CN106533590B - Uplink channel quality measurement method based on receiving end EVM - Google Patents

Abstract

The present invention provides an uplink channel quality measurement method based on the receiving end EVM, which is used to solve the technical problem of low system throughput existing in the existing channel quality measurement method. The implementation steps are: constructing an uplink adaptive system model, and establish the mathematical relationship of the frequency domain signal between the transmitter and receiver of the model; according to the pilot configuration form of the 3GPP standard, extract the pilot signal from the frequency domain signal of the receiver; estimate the system frequency domain channel response; estimate based on The measurement signal of the receiving end EVM channel quality measurement algorithm; Turbo code decoding is performed on the estimated value of the transmitting end time domain signal to obtain the decoded bit data, and the bit data is re-coded and re-modulated to obtain an ideal reference signal; Using the error vector magnitude calculation formula, according to the measurement signal and the ideal reference signal, the receiving end EVM is calculated; the modulation and coding mode MCS is selected. The present invention has high channel quality measurement accuracy, low computational complexity and high robustness, and is suitable for wireless communication systems.

Description

Uplink channel quality measurement method based on receiver EVM

technical field

The invention belongs to the field of wireless communication, and relates to a channel quality measurement method, in particular to a channel quality measurement method of an uplink adaptive system based on the error vector magnitude of a receiving end, which is suitable for terrestrial cellular communication systems, satellite communication systems, Massive - Wireless communication systems such as MIMO systems and point-to-point link transmission systems.

Background technique

In a wireless communication system, the channel is a time-varying channel with multi-path effects. If the transmitter knows the prior information of the channel, it can select a modulation mode and coding rate that are more suitable for channel transmission to transmit data, thereby reducing various Factors affect the received signal to better adapt to changing channel transmission conditions and improve system performance. It can be seen that in order to improve the performance of the communication system, the research of link adaptation is very necessary. Uplink adaptation includes power control techniques and rate control techniques. The power control technology maintains a certain signal-to-noise ratio at the receiving end by dynamically adjusting the transmit power, thereby ensuring the transmission quality of the link. The rate control technology is the main technology used in link adaptation, which is often referred to as adaptive modulation and coding technology (AMC). The eNodeB dynamically selects the modulation and coding methods according to the channel environment change information provided by the user terminal. (MCS), which maximizes the performance of the system and ensures the transmission quality of the link while meeting the BLER limit of the system. Specifically, users who are closer to the base station and have better channel conditions will be assigned higher-order modulation methods and higher coding rates; while users who are far away from the base station and have poor channel conditions, the receiving end in order to ensure correct For demodulation, more redundant information is required, but more redundant information will reduce the coding rate, so a lower-order modulation method and a lower coding rate are allocated.

In the uplink adaptive system, the receiving end needs to feed back channel state information to the transmitting end, which requires channel quality measurement. The existing methods generally use the signal-to-interference and noise ratio (SINR) to characterize the transmission quality of the channel, that is, Taking SINR as a metric for channel quality measurement, the receiving end selects MCS according to the estimated SINR, and then feeds it back to the transmitting end. The transmitting end performs the optimal configuration of the next transmission parameters according to the feedback channel quality information. Most of the research in this area focuses on the measurement of effective SINR. The basic idea is to first estimate the SINR of each sub-carrier, and then map the SINR values of these sub-carriers into a system that can reflect the average value of the entire link through a certain mapping method. The effective SINR of the performance, and MCS selection is performed according to this effective SINR. Commonly used methods include exponentially efficient SINR mapping (EESM) algorithm, mutual information efficient SINR mapping (MIESM) algorithm, average mutual information per bit (MMIB) algorithm, average effective SINR mapping (AESM) algorithm and harmonic mean algorithm (Harm-mean) . Among them, the two algorithms, EESM and MIESM, have similarities and are derived from effective SINR mapping (ESM). The difference is that they use different mapping functions, namely the exponential mapping function and the relevant mutual information mapping function. However, in these two algorithms, the mapping function contains a tuning factor, which needs to be obtained by offline simulation, and the parameter value depends on the MCS, channel state and antenna configuration, so it is difficult to achieve the optimal, and the robustness is poor, so This results in a loss of system throughput performance. MMIB refers to the average mutual information between the coded bits and their log-likelihood ratios (LLRs). MMIB channel quality measurement refers to the equivalent of a wireless channel as multiple parallel bit LLR channels. By calculating the coded bits and their corresponding receivers The mutual information between the LLR values establishes a one-to-one correspondence between the effective SINR and the bit-level average mutual information. However, since the calculation of mutual information is very complicated, it is difficult to obtain its precise mapping formula, so the accuracy of the MMIB algorithm is relatively low. In addition, the performance of the algorithm depends to a large extent on the modulation order, that is to say, the algorithm is very sensitive to the change of the modulation order, and is not suitable for wireless channels with time-varying characteristics. The Harm-mean algorithm takes the harmonic mean of the SINRs of multiple subcarriers as the equivalent SINR, while the AESM algorithm considers that in the SC-FDMA system, the SINRs of all subcarriers are approximately equal, so the average operation can be used to simplify calculate. However, these two methods have certain limitations, and are only suitable for scenarios where frequency selection is not serious. Although the complexity of these two algorithms is small, their calculation accuracy is correspondingly poor.

It can be seen from the above analysis that the above methods are equivalent to a many-to-one mapping, so the effective SINRs of two different sub-channels may be the same, ignoring the differences between different sub-channels, and selecting the same MCS, and this MCS may not all be optimal for these sub-channels, so the measurement accuracy is low and the robustness is poor, thus affecting the system performance and causing the throughput to drop. Therefore, if SINR is used as a measurement index of channel quality, the accuracy of its measurement value is very critical to the improvement of system throughput performance. However, in wireless communication, there are many factors that affect the change of SINR value, such as fading channel, interference and noise, etc., so it is very difficult and challenging to ensure its accuracy.

In the uplink, the signal will change due to the influence of noise or interference through the channel. If the channel condition is good, the change of the signal passing through the channel is small. On the contrary, if the channel condition is poor, the signal passing through the channel will change greatly. Therefore, in addition to the traditional channel quality measurement based on SINR, the channel quality can also be judged by measuring the degree of change of the signal passing through the channel, and the error vector magnitude EVM (Error Vector Magnitude) is an indicator to measure this change, because The influence of interference and noise on the signal can be shown intuitively by the deviation of the signal from the standard constellation point on the constellation diagram. The receiver EVM is defined as the ratio between the square root value of the average power of the receiver error vector signal and the square root value of the receiver reference signal average power. It can be seen that the size of the receiving end EVM can reflect the channel quality. When the channel condition is good, the signal is less affected by the channel and the receiving end EVM is small; when the channel condition is poor, the signal is greatly affected by the channel, and the receiving end EVM is large. And compared with estimating the SINR of each subcarrier, the EVM of the receiving end of the system can be obtained more easily.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the above-mentioned deficiencies of the prior art, and proposes a method for measuring uplink channel quality based on the EVM of the receiving end. The mapping table is used to select the appropriate MCS and feed it back to the transmitter to configure the optimal parameters for the next transmission. The purpose is to ensure that the BLER limit of the uplink adaptive link is met, and to solve the existing channel quality measurement methods. The technical problem of low system throughput due to low channel quality measurement accuracy and high computational complexity.

To achieve the above object, the technical scheme adopted by the present invention comprises the following steps:

(1) Construct the uplink adaptive system model, and establish the mathematical relationship of the frequency domain signal between the transmitter and receiver of the model. The implementation steps are:

(1a) Using multiple access technology, construct the uplink adaptive system model of terrestrial cellular communication system, or satellite communication system, or Massive-MIMO system or point-to-point link transmission system: including Turbo coding, modulation, layer mapping, DFT transformation And the transmitter of the symbol generation module, including the receiver of FFT transformation, channel estimation, frequency domain equalization, IDFT transformation, demodulation and decoding, MIMO channel, including channel quality measurement and adaptation based on the error vector magnitude EVM of the receiver Modulation and coding adaptive feedback module;

(1b) Establish the mathematical relationship of the frequency domain signal between the transmitter and the receiver of the uplink adaptive system model Obtain the frequency domain signal y ^f of the receiver:

where Y is the receiver frequency domain signal, H is the system frequency domain channel response, FM is the normalized DFT transform of size _M , k,l=1,2,...,M, N _T is the number of transmitting antennas, I _NT is a unit vector with dimension N _T , x is the modulated signal at the transmitting end, V is the mean value of 0, and the variance is Gaussian white noise;

(2) At the receiving end, according to the pilot frequency configuration form in the 3GPP standard, extract the pilot frequency signal Y _p from the frequency domain signal Y at the receiving end of the uplink adaptive system model:

Y _p =H _p S _p +V _p

Among them, S _p , H _p and V _p refer to the pilot signal, pilot channel response and white Gaussian noise of the transmitter respectively;

(3) At the receiving end, estimate the system frequency domain channel response H:

(3a) Using the channel estimation algorithm, estimate the pilot channel response _Hp according to the pilot signal _Yp , and obtain the estimated value of the pilot channel response

(3b) Using the time domain interpolation algorithm, according to the pilot channel response Calculate the system frequency domain channel response estimate

(4) Estimate the measurement signal Z based on the EVM channel quality measurement algorithm at the receiving end:

(4a) Using the frequency domain equalization algorithm, according to the receiver frequency domain signal matrix y _f in step (1b) and the system frequency domain channel response obtained in step (3) Calculate the estimated value of the frequency domain signal at the transmitter

(4b) Estimated value of the frequency domain signal at the transmitter Perform IFFT transformation to obtain the estimated value of the time-domain signal at the transmitter That is, the measurement signal Z based on the receiving end error vector magnitude EVM channel quality measurement algorithm;

(5) At the receiving end, estimate the value of the time domain signal at the transmitting end Turbo code decoding is performed to obtain the decoded bit data, and the bit data is re-encoded and re-modulated to obtain an ideal reference signal R;

(6) using the error vector magnitude calculation formula, according to the measurement signal Z obtained in step (4) and the ideal reference signal R obtained in step (5), calculate the receiving end EVM;

(7) At the receiving end, select the modulation and coding mode MCS:

(7a) Off-line simulation is carried out on the uplink adaptive system model, and the mapping table between the receiving end EVM and the MCS is obtained;

(7b) Perform multiple simulations on the uplink adaptive system model, select the corresponding MCS from the mapping table between the receiving end EVM and the MCS according to the instantaneous receiving end EVM value in each simulation cycle, and feed it back to The transmitter is used to configure the optimal parameters for the next transmission.

Compared with the prior art, the present invention has the following advantages:

(1) The present invention obtains the mapping table between the receiving end EVM and the MCS through off-line simulation, and selects its corresponding MCS from the mapping table between the receiving end EVM and the MCS according to the instantaneous receiving end EVM of the system, which is a one-to-one Therefore, the channel quality measurement accuracy is higher, and the throughput performance of the system is effectively improved under the condition that the system BLER limit is met;

(2) The present invention adopts the channel quality measurement method based on the EVM of the receiving end when measuring the channel quality of the uplink adaptive system, which has low computational complexity and high robustness, and can be better applied to the uplink. The channel adaptive system further improves the throughput performance of the system.

Description of drawings

Fig. 1 is the realization flow diagram of the present invention;

2 is a schematic diagram of an uplink adaptive system model of the present invention;

Fig. 3 is the schematic diagram of the channel quality measurement index EVM adopted by the present invention;

Fig. 4 is the acquisition flow chart of the mapping table between the receiving end EVM and MCS in the present invention;

Fig. 5 is a receiving end EVM and BLER simulation graph of the present invention;

FIG. 6 is a system BLER and throughput simulation diagram of the present invention.

Detailed ways

The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

Referring to Fig. 1, the implementation steps of the present invention are:

Step 1: Adopt SC-FDMA multiple access technology to construct an uplink adaptive system model of a terrestrial cellular communication system, or a satellite communication system, or a Massive-MIMO system or a point-to-point link transmission system, and what this embodiment constructs is a terrestrial cellular communication system. The LTE system in communication, as shown in Figure 2, includes Turbo coding, modulation, layer mapping, DFT transform and the transmitter of the SC-FDMA symbol generation module, including FFT transform, channel estimation, frequency domain equalization, IDFT transform, demodulation And the receiving end of the decoding, the MIMO channel, includes a channel quality measurement based on the error vector magnitude EVM of the receiving end and an adaptive feedback module of Adaptive Modulation and Coding (AMC).

Step 2, establish the mathematical relationship of the frequency domain signal between the transmitter and receiver of the constructed uplink adaptive system model Obtain the frequency domain signal y ^f of the receiver:

in is an IDFT transform of size M, h _r,n =diag{[h ₁ ] _r,n ,[h ₂ ] _r,n ,...,[h _M ] _r,n }, [h _m ] _{r, n} refers to the frequency domain channel response of the nth transmit antenna and the mth subcarrier under the rth receive antenna, v _r is the mean value of 0 in the frequency domain and the variance of The noise vector of , x _n =[x _n,1 ,x _n,2 ,...,x _n,M ] ^T is the time domain signal modulated by M-QAM at the transmitting end, N _T is the number of transmitting antennas, N _R is the number of receiving antennas, and h is the system time-domain channel response.

Represent the receiver frequency domain signal in matrix form y _f :

Among them, Y is the frequency domain signal of the receiver, H is the system frequency domain channel response, V is the mean value of 0, and the variance is Gaussian white noise.

Step 3, at the receiving end, according to the pilot frequency configuration form of the 3GPPLTE standard, extract the pilot frequency signal Y _p from the frequency domain signal Y at the receiving end of the uplink adaptive system model:

In a time slot, different SC-FDMA symbols may have different CP (Cyclic Prefix) lengths. After the signal passes through the channel, it is received and processed at the receiving end. After receiving the uplink signal passing through the channel, the receiving end performs time-frequency synchronization on the received signal, and extracts the pilot signal Y _p from the frequency domain signal Y of the receiving end that has undergone time-frequency synchronization:

Y _p =H _p S _p +V _p

Among them, S _p , H _p and V _p refer to the pilot signal, the pilot channel response and the Gaussian white noise at the transmitting end, respectively. The pilot signal _Sp at the transmitting end is known.

Step 4, at the receiving end, estimate the system frequency domain channel response H:

Step 4a, based on the pilot signal Y _p extracted in step 3, use the LS channel estimation algorithm or the MMSE channel estimation algorithm to estimate the pilot channel response H _p to obtain an estimated value of the pilot channel response

If LS channel estimation is used, the channel response estimation coefficient is:

If MMSE channel estimation is used, the channel response estimation coefficient is:

in, is the covariance matrix of the pilot channel response and the received signal, is the received signal autocovariance matrix.

The LS channel estimation algorithm is widely used because it is simple and easy to implement and does not require the statistical characteristics of the channel, but the estimation result of the LS algorithm is easily affected by noise, especially when the signal-to-noise ratio is low, the estimation accuracy will be greatly reduced. The MMSE algorithm has a good suppression effect on the inter-subcarrier interference and Gaussian white noise. In this embodiment, the MMSE channel estimation algorithm is adopted.

Step 4b, use the time domain interpolation algorithm to respond according to the pilot channel Calculate the system frequency domain channel response estimate Proceed as follows:

Step 4b1, use the FFT/IFFT method to respond to the estimated value of the pilot channel obtained in step 4a Perform IFFT transform to obtain the time-domain pilot channel response estimate

in, is the IDFT transform of size N _p , where N _p is the number of pilot channel responses, n=0,1,...,N _p -1, T is the matrix transpose.

Step 4b2, estimate the time-domain pilot channel response Interpolate to get the estimated value of the system time domain channel response

where N is the number of system channel responses.

Step 4b3, estimate the system time domain channel response Perform FFT operation to obtain the system frequency domain channel response

where F _N is the DFT transform of size N, k=0,1,...,N-1.

Step 5, estimate the measurement signal Z based on the receiving end error vector magnitude EVM channel quality measurement algorithm:

Step 5a, adopt the ZF frequency domain equalization algorithm or the MMSE frequency domain equalization algorithm, according to the receiving end frequency domain signal matrix y _f in step 2 and the system frequency domain channel response obtained in step 4 Calculate the estimated value of the frequency domain signal at the transmitter

If the ZF frequency domain equalization algorithm is used, the equalization coefficient is The estimated value of the frequency domain signal at the transmitter is:

If the MMSE equalization algorithm is used, the equalization coefficient is The estimated value of the frequency domain signal at the transmitter is:

In this embodiment, the ZF frequency domain equalization algorithm is adopted.

Step 5b, the estimated value of the frequency domain signal at the transmitter Perform IFFT transformation to obtain the estimated value of the time-domain signal at the transmitter That is, the measurement signal Z based on the receiving end error vector magnitude EVM channel quality measurement algorithm:

Step 6: Estimate the time-domain signal at the transmitter Turbo code decoding is performed to obtain the decoded bit data, and the bit data is re-encoded and re-modulated to obtain an ideal reference signal R:

Step 6a, first estimate the value of the time domain signal at the transmitting end Perform Turbo code logarithmic MAP decoding to obtain the estimated value of the time-domain signal at the transmitter bit data soft information

Again this soft message Make a hard decision to obtain the estimated value of the time-domain signal at the transmitter bit data Its judgment formula is:

Step 6b, for bit data Recoding and remodulation are performed to obtain an ideal reference signal R.

Step 7, using the error vector magnitude calculation formula, according to the measurement signal Z obtained in step 5 and the ideal reference signal R obtained in step 6, calculate the receiving end EVM:

The receiving end EVM is defined as the ratio between the square root value of the average power of the error vector signal at the receiving end and the square root value of the average power of the reference signal, that is, the difference between the root mean square value (RMS: Root MeanSquare) of the error vector signal and the reference signal. ratio, as shown in Figure 3.

Step 7a, adopts the error vector magnitude calculation formula to calculate the EVM _i of each subframe of the receiving end in the uplink shared data channel:

Step 7b, calculate the average value of the receiving end EVM _i of all subframes under one frame, obtain the receiving end EVM, and the realization formula is:

Among them, i is the subframe sequence number, and L is the total number of subframes.

Step 8, select the modulation and coding method MCS:

Step 8a, carry out off-line simulation to the uplink adaptive system model, obtain the mapping table between the receiving end EVM and the MCS, and obtain by offline simulation of the uplink adaptive system model using the SISO AWGN channel. The implementation process is shown in Figure 4. The MCS used in this embodiment is 1 to 15:

Step 8a1, let MCS=1.

Step 8a2, perform off-line loop simulation on the uplink adaptive system model using the SISO AWGN channel, and find out the SNR value range that can make the BLER evenly distributed between 0 and 1 from the simulation results.

Step 8a3, under each SNR within the SNR value range, perform offline loop simulation on the uplink adaptive system model using the SISO AWGN channel, obtain the receiving end EVM and BLER corresponding to each cycle, and analyze these receiving ends. The EVM and BLER are connected to obtain the simulation curve between the receiving end EVM and BLER, as shown in Figure 5.

Step 8a4, on the simulation curve between the receiving end EVM and BLER, find out the receiving end EVM corresponding to BLER=0.1 and store it, and store the current MCS at the same time.

Step 8a5, make MCS=MCS+1, repeat steps 8a2～8a4, until MCS=15, and extract the mapping table between the receiving end EVM and the corresponding MCS from the stored multiple receiving end EVMs and MCS, such as Table 1 shows:

Table 1

Step 8b, carry out multiple simulations to the uplink adaptive system model, select its corresponding MCS from Table 1 according to the instantaneous receiving end EVM value in each simulation cycle, and feed it back to the transmitting end, for the next time. Configure the optimal parameters for transmission.

The effect of the present invention can be further illustrated by the following simulation:

1. Simulation conditions

Simulation software: using Matlab;

Simulation scenario: The parameter settings for transmission are based on the 3GPP LTE standard, the system bandwidth is 1.4MHz, the number of transmit antennas is 1, the number of receiving worlds is 2, and the VehA channel is assumed. It is assumed that there are no problems such as time-frequency synchronization and I/Q imbalance. The specific simulation parameters are shown in Table 2:

Table 2: Simulation Parameters Table

parameter Numerical value community 7 districts Cell radius 500m Carrier frequency/system bandwidth 2GHz/1.4MHz channel model VehA SNR(dB) 5:5:40 targetBLER less than or equal to 10% Antenna configuration 1 send 2 receive (1×2) BS receiver type ZF receiver channel estimation MMSE

2. Simulation content and result analysis

Using the above simulation conditions, the present invention and the existing channel quality measurement algorithms are simulated, such as the EESM algorithm, the MIESM algorithm, the AESM algorithm and the Harm-mean algorithm, to obtain the BLER and the BLER of the present invention and the existing channel quality measurement algorithm under different SNRs. The throughput performance comparison chart is shown in Figure 6.

The abscissa in Fig. 6(a) and Fig. 6(b) represents the SNR in the current scene, and the unit is dB. The ordinate of Fig. 6(a) represents the system BLER, and the ordinate of Fig. 6(b) represents the system throughput, and the unit is bit/s. , in Fig. 6, the curve marked with a small vertical bar is the BLER and throughput simulation result curve of the EESM algorithm; the curve marked by a square is the BLER and throughput simulation result curve of the MIESM algorithm; the curve marked with a five-pointed star is the AESM algorithm The curve of BLER and throughput simulation result of the Hram-mean algorithm is the curve marked by a diamond; the curve marked by a circle is the BLER and throughput simulation result curve of the system when the present invention is applied.

From Fig. 6(a), it can be seen that for all channel quality measurement algorithms, the system BLER and throughput performance improves as the SNR increases. For the EESM, MIESM, AESM and Harm-mean algorithms, when the SNR exceeds 25dB, the target BLER is reached, and for the algorithm proposed in the present invention, when the SNR exceeds 13dB, the target BLER can be achieved. It can be seen that, The present invention has better BLER performance and can better satisfy the limitation of system BLER. As can be seen from Fig. 6(b), with the increase of SNR, compared with the existing channel quality measurement algorithm, the measurement accuracy of the present invention is higher, and the throughput performance is significantly improved. Therefore, compared with the existing channel quality measurement algorithms such as EESM, MIESM, AESM and Harm-mean, the present invention can more effectively improve the throughput performance of the system under the condition that the requirements of the system BLER are met.

Claims (9)

1. A method for measuring the quality of an uplink channel based on the Error Vector Magnitude (EVM) of a receiving end comprises the following steps:

(1) constructing an uplink adaptive system model, and establishing a mathematical relation of frequency domain signals between a transmitting end and a receiving end of the model, wherein the method comprises the following steps:

(1a) adopting a multiple access technology to construct an uplink adaptive system model of a ground cellular communication system, or a satellite communication system, or a Massive-MIMO system or a point-to-point link transmission system: the MIMO channel comprises a self-adaptive feedback module for measuring the channel quality and self-adaptive modulation and coding based on the error vector amplitude EVM of the receiving end;

(1b) establishing a mathematical relation y of frequency domain signals between a transmitting end and a receiving end of an uplink self-adaptive system model_i ^fTo obtain the frequency domain signal y of the receiving end^f：

Where Y is the receiving end frequency domain signal, H is the system frequency domain channel response, F_MFor a normalized DFT transform of size M,k,l＝1,2,...,M，N_Tin order to determine the number of the transmitting antennas,is dimension N_TX is the modulation signal of the transmitting end, V is the mean value of 0, and the variance isWhite gaussian noise of (1);

(2) at the receiving end, according to the pilot configuration form in the 3GPP standard, the pilot signal Y is extracted from the frequency domain signal Y at the receiving end of the uplink self-adaptive system model_p：

Y_p＝H_pS_p+V_p

Wherein S is_p、H_pAnd V_pRespectively indicating a pilot signal of a transmitting end, pilot channel response and Gaussian white noise;

(3) at a receiving end, estimating the system frequency domain channel response H:

(3a) using a channel estimation algorithm based on the pilot signal Y_pResponse to pilot channel H_pEstimating to obtain pilot channel response estimation value

(3b) Using time domain interpolation algorithm, based on pilot channel response estimation valueCalculating system frequency domain channel response estimated value

(4) Estimating a measurement signal Z based on a receiving end error vector magnitude EVM channel quality measurement algorithm:

(4a) adopting a frequency domain equalization algorithm to obtain a frequency domain signal y of the receiving end in the step (1b)^fAnd the system frequency domain channel response estimated value obtained in the step (3)Calculating an estimated value of a frequency domain signal of a transmitting end(4b) Estimation value of frequency domain signal of transmitting terminalIFFT conversion is carried out to obtain the time domain signal estimation value of the transmitting terminalNamely a measurement signal Z based on the EVM channel quality measurement algorithm of the error vector magnitude of the receiving end;

(5) at the receiving end, the time domain signal estimation value of the transmitting end is carried outDecoding Turbo code to obtain decoded bit data, and re-encoding and re-modulating the bit data to obtainAn ideal reference signal R;

(6) calculating the EVM of the receiving end according to the measurement signal Z obtained in the step (4) and the ideal reference signal R obtained in the step (5) by adopting an error vector magnitude calculation formula;

(7) at the receiving end, a modulation coding scheme MCS is selected:

(7a) performing off-line simulation on the uplink adaptive system model to obtain a mapping table between the EVM and the MCS of the receiving end;

(7b) and simulating the uplink self-adaptive system model for multiple times, selecting a corresponding MCS from a mapping table between the receiving end EVM and the MCS according to the instantaneous receiving end EVM value in each simulation cycle, and feeding back the MCS to the transmitting end for configuring the optimal parameters of the next transmission.

2. The method as claimed in claim 1, wherein the mathematical relationship of the frequency domain signals between the transmitting end and the receiving end of the model of the uplink adaptive system in step (1) is a mathematical relationship between the Error Vector Magnitude (EVM) of the receiving endThe expression is as follows:

whereinIs an IDFT transform of size M, h_r,n＝diag{[h₁]_r,n,[h₂]_r,n,...,[h_M]_r,n}，[h_m]_r,nRefers to the frequency domain channel response of the m sub-carrier under the nth transmitting antenna and the r receiving antenna, v_rMean value of 0 and variance of frequency domainOfAcoustic vector, x_n＝[x_n,1,x_n,2,...,x_n,M]^TIs a time domain signal N of a transmitting terminal after M-QAM modulation_TTo transmit antenna number, H_MIs h_r,nForm of Fourier transform of v_nIs the channel noise.

3. The method for measuring uplink channel quality based on EVM of receiver-side error vector magnitude as claimed in claim 1, wherein the channel estimation algorithm in step (3a) adopts LS channel estimation algorithm or MMSE channel estimation algorithm: if the LS channel estimation algorithm is adopted, the channel response estimation coefficient isIf MMSE channel estimation algorithm is adopted, the channel response estimation coefficient isWherein,is the covariance matrix of the pilot channel response and the received signal,is the received signal auto-covariance matrix.

4. The method as claimed in claim 1, wherein the system frequency domain channel response estimate in step (3b) is obtained by performing the uplink channel quality measurement method based on receiver-side Error Vector Magnitude (EVM)The acquisition steps are as follows:

(3b1) using FFT/IFFT method to estimate the pilot channel response obtained in step (3a)IFFT conversion is carried out to obtain a time domain pilot channel response estimated value

Wherein,is of size N_pIDFT of, N_pAs to the number of pilot channel responses,n＝0,1,...,N_p-1, T is a matrix transpose;

(3b2) for time domain pilot channel response estimation valueInterpolation is carried out to obtain the estimated value of the time domain channel response of the system

Wherein N is the number of system channel responses;

(3b3) channel response estimation value for system time domainFFT operation is carried out to obtain the system frequency domainChannel response estimation

Wherein F_NFor a DFT transform of size N,k＝0,1,...,N-1。

5. the uplink channel quality measurement method based on receiving-end Error Vector Magnitude (EVM) as claimed in claim 1, wherein the frequency domain equalization algorithm in step (4a) adopts ZF frequency domain equalization algorithm or MMSE frequency domain equalization algorithm: if ZF frequency domain equalization algorithm is adopted, the equalization coefficient isThe estimated value of the frequency domain signal at the transmitting end isIf MMSE equalization algorithm is adopted, the equalization coefficient isThe estimated value of the frequency domain signal at the transmitting end is

6. The method as claimed in claim 1, wherein the measurement signal Z based on the EVM channel quality measurement algorithm in step (4b) is implemented by the following formula:

whereinIs an M-point Fourier transform inverse matrix.

7. The method as claimed in claim 1, wherein the step of obtaining the ideal reference signal R in step (5) comprises:

(5a) firstly, the time domain signal estimation value of a transmitting terminal is carried outTurbo code logarithm MAP decoding is carried out to obtain the time domain signal estimated value of the transmitting terminalSoft information of bit data of

Then the soft information is processedHard decision is carried out to obtain a time domain signal estimation value of a transmitting terminalBit data ofThe decision formula is:

(5b) for bit dataAnd performing recoding and remodulation to obtain an ideal reference signal R.

8. The method for measuring uplink channel quality based on receiver-side Error Vector Magnitude (EVM) as claimed in claim 1, wherein said calculating receiver-side EVM in step (6) is implemented by the steps of:

(6a) calculating EVM of each subframe of a receiving end in an uplink shared data channel by adopting an error vector magnitude calculation formula_i：

(6b) Calculating EVM of all sub-frame receiving ends under one frame_iObtaining the receiving end EVM by the average value of the data, wherein the realization formula is as follows:

wherein i is the subframe number, and L is the total number of subframes.

9. The method as claimed in claim 1, wherein the mapping table between the receiving end EVM and the MCS in step (7a) is obtained by:

(7a1) initializing, and setting MCS as 1;

(7a2) performing off-line cyclic simulation on an uplink adaptive system model adopting a SISO AWGN channel, and finding out a value range of SNR (signal to noise ratio) which can enable BLER (block error ratio) to be uniformly distributed between 0 and 1 from a simulation result;

(7a3) performing off-line circulation simulation on an uplink adaptive system model adopting a SISO AWGN channel under each SNR in an SNR value range to obtain receiving end EVM and BLER corresponding to each circulation, and connecting the receiving end EVM and BLER to obtain a simulation curve between the receiving end EVM and the BLER;

(7a4) finding out and storing a receiving end EVM corresponding to BLER (0.1) on a simulation curve between the receiving end EVM and BLER, and simultaneously storing the current MCS;

(7a5) the steps (7a2) to (7a4) are repeated until MCS becomes 28, and a mapping table between the receiver EVM and the corresponding MCS is extracted from the stored plurality of receiver EVMs and MCSs.