CN103873168A

CN103873168A - Multiple bandwidth sampling-based data preprocessing method in EVM (Error Vector Magnitude) measurement

Info

Publication number: CN103873168A
Application number: CN201410086950.0A
Authority: CN
Inventors: 郝金光; 程能杰; 裴文江; 金海忠
Original assignee: NANJING RUANYI TESTING TECHNOLOGY Co Ltd
Current assignee: Anhui Taiyue Ruitong Technology Co ltd
Priority date: 2014-03-10
Filing date: 2014-03-10
Publication date: 2014-06-18
Anticipated expiration: 2034-03-10
Also published as: CN103873168B

Abstract

本发明公开了一种基于多倍带宽采样的EVM测量中的数据预处理方法，属于通信系统的EVM测量领域。该方法先计算接收序列与训练序列的互相关系数的模，然后将大于阈值的模值里的序列变量组成集合A，在该集合A中将训练序列周期数量的间隔为训练序列周期的元素组成集合，将这些集合的非空首元素组成集合B，在集合B中查找间隔为1的连续的N个元素构成集合C，N为IQ速率与带宽的比率，由接收序列和集合C生成预处理数据序列，最后计算合成对应的新预处理数据序列。本发明运用到了原始数据的全部信息，有效提高了EVM测量的准确性。The invention discloses a data preprocessing method in EVM measurement based on multiple bandwidth sampling, and belongs to the field of EVM measurement of communication systems. This method first calculates the modulus of the cross-correlation coefficient between the receiving sequence and the training sequence, and then forms the sequence variables in the modulus value greater than the threshold into a set A, and in this set A, the interval of the number of training sequence periods is composed of elements of the training sequence period Set, the non-empty first elements of these sets are formed into set B, and the continuous N elements with an interval of 1 are searched in set B to form set C, N is the ratio of IQ rate to bandwidth, and preprocessing is generated by receiving sequence and set C Data sequence, and finally calculate and synthesize the corresponding new preprocessed data sequence. The invention utilizes all the information of the original data and effectively improves the accuracy of EVM measurement.

Description

Data Preprocessing Method in EVM Measurement Based on Multiple Bandwidth Sampling

技术领域technical field

本发明涉及一种基于多倍带宽采样的EVM测量中的数据预处理方法，属于通信系统的EVM测量领域。The invention relates to a data preprocessing method in EVM measurement based on multiple bandwidth sampling, and belongs to the field of EVM measurement of communication systems.

背景技术Background technique

在通信系统如TD、LTE中，通常用误差矢量幅度（EVM）来表示信号调制质量，EVM的直接原因是接收端调制信号的相位误差和幅度误差。EVM的产生一方面是由于射频器本地振荡器（LO）存在本振泄漏、相位噪声及功放非线性失真，另一方面是由于算法性能差异（包括信道估计、信号检测等）出现误差导致，所以EVM值也可以体现一些算法性能的优劣。In communication systems such as TD and LTE, the error vector magnitude (EVM) is usually used to represent the signal modulation quality. The direct cause of EVM is the phase error and amplitude error of the modulated signal at the receiving end. On the one hand, the generation of EVM is due to local oscillator leakage, phase noise, and nonlinear distortion of the power amplifier in the local oscillator (LO) of the radio frequency device, and on the other hand, it is caused by errors in algorithm performance differences (including channel estimation, signal detection, etc.), so The EVM value can also reflect the pros and cons of some algorithm performance.

在多倍带宽采样率系统中，采样数据IQ速率是码流速率的N倍，在一般的数据处理中，通常是对N倍数据进行N倍的下采样，然后进行数据后续处理，该方法在OFDM接收机中对解码的影响不大，因为被舍弃的N-1个数据实际上还是在可纠正范围内，可以通过接收机处理模块中的信道估计以及Viterbi解码进行纠正，但这种数据处理方法对于EVM测量的影响较大，原因是抽样取得的数据仅仅含有原始数据的一部分信息，尽管不会对解码造成影响，但会增加抽样点偏离星座图标准点的距离，因而会影响测试仪的EVM测试性能。In a multi-bandwidth sampling rate system, the IQ rate of the sampled data is N times the rate of the code stream. In general data processing, N times the data is usually down-sampled by N times, and then the data is subsequently processed. This method is The influence on decoding in the OFDM receiver is not large, because the discarded N-1 data is actually within the correctable range, which can be corrected by channel estimation and Viterbi decoding in the receiver processing module, but this data processing The method has a great impact on the EVM measurement, because the data obtained by sampling only contains part of the original data information, although it will not affect the decoding, but it will increase the distance between the sampling point and the standard point of the constellation diagram, thus affecting the tester. EVM test performance.

发明内容Contents of the invention

本发明针对背景技术中的不足，提出一种改进的EVM测量中的数据预处理方法，以解决因抽样数据未包含原始数据的全部信息而导致EVM测试受影响的问题。Aiming at the deficiencies in the background technology, the present invention proposes an improved data preprocessing method in EVM measurement to solve the problem that the EVM test is affected because the sampling data does not contain all the information of the original data.

该方法包括如下步骤：The method comprises the steps of:

步骤1：计算接收序列与训练序列的互相关系数的模r(n)：Step 1: Calculate the modulo r(n) of the cross-correlation coefficient between the received sequence and the training sequence:

$r r ((n no)) = = | | \frac{{Σ Σ}_{m m = = 00}^{L L - - 11} s the s ((n no + + m m)) \cdot &Center Dot; {p p}^{* *} ((m m))}{\sqrt{{Σ Σ}_{m m = = 00}^{L L - - 11} {[[s the s ((n no + + m m)) - - \overset{&OverBar; &OverBar;}{s the s} ((n no))]]}^{22}} \cdot \cdot \sqrt{{Σ Σ}_{m m = = 00}^{L L - - 11} {[[p p ((m m)) - - \overset{&OverBar; &OverBar;}{p p}]]}^{22}}} | |$

其中：s(n)为接收序列；p(m)为训练序列；p^*(m)为训练序列的共轭；L为训练序列周期，下同；为L个连续输入采样点的均值；

为训练序列的均值；Among them: s(n) is the receiving sequence; p(m) is the training sequence; p ^* (m) is the conjugate of the training sequence; L is the period of the training sequence, the same below; is the mean value of L consecutive input sampling points;

is the mean value of the training sequence;

步骤2：通过步骤1获取集合

其中δ_t为预设的阈值；Step 2: Get the collection by step 1

Where δ _t is a preset threshold;

步骤3：在集合A中将M个间隔为L的元素组成集合，将符合条件的所有集合的首元素组成集合

上述M为训练序列周期数，下同；Step 3: In the set A, form a set of M elements with an interval of L, and form a set with the first elements of all sets that meet the conditions

The above M is the number of training sequence cycles, the same below;

步骤4：在集合B中查找出间隔为1的连续的N个元素，并将这N个元素组成的合并序列构成集合 $C = {b (m_{q_{0}}), b (m_{q_{1}}), . . ., b (m_{q_{N - 1}})},$ 其中 $b (m_{q_{1}}) - b (m_{q_{0}}) = 1, b (m_{q_{2}}) - b (m_{q_{1}}) = 1, . . .,$ 上述N为IQ速率与带宽的比率，下同；Step 4: Find consecutive N elements with an interval of 1 in the set B, and form the merge sequence composed of these N elements into a set $C = {b (m_{q_{0}}), b (m_{q_{1}}), . . ., b (m_{q_{N - 1}})},$ in $b (m_{q_{1}}) - b (m_{q_{0}}) = 1, b (m_{q_{2}}) - b (m_{q_{1}}) = 1, . . .,$ The above N is the ratio of IQ rate to bandwidth, the same below;

步骤5：由接收序列s(n)和集合C构成预处理数据序列S₀,S₁,...,S_N-1，其中：Step 5: Constitute the preprocessing data sequence S ₀ , S ₁ ,...,S _N-1 from the received sequence s(n) and the set C, where:

${S S}_{x x} = = {{s the s ((b b (({m m}_{{q q}_{x x}})))),, s the s ((b b (({m m}_{{q q}_{x x}} + + N N)))),, s the s ((b b (({m m}_{{q q}_{x x}} + + 22 N N)))),, . . . . . .}} ((x x = = 0,1 0,1,, . . . . . .,, N N - - 11))$

步骤6：将步骤5所得的预处理数据序列计算合成对应的新预处理数据序列S：Step 6: Calculate and synthesize the preprocessed data sequence obtained in step 5 into a corresponding new preprocessed data sequence S:

S=α₀·S₀+α₁·S₁+...+α_N-1·S_N-1 S=α ₀ ·S ₀ +α ₁ ·S ₁ +...+α _N-1 ·S _N-1

其中： $α_{k} = \frac{\frac{1}{β_{k}}}{\frac{1}{β_{0}} + \frac{1}{β_{1}} + . . . + \frac{1}{β_{N - 1}}} (k = 0,1, . . ., N - 1)$ in: $α_{k} = \frac{\frac{1}{β_{k}}}{\frac{1}{β_{0}} + \frac{1}{β_{1}} + . . . + \frac{1}{β_{N - 1}}} (k = 0,1, . . ., N - 1)$

${β β}_{k k} = = \frac{11}{M m} \cdot &Center Dot; {Σ Σ}_{m m = = 00}^{M m - - 11} r r ((C C ((k k)) + + m m \cdot &Center Dot; L L)) ((k k = = 0,1 0,1,, . . . . . .,, N N - - 11)) . .$

进一步地，所述步骤1中

的表达式为：

所述步骤1中

的表达式为：

\overset{&OverBar;}{p} = \frac{1}{L} \cdot Σ_{m = 0}^{L - 1} p (m) .

Further, in the step 1

The expression is:

In the step 1

The expression is:

\overset{&OverBar;}{p} = \frac{1}{L} \cdot Σ_{m = 0}^{L - 1} p (m) .

技术效果：Technical effect:

完善了基于多倍带宽采样的EVM测量中的数据预处理算法，联合了数据处理中的N点信息，使抽样数据包含有原始数据的全部信息，从而减少了抽样点偏离星座图标准点的距离，使EVM测量更加准确，提高了测试仪所测试的EVM指标。Improve the data preprocessing algorithm in the EVM measurement based on multiple bandwidth sampling, combine the N point information in the data processing, so that the sampled data contains all the information of the original data, thereby reducing the distance between the sampled point and the standard point of the constellation diagram , so that the EVM measurement is more accurate, and the EVM index tested by the tester is improved.

具体实施方式Detailed ways

下面对本发明作进一步说明。The present invention will be further described below.

本发明方法是对现有EVM测量中数据预处理部分的改进，主要包括如下步骤：The inventive method is an improvement to the data preprocessing part in the existing EVM measurement, and mainly comprises the following steps:

步骤1：计算接收序列与所采用训练序列的互相关系数的模r(n)：Step 1: Calculate the modulus r(n) of the cross-correlation coefficient between the received sequence and the adopted training sequence:

$r r ((n no)) = = | | \frac{{Σ Σ}_{m m = = 00}^{L L - - 11} s the s ((n no + + m m)) \cdot &Center Dot; {p p}^{* *} ((m m))}{\sqrt{{Σ Σ}_{m m = = 00}^{L L - - 11} {[[s the s ((n no + + m m)) - - \overset{&OverBar; &OverBar;}{s the s} ((n no))]]}^{22}} \cdot &Center Dot; \sqrt{{Σ Σ}_{m m = = 00}^{L L - - 11} {[[p p ((m m)) - - \overset{&OverBar; &OverBar;}{p p}]]}^{22}}} | |$

其中：s(n)为接收序列，n=0,1,2,...；p(m)为训练序列，m=0,1,...,L-1；p^*(m)为训练序列的共轭；L为训练序列周期，下同；

为L个连续输入采样点的均值；

为训练序列的均值；Among them: s(n) is the receiving sequence, n=0,1,2,...; p(m) is the training sequence, m=0,1,...,L-1; p ^* (m) is Conjugation of the training sequence; L is the period of the training sequence, the same below;

is the mean value of L consecutive input sampling points;

is the mean value of the training sequence;

的表达式为 $\overset{&OverBar;}{s} (n) = \frac{1}{L} \cdot Σ_{m = 0}^{L - 1} s (n + m);$ The expression is $\overset{&OverBar;}{the s} (no) = \frac{1}{L} &Center Dot; Σ_{m = 0}^{L - 1} the s (no + m);$

的表达式为

由于所采用训练序列已知，因此

可以提前计算好作为常数使用；

The expression is

Since the training sequence used is known, so

It can be calculated in advance and used as a constant;

步骤2：记录步骤1计算所得的模r(n)大于阈值δ_t时r(n)对应的n值，并将这些n值组成集合A，即：Step 2: Record the n value corresponding to r(n) when the modulus r(n) calculated in step 1 is greater than the threshold _δt , and form these n values into a set A, namely:

通过步骤1获取集合

δ_t为预设的阈值，δ_t∈(0,1)；Get the collection by step 1

δ _t is the preset threshold, δ _t ∈ (0,1);

步骤3：在集合A中将M个间隔为L的元素组成集合，在集合A中可以组成若干个符合条件的集合，将组成的符合条件的所有集合的首元素组成集合B；Step 3: In the set A, M elements with an interval of L are formed into a set. In the set A, several sets that meet the conditions can be formed, and the first elements of all the sets that meet the conditions are formed into the set B;

的首元素记为b(m)，k_m′＝[m′，m′+L，...，m′+(M-1)L](m′＝0，1，...)；

The first element of is denoted as b(m), k _m' = [m', m'+L,..., m'+(M-1)L](m'=0, 1,...);

上述M为训练序列周期数，下同；

The above M is the number of training sequence cycles, the same below;

步骤4：在集合B中查找出间隔为1的连续的N个元素，若存在这样一组N个元素，则表示已经找到对应的合并序列，将这N个元素组成的合并序列构成集合C，即：Step 4: Find consecutive N elements with an interval of 1 in the set B. If such a set of N elements exists, it means that the corresponding merged sequence has been found, and the merged sequence composed of these N elements forms a set C. Right now:

$C = {b (m_{q_{0}}), b (m_{q_{1}}), . . ., b (m_{q_{N - 1}})},$ 其中 $b (m_{q_{1}}) - b (m_{q_{0}}) = 1, b (m_{q_{2}}) - b (m_{q_{1}}) = 1, . . .,$ 上述N为IQ速率与带宽的比率，下同； $C = {b (m_{q_{0}}), b (m_{q_{1}}), . . ., b (m_{q_{N - 1}})},$ in $b (m_{q_{1}}) - b (m_{q_{0}}) = 1, b (m_{q_{2}}) - b (m_{q_{1}}) = 1, . . .,$ The above N is the ratio of IQ rate to bandwidth, the same below;

步骤5：由接收序列s(n)并根据集合C构成预处理数据序列S₀,S₁,...,S_N-1，其中：Step 5: Construct the preprocessed data sequence S ₀ , S ₁ ,...,S _N-1 from the received sequence s(n) according to the set C, where:

其中：in:

${α α}_{k k} = = \frac{\frac{11}{{β β}_{k k}}}{\frac{11}{{β β}_{00}} + + \frac{11}{{β β}_{11}} + + . . . . . . + + \frac{11}{{β β}_{N N - - 11}}} ((k k = = 0,1 0,1,, . . . . . .,, N N - - 11));; {β β}_{k k} = = \frac{11}{M m} \cdot &Center Dot; {Σ Σ}_{m m = = 00}^{M m - - 11} r r ((C C ((k k)) + + m m \cdot &Center Dot; L L)) ((k k = = 0,1 0,1,, . . . . . .,, N N - - 11)) . .$

本发明的方法测试：Method test of the present invention:

基于OFDM有关协议，利用802.11a综测仪实施本方法的性能测试，在EVM测试中的其他算法环节保持不变，仅改变数据预处理算法部分，在测试中利用短训练序列，相关参数设定为：N=2，L=16，M=10。Based on OFDM-related protocols, the 802.11a comprehensive tester is used to implement the performance test of this method. The other algorithm links in the EVM test remain unchanged, only the data preprocessing algorithm part is changed, and the short training sequence is used in the test. The relevant parameter settings For: N=2, L=16, M=10.

场景1：WiFi模块数据测试Scenario 1: WiFi module data test

通过星座图分析，利用现有方法对应的EVM值为-29.08296dB，利用本发明方法对应的EVM值为-30.791124dB。Through constellation diagram analysis, the EVM value corresponding to the existing method is -29.08296dB, and the EVM value corresponding to the method of the present invention is -30.791124dB.

场景2：综测仪回环测试Scenario 2: Comprehensive tester loopback test

通过星座图分析，利用现有方法对应的EVM值为-31.1202dB，利用本发明方法对应的EVM值为-32.8273dB。Through constellation diagram analysis, the EVM value corresponding to the existing method is -31.1202dB, and the EVM value corresponding to the method of the present invention is -32.8273dB.

可见，本发明方法在EVM测量中是有效的，可以提高测试仪所测试的EVM指标。It can be seen that the method of the present invention is effective in EVM measurement, and can improve the EVM index tested by the tester.

Claims

1. a data preprocessing method in the EVM measurement based on multiple bandwidth sampling, is characterized in that comprising the steps:

Step 1: Calculate the modulus r(n) of the cross-correlation coefficient between the received sequence and the training sequence:

r r ((n no)) = = | | \frac{{Σ Σ}_{m m = = 00}^{L L - - 11} s the s ((n no + + m m)) \cdot &Center Dot; {p p}^{* *} ((m m))}{\sqrt{{Σ Σ}_{m m = = 00}^{L L - - 11} {[[s the s ((n no + + m m)) - - \overset{&OverBar; &OverBar;}{s the s} ((n no))]]}^{22}} \cdot &Center Dot; \sqrt{{Σ Σ}_{m m = = 00}^{L L - - 11} {[[p p ((m m)) - - \overset{&OverBar; &OverBar;}{p p}]]}^{22}}} | |

Among them: s(n) is the receiving sequence; p(m) is the training sequence; p ^* (m) is the conjugate of the training sequence; L is the period of the training sequence, the same below;

is the mean value of L consecutive input sampling points;

is the mean value of the training sequence;

Step 2: Get the collection by step 1

Where δ _t is a preset threshold;

Step 3: In the set A, form a set of M elements with an interval of L, and form a set with the first elements of all sets that meet the conditions

The above M is the number of training sequence cycles, the same below;

Step 4: Find consecutive N elements with an interval of 1 in the set B, and form the merge sequence composed of these N elements into a set

C = {b (m_{q_{0}}), b (m_{q_{1}}), . . ., b (m_{q_{N - 1}})},

in

b (m_{q_{1}}) - b (m_{q_{0}}) = 1, b (m_{q_{2}}) - b (m_{q_{1}}) = 1, . . .,

The above N is the ratio of IQ rate to bandwidth, the same below;

Step 5: Constitute the preprocessing data sequence S ₀ , S ₁ ,...,S _N-1 from the received sequence s(n) and the set C, where:

{S S}_{x x} = = {{s the s ((b b (({m m}_{{q q}_{x x}})))),, s the s ((b b (({m m}_{{q q}_{x x}} + + N N)))),, s the s ((b b (({m m}_{{q q}_{x x}} + + 22 N N)))),, . . . . . .}} ((x x = = 0,1 0,1,, . . . . . .,, N N - - 11))

Step 6: Calculate and synthesize the preprocessed data sequence obtained in step 5 into a corresponding new preprocessed data sequence S:

S=α ₀ ·S ₀ +α ₁ ·S ₁ +...+α _N-1 ·S _N-1

in:

α_{k} = \frac{\frac{1}{β_{k}}}{\frac{1}{β_{0}} + \frac{1}{β_{1}} + . . . + \frac{1}{β_{N - 1}}} (k = 0,1, . . ., N - 1)

{β β}_{k k} = = \frac{11}{M m} \cdot &Center Dot; {Σ Σ}_{m m = = 00}^{M m - - 11} r r ((C C ((k k)) + + m m \cdot &Center Dot; L L)) ((k k = = 0,1 0,1,, . . . . . .,, N N - - 11)) . .

2. the data preprocessing method in the EVM measurement based on multiple bandwidth sampling according to claim 1, is characterized in that:

In the step 1 The expression is:

In the step 1 The expression is: