclosed are a distortion correction apparatus and method for a non-linear system. The apparatus comprises: an adaptation module and a pre-correction module. The adaptation module comprises: a data collection unit for collecting host link data and feedback link data; a signal processing unit for pre-processing the collected host link data and feedback link data; a correction parameter identification unit for identifying parameters according to the pre-processed host link data and feedback link data to obtain the correction parameters of a non-linear system; and a pre-correction module for pre-correcting the host link data according to the correction parameters. While the existing digital pre-processing method fails to meet the requirement of high linearity, the technical solution of the present invention solves the problem, thus improving the identification accuracy of the correction parameters.
Description
非线性系统失真校正装置及方法 技术领域 本发明涉及通信领域， 具体而言， 涉及一种非线性系统失真校正装置及方法。 背景技术 随着移动通信的发展， 频谱资源越来越稀缺， 为了提高频谱利用效率， 往往采用 高效率的调制方式， 然而这些调制方式在功率放大器工作在接近饱和区时却产生了交 调干扰， 这导致功率放大器产生严重的非线性失真。 解决功率放大器非线性失真问题 的一个途径是采用功率回退技术，但这又导致了功率放大器的低效率和高功耗。因此， 频率利用率和功放效率的折衷要求采用某种处理技术对功放的非线性失真进行校正， 数字预失真技术以其成本不高和性能较好的优势成为当前非线性系统失真校正的首要 选择。 在移动通信系统中， 功率放大器的特性随着环境温度、 器件老化而改变， 因此， 为了提高功放非线性失真的改善效果， 需要对校正参数进行自适应。 目前已有的数字 预失真处理方法通常采用一种间接学习结构对校正参数进行自适应。 例如， 建立一个 逆模型（即校正参数）， 使功放的输出通过该模型的响应逼近功放的输入， 由于这种方 法在逆模型的建立过程中， 信号中的噪声分布使得模型参数最终收敛于一个有偏值。 因此， 上述方法在功放线性指标要求不高的场合非常有效，但对于高线性要求的系统， 已有的数字预处理方法效果不能达到最佳。 针对这一问题， 目前尚未提出有效的解决 方案。 发明内容 本发明提供了一种非线性系统失真校正装置及方法， 以解决上述问题。 根据本发明的一个方面， 提供了一种非线性系统失真校正装置， 包括： 自适应器 模块、 预校正器模块， 其中， 自适应器模块包括： 数据采集单元， 设置为采集主链路 数据和反馈链路数据； 信号处理单元， 设置为对采集到的主链路数据和反馈链路数据 进行预处理； 校正参数辨识单元， 设置为根据预处理后的主链路数据和反馈链路数据 进行参数辨识， 得到非线性系统的校正参数； 预校正器模块， 设置为根据校正参数对 主链路数据进行预校正处理。
校正参数辨识单元包括： 矩阵构造子单元， 设置为根据失真校正模型、 预校正后 的信号以及预处理后的主链路数据和反馈链路数据构建参数辨识矩阵和目标矩阵； 伪 逆计算子单元， 设置为计算参数辨识矩阵的伪逆矩阵； 参数辨识子单元， 设置为根据 目标矩阵、 参数辨识矩阵的伪逆矩阵及预定的参数辨识算法进行参数辨识， 得到非线 性系统的校正参数。 上述失真校正模型包括以下之一：通用记忆多项式模型、 Wiener模型、 Hammerstein 模型、 Volterra模型、 神经网络、 小波网络； 和 /或， 计算参数辨识矩阵和目标矩阵的 伪逆矩阵的算法包括以下之一： 奇异值分解、 QR分解、 Cholesky分解； 和 /或， 预定 的参数辨识算法包括以下之一： 最小二乘算法、 递归最小二乘算法、 最小均方算法。 预校正器模块包括： 地址索引单元， 设置为对主链路信号的幅值或功率进行线性 或非线性映射， 产生索引地址信息； 校正信号生成单元， 设置为在校正参数中查找索 引地址信息对应的内容， 生成失真校正信号； 预校正处理单元， 设置为根据失真校正 信号对主链路信号进行预校正处理。 地址索引单元对主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址 信息的映射算法可以包括：
or fa ( x )， 其中， a 为校正信号的地址信息， |x|和 |x|2为信号的模值和功率， /。(·)为映射 函数， or为或； 预校正处理单元根据失真校正信号对主链路信号进行预校正处理的算法可以包 括： y(n) = Fu x (U, X) , TECHNICAL FIELD The present invention relates to the field of communications, and in particular to a nonlinear system distortion correcting apparatus and method. BACKGROUND With the development of mobile communication, spectrum resources are increasingly scarce. In order to improve spectrum utilization efficiency, high-efficiency modulation methods are often used. However, these modulation methods generate intermodulation interference when the power amplifier operates close to the saturation region. This causes the power amplifier to produce severe nonlinear distortion. One way to solve the problem of nonlinear distortion in power amplifiers is to use power back-off techniques, which in turn leads to low efficiency and high power consumption of the power amplifier. Therefore, the trade-off between frequency utilization and power amplifier efficiency requires some processing technology to correct the nonlinear distortion of the power amplifier. Digital pre-distortion technology is the primary choice for current nonlinear system distortion correction because of its low cost and good performance. . In a mobile communication system, the characteristics of the power amplifier change with the ambient temperature and the aging of the device. Therefore, in order to improve the improvement effect of the nonlinear distortion of the power amplifier, the correction parameters need to be adaptive. The existing digital predistortion processing methods usually adopt an indirect learning structure to adapt the correction parameters. For example, an inverse model (ie, a correction parameter) is established to make the output of the power amplifier approach the input of the power amplifier through the response of the model. Because of the method in the process of establishing the inverse model, the noise distribution in the signal causes the model parameters to finally converge to one. There is a bias value. Therefore, the above method is very effective in the case where the linearity index of the power amplifier is not high, but for the system with high linearity requirements, the existing digital preprocessing method cannot achieve the best effect. In response to this problem, no effective solution has yet been proposed. SUMMARY OF THE INVENTION The present invention provides a nonlinear system distortion correcting apparatus and method to solve the above problems. According to an aspect of the present invention, a non-linear system distortion correction apparatus is provided, including: an adaptor module, a pre-corrector module, wherein the adaptor module includes: a data acquisition unit configured to collect main link data and Feedback link data; a signal processing unit configured to perform pre-processing on the collected main link data and feedback link data; and a correction parameter identification unit configured to perform pre-processed main link data and feedback link data Parameter identification, obtaining correction parameters of the nonlinear system; The pre-corrector module is configured to perform pre-correction processing on the main link data according to the correction parameters. The correction parameter identification unit comprises: a matrix construction subunit, configured to construct a parameter identification matrix and a target matrix according to the distortion correction model, the pre-corrected signal, and the preprocessed main link data and the feedback link data; the pseudo inverse calculation subunit , the pseudo-inverse matrix is set to calculate the parameter identification matrix; the parameter identification sub-unit is set to perform parameter identification according to the target matrix, the pseudo-inverse matrix of the parameter identification matrix and a predetermined parameter identification algorithm, and the correction parameters of the nonlinear system are obtained. The above distortion correction model includes one of the following: a general memory polynomial model, a Wiener model, a Hammerstein model, a Volterra model, a neural network, a wavelet network; and/or an algorithm for calculating a pseudo-inverse matrix of a parameter identification matrix and a target matrix, including one of the following : singular value decomposition, QR decomposition, Cholesky decomposition; and/or, the predetermined parameter identification algorithm includes one of the following: a least squares algorithm, a recursive least squares algorithm, and a least mean square algorithm. The pre-corrector module includes: an address indexing unit configured to perform linear or non-linear mapping on the amplitude or power of the main link signal to generate index address information; and a correction signal generating unit configured to search for the index address information in the correction parameter The content of the distortion correction signal is generated; the pre-correction processing unit is configured to perform pre-correction processing on the main link signal according to the distortion correction signal. The address indexing unit performs linear or non-linear mapping on the amplitude or power of the main link signal, and the mapping algorithm for generating the index address information may include: Or f a ( x ), where a is the address information of the correction signal, and |x| and |x| 2 are the modulus and power of the signal, /. (·) is a mapping function, or is or; the algorithm for pre-correction processing of the main link signal by the pre-correction processing unit according to the distortion correction signal may include: y(n) = F ux (U, X) ,
U = [U(n), U(n - l), ..., U(n - K)]， U = [U(n), U(n - l), ..., U(n - K)],
X = [x(n), x(n _ 1), ... , x(n - J)] , 其中， 为根据索引地址信息查找得到的失真校正信号向量， 为主链路信号向 量， f为校正信号的最大延迟， J为主链路信号的最大延迟， 《为信号采样时间序号， _y为预校正后信号； χ(·)为预校正函数。
根据本发明的另一方面， 提供了一种非线性系统失真校正方法， 包括： 采集主链 路数据和反馈链路数据； 对采集到的主链路数据和反馈链路数据进行预处理； 根据预 处理后的主链路数据和反馈链路数据进行参数辨识， 得到非线性系统的校正参数； 根 据校正参数对主链路数据进行预校正处理。 根据预处理后的主链路数据和反馈链路数据进行参数辨识， 得到非线性系统的校 正参数包括： 根据失真校正模型、 预校正后的信号以及预处理后的主链路数据和反馈 链路数据构建参数辨识矩阵和目标矩阵； 计算参数辨识矩阵的伪逆矩阵； 根据目标矩 阵、 参数辨识矩阵的伪逆矩阵及预定的参数辨识算法进行参数辨识， 得到非线性系统 的校正参数。 上述失真校正模型包括以下之一：通用记忆多项式模型、 Wiener模型、 Hammerstein 模型、 Volterra模型、 神经网络、 小波网络； 和 /或， 计算参数辨识矩阵和目标矩阵的 伪逆矩阵的算法包括以下之一： 奇异值分解、 QR分解、 Cholesky分解； 和 /或， 预定 的参数辨识算法包括以下之一： 最小二乘算法、 递归最小二乘算法、 最小均方算法。 根据校正参数对主链路数据进行预校正处理包括： 对主链路信号的幅值或功率进 行线性或非线性映射， 产生索引地址信息； 在上述校正参数中查找上述索引地址信息 对应的内容， 生成失真校正信号； 根据失真校正信号对主链路信号进行预校正处理。 对主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址信息的映射算 法可以包括：
or fa ( x )， 其中， a 为校正信号的地址信息， |x|和 |x|2为信号的模值和功率， /。(·)为映射 函数， or为或； 根据失真校正信号对主链路信号进行预校正处理的算法可以包括： y(n) = Fu x (U, X) , X = [x(n), x(n _ 1), ... , x(n - J)] , where is the distortion correction signal vector found according to the index address information, the main link signal vector, f To correct the maximum delay of the signal, J is the maximum delay of the main link signal, "for the signal sampling time serial number, _y is the pre-corrected signal; χ (·) is the pre-correction function. According to another aspect of the present invention, a nonlinear system distortion correction method is provided, including: collecting primary link data and feedback link data; and performing pre-processing on the collected primary link data and feedback link data; The pre-processed main link data and the feedback link data are subjected to parameter identification, and the correction parameters of the nonlinear system are obtained; and the main link data is pre-corrected according to the correction parameters. The parameter identification is performed according to the preprocessed main link data and the feedback link data, and the correction parameters of the nonlinear system are obtained: according to the distortion correction model, the pre-corrected signal, and the preprocessed main link data and the feedback link. The data constructs the parameter identification matrix and the target matrix; calculates the pseudo-inverse matrix of the parameter identification matrix; performs parameter identification according to the target matrix, the pseudo-inverse matrix of the parameter identification matrix and the predetermined parameter identification algorithm, and obtains the correction parameters of the nonlinear system. The above distortion correction model includes one of the following: a general memory polynomial model, a Wiener model, a Hammerstein model, a Volterra model, a neural network, a wavelet network; and/or an algorithm for calculating a pseudo-inverse matrix of a parameter identification matrix and a target matrix, including one of the following : singular value decomposition, QR decomposition, Cholesky decomposition; and/or, the predetermined parameter identification algorithm includes one of the following: a least squares algorithm, a recursive least squares algorithm, and a least mean square algorithm. Performing pre-correction processing on the main link data according to the correction parameter includes: linearly or non-linearly mapping the amplitude or power of the main link signal to generate index address information; searching for the content corresponding to the index address information in the correction parameter, Generating a distortion correction signal; pre-correcting the main link signal according to the distortion correction signal. The linear or non-linear mapping of the amplitude or power of the primary link signal, the mapping algorithm for generating the index address information may include: Or f a ( x ), where a is the address information of the correction signal, and |x| and |x| 2 are the modulus and power of the signal, /. (·) is a mapping function, or is or; The algorithm for pre-correcting the main link signal according to the distortion correction signal may include: y(n) = F ux (U, X) ,
U = [U(n), U(n - l), ..., U(n - K)]， U = [U(n), U(n - l), ..., U(n - K)],
X = [x(n), x(n _ 1), ... , x(n - J)] ,
其中， 为根据索引地址信息查找得到的失真校正信号向量， 为主链路信号向 量， f为校正信号的最大延迟， J为主链路信号的最大延迟， 《为信号采样时间序号，X = [x(n), x(n _ 1), ... , x(n - J)] , Wherein, the distortion correction signal vector obtained by searching according to the index address information is the main link signal vector, f is the maximum delay of the correction signal, and J is the maximum delay of the main link signal, “is the signal sampling time serial number,
_y为预校正后信号； χ(·)为预校正函数。 通过本发明， 采用同时采集主链路数据和反馈链路数据， 在对其进行了预处理后 一起作为参数辨识的基础， 生成校正参数以进行主链路数据预校正的方案， 解决了现 有数字预处理方法不能满足高线性要求的问题， 进而达到了提高校正参数的辨识精度 的效果。 附图说明 此处所说明的附图用来提供对本发明的进一步理解， 构成本申请的一部分， 本发 明的示意性实施例及其说明用于解释本发明， 并不构成对本发明的不当限定。 在附图 中： 图 1是根据本发明实施例的非线性系统失真校正装置的结构框图； 图 2是根据本发明第一优选实施例的非线性系统失真校正装置的结构框图； 图 3是根据本发明第二优选实施例的非线性系统失真校正装置的结构框图； 图 4是现有非线性系统失真校正装置的结构示意图； 图 5是根据本发明实例的非线性系统失真校正装置的结构示意图； 图 6是根据本发明实例的预校正器的基本结构图； 图 7是根据本发明实例的自适应器的基本结构图； 图 8是根据本发明实例一的功放预校正装置的具体结构示意图； 图 9是根据本发明实例二的功放预校正装置的具体结构示意图； 图 10是根据本发明实施例的非线性系统失真校正方法的流程图； 图 11是根据本发明优选实施例的非线性系统失真校正方法的流程图。
具体实施方式 下文中将参考附图并结合实施例来详细说明本发明。 需要说明的是， 在不冲突的 情况下， 本申请中的实施例及实施例中的特征可以相互组合。 图 1是根据本发明实施例的非线性系统失真校正装置的结构框图。 如图 1所示， 根据本发明实施例的非线性系统失真校正装置包括： 自适应器模块 12、 预校正器模块 14， 其中， 自适应器模块 12包括： 数据采集单元 122， 设置为采集主链路数据和反馈链路数据； 信号处理单元 124，连接至数据采集单元 122， 设置为对采集到的主链路数据和反 馈链路数据进行预处理； 校正参数辨识单元 126，连接至信号处理单元 124， 设置为根据预处理后的主链路 数据和反馈链路数据进行参数辨识， 得到非线性系统的校正参数； 预校正器模块 14，连接至自适应器模块 12， 设置为根据校正参数对主链路数据进 行预校正处理。 上述装置采用了基于正向迭代的自适应方式， 将主链路数据和反馈链路数据都作 为计算校正参数的数据基础， 解决了功率放大器的非线性失真带来的频谱扩散问题， 提高了校正参数的辨识精度。 本实例中， 主链路数据也可以称为未经预校正的信号或 预校正器模块 14的输入信号，反馈链路数据也可以称为经过了非线性系统的信号或非 线性系统的输出信号。 信号处理单元 124所进行的预处理与现有技术中为了得到校正参数所进行的预处 理相同， 可以包括： 移频、 滤波、 信号校正等一般性的处理， 通过预处理即可在进行 参数辨识前， 对基础数据进行整理， 以进行后续的处理。 对于校正参数的识别， 在根据本实施例装置引入了主链路数据之后， 可以根据多 种方式来进行参数识别， 在本实施例中， 提供了一种较优的实施方式， 图 2是根据本 发明第一优选实施例的非线性系统失真校正装置的结构框图， 如图 2所示， 校正参数 辨识单元 126可以进一步包括： 矩阵构造子单元 1262， 设置为根据失真校正模型、 预校正后的信号以及预处理后 的主链路数据和反馈链路数据构建参数辨识矩阵和目标矩阵；
伪逆计算子单元 1264，连接至矩阵构造子单元 1262， 设置为计算参数辨识矩阵的 伪逆矩阵； 参数辨识子单元 1266， 连接至矩阵构造子单元 1262及伪逆计算子单元 1264， 设 置为根据目标矩阵、 参数辨识矩阵的伪逆矩阵及预定的参数辨识算法进行参数辨识， 得到非线性系统的校正参数。 校正参数辨识分为三步， 首先， 构造参数辨识所需矩阵， 即参数辨识矩阵和目标 矩阵， 参数辨识矩阵和目标矩阵是由预处理后的主链路信号、 预处理后的反馈信号和 预校正后的信号（即非线性系统的输入信号）通过失真校正模型共同构造而成； 其次， 计算参数辨识矩阵的伪逆矩阵； 最后， 采用预定的参数辨识算法对上述目标矩阵及参 数辨识矩阵的伪逆矩阵进行参数辨识， 得出非线性系统的校正参数。 优选的， 上述失真校正模型可以包括以下之一： 通用记忆多项式模型、 Wiener模 型、 Hammerstein模型、 Volterra模型、 神经网络、 小波网络； 禾 P/或， 计算参数辨识矩 阵和目标矩阵的伪逆矩阵的算法可以包括以下之一： 奇异值分解、 QR分解、 Cholesky 分解； 和 /或， 预定的参数辨识算法可以包括以下之一： 最小二乘算法、 递归最小二乘 算法、 最小均方算法。 在具体实施过程中， 可使用的模型和算法包括但不限于上述的模型和算法， 可以 根据具体需要进行扩展并进行不同的搭配。 上述的模型和算法在现有技术中都有明确 的含义及应用方法， 本实例中不再赘述。 对于预校正处理， 也可以由多种方式来进行， 在本实施例中， 提供了一种较优的 实施方式， 如图 3所示， 图 3是根据本发明第二优选实施例的非线性系统失真校正装 置的结构框图。 需要说明的是， 图 2及图 3所示的非线性系统失真校正装置， 可以按 各自结构单独使用也可以对其结构进行结合使用。 如图 3所示， 预校正器模块 14可以进一步包括： 地址索引单元 142， 设置为对主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址信息； 校正信号生成单元 144，连接至地址索引单元 142， 设置为在上述校正参数中查找 上述索引地址信息对应的内容， 生成失真校正信号； 预校正处理单元 146，连接至校正信号生成单元 144， 设置为根据失真校正信号对 主链路信号进行预校正处理。
预校正也可以分为三步完成， 首先， 对主链路信号的幅值或功率进行线性或非线 性映射， 产生索引地址信息； 其次， 在正参数辨识单元 126生成的校正参数中查找上 述索引地址信息对应的内容， 生成失真校正信号； 最后， 根据失真校正信号对主链路 信号进行预校正处理， 得到预校正后的信号， 以抵消后续非线性系统产生的非线性失 真。 优选的， 地址索引单元 142对主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址信息的映射算法可以包括：
or fa ( x )， 其中， a 为校正信号的地址信息， |x|和 |x|2为信号的模值和功率， /。(·)为映射 函数， or为或； 预校正处理单元 146根据失真校正信号对主链路信号进行预校正处理的算法可以 包括： y(n) = Fu x (U, X) , _y is the pre-corrected signal; χ (·) is the pre-correction function. Through the invention, the main link data and the feedback link data are simultaneously collected, and after being pre-processed together, the correction parameters are generated as a basis for parameter identification, and the main link data pre-correction scheme is solved, thereby solving the existing solution. The digital preprocessing method can not meet the problem of high linearity requirements, and thus achieves the effect of improving the identification accuracy of the correction parameters. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are set to illustrate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram showing a configuration of a nonlinear system distortion correcting apparatus according to an embodiment of the present invention; FIG. 2 is a block diagram showing a configuration of a nonlinear system distortion correcting apparatus according to a first preferred embodiment of the present invention; FIG. 4 is a schematic structural diagram of a nonlinear system distortion correction apparatus according to a second preferred embodiment of the present invention; FIG. 4 is a schematic structural diagram of a nonlinear system distortion correction apparatus according to an example of the present invention; 6 is a basic structural diagram of a pre-corrector according to an example of the present invention; FIG. 7 is a basic structural diagram of an adaptor according to an example of the present invention; FIG. 8 is a detailed structural diagram of a power amplifier pre-correcting apparatus according to an example 1 of the present invention; 9 is a detailed structural diagram of a power amplifier pre-correcting apparatus according to Example 2 of the present invention; FIG. 10 is a flowchart of a nonlinear system distortion correcting method according to an embodiment of the present invention; FIG. 11 is a nonlinear diagram according to a preferred embodiment of the present invention. Flow chart of the system distortion correction method. BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, the present invention will be described in detail with reference to the accompanying drawings. It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict. 1 is a block diagram showing the structure of a nonlinear system distortion correcting apparatus according to an embodiment of the present invention. As shown in FIG. 1 , the nonlinear system distortion correction apparatus according to the embodiment of the present invention includes: an adaptor module 12 and a pre-corrector module 14 , wherein the adaptor module 12 includes: a data acquisition unit 122 configured to collect the main Link data and feedback link data; signal processing unit 124, coupled to data acquisition unit 122, configured to pre-process the collected primary link data and feedback link data; calibration parameter identification unit 126, coupled to signal processing The unit 124 is configured to perform parameter identification according to the preprocessed main link data and the feedback link data to obtain a calibration parameter of the nonlinear system; the precorrector module 14 is connected to the adaptor module 12, and is set according to the calibration parameter. Pre-correction processing of the main link data. The above device adopts an adaptive method based on forward iteration, and uses the main link data and the feedback link data as the data basis for calculating the correction parameters, solves the spectrum diffusion problem caused by the nonlinear distortion of the power amplifier, and improves the correction. The identification accuracy of the parameters. In this example, the main link data may also be referred to as an unpre-corrected signal or an input signal of the pre-corrector module 14, and the feedback link data may also be referred to as a signal passing through a nonlinear system or an output signal of a nonlinear system. . The preprocessing performed by the signal processing unit 124 is the same as the preprocessing performed in the prior art to obtain the correction parameters, and may include: general processing such as frequency shifting, filtering, signal correction, etc., and parameter identification can be performed by preprocessing. Before, the basic data is sorted for subsequent processing. For the identification of the correction parameters, after the main link data is introduced by the device according to the embodiment, the parameter identification can be performed according to various manners. In this embodiment, a preferred implementation manner is provided, and FIG. 2 is based on A block diagram of a nonlinear system distortion correction apparatus according to a first preferred embodiment of the present invention, as shown in FIG. 2, the correction parameter identification unit 126 may further include: a matrix construction sub-unit 1262, configured to be based on a distortion correction model, pre-corrected Constructing a parameter identification matrix and a target matrix by using the signal and the preprocessed main link data and the feedback link data; The pseudo inverse calculation subunit 1264 is connected to the matrix construction subunit 1262, and is configured to calculate a pseudo inverse matrix of the parameter identification matrix; the parameter identification subunit 1266 is connected to the matrix construction subunit 1262 and the pseudo inverse calculation subunit 1264, and is set according to The target matrix, the pseudo-inverse matrix of the parameter identification matrix and the predetermined parameter identification algorithm are used for parameter identification, and the correction parameters of the nonlinear system are obtained. The calibration parameter identification is divided into three steps. First, construct the parameter identification matrix, which is the parameter identification matrix and the target matrix. The parameter identification matrix and the target matrix are the pre-processed main link signals, the pre-processed feedback signals and the pre-processing. The corrected signal (ie, the input signal of the nonlinear system) is constructed by the distortion correction model; secondly, the pseudo inverse matrix of the parameter identification matrix is calculated; finally, the predetermined parameter identification algorithm is used for the target matrix and the parameter identification matrix. The pseudo inverse matrix is used for parameter identification, and the correction parameters of the nonlinear system are obtained. Preferably, the distortion correction model may include one of the following: a general memory polynomial model, a Wiener model, a Hammerstein model, a Volterra model, a neural network, a wavelet network; a P/or, a parameter identification matrix and a pseudo-inverse matrix of the target matrix. The algorithm may include one of the following: singular value decomposition, QR decomposition, Cholesky decomposition; and/or, the predetermined parameter identification algorithm may include one of the following: a least squares algorithm, a recursive least squares algorithm, a least mean square algorithm. In the specific implementation process, the models and algorithms that can be used include, but are not limited to, the above-mentioned models and algorithms, and can be expanded and matched according to specific needs. The above-mentioned models and algorithms have clear meanings and application methods in the prior art, and will not be described in detail in this example. For the pre-correction process, it can also be performed in various ways. In this embodiment, a preferred embodiment is provided. As shown in FIG. 3, FIG. 3 is a nonlinearity according to a second preferred embodiment of the present invention. A block diagram of the system distortion correction device. It should be noted that the nonlinear system distortion correcting device shown in FIGS. 2 and 3 may be used alone or in combination of its structure. As shown in FIG. 3, the pre-corrector module 14 may further include: an address indexing unit 142 configured to linearly or non-linearly map the amplitude or power of the main link signal to generate index address information; the correction signal generating unit 144, The connection to the address indexing unit 142 is configured to search for the content corresponding to the index address information in the above-mentioned correction parameter to generate a distortion correction signal. The pre-correction processing unit 146 is connected to the correction signal generation unit 144, and is configured to be paired according to the distortion correction signal. The link signal is pre-corrected. Pre-correction can also be completed in three steps. First, linear or non-linear mapping of the amplitude or power of the main link signal is performed to generate index address information. Second, the index is searched for in the correction parameter generated by the positive parameter identification unit 126. The content corresponding to the address information generates a distortion correction signal. Finally, the main link signal is pre-corrected according to the distortion correction signal to obtain a pre-corrected signal to cancel the nonlinear distortion generated by the subsequent nonlinear system. Preferably, the address indexing unit 142 performs linear or non-linear mapping on the amplitude or power of the primary link signal, and the mapping algorithm for generating the indexed address information may include: Or f a ( x ), where a is the address information of the correction signal, and |x| and |x| 2 are the modulus and power of the signal, /. (·) is a mapping function, or is or; the algorithm for pre-correction processing of the main link signal by the pre-correction processing unit 146 according to the distortion correction signal may include: y(n) = F ux (U, X) ,
U = [U(n), U(n - l), ..., U(n - K)]， U = [U(n), U(n - l), ..., U(n - K)],
;r = [xO), x( - i) x( - J)]， 其中， 为根据索引地址信息查找得到的失真校正信号向量， 为主链路信号向 量， f为校正信号的最大延迟， J为主链路信号的最大延迟， 《为信号采样时间序号， _y为预校正后信号； χ(·)为预校正函数。 地址索引单元 142在生成索引地址信息时， 是对主链路信号的幅值或功率进行线 性映射还是非线性映射体现在为映射函数 /。(·)的选择上， 而具体选择怎样的映射函数 需要根据实际情况确定。 同样， 预校正处理单元 146根据失真校正信号对主链路信号 进行与校正处理的关键， 也在于预校正函数 ^0的选择， 需要根据实际情况确定。 下面结合实例对上述优选实施例进行详细说明。 为了帮助理解本发明， 首先对现有的非线性系统失真校正装置难以满足高线性指 标要求的原因进行简单的说明。 如图 4所示， 对于 GSM多载波系统来说， 图中的非 线性系统主要是功放， 功放在对信号进行放大的同时， 也对信号的幅度和相位产生了
非线性失真， 这些失真在时域上造成信号包络失真， 在频域上造成了频谱扩散， 从而 导致临道泄露功率比恶化和信号解调指标差。 在传统的功放非线性校正装置中， 校正 参数是根据功放的输入数字信号和耦合的反馈数字信号， 通过建立功放的逆模型得到 功放的非线性校正参数。 上述方案的缺点是在构建功放逆模型的过程中， 改变了反馈 信号中观测噪声的分布特性， 从而影响了功放逆模型的参数辨识精度， 在线性指标要 求高的场合， 其非线性失真校正性不能满足要求。 图 5是根据本发明实例的非线性系统失真校正装置的结构示意， 亦展现了该装置 在通信系统中的位置。如图 5所示， 整个非线性失真校正装置包括： 信号发生器模块、 预校正器模块、 DAC模块、 ADC模块、 非线性系统模块、 自适应器模块和控制信号 模块。 这里， 信号发生器模块、 DAC模块、 ADC模块， 为在本实例具体实施过程需要 添加的基础功能模块， 用于提供原始信号及进行数模转换； 非线性系统模块即造成非 线性失真的模块。 在本实例中： 信号发生器模块会产生主链路信号， 即待非线性处理的数字信号。 信号发生器模 块产生的主链路信号， 经过预校正器模块的预校正处理， 得到的预校正后的信号； 预 校正后的信号经过数模转换以及非线性系统的处理得出非线性系统的输出信号， 非线 性系统的输出信号经过模数转换以后， 得到反馈数据信号。 在自适应器模块中， 对主链路信号和反馈数字信号进行信号预处理以后， 建立非 线性系统的失真模型， 采用参数辨识算法， 辨识得到校正参数， 并下载到预校正器， 从而实现校正参数的自适应处理。 预校正器模块对主链路信号进行数字预校正处理， 得到预校正后的信号。 预校正 器模块根据信号的幅度和相位信息， 对主链路信号进行预校正， 该校正信息与非线性 系统产生的失真信号的幅度相等、 相位相反， 因此可以抵消非线性系统对主链路信号 造成的失真。 预校正后信号经过 DAC模块实现从数字域到模拟域的转换， 并通过非线性系统 模块实现信号的非线性处理。 非线性系统的输出信号通过 ADC模块， 最终得到反馈 数字信号。
自适应器模块定时完成主链路信号、 预校正后信号和反馈数字信号的采集， 对采 集信号进行必要的预处理以后， 采用参数辨识算法辨识得到校正参数， 并下载到预校 正器。 在具体实施过程中， 还可以设置一控制信号模块， 以相对独立的实现对自适应器 模块的控制， 包括控制自适应器模块中数据的采集、 信号预处理流程、 校正参数辨识 和校正参数的下载等； 图 6是根据本发明实例的预校正器模块的基本结构图， 包括地址索引单元、 校正 信号产生单元和预校正处理单元。 地址索引单元和校正信息产生单元主要负责对输入信号的幅值或者功率进行线性 或非线性映射， 产生索引地址信息， 并根据该地址信息得到与输入数据对应的校正信 号。 地址索引单元可以采用的映射关系如下式：
其中， a 为校正信号的地址信息， |x|和 |x|2为信号的模值和功率， /。(·)为映射 函数， 例如， /。(·)采用对数函数。 本发明的映射函数不限于上述对数映射。 预校正处理单元主要负责对主链路信号进行预校正处理。 预校正单元可以采用的 公式如下： y(n) = F„x(U,X) (2) ;r = [xO), x( - i) x( - J)], where is the distortion correction signal vector found from the index address information, the main link signal vector, and f is the maximum delay of the correction signal, J The maximum delay of the main link signal, "is the signal sampling time serial number, _y is the pre-corrected signal; χ (·) is the pre-correction function. When generating the index address information, the address indexing unit 142 linearly maps the amplitude or power of the main link signal or the nonlinear mapping as a mapping function/. The choice of (·), and the specific choice of the mapping function needs to be determined according to the actual situation. Similarly, the pre-correction processing unit 146 performs the key processing of the main link signal according to the distortion correction signal, and also the selection of the pre-correction function ^0, which needs to be determined according to the actual situation. The above preferred embodiments will be described in detail below with reference to examples. In order to help understand the present invention, first, the reason why the conventional nonlinear system distortion correcting device is difficult to meet the high linearity index requirement will be briefly explained. As shown in Figure 4, for the GSM multi-carrier system, the nonlinear system in the figure is mainly a power amplifier. The power amplifier is used to amplify the signal, and also produces the amplitude and phase of the signal. Nonlinear distortion, which causes signal envelope distortion in the time domain, causing spectral spread in the frequency domain, resulting in poor leakage power degradation and signal demodulation index. In the conventional power amplifier nonlinear correction device, the correction parameter is based on the input digital signal of the power amplifier and the coupled feedback digital signal, and the nonlinear correction parameter of the power amplifier is obtained by establishing an inverse model of the power amplifier. The shortcoming of the above scheme is that in the process of constructing the power amplifier inverse model, the distribution characteristics of the observed noise in the feedback signal are changed, which affects the parameter identification accuracy of the power amplifier inverse model. In the case where the linear index requires high, the nonlinear distortion correction property Can not meet the requirements. Figure 5 is a block diagram showing the configuration of a nonlinear system distortion correcting apparatus according to an example of the present invention, and also showing the position of the apparatus in the communication system. As shown in FIG. 5, the entire nonlinear distortion correction device includes: a signal generator module, a pre-corrector module, a DAC module, an ADC module, a nonlinear system module, an adaptor module, and a control signal module. Here, the signal generator module, the DAC module, and the ADC module are basic functional modules that need to be added in the specific implementation process of the present example to provide original signals and perform digital-to-analog conversion; the nonlinear system modules are modules that cause nonlinear distortion. In this example: The signal generator module generates a primary link signal, a digital signal to be nonlinearly processed. The main link signal generated by the signal generator module is pre-corrected by the pre-corrector processing of the pre-corrector module, and the pre-corrected signal is subjected to digital-to-analog conversion and processing by a nonlinear system to obtain a nonlinear system. The output signal, after the analog signal of the nonlinear system is subjected to analog-to-digital conversion, obtains a feedback data signal. In the adaptor module, after pre-processing the signal of the main link signal and the feedback digital signal, the distortion model of the nonlinear system is established, and the parameter identification algorithm is used to identify the correction parameters and download to the pre-corrector to implement the correction. Adaptive processing of parameters. The pre-corrector module performs digital pre-correction processing on the main link signal to obtain a pre-corrected signal. The pre-corrector module pre-corrects the main link signal according to the amplitude and phase information of the signal, and the correction information has the same amplitude and opposite phase as the distortion signal generated by the nonlinear system, thereby canceling the nonlinear system to the main link signal The distortion caused. The pre-corrected signal is converted from the digital domain to the analog domain by the DAC module, and the nonlinear processing of the signal is realized by the nonlinear system module. The output signal of the nonlinear system passes through the ADC module, and finally the feedback digital signal is obtained. The adaptive module periodically completes the acquisition of the main link signal, the pre-corrected signal, and the feedback digital signal. After performing the necessary pre-processing on the acquired signal, the parameter identification algorithm is used to identify the corrected parameter and download it to the pre-corrector. In the specific implementation process, a control signal module may be further provided to control the adaptive device module relatively independently, including controlling data acquisition, signal preprocessing flow, correction parameter identification and correction parameters in the adaptive device module. FIG. 6 is a basic structural diagram of a pre-corrector module according to an example of the present invention, including an address index unit, a correction signal generating unit, and a pre-correction processing unit. The address indexing unit and the correction information generating unit are mainly responsible for linearly or non-linearly mapping the amplitude or power of the input signal, generating index address information, and obtaining a correction signal corresponding to the input data based on the address information. The mapping relationship that the address index unit can adopt is as follows: Where a is the address information of the correction signal, and |x| and |x| 2 are the modulus and power of the signal, /. (·) is a mapping function, for example, /. (·) uses a logarithmic function. The mapping function of the present invention is not limited to the above-described logarithmic mapping. The pre-correction processing unit is mainly responsible for pre-correcting the main link signal. The formula that the pre-correction unit can take is as follows: y(n) = F„ x (U,X) (2)
U = [U(nl U(n -!),..., U(n- K)] (3) U = [U(nl U(n -!),..., U(n- K)] (3)
X = [x(n), x(n-\),...,x(n-J)] (4) 其中， 为根据地址查找得到的失真校正信号向量， 为主链路信号向量， K为 校正信号的最大延迟， J为主链路信号的最大延迟， 《为信号采样时间序号， _y为预 校正后信号， χ(·)为预校正函数。 总的来说， 预校正处理模块执行的步骤如下:
步骤 1， 地址产生。 计算主链路信号的幅值或功率， 根据式 （1 ) 计算得到校正信 号的索引地址。 步骤 2， 计算失真校正信号。 使用产生的索引地址在校正参数中查找对应的内容。 步骤 3， 预校正处理。 使用失真校正信号， 按照式 （2)、 ( 3 ) 和 （4) 对主链路信 号进行预校正处理， 得到预校正后信号。 图 7是根据本发明实例的自适应器的基本结构图， 包括数据采集单元、 信号处理 单元、 校正参数辨识单元。 数据采集单元主要负责采集校正参数辨识所需的处理数据， 包括主链路信号、 反 馈数字信号， 也可用于对预校正后的信号直接进行采集。 需要说明的是， 在预校正后 的信号的采集不是必须的， 也可以通过对采集的主链路信号进行预校正处理， 间接得 到预校正后信号。 信号处理单元主要负责对采集的数字信号进行必要的数据预处理。 校正参数辨识单元主要负责把预处理后的信号按照预定的失真模型构建得到参数 辨识矩阵 R和目标矩阵 /)， 采用参数辨识算法， 辨识得到校正参数， 并把校正参数下 载到校正信息产生单元中。 在具体实施过程中， 如是需要经常更新校正参数， 可以再单独设置一参数下载单 元， 负责保存和更新校正参数， 再把校正参数下载到校正信息产生单元中。 失真模型可以采用通用记忆多项式模型， 如下式：
其中， 为模型输入信号， 为信号延迟， P为模型阶数， J,K为最大延迟， P 为模型最高阶数， 为模型系数。 对应到本发明中， 为信号发生器模块的输出信 号， 为目标矩阵 /) ; 可用的失真模型不限于通用记忆多项式模型，也可以是 Wiener模型、 Hammerstein 模型、 Volterra模型、 神经网络和小波网络等。 校正参数辨识可以采用最小二乘辨识算法。
W = R~lD ( 5 )
其中， R为辨识矩阵， /)为目标矩阵 （/)的构建方法在下文中举例说明）， W为 校正参数， （ 1为求伪逆运算， 其他表达式的意义与式 （5 ) —致。 同样的， 可用的算法不限于式（6)所示的最小二乘辨识算法， 还包括递归最小二 乘算法和最小均方算法等其他迭代算法。 对应的， 本发明辨识矩阵 R的构造不限于式 ( 7) 所示的表达式， 可以根据失真模型而改变。 总的来说， 校正参数辨识单元执行的步骤如下： 步骤 1， 构造参数辨识所需矩阵。 根据式 （7) 构造出参数辨识矩阵 R和目标矩阵 D , 参数辨识矩阵 R和目标矩阵 /)是由主链路信号、 反馈信号和预校正后信号共同构 造。 步骤 2， 计算伪逆。 采用奇异值分解、 QR分解或 Cholesky分解等矩阵求逆方法 计算参数辨识矩阵 R的伪逆。 步骤 3， 辨识校正参数。 采用最小二乘算法、 RLS算法或 LMS算法等辨识得到校 正参数。 图 8是根据本发明实例一的功放预校正装置的具体结构示意图。 在本实例中， 非 线性系统为功放， 各模块没有在图中明示， 而是通过实现的功能表示。 整个系统包括作为应用基础的基带信号模块、通道滤波模块、预校正器模块、 DAC 模块、 ADC模块、 上变频模块、 下变频模块、 LO模块、 功放模块和衰减器模块。 在 本实例中，预校正后的信号按照式（8 )所示的预校正函数得到。构造参数辨识矩阵时， 参数辨识矩阵 R根据式（7)构造， 目标矩阵/)由反馈信号与预校正后信号的差值构造， 如式 （9) 所示。 其他模块处理方式与上述的及现有技术相同。 y = x - U * X ( 8 ) D = z -y ( 9) 其中， _y为预校正后信号， X为主链路信号， 为校正信号， 为主链路信号通 过延迟得到的向量。
图 9是根据本发明实例二的功放预校正装置的具体结构示意图。 在本实例中， 非 线性系统为功放， 各模块没有在图中明示， 而是通过实现的功能表示。 整个系统包括作为应用基础的基带信号模块、通道滤波模块、预校正器模块、 DAC 模块、 ADC模块、 上变频模块、 下变频模块、 LO模块、 功放模块、 和衰减器模块。 在本实例中， 多次项构造器根据式 （10)和 （11) 构造多次项序列 M， 补偿器根据式 (12) 构造校正信号， 预校正后的信号按照式 （13) 所示的预校正函数得到。 在构造 参数辨识矩阵时， 参数辨识矩阵 R根据式（7)构造， 目标矩阵/)由预校正后信号与反 馈信号的差值构造， 如式 （13) 所示。 其他模块处理方式与上述的及现有技术相同。 = [ 0, ,,..., ^...] (10)
其中， 为多项式序列， P为多项式最高阶数， |·|为模值运算， X为多载波合路 信号， Α为信号延迟。 X = [x(n), x(n-\),...,x(nJ)] (4) where is the distortion correction signal vector obtained from the address search, the main link signal vector, K is the correction The maximum delay of the signal, J is the maximum delay of the main link signal, "for the signal sampling time serial number, _y is the pre-corrected signal, and χ (·) is the pre-correction function. In summary, the steps performed by the pre-correction processing module are as follows: Step 1, the address is generated. Calculate the amplitude or power of the main link signal, and calculate the index address of the corrected signal according to equation (1). Step 2, calculating a distortion correction signal. Use the generated index address to find the corresponding content in the correction parameters. Step 3. Pre-correction processing. Using the distortion correction signal, the main link signal is pre-corrected according to equations (2), (3), and (4) to obtain a pre-corrected signal. 7 is a basic structural diagram of an adaptor according to an example of the present invention, including a data acquisition unit, a signal processing unit, and a correction parameter identification unit. The data acquisition unit is mainly responsible for collecting the processing data required for the calibration parameter identification, including the main link signal and the feedback digital signal, and can also be used for directly collecting the pre-corrected signal. It should be noted that the acquisition of the pre-corrected signal is not necessary, and the pre-corrected signal may be indirectly obtained by performing pre-correction processing on the collected main link signal. The signal processing unit is mainly responsible for performing necessary data preprocessing on the acquired digital signals. The correction parameter identification unit is mainly responsible for constructing the parameter identification matrix R and the target matrix/) according to a predetermined distortion model, using the parameter identification algorithm to identify the correction parameters, and downloading the correction parameters to the correction information generation unit. . In the specific implementation process, if it is necessary to update the calibration parameters frequently, a parameter download unit may be separately set, which is responsible for saving and updating the correction parameters, and then downloading the correction parameters into the correction information generating unit. The distortion model can use a general memory polynomial model, as follows: Among them, the input signal for the model is the signal delay, P is the model order, J, K is the maximum delay, and P is the highest order of the model, which is the model coefficient. Corresponding to the present invention, the output signal of the signal generator module is the target matrix /); the available distortion model is not limited to the general memory polynomial model, but also the Wiener model, the Hammerstein model, the Volterra model, the neural network, the wavelet network, etc. . The calibration parameter identification can use a least squares identification algorithm. W = R~ l D ( 5 ) Where R is the identification matrix, /) is the construction method of the target matrix (/) is exemplified below), W is the correction parameter, ( 1 is the pseudo-inverse operation, and the meaning of other expressions is the same as equation (5). Similarly, the available algorithm is not limited to the least squares identification algorithm shown in the formula (6), and includes other iterative algorithms such as a recursive least squares algorithm and a minimum mean square algorithm. Correspondingly, the configuration of the identification matrix R of the present invention is not limited. The expression shown in equation (7) can be changed according to the distortion model. In general, the steps performed by the correction parameter identification unit are as follows: Step 1, constructing the required matrix for parameter identification. Constructing parameter identification according to equation (7) The matrix R and the target matrix D, the parameter identification matrix R and the target matrix /) are jointly constructed by the main link signal, the feedback signal and the pre-corrected signal. Step 2, calculate the pseudo inverse. The pseudo-inverse of the parameter identification matrix R is calculated by matrix inversion method such as singular value decomposition, QR decomposition or Cholesky decomposition. Step 3. Identify the correction parameters. The correction parameters are obtained by using a least squares algorithm, an RLS algorithm, or an LMS algorithm. FIG. 8 is a schematic diagram showing the specific structure of a power amplifier pre-correcting apparatus according to an example 1 of the present invention. In this example, the nonlinear system is a power amplifier, and each module is not explicitly shown in the figure, but is represented by a function realized. The entire system includes a baseband signal module, a channel filter module, a pre-corrector module, a DAC module, an ADC module, an up-conversion module, a down-conversion module, an LO module, a power amplifier module, and an attenuator module. In the present example, the pre-corrected signal is obtained according to the pre-correction function shown in equation (8). When constructing the parameter identification matrix, the parameter identification matrix R is constructed according to equation (7), and the target matrix /) is constructed by the difference between the feedback signal and the pre-corrected signal, as shown in equation (9). Other modules are handled in the same manner as described above and in the prior art. y = x - U * X ( 8 ) D = z -y ( 9) where _y is the pre-corrected signal, X is the main link signal, and is the correction signal, the vector obtained by delaying the main link signal. FIG. 9 is a schematic diagram showing the specific structure of a power amplifier pre-correcting apparatus according to Example 2 of the present invention. In this example, the nonlinear system is a power amplifier, and each module is not explicitly shown in the figure, but is represented by a function realized. The entire system includes a baseband signal module, a channel filter module, a pre-corrector module, a DAC module, an ADC module, an up-conversion module, a down-conversion module, an LO module, a power amplifier module, and an attenuator module as application basis. In this example, the multiple item constructor constructs a plurality of item sequences M according to equations (10) and (11), and the compensator constructs a correction signal according to equation (12), and the pre-corrected signal is according to equation (13). The pre-correction function is obtained. When constructing the parameter identification matrix, the parameter identification matrix R is constructed according to equation (7), and the target matrix /) is constructed by the difference between the pre-corrected signal and the feedback signal, as shown in equation (13). Other modules are handled in the same manner as described above and in the prior art. = [ 0 , ,,..., ^...] (10) Among them, is a polynomial sequence, P is the highest order of the polynomial, |·| is the modulo operation, X is the multi-carrier combined signal, and Α is the signal delay.
U = M"W (12) 其中， 为失真补偿信号， 为预失真参数， 为多次项序列。 y = x + U*X (13) U = M"W (12) where, is the distortion compensation signal, which is the predistortion parameter, which is a sequence of multiple items. y = x + U*X (13)
D=y-z (14) 图 10是根据本发明实施例的非线性系统失真校正方法的流程图。 如图 10所示， 根据本发明实施例的非线性系统失真校正方法包括： 步骤 S1002, 采集主链路数据和反馈链路数据； 步骤 S1004, 对采集到的主链路数据和反馈链路数据进行预处理； 步骤 S1006, 根据预处理后的主链路数据和反馈链路数据进行参数辨识， 得到非 线性系统的校正参数； 步骤 S1008, 根据校正参数对主链路数据进行预校正处理。
上述方法采用了基于正向迭代的自适应方式， 将主链路数据和反馈链路数据都作 为计算校正参数的数据基础， 解决了功率放大器的非线性失真带来的频谱扩散问题， 提高了校正参数的辨识精度。 优选地， 步骤 S1006可以进一步包括以下处理： ( 1 )根据失真校正模型、预校正后的信号以及预处理后的主链路数据和反馈链路 数据构建参数辨识矩阵和目标矩阵； D = yz (14) FIG. 10 is a flowchart of a nonlinear system distortion correction method according to an embodiment of the present invention. As shown in FIG. 10, the nonlinear system distortion correction method according to the embodiment of the present invention includes: Step S1002: collecting primary link data and feedback link data; Step S1004, collecting collected primary link data and feedback link data Performing preprocessing; Step S1006: performing parameter identification according to the preprocessed main link data and the feedback link data to obtain a correction parameter of the nonlinear system; and in step S1008, performing pre-correction processing on the main link data according to the correction parameter. The above method adopts an adaptive method based on forward iteration, and uses both the main link data and the feedback link data as the data basis for calculating the correction parameters, solving the spectrum diffusion problem caused by the nonlinear distortion of the power amplifier, and improving the correction. The identification accuracy of the parameters. Preferably, step S1006 may further include the following processes: (1) constructing a parameter identification matrix and a target matrix according to the distortion correction model, the pre-corrected signal, and the preprocessed main link data and the feedback link data;
(2) 计算参数辨识矩阵的伪逆矩阵； (2) calculating a pseudo inverse matrix of the parameter identification matrix;
(3 )根据目标矩阵、参数辨识矩阵的伪逆矩阵及预定的参数辨识算法进行参数辨 识， 得到非线性系统的校正参数。 校正参数辨识分为三步， 首先， 构造参数辨识所需矩阵， 即参数辨识矩阵和目标 矩阵， 参数辨识矩阵和目标矩阵是由预处理后的主链路信号、 预处理后的反馈信号和 预校正后的信号（即非线性系统的输入信号）通过失真校正模型共同构造而成； 其次， 计算参数辨识矩阵的伪逆矩阵； 最后， 采用预定的参数辨识算法对上述目标矩阵及参 数辨识矩阵的伪逆矩阵进行参数辨识， 得出非线性系统的校正参数。 优选地， 上述失真校正模型可以包括以下之一： 通用记忆多项式模型、 Wiener模 型、 Hammerstein模型、 Volterra模型、 神经网络、 小波网络； 禾 P/或， 计算参数辨识矩 阵和目标矩阵的伪逆矩阵的算法可以包括以下之一： 奇异值分解、 QR分解、 Cholesky 分解； 和 /或， 预定的参数辨识算法可以包括以下之一： 最小二乘算法、 递归最小二乘 算法、 最小均方算法。 在具体实施过程中， 可使用的模型和算法包括但不限于上述的模型和算法， 可以 根据具体是需要进行扩展并进行不同的搭配。 优选地， 步骤 S1008可以进一步包括以下处理： (3) According to the target matrix, the pseudo-inverse matrix of the parameter identification matrix and the predetermined parameter identification algorithm, the parameters are identified, and the correction parameters of the nonlinear system are obtained. The calibration parameter identification is divided into three steps. First, construct the parameter identification matrix, which is the parameter identification matrix and the target matrix. The parameter identification matrix and the target matrix are the pre-processed main link signals, the pre-processed feedback signals and the pre-processing. The corrected signal (ie, the input signal of the nonlinear system) is constructed by the distortion correction model; secondly, the pseudo inverse matrix of the parameter identification matrix is calculated; finally, the predetermined parameter identification algorithm is used for the target matrix and the parameter identification matrix. The pseudo inverse matrix is used for parameter identification, and the correction parameters of the nonlinear system are obtained. Preferably, the distortion correction model may include one of the following: a general memory polynomial model, a Wiener model, a Hammerstein model, a Volterra model, a neural network, a wavelet network; a P/or, a parameter identification matrix and a pseudo-inverse matrix of the target matrix. The algorithm may include one of the following: singular value decomposition, QR decomposition, Cholesky decomposition; and/or, the predetermined parameter identification algorithm may include one of the following: a least squares algorithm, a recursive least squares algorithm, a least mean square algorithm. In the specific implementation process, the models and algorithms that can be used include, but are not limited to, the above-mentioned models and algorithms, and may be expanded and differently matched according to specific needs. Preferably, step S1008 may further include the following processing:
( 1 ) 对主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址信息； (1) linearly or non-linearly mapping the amplitude or power of the main link signal to generate index address information;
(2) 在上述校正参数中查找上述索引地址信息对应的内容， 生成失真校正信号； (3 ) 根据失真校正信号对主链路信号进行预校正处理。 预校正也可以分为三步完成， 首先， 对主链路信号的幅值或功率进行线性或非线 性映射， 产生索引地址信息； 其次， 在校正参数中查找上述索引地址信息对应的内容，
生成失真校正信号； 最后， 根据失真校正信号对主链路信号进行预校正处理， 得到预 校正后的信号， 以抵消后续非线性系统产生的非线性失真。 优选地， 对主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址信息 的映射算法可以包括：
其中， a 为校正信号的地址信息， |x|和 |x|2为信号的模值和功率， /。(·)为映射 函数， or为或； 根据失真校正信号对主链路信号进行预校正处理的算法可以包括： y(n) = Fu x (U, X) , U = [U(n), U(n - l), ..., U(n - K)] , (2) searching for the content corresponding to the index address information in the above correction parameter to generate a distortion correction signal; (3) performing pre-correction processing on the main link signal according to the distortion correction signal. Pre-correction can also be divided into three steps. First, linear or non-linear mapping of the amplitude or power of the main link signal is generated to generate index address information. Secondly, the content corresponding to the index address information is searched for in the correction parameter. A distortion correction signal is generated. Finally, the main link signal is pre-corrected according to the distortion correction signal to obtain a pre-corrected signal to cancel the nonlinear distortion generated by the subsequent nonlinear system. Preferably, the linear or non-linear mapping of the amplitude or power of the primary link signal, the mapping algorithm for generating the index address information may include: Where a is the address information of the correction signal, and |x| and |x| 2 are the modulus and power of the signal, /. (·) is a mapping function, or is or; The algorithm for pre-correcting the main link signal according to the distortion correction signal may include: y(n) = F ux (U, X) , U = [U(n), U(n - l), ..., U(n - K)] ,
X = [x(n), χ(η _ 1), ... , x(n _ J)]， 其中， 为根据索引地址信息查找得到的失真校正信号向量， 为主链路信号向 量， f为校正信号的最大延迟， J为主链路信号的最大延迟， 《为信号采样时间序号， _y为预校正后信号； χ(·)为预校正函数。 在生成索引地址信息时， 是对主链路信号的幅值或功率进行线性映射还是非线性 映射体现在为映射函数 /。(·)的选择上， 而具体选择怎样的映射函数需要根据实际情况 确定。 同样， 根据失真校正信号对主链路信号进行与校正处理的关键， 也在于预校正 函数 χ(·)的选择， 需要根据实际情况确定。 综上所述， 如图 11所示， 根据本发明优选实施例的非线性系统失真校正方法在具 体实施过程中可以包括如下步骤： 步骤 S 1102, 接收基带信号； 步骤 S 1104, 基带信号经过通道滤波器模块， 实现脉冲成型和采样率变换， 得到 主链路信号； 步骤 S 1106, 从功放输出口耦合反馈信号；
步骤 S1108, 耦合反馈信号的经过下变频完成载波频点变换； 步骤 S1110, 在经过 ADC得到反馈链路信号； 步骤 S1112, 根据主链路信号、 反馈链路信号以及预校正后信号进行校正参数辨 识， 确定校正参数。 步骤 S1114, 主链路信号经过预校正处理得到预校正后的信号； 步骤 S1116, 预校正后的信号经过 DAC完成数字信号到模拟信号的转换。 从以上的描述中， 可以看出本发明提供的技术方案采用了基于正向迭代的自适应 技术， 与传统的预失真技术相比， 克服了校正参数辨识过程中的噪声特性变化问题， 从而提高了校正参数的辨识精度， 并且改善了校正参数的自适应收敛性能， 且不提高 系统的硬件资源， 使本发明更适合高线性指标要求的场合。 而且， 本发明提供的技术 方案不限于只对 GSM 多载波信号进行预校正， 对于 GSM、 CDMA、 UMTS、 TD-SCDMA, LTE、 WiMAX 以及各种混模信号， 其预校正效果同样优于传统的预校 正技术， 适用于 GSM、 CDMA、 UMTS、 TD-SCDMA、 LTE和 WiMAX单模或多模系 统。 显然， 本领域的技术人员应该明白， 上述的本发明的各模块或各步骤可以用通用 的计算装置来实现， 它们可以集中在单个的计算装置上， 或者分布在多个计算装置所 组成的网络上， 可选地， 它们可以用计算装置可执行的程序代码来实现， 从而， 可以 将它们存储在存储装置中由计算装置来执行， 并且在某些情况下， 可以以不同于此处 的顺序执行所示出或描述的步骤， 或者将它们分别制作成各个集成电路模块， 或者将 它们中的多个模块或步骤制作成单个集成电路模块来实现。 这样， 本发明不限制于任 何特定的硬件和软件结合。 以上所述仅为本发明的优选实施例而已， 并不用于限制本发明， 对于本领域的技 术人员来说， 本发明可以有各种更改和变化。 凡在本发明的精神和原则之内， 所作的 任何修改、 等同替换、 改进等， 均应包含在本发明的保护范围之内。
X = [x(n), χ(η _ 1), ... , x(n _ J)], where is the distortion correction signal vector found based on the index address information, the main link signal vector, f To correct the maximum delay of the signal, J is the maximum delay of the main link signal, "for the signal sampling time serial number, _y is the pre-corrected signal; χ (·) is the pre-correction function. When the index address information is generated, whether the amplitude or power of the main link signal is linearly mapped or the nonlinear mapping is embodied as a mapping function /. The choice of (·), and the specific choice of the mapping function needs to be determined according to the actual situation. Similarly, the key to performing the correction processing on the main link signal according to the distortion correction signal is also that the selection of the pre-correction function χ (·) needs to be determined according to the actual situation. In summary, as shown in FIG. 11, the nonlinear system distortion correction method according to the preferred embodiment of the present invention may include the following steps in the specific implementation process: Step S1102, receiving a baseband signal; Step S1104, baseband signal passing through the channel a filter module, implementing pulse shaping and sampling rate conversion to obtain a main link signal; and step S 1106, coupling a feedback signal from the power amplifier output port; Step S1108, the carrier frequency point conversion is completed by down-conversion of the coupled feedback signal; step S1110, obtaining a feedback link signal through the ADC; step S1112, performing correction parameter identification according to the main link signal, the feedback link signal, and the pre-corrected signal , Determine the calibration parameters. Step S1114, the main link signal is subjected to pre-correction processing to obtain a pre-corrected signal; in step S1116, the pre-corrected signal is converted by the DAC to the analog signal. From the above description, it can be seen that the technical solution provided by the present invention adopts an adaptive technique based on forward iteration, and overcomes the problem of variation of noise characteristics in the process of correcting parameter identification, thereby improving the problem compared with the conventional predistortion technique. The identification accuracy of the correction parameters is improved, and the adaptive convergence performance of the correction parameters is improved, and the hardware resources of the system are not improved, so that the present invention is more suitable for the occasions with high linearity requirements. Moreover, the technical solution provided by the present invention is not limited to pre-correction only for GSM multi-carrier signals, and the pre-correction effect is superior to the conventional ones for GSM, CDMA, UMTS, TD-SCDMA, LTE, WiMAX, and various mixed-mode signals. Pre-correction technology for GSM, CDMA, UMTS, TD-SCDMA, LTE and WiMAX single-mode or multi-mode systems. Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general-purpose computing device, which can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Alternatively, they may be implemented by program code executable by the computing device, such that they may be stored in the storage device by the computing device and, in some cases, may be different from the order herein. The steps shown or described are performed, or they are separately fabricated into individual integrated circuit modules, or a plurality of modules or steps are fabricated as a single integrated circuit module. Thus, the invention is not limited to any specific combination of hardware and software. The above is only the preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes can be made to the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and scope of the present invention are intended to be included within the scope of the present invention.
Claims
1. 一种非线性系统失真校正装置， 包括： 自适应器模块、 预校正器模块， 其中， 所述自适应器模块包括： 数据采集单元， 设置为采集主链路数据和反馈链 路数据； 信号处理单元， 设置为对采集到的所述主链路数据和所述反馈链路数 据进行预处理； 校正参数辨识单元， 设置为根据预处理后的所述主链路数据和 所述反馈链路数据进行参数辨识， 得到非线性系统的校正参数； A non-linear system distortion correction apparatus, comprising: an adaptor module, a pre-corrector module, wherein the adaptor module comprises: a data acquisition unit configured to collect main link data and feedback link data; a signal processing unit, configured to preprocess the collected main link data and the feedback link data; and the correction parameter identification unit is configured to be according to the preprocessed main link data and the feedback chain The road data is parameterized to obtain the calibration parameters of the nonlinear system;
所述预校正器模块， 设置为根据所述校正参数对所述主链路数据进行预校 正处理。 The pre-corrector module is configured to perform pre-correction processing on the main link data according to the correction parameter.
2. 根据权利要求 1所述的装置， 其中， 所述校正参数辨识单元包括： 2. The device according to claim 1, wherein the correction parameter identification unit comprises:
矩阵构造子单元， 设置为根据失真校正模型、 预校正后的信号以及预处理 后的所述主链路数据和所述反馈链路数据构建参数辨识矩阵和目标矩阵； 伪逆计算子单元， 设置为计算所述参数辨识矩阵的伪逆矩阵； 参数辨识子单元， 设置为根据所述目标矩阵、 所述参数辨识矩阵的伪逆矩 阵及预定的参数辨识算法进行参数辨识， 得到所述非线性系统的校正参数。 a matrix construction subunit, configured to construct a parameter identification matrix and a target matrix according to the distortion correction model, the pre-corrected signal, and the preprocessed main link data and the feedback link data; a pseudo inverse calculation subunit, setting The parameter identification subunit is configured to perform parameter identification according to the target matrix, a pseudo inverse matrix of the parameter identification matrix, and a predetermined parameter identification algorithm to obtain the nonlinear system. Correction parameters.
3. 根据权利要求 2所述的装置， 其中， 3. The device according to claim 2, wherein
所述失真校正模型包括以下之一： 通用记忆多项式模型、 Wiener模型、 Hammerstein模型、 Volterra模型、 神经网络、 小波网络； 和 /或， The distortion correction model includes one of the following: a general memory polynomial model, a Wiener model, a Hammerstein model, a Volterra model, a neural network, a wavelet network; and/or,
计算所述参数辨识矩阵的伪逆矩阵的算法包括以下之一： 奇异值分解、 QR 分解、 Cholesky分解； 和 /或， 所述预定的参数辨识算法包括以下之一： 最小二乘算法、 递归最小二乘算 法、 最小均方算法。 The algorithm for calculating the pseudo inverse matrix of the parameter identification matrix includes one of the following: singular value decomposition, QR decomposition, Cholesky decomposition; and/or, the predetermined parameter identification algorithm includes one of the following: a least squares algorithm, minimum recursion Two-squares algorithm, least mean square algorithm.
4. 根据权利要求 1所述的装置， 其中， 所述预校正器模块包括： 4. The apparatus according to claim 1, wherein the pre-corrector module comprises:
地址索引单元， 设置为对所述主链路信号的幅值或功率进行线性或非线性 映射， 产生索引地址信息； An address indexing unit configured to linearly or non-linearly map the amplitude or power of the primary link signal to generate index address information;
校正信号生成单元， 设置为在所述校正参数中查找所述索引地址信息对应 的内容， 生成失真校正信号； 预校正处理单元， 设置为根据所述失真校正信号对所述主链路信号进行预 校正处理。 a correction signal generating unit, configured to search for content corresponding to the index address information in the correction parameter, to generate a distortion correction signal; The pre-correction processing unit is configured to perform pre-correction processing on the main link signal according to the distortion correction signal.
5. 根据权利要求 4所述的装置， 其中， 5. The apparatus according to claim 4, wherein
所述地址索引单元对所述主链路信号的幅值或功率进行线性或非线性映 射， 产生索引地址信息的映射算法包括： 
or fa ( x )， 其中， a 为校正信号的地址信息， |x|和 |x|2为信号的模值和功率， /。(·)为 映射函数， or为或； The address indexing unit performs linear or non-linear mapping on the amplitude or power of the primary link signal, and the mapping algorithm for generating index address information includes: Or f a ( x ), where a is the address information of the correction signal, and |x| and |x| 2 are the modulus and power of the signal, /. (·) is a mapping function, or is or;
所述预校正处理单元根据所述失真校正信号对所述主链路信号进行预校正 处理的算法包括： The algorithm for performing pre-correction processing on the main link signal according to the distortion correction signal by the pre-correction processing unit includes:
y(n) = Fu x (U, X) , y(n) = F ux (U, X) ,
U = [U(n), U(n - l), ..., U(n - K)]， U = [U(n), U(n - l), ..., U(n - K)],
X = [x(n), x(n _ 1), ... , x(n - J)] , 其中， 为根据所述索引地址信息查找得到的失真校正信号向量， 为所 述主链路信号向量， f为所述校正信号的最大延迟， J为所述主链路信号的最 大延迟， 《为信号采样时间序号， _y为预校正后信号， χ(·)为预校正函数。 X = [x(n), x(n _ 1), ..., x(n - J)] , wherein the distortion correction signal vector obtained by searching according to the index address information is the main link The signal vector, f is the maximum delay of the correction signal, J is the maximum delay of the main link signal, "is the signal sampling time number, _y is the pre-corrected signal, and χ (·) is the pre-correction function.
6. 一种非线性系统失真校正方法， 包括： 6. A nonlinear system distortion correction method, comprising:
采集主链路数据和反馈链路数据； Collecting primary link data and feedback link data;
对采集到的所述主链路数据和所述反馈链路数据进行预处理； 根据预处理后的所述主链路数据和所述反馈链路数据进行参数辨识， 得到 非线性系统的校正参数； Pre-processing the collected main link data and the feedback link data; performing parameter identification according to the pre-processed main link data and the feedback link data, and obtaining a calibration parameter of the nonlinear system ;
根据所述校正参数对所述主链路数据进行预校正处理。 Performing pre-correction processing on the main link data according to the correction parameter.
7. 根据权利要求 6所述的方法， 其中， 根据预处理后的所述主链路数据和所述反 馈链路数据进行参数辨识， 得到非线性系统的校正参数包括： The method according to claim 6, wherein the parameter identification is performed according to the preprocessed main link data and the feedback link data, and the calibration parameters of the nonlinear system are obtained:
根据失真校正模型、 预校正后的信号以及预处理后的所述主链路数据和所 述反馈链路数据构建参数辨识矩阵和目标矩阵； Constructing a parameter identification matrix and a target matrix according to the distortion correction model, the pre-corrected signal, and the pre-processed main link data and the feedback link data;
计算所述参数辨识矩阵的伪逆矩阵; 根据所述目标矩阵、 所述参数辨识矩阵的伪逆矩阵及预定的参数辨识算法 进行参数辨识， 得到所述非线性系统的校正参数。 根据权利要求 7所述的方法， 其中， Calculating a pseudo inverse matrix of the parameter identification matrix; Parameter identification is performed according to the target matrix, the pseudo inverse matrix of the parameter identification matrix, and a predetermined parameter identification algorithm, and the calibration parameters of the nonlinear system are obtained. The method according to claim 7, wherein
所述失真校正模型包括以下之一： 通用记忆多项式模型、 Wiener模型、 Hammerstein模型、 Volterra模型、 神经网络、 小波网络； 和 /或， The distortion correction model includes one of the following: a general memory polynomial model, a Wiener model, a Hammerstein model, a Volterra model, a neural network, a wavelet network; and/or,
计算所述参数辨识矩阵的伪逆矩阵的算法包括以下之一： 奇异值分解、 QR 分解、 Cholesky分解； 和 /或， 所述预定的参数辨识算法包括以下之一： 最小二乘算法、 递归最小二乘算 法、 最小均方算法。 根据权利要求 6所述的方法， 其中， 根据所述校正参数对所述主链路数据进行 预校正处理包括： The algorithm for calculating the pseudo inverse matrix of the parameter identification matrix includes one of the following: singular value decomposition, QR decomposition, Cholesky decomposition; and/or, the predetermined parameter identification algorithm includes one of the following: a least squares algorithm, minimum recursion Two-squares algorithm, least mean square algorithm. The method according to claim 6, wherein the pre-correcting processing of the main link data according to the correction parameter comprises:
对所述主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址信 息； Linearly or non-linearly mapping the amplitude or power of the primary link signal to generate indexed address information;
在所述校正参数中查找所述索引地址信息对应的内容，生成失真校正信号； 根据所述失真校正信号对所述主链路信号进行预校正处理。 根据权利要求 9所述的方法， 其中， 对所述主链路信号的幅值或功率进行线性或非线性映射， 产生索引地址信 息的映射算法包括： 
or fa ( x )， 其中， a 为校正信号的地址信息， |x|和 |x|2为信号的模值和功率， /。(·)为 映射函数， or为或； Searching for content corresponding to the index address information in the correction parameter, generating a distortion correction signal; performing pre-correction processing on the main link signal according to the distortion correction signal. The method according to claim 9, wherein the mapping algorithm for generating index address information comprises linear or non-linear mapping of the amplitude or power of the primary link signal comprises: Or f a ( x ), where a is the address information of the correction signal, and |x| and |x| 2 are the modulus and power of the signal, /. (·) is a mapping function, or is or;
根据所述失真校正信号对所述主链路信号进行预校正处理的算法包括： y(n) = Fu x (U, X) , An algorithm for performing pre-correction processing on the main link signal according to the distortion correction signal includes: y(n) = F ux (U, X)
U = [U(n), U(n - l), ..., U(n - K)]， U = [U(n), U(n - l), ..., U(n - K)],
X = [x(n), x(n _ 1), ... , x(n - J)] , 其中， 为根据所述索引地址信息查找得到的失真校正信号向量， 为所 述主链路信号向量， f为所述校正信号的最大延迟， J为所述主链路信号的最 大延迟， 《为信号采样时间序号， _y为预校正后信号， χ(·)为预校正函数。 X = [x(n), x(n _ 1), ... , x(n - J)] , Wherein, the distortion correction signal vector obtained by searching according to the index address information is the main link signal vector, f is the maximum delay of the correction signal, and J is the maximum delay of the main link signal, The signal sampling time serial number, _y is the pre-corrected signal, and χ (·) is the pre-correction function.
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| CN1206251A (en) * | 1997-06-30 | 1999-01-27 | 哈里公司 | Adaptive pre-distortion apparatus for linearizing amplifier output within data transmission system |
| WO2008006069A2 (en) * | 2006-07-07 | 2008-01-10 | Scintera Networks, Inc. | Pre-distortion apparatus |
| CN101022434A (en) * | 2007-03-27 | 2007-08-22 | 华为技术有限公司 | Predistorting device and method |
| CN102281221A (en) * | 2011-06-23 | 2011-12-14 | 中兴通讯股份有限公司 | Nonlinear systematic distortion correction device and method |
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| Publication number | Publication date |
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| CN102281221A (en) | 2011-12-14 |
| CN102281221B (en) | 2017-03-15 |
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