CN101572581A - Method and device for confirming signal-interference-noise radio - Google Patents

Abstract

The invention discloses a method and a device for confirming a signal-interference-noise radio (SINR). The method comprises the steps of obtaining channel response information, calculating related coefficient sum of row vectors and confirming the SINR of each data stream by utilizing the channel response information and the related coefficient sum. The device comprises a channel response information acquiring module and a calculating module. The scheme of the invention can effectively lower the complexity for confirming the SINR.

Description

A kind of Signal to Interference plus Noise Ratio is determined method and device

Technical field

The present invention relates to the treatment technology in the wireless communication system, relate in particular to Signal to Interference plus Noise Ratio in a kind of multi-user wireless communication system (signal interference noise ratio, SINR) definite method and device.

Background technology

(many antennas of transmitting terminal and/or receiving terminal can bring space diversity gain and/or spatial reuse gain in the multi-user system, thereby have systematic function preferably for single-input-single-output, SISO) system than traditional single-shot list receipts.What therefore, the multi-user system with many antennas had become next generation mobile communication system mainly is absorbed in one of point.

Under spatial multiplexing mode, different data flow is distributed on each separate space channel to be transmitted.Like this, transmission make the throughput of multi-user system increase greatly, thereby the data transmission capabilities of this system can be effectively improved in the time of a plurality of data flow.

In the actual multi-user system, owing to channel response matrix being carried out singular value (singular value decomposition as can't direct communication between each terminal of receiving terminal causing to adopt, SVD) decompose, thereby can't obtain the parallel spatial channel of acquisition the best of the minimum and total transmittability maximum of phase mutual interference.At this problem, present solution is, in the signal processing of transmitting terminal, introduce linearity or nonlinear precoding processing operation, promptly utilize and make scheduled user's the pre-coding matrix of total transmittability maximum obtain equivalent channel state information matrix, total transmittability here comprises capacity, throughput etc., thereby the interference of eliminating or reducing between space channel also improves total transmittability, and above-mentioned total transmittability to be SINR by each data flow determined.Linear predictive coding wherein receives much concern because of it has lower complexity, linear predictive coding commonly used comprises unitary pre-coding (unitaryprecoding), non-unitary pre-coding (non-unitary precoding), zero-forcing beamforming precoding (zero-forcing beam forming precoding) and least mean-square error (minimum meansquare error, MMSE) precoding etc.In addition, in the multi-user system under the spatial multiplexing mode, carrying out transfer of data in order to select more excellent a plurality of users, usually also is with based on carrying out the multi-subscriber dispatching operation by total transmittability of the scheduled user that SINR was determined of each data flow.

As seen, SINR is a key index in the multi-user system communication process.Fig. 1 shows the flow chart that existing SINR determines method.Referring to Fig. 1, this method comprises:

In step 101, obtain channel response information.

Channel response information in this step can be channel state information matrix or equivalent channel state information matrix.The equivalent channel state information matrix here is meant the result who utilizes after pre-coding matrix is handled channel state information matrix.

In step 102, utilize channel response information to calculate weight coefficient.

Obtain weight coefficient W by following formula 1 in this step:

W=H ' * (HH ') ^-1Formula 1

Wherein W is a weight coefficient, and H is the channel response information that obtains in the above-mentioned steps 101, and H ' is the associate matrix of matrix H, () ^-1Expression is got contrary to the content in the bracket.

In step 103, calculate the phase place of every column vector in the weight coefficient.

Calculate the phase place of every column vector among the weight coefficient W in this step by following formula 2:

W (k)=W (k)/| W (k) | formula 2

Wherein W (k) is the phase place of weight coefficient W k column vector, and W (k) is the k column vector among the weight coefficient W, | W (k) | be the amplitude of weight coefficient W k column vector.

After the operation of carrying out this step, the energy of each row is 1 among the weight coefficient W, and then also can to see as be normalized to weight coefficient W to the operation of asking for phase place of this step.

In step 104, calculate the SINR of each data flow.

Calculate the SINR of each data flow in this step by following formula 3:

$\mathrm{SINR}\left(k\right)=\frac{P_k{\left|h_k\stackrel{\‾}{W\left(k\right)}\right|}^2}{N_0}$ Formula 3

Wherein SINR (k) is the Signal to Interference plus Noise Ratio of k data flow, P _kBe the received power of this data flow, N ₀Be noise.

So far, finish the flow process of the existing SINR of determining.

In above-mentioned existing scheme, suppose that number of transmission antennas and data flow number are Nt, reception antenna quantity is Nr, and wherein Nr is greater than or equal to Nt, and then weight coefficient W is the matrix of Nt * Nt.Asking in the weight coefficient process of step 102, need to carry out two submatrixs and take advantage of operation and a matrix inversion operation, promptly need to be about (2 * Nt ³+ Nt ²) inferior complex operation; When step 103 is calculated the phase place of all column vectors among the weight coefficient W, need carry out Nt time complex operation; And all need to carry out complex operation one time when in step 104, determining the SINR of each data flow, and generally the number of data flow can be identical with number of transmission antennas, then needs to carry out Nt time complex operation so when determining the SINR of all data flow.Like this, in a dispatching cycle, the complexity in existing definite SINR process is: (2 * Nt ³+ Nt ²+ 2Nt) inferior complex operation, as seen, complexity is all relevant with 2 rank indexes with 3 rank of number of transmission antennas.Such complexity can be born for the system of single transmit antenna, only is 5 complex operations; Yet, when number of transmission antennas more for a long time, the complexity of existing scheme can sharply increase, for example, when Nt=2, its complexity is: (2 * 2 ³+ 2 ²+ 2 * 2) time complex operation=24, when Nt=4, its complexity is: (2 * 4 ³+ 4 ²+ 2 * 4)=152 time complex operation when Nt=8, then is (2 * 8 ³+ 8 ²+ 2 * 8) time complex operation=1104.If above-mentioned SINR determines that scheme is applied to that pre-coding matrix is selected or processes such as multi-subscriber dispatching in, then complexity can be along with pre-coding matrix number or number of users and linear increasing, and thus, needs the complex operation carried out more.

Therefore, existing SINR determines method for the situation of multiple transmit antennas, and its complexity is higher.Further, because higher computation complexity requires multi-user system to be merely able to the equipment that adopts performance higher, thereby increased system cost.Have, repeatedly complex operation can expend the long time again, and the equipment of the auxiliary like this SINR of determining is had to when carrying out these complex operations or handled other tasks afterwards, can cause the response time of task so longer, the reduction systematic function.

Summary of the invention

The invention provides a kind of SINR and determine method, can have lower complexity.

Determine to comprise in the method at SINR of the present invention:

Obtain channel response information, calculate in this channel response information each row vector coefficient correlation and, and utilize this channel response information and this coefficient correlation and determine the SINR of each data flow.

In one embodiment, coefficient correlation of each row vector and before in this channel response information of described calculating further comprises: to the capable vectorial normalized of this channel response information;

Coefficient correlation of each row vector and be in this channel response information of described calculating: calculate per two vectorial normalization results' of row coefficient correlation, according to the coefficient correlation that obtains, calculate described coefficient correlation and.

Wherein, describedly be: calculate energy of each row vector in the described channel response information, utilize energy of each row vector, the capable vector of correspondence is carried out normalized to the capable vectorial normalized of this channel response information.

Wherein, described energy that utilizes each row vector, carry out normalized to the capable vector of correspondence and be:

Energy to each row vector carries out the evolution operation, obtains the amplitude of corresponding row vector;

With the amplitude of each row vector, obtain the vectorial normalization result of row of each row vector divided by self.

Wherein, per two the vectorial normalization results' of row of described calculating coefficient correlation is: by formula ρ _Ij=| h _i' * h _j| calculate the coefficient correlation between the vectorial normalization result of per two row, wherein ρ _IjBe the coefficient correlation between i the vectorial normalization result of row and j the vectorial normalization result of row, h _i' be i the vectorial normalization result's of row conjugate transpose vector, h _jBe j the vectorial normalization result of row, i and j are the integer in the closed interval [1, Nt], and i＜j, and Nt is the data flow number, || the expression modulo operation;

The coefficient correlation that described basis obtains is calculated described coefficient correlation and is: passes through formula $\mathrm{Corr}=\underset{i<j}{\mathrm{\&Sigma;}}{\mathrm{\&rho;}}_{\mathrm{ij}}^2$ Calculate described coefficient correlation and, wherein Corr for coefficient correlation of each row vector with, the symbol ∑ is represented sum operation.

In another embodiment, the coefficient correlation of each row vector and be in this channel response information of described calculating: calculate the coefficient correlations of per two row vectors, according to the coefficient correlation that obtains, calculate described coefficient correlation and.

Wherein, the coefficient correlation of per two the row vectors of described calculating is: pass through formula ${\mathrm{\&rho;}}_{\mathrm{ij}}=\frac{|h_i^{\&prime;}\&times;h_j|}{\left|h_i\right|\&CenterDot;\left|h_j\right|}$ Calculate the coefficient correlation between per two capable vectors, wherein ρ _IjBe the coefficient correlation between i row vector and j the capable vector, h _i' be the conjugate transpose vector of i row vector, h _jBe j row vector, || the expression modulo operation, i and j are the integer in the closed interval [1, Nt], and i＜j, Nt is the data flow number;

The coefficient correlation that described basis obtains is calculated described coefficient correlation and is: passes through formula $\mathrm{Corr}=\underset{i<j}{\mathrm{\&Sigma;}}{\mathrm{\&rho;}}_{\mathrm{ij}}^2$ Calculate described coefficient correlation and, wherein Corr for coefficient correlation of each row vector with, the symbol ∑ is represented sum operation.

Preferably, determine further to comprise before the SINR of each data flow: the energy that calculates each row vector in the described channel response information.

Wherein, the energy of each row vector is in the described channel response information of described calculating:

According to formula | h _k| ²=| h _K1| ²+ | h _K2| ²+ ... + | h _Ki| ²+ ... + | h _KNt| ²Calculate the energy of described row vector, wherein h _kBe k in described channel response information row vector, h _KiBe i element in k the capable vector of described channel response information, | h _Ki| for to element h _KiCarry out modulo operation, and k and i be the integer in the closed interval [1, Nt], Nt is the data flow number.

Wherein, before the SINR of described definite each data flow, further comprise: the received power of obtaining each data flow;

Describedly utilize this channel response information and this coefficient correlation and determine that the SINR of each data flow is: according to the coefficient correlation of the energy of the received power of each data flow, corresponding row vector and each row vector and, calculate the SINR of corresponding data stream.

Wherein, the energy of described received power, corresponding row vector and the coefficient correlation of each row vector according to each data flow and, the SINR that calculates corresponding data stream is:

Pass through formula $\mathrm{SINR}\left(k\right)=\frac{P_k}{N_0}{\left|h_k\right|}^2(1-\mathrm{Corr})$ Calculate the SINR of each data flow, wherein SINR (k) is the SINR of k data flow, P _kBe the received power of k data flow, N ₀Be noise, | h _k| ²Be the energy of k row vector of described channel response information, Corr for the coefficient correlation of each row vector and, k is the interior integer in closed interval [1, Nt], Nt is the data flow number.

Wherein, described channel response information is: channel state information matrix, perhaps, equivalent channel state information matrix.

The present invention also provides a kind of SINR to determine device, can have lower complexity.

Determine to comprise in the device at SINR of the present invention: channel response information acquisition module and computing module, wherein,

Described channel response information acquisition module is used to obtain channel response information;

Described computing module be used to calculate each row vector coefficient correlation and, and utilize this channel response information and this coefficient correlation and determine the SINR of each data flow.

Preferably, described device further comprises: the normalized module is used for the capable vectorial normalized of this channel response information;

Described computing module calculates per two vectorial normalization results' of row coefficient correlation, according to the coefficient correlation that obtains, calculate described coefficient correlation and.

Wherein, energy of each row vector in the further calculating channel response information of described normalized module utilizes energy of each row vector, and the capable vector of correspondence is carried out described normalized.

Wherein, described normalized module comprises: energy meter operator module and processing sub, wherein,

Described energy meter operator module is used for the energy of each row vector of calculating channel response information;

Described processing sub is used to utilize energy of each row vector, and the capable vector of correspondence is carried out normalized.

Wherein, described computing module comprises: correlation calculations submodule and SINR determine submodule, wherein,

Described correlation calculations submodule is used to calculate the vectorial normalization result's of each row coefficient correlation, and obtain coefficient correlation and;

Described SINR determine submodule be used to utilize the energy of row vector of received power, this data flow correspondence of each data flow and coefficient correlation and, calculate the SINR of corresponding data stream.

Preferably, described device further comprises: the received power acquisition module is used to obtain the received power of each data flow;

Described computing module comprises: energy meter operator module, correlation calculations submodule and SINR determine submodule, and wherein, described energy meter operator module is used for the energy of each row vector of calculating channel response information; Described correlation calculations submodule is used to calculate the coefficient correlation of each row vector, and obtain each row vector coefficient correlation and; Described SINR determine submodule be used to utilize the energy of row vector of received power, this data flow correspondence of each data flow and coefficient correlation and, calculate the SINR of corresponding data stream.

Preferably, described device further comprises: the received power acquisition module is used to obtain the received power of each data flow.

By such scheme as seen, among the present invention when determining SINR, at first calculate each row vector in the channel response information that gets access to coefficient correlation and, again according to the coefficient correlation of channel response information and each row vector and, determine the SINR of each data flow.Because the present invention considers existing SINR and determines that the amplitude of weight coefficient in the method can appear in molecule and the denominator simultaneously, we can say that the amplitude of weight coefficient does not influence the result of SINR.And because the amplitude of weight coefficient is relevant with the amplitude of channel response information, so the present invention is the coefficient correlation of each row vector in the calculating channel response information in the initial step of determining SINR, realize and existing method in being similar to of weight coefficient.So just need not as existing method all to include in the parameters whole when calculating weight coefficient the amplitude of whole channel response informations, but simplify definite process of SINR, thereby reduced complexity effectively by the computing between the capable vector that has reduced exponent number.

Description of drawings

To make clearer above-mentioned and other feature and advantage of the present invention of those of ordinary skill in the art by describe exemplary embodiment of the present invention in detail with reference to accompanying drawing below, in the accompanying drawing:

Fig. 1 determines the flow chart of method for existing SINR;

Fig. 2 determines the exemplary process diagram of method for SINR among the present invention;

Fig. 3 determines the exemplary block diagram of device for SINR among the present invention;

Fig. 4 determines the detail flowchart of method for SINR in the embodiment of the invention 1;

Fig. 5 determines the structural representation of device for SINR in the embodiment of the invention 1;

Fig. 6 determines the detail flowchart of method for SINR in the embodiment of the invention 2;

Fig. 7 determines the structural representation of device for SINR in the embodiment of the invention 2;

Fig. 8 determines the scheduling time analogous diagram of the greedy algorithm of method for adopting existing SINR and determine method and adopt SINR among the embodiment 1;

Fig. 9 determines that for adopting existing SINR SINR determines the complexity analogous diagram that the pre-coding matrix of method is selected among method and the embodiment 1.

Embodiment

For making purpose of the present invention, technical scheme clearer, below with reference to the accompanying drawing embodiment that develops simultaneously, the present invention is described in further detail.

The present invention as key parameter, utilizes this vector normalization result progressively to obtain the SINR of each data flow the vectorial normalization result of the row of channel response information when determining SINR.

Fig. 2 shows the exemplary process diagram that SINR among the present invention determines method.Referring to Fig. 2, this method comprises:

In step 201, obtain channel response information.

In step 202, calculate each row vector coefficient correlation and, and utilize this channel response information and this coefficient correlation and determine the SINR of each data flow.

Fig. 3 shows the exemplary block diagram that SINR among the present invention determines device.Referring to Fig. 3, this device comprises: channel response information acquisition module and computing module.Wherein, the channel response information acquisition module is used to obtain channel response information; Computing module be used to calculate each row vector coefficient correlation and, and utilize this channel response information and this coefficient correlation and determine the SINR of each data flow.

From foregoing description as seen, among the present invention when determining SINR, at first calculate each row vector in the channel response information that gets access to coefficient correlation and, again according to the coefficient correlation of channel response information and each row vector and, determine the SINR of each data flow.Because the present invention considers existing SINR and determines that the amplitude of weight coefficient in the method can appear in molecule and the denominator simultaneously, we can say that the amplitude of weight coefficient does not influence the result of SINR.And because the amplitude of weight coefficient is relevant with the amplitude of channel response information, so the present invention is the coefficient correlation of each row vector in the calculating channel response information in the initial step of determining SINR, realize and existing method in being similar to of weight coefficient.So just need not as existing method all to include in the parameters whole when calculating weight coefficient the amplitude of whole channel response informations, but simplify definite process of SINR, thereby reduced complexity effectively by the computing between the capable vector that has reduced exponent number.

With the data flow number is that the multi-user system of Nt is an example, the data flow number here can be less than or equal to number of transmission antennas, calculate among the present invention channel response information capable vector correlation coefficient and the time need carry out time complex operation of 3Nt * (Nt-1)/2, and need not to carry out complex operation when utilizing the coefficient correlation of channel response information and row vector and determining the SINR of each data flow, therefore determine among the present invention that the complexity in the SINR process is: [3Nt * (Nt-1)/2] inferior complex operation, visible only is 2 powers of Nt to the amount of complexity effect maximum here.As seen Nt is greater than or equal at 2 o'clock, and the complexity among the present invention is far smaller than the complexity in the existing method, and Nt is big more, and the present invention program is big more to the reduction degree of complexity.

Below will describe the detailed process of determining SINR among the present invention in detail.

Embodiment 1

In the present embodiment coefficient correlation of calculating each row vector and before, at first calculate the energy of each row vector in the channel response information, and utilize the energy that calculates that each row vector is carried out normalized, utilize the vectorial normalization result of each row to calculate per two vectorial normalization results' of row coefficient correlation again.In the present embodiment, the coefficient correlation of each row vector and be the vectorial normalization result of each row coefficient correlation and.

Fig. 4 shows the detail flowchart that SINR in the present embodiment determines method, referring to Fig. 4, obtains the SINR of each data flow in the present embodiment according to following step:

In step 401, obtain channel response information.

Similar to the step 101 of existing method, the channel response information in this step also can be channel state information matrix or equivalent channel state information matrix.

The channel state information matrix here is meant from every transmit antennas to the channel state information matrix the every reception antenna.This channel state information matrix can obtain by conventional channel estimating, for example: utilize descending pilot frequency information or utilize up pilot frequency information to carry out channel estimating at network side in end side; Also can obtain channel state information matrix by feedback channel, for example in the system of Frequency Division Duplexing (FDD), after end side is carried out channel estimating, channel is quantized, utilize uplink feedback channel will quantize channel state information matrix afterwards then and feed back to network side.

The equivalent channel state information matrix here is meant the result who utilizes after pre-coding matrix is handled channel state information matrix.This equivalence channel state information matrix can apply the pilot signal acquisition of precoding technique by direct measurement, also can utilize modes such as channel estimating or feedback to obtain channel state information matrix earlier, then this channel state information matrix is combined with corresponding pre-coding matrix, thereby obtain equivalent channel state information matrix.

In step 402, the energy of each row vector in the calculating channel response information.

Suppose that channel response information is H, h _kFor k among channel response information H row vector, then calculate h according to following formula 4 in this step _kEnergy:

| h _k| ²=| h _K1| ²+ | h _K2| ²+ ... + | h _Ki| ²+ ... + | h _KNt| ²Formula 4

Wherein, h _KiI element in k the capable vector of expression channel response information H, | h _Ki| for getting element h _KiAbsolute value, and k and i be the integer in the closed interval [1, Nt], Nt is the data flow number, | h _Ki| for to element h _KiCarry out modulo operation.

In step 403, utilize energy of each row vector, the capable vector of correspondence is carried out normalized.

With k the vectorial h of row _kBe example, in this step, at first to the vectorial h of the row that obtains in the abovementioned steps 402 _kEnergy carry out evolution operation, obtain this row vector h _kAmplitude | h _k|, will go vectorial h then _kDivided by the amplitude of self | h _k|, calculate h _kThe vectorial normalization of row h as a result _kIn other words, in this step, 5 calculate k vectorial normalized result of row that row is vectorial among the channel response information H according to the following equation:

h _k=h _k/ | h _k| formula 5

After obtaining the vectorial normalization result of whole row, can utilize the vectorial normalization result of each row to form the normalization matrix H of channel response information H according to the vector position before the normalized.

In step 404, calculate each the row vectorial normalization result coefficient correlation, and obtain coefficient correlation and.

Each row vector in the channel response information is all represented a channel, and channel relevancy is a key index of determining SINR, so calculates coefficient correlation between per two different rows vector normalization result by following formula 6 in this step:

ρ _Ij=| h _i' * h _j| formula 6

ρ wherein _IjBe the coefficient correlation between i the vectorial normalization result of row and j the vectorial normalization result of row, h _i' be i the vectorial normalization result's of row conjugate transpose vector, i and j are the integer in the closed interval [1, Nt], and i＜j, Nt is the data flow number, || the expression modulo operation.Here the reason of i＜j is, after channel response information H being executed the vectorial normalization of row, HH ' is a correlation matrix, and promptly the diagonal entry of HH ' is 1, and the amplitude of other elements is all less than 1, and this matrix is the conjugation symmetrical matrix.So, coefficient correlation between i vectorial normalization result of row and j the vectorial normalization result of row equals the coefficient correlation between j the vectorial normalization result of row and i the vectorial normalization result of row, therefore only need obtain going in twos under i＜j situation the coefficient correlation between vectorial normalization result, and need not to calculate the situation of i 〉=j.

According to the common technology of this area, coefficient correlation and its phase place between two vectors are closely related, and irrelevant with its amplitude, therefore, we can say, the coefficient correlation between two vectors equals these two coefficient correlations between vectorial normalization result.

After the coefficient correlation that has obtained the vectorial normalization result of each row, according to the following equation 7 calculate the vectorial normalization result of each row coefficient correlation and, as the coefficient correlation of each row vector and:

$\mathrm{Corr}=\underset{i<j}{\mathrm{\&Sigma;}}{\mathrm{\&rho;}}_{\mathrm{ij}}^2$ Formula 7

Wherein, Corr for the vectorial normalization result's of each row coefficient correlation and, also be each row vector coefficient correlation and, the symbol ∑ is represented sum operation.

In step 405, utilize the energy of the received power of each data flow, corresponding row vector and coefficient correlation and, calculate the SINR of corresponding data stream.

The correlation of considering two interchannels is big more, and the SINR of the signal of these two channel is more little, 8 SINR that calculate each data flow according to the following equation in this step:

$\mathrm{SINR}\left(k\right)=\frac{P_k}{N_0}{\left|h_k\right|}^2(1-\mathrm{Corr})$ Formula 8

Wherein SINR (k) is the SINR of k data flow, P _kBe the received power of k data flow, N ₀Be noise, | h _k| ²Be the energy of k the row vector of channel response information H, Corr for the coefficient correlation of each row vector with.In addition, the received power P of k data flow here _kBy network side is that the transmitted power of k data flow distribution and the large scale decline of channel determine that transmitted power wherein can utilize existing any power distribution algorithm to obtain, or the constant power value that sets in advance; Noise N ₀In can comprise adjacent area interference.

So far, the SINR that finishes in the present embodiment determines flow process.

Fig. 5 shows the structural representation that SINR in the present embodiment determines device.Referring to Fig. 5, present embodiment has carried out refinement to the computing module among Fig. 3, and has increased normalized module and received power acquisition module on the basis of Fig. 3.

Channel response information acquisition module in the present embodiment is used to obtain channel state information matrix or equivalent channel state information matrix, as channel response information.

The normalized module is used for the capable vectorial normalized of this channel response information, and energy of each row vector in the calculating channel response information, and utilizes energy of each row vector, and the capable vector of correspondence is carried out described normalized.Normalized module among Fig. 5 comprises: energy meter operator module and processing sub.Energy meter operator module wherein is used for the energy of each row vector of calculating channel response information; Processing sub is used to utilize energy of each row vector, and the capable vector of correspondence is carried out normalized.

Received power acquisition module in the present embodiment is used for obtaining according to above-mentioned steps 405 pointed modes the received power of each data flow.

Correspondingly, computing module utilize the energy of row vector of received power, this data flow correspondence of each data flow and coefficient correlation and, calculate the SINR of corresponding data stream.This computing module comprises: correlation calculations submodule and SINR determine submodule.Correlation calculations submodule wherein is used to calculate the vectorial normalization result's of each row coefficient correlation, and obtain each row vector coefficient correlation and; SINR determine submodule be used to utilize the energy of row vector of received power, this data flow correspondence of each data flow and coefficient correlation and, calculate the SINR of corresponding data stream.

The SINR of present embodiment determines in the method and apparatus, after obtaining channel response information, at first to the capable vectorial normalized of channel response information, the amplitude of each row vector is removed, with the vectorial normalization result's of each row coefficient correlation and as the coefficient correlation of each row vector and, and utilize this coefficient correlation and and channel response information progressively obtain the SINR of each data flow.Compare with the operation of last removal amplitude in the existing method, present embodiment is removed amplitude in advance and is utilized coefficient correlation and substitute under the mode that has weight coefficient now, the amplitude of channel response information can in the processing of each step, not participate in this matrix of more complicated take advantage of with computing such as matrix inversion in, therefore the complex operation number of times of present embodiment significantly reduces, and complexity can access effectively and reduce.

With the data flow number is that the multi-user system of Nt is an example, need to carry out time complex operation of 2Nt complex operation and Nt * (Nt-1)/2 when carrying out the vectorial normalized of row of channel response information in the present embodiment and calculating coefficient correlation respectively, and need not to carry out complex operation when utilizing channel response information and vector correlation coefficient and determining the SINR of each data flow, so present embodiment determines that the complexity in the SINR process is: time complex operation of 2Nt+Nt * (Nt-1)/2=Nt * (Nt+3)/2.

For instance, when Nt=2, its complexity is: 2 * (2+3)/2=5 time complex operation, less than the complexity of 24 complex operations in the existing method; When Nt=4, its complexity is: 4 * (4+3)/2=14 time complex operation, the complexity of 152 complex operations in the existing method; When Nt=8, its complexity is: 8 * (8+3)/2=44 time complex operation, the complexity of 1104 complex operations in the existing method.

Embodiment 2

Do not carry out the vectorial normalized of row of the channel response information among the embodiment 1 in the present embodiment, but directly utilize in the channel response information capable vector calculation coefficient correlation and, determine the SINR of each data flow again according to the method in the step 405.

Fig. 6 shows the detail flowchart that SINR in the present embodiment determines method, referring to Fig. 6, obtains the SINR of each data flow in the present embodiment according to following step:

In step 601, obtain channel response information.

Channel response information in this step also can be channel state information matrix or equivalent channel state information matrix.

In step 602, calculate coefficient correlation of each row vector, and obtain coefficient correlation and.

Direct coefficient correlations of per two row vectors in the calculating channel response information in this step, according to the coefficient correlation that obtains, calculate coefficient correlation and.Specifically, calculate coefficient correlation between per two different rows vectors by following formula 9:

${\mathrm{\&rho;}}_{\mathrm{ij}}=\frac{|h_i^{\&prime;}\&times;h_j|}{\left|h_i\right|\&CenterDot;\left|h_j\right|}$ Formula 9

ρ wherein _IjBe the coefficient correlation between i row vector and j the capable vector, h _i' be the conjugate transpose vector of i row vector, h _jBe j row vector, || the expression modulo operation, i and j are the integer in the closed interval [1, Nt], and i＜j, Nt is the data flow number.Similar to embodiment 1, therefore only need obtain going in twos under i＜j situation the coefficient correlation between vectorial normalization result in this step, and need not to calculate the situation of i 〉=j.

After the coefficient correlation that has obtained the vectorial normalization result of each row, according to the following equation 10 calculate each row vector coefficient correlation and:

$\mathrm{Corr}=\underset{i<j}{\mathrm{\&Sigma;}}{\mathrm{\&rho;}}_{\mathrm{ij}}^2$ Formula 10

Wherein, Corr for the vectorial normalization result's of each row coefficient correlation and, also be each row vector coefficient correlation and, the symbol ∑ is represented sum operation.

In step 603, the energy of each row vector in the calculating channel response information.

This step adopts the mode identical with step 402 among the embodiment 1 to calculate the energy of each row vector | h _k| ²In addition, the operation of calculating energy also can be carried out between step 601 and step 602 in this step.

In step 604, utilize the energy of the received power of each data flow, corresponding row vector and coefficient correlation and, calculate the SINR of corresponding data stream.

The operation of this step is identical with step 405 among the embodiment 1.Promptly utilize formula $\mathrm{SINR}\left(k\right)=\frac{P_k}{N_0}{\left|h_k\right|}^2(1-\mathrm{Corr})$ Calculate the SINR of each data flow.Wherein, SINR (k) is the SINR of k data flow, P _kBe the received power of k data flow, N ₀Be noise, | h _k| ²Be the energy of k the row vector of channel response information H, Corr for the coefficient correlation of each row vector with.In addition, the noise N here ₀In also can comprise adjacent area interference.

So far, the SINR that finishes in the present embodiment determines flow process.

Fig. 7 shows the structural representation that SINR in the present embodiment determines device.Referring to Fig. 7, present embodiment has carried out refinement to the computing module among Fig. 3, and has increased the received power acquisition module on the basis of Fig. 3.

Channel response information acquisition module in the present embodiment is used to obtain channel state information matrix or equivalent channel state information matrix, as channel response information.

Received power acquisition module in the present embodiment is used to obtain the received power of each data flow.

Correspondingly, computing module utilize the energy of row vector of received power, this data flow correspondence of each data flow and each row vector coefficient correlation and, calculate the SINR of corresponding data stream.This computing module comprises: energy meter operator module, correlation calculations submodule and SINR determine submodule.Energy meter operator module wherein is used for the energy of each row vector of calculating channel response information; The correlation calculations submodule is used to calculate the coefficient correlation of each row vector, and obtain each row vector coefficient correlation and; SINR determine submodule be used to utilize the energy of row vector of received power, this data flow correspondence of each data flow and coefficient correlation and, calculate the SINR of corresponding data stream.

Directly utilize each row vector calculation of channel response information to go out coefficient correlation between per two different rows vectors in the present embodiment, only need two capable vectors and amplitude thereof to participate in the computing at every turn, and need not to need whole channel response informations and amplitude thereof all to participate in computing in the existing method of picture.Because the complexity that the complexity of row vector calculation is calculated well below whole channel response informations, so the method and apparatus in the present embodiment all can reduce the complexity of SINR deterministic process effectively.

Further, owing to determine among the foregoing description 1 and the embodiment 2 that the complexity of SINR process is lower,, make the cost of multi-user system obtain reducing significantly therefore as long as adopt the equipment of medium-performance can satisfy processing requirements.Moreover the significantly reducing of complex operation number of times among these two embodiment shortened SINR effectively and fixed time really, so just can make response to other tasks in time, reduces time of delay, thereby improves systematic function.

SINR among above-mentioned Fig. 4 and Fig. 6 determines that the SINR among flow process and Fig. 5 and Fig. 7 determines that device not only goes in the situation based on zero forcing algorithm, and the SINR that also can utilize such scheme to obtain is similar to the SINR based on the MMSE algorithm.

SINR in the present embodiment determines that scheme both can be applied to can be applied in the pre-coding matrix selection course again in the multi-subscriber dispatching process.

Application scenarios one: multi-subscriber dispatching.

(Multiple-Input-Single-Output) system is an example with the single output of many inputs, and the network equipment such as the base station has many transmit antennas, and each terminal has a reception antenna.Disturbing based on orthogonal channel is zero principle, and the base station utilizes the wave beam forming matrix that the data flow that sends to different receiving terminals is carried out wave beam forming in the space division multiplexing process, to eliminate the interference between the different data streams, improves systematic function.In order to select suitable terminal to carry out space division multiplexing, can utilize overall traversal search mode, realize multi-subscriber dispatching based on the greedy algorithm mode or the semi-orthogonal/quadrature terminal selection algorithm mode of capacity.

In the greedy algorithm mode based on capacity is example, and capacity is a basic consideration factor of selecting terminal.Under this mode, each terminal of selecting all satisfies this terminal and the condition of the total capacity maximum of selecteed terminal before.When calculating total capacity, just can utilize the method among embodiment 1 or the embodiment 2 to determine SINR, calculate total capacity again, and utilize total capacity to determine whether current terminal can be selected.

Specifically, suppose total N terminal, when choosing n terminal, calculate the total capacity of preceding n terminal by following formula 11:

$C\left(n\right)={\log }_2\left\{\underset{j=1}{\overset{n}{\mathrm{\&Pi;}}}[1+\mathrm{SINR}\left(j\right)]\right\}$ Formula 11

Wherein, C (n) is the total capacity of a preceding n terminal, and symbol ∏ represents even to take advantage of, and SINR (j) is the SINR of n terminal corresponding data flow, and n is the integer in the closed interval [1, N], and j is the integer in the closed interval [1, n].

Have lower complexity owing to determine the scheme of SINR among the embodiment of the invention 1 and the embodiment 2, the computation complexity of total capacity also can correspondingly reduce in the then above-mentioned multi-subscriber dispatching process, thereby can simplify the multi-subscriber dispatching process effectively.

Suppose that the base station has 4 transmit antennas, each terminal has a reception antenna, and bandwidth is 20MHz, comprises 2048 subcarriers altogether, and be 1ms dispatching cycle.Fig. 8 shows and adopts existing SINR to determine method and adopt SINR among the embodiment 1 to determine the scheduling time analogous diagram of the greedy algorithm of method.Referring to Fig. 8, the lines that have a square frame represent to adopt existing SINR to determine the scheduling time of the tradition of method based on the greedy algorithm correspondence of capacity, and the lines that have a circle represent to adopt SINR among the embodiment 1 to determine the scheduling time based on the greedy algorithm correspondence of capacity of method.Both are except SINR determines the method difference, and all the other conditions are all identical.Can find out clearly, though the scheduling time of greedy algorithm increases along with the increase of number of users, be that complexity increases along with the increase of number of users, but because SINR determines that the complexity of method has obtained reducing significantly in the present embodiment, make and adopt this SINR to determine that the complexity of the greedy algorithm of method reduces greatly, correspondingly, scheduling time also shortens greatly.

Application scenarios two: pre-coding matrix is selected.

In order to improve the transmission signals quality, such as multiple-input and multiple-output (Multiple-Input-Multiple-Output, MIMO) in the multi-user system of system and so on, transmitting terminal or receiving terminal are according to channel conditions, from the code book that sets in advance, select suitable pre-coding matrix, so that transmitting terminal utilizes selected pre-coding matrix to carry out wave beam forming, make the interference of signal in transmission course to minimize.

A kind of pre-coding matrix selection scheme is, sets in advance the code book that comprises a plurality of pre-coding matrixes, for example has 16 pre-coding matrixes in the code book; Determine that according to the SINR among the embodiment of the invention 1 or the embodiment 2 method calculates several SINR of each pre-coding matrix correspondence, utilize several SINR that calculate to calculate or estimate the transmittability of each data flow again, comprise capacity, throughput etc., with the pre-coding matrix of total transmittability maximum as this selected pre-coding matrix.This pre-coding matrix selection operation both can be finished at transmitting terminal, also can finish at receiving terminal.After determining this selected pre-coding matrix, transmitting terminal or receiving terminal feed back the relevant information of this pre-coding matrix to the opposite end.

When calculating the SINR of pre-coding matrix correspondence, the channel response information that gets access in the step 601 of the step 401 of embodiment 1 or embodiment 2 is an equivalent channel state information matrix, promptly equals the product of channel state information matrix and this pre-coding matrix.

Fig. 9 determines that for adopting existing SINR SINR determines the complexity analogous diagram that the pre-coding matrix of method is selected among method and the embodiment 1, and transverse axis wherein is the quantity of pre-coding matrix in the code book, and the longitudinal axis is the complexity of selecting with encoder matrix.Referring to Fig. 9, suppose that number of transmit antennas is greater than reception antenna quantity, and the data flow transmitted number equals reception antenna quantity simultaneously, the solid line lines that have a circle are represented to have in the mimo system of 8 reception antennas and are adopted existing SINR to determine the complexity that the pre-coding matrix of method is selected, the dotted line lines that have a circle are represented to have and are adopted in the mimo system of 8 reception antennas that SINR determines the complexity that the pre-coding matrix of method is selected among the embodiment 1, the solid line lines that have a square frame are represented to have in the mimo system of 4 reception antennas and are adopted existing SINR to determine the complexity that the pre-coding matrix of method is selected, and the solid line lines that have a circle are represented to have and adopted in the mimo system of 4 reception antennas that SINR determines the complexity that the pre-coding matrix of method is selected among the embodiment 1.As can be seen from Fig. 9, under the reception antenna situation identical with pre-coding matrix quantity, SINR determines that the complexity ratio that the pre-coding matrix of method is selected adopts existing SINR to determine that the complexity of the pre-coding matrix selection of method has reduced an order of magnitude at least among the employing embodiment 1.Therefore, we can say that SINR determines that it is the immediate cause that pre-coding matrix selects complexity to reduce that the complexity of scheme reduces among the embodiment 1.

Except above-mentioned two application scenarioss, the SINR among the embodiment of the invention 1 and the embodiment 2 determines that scheme can also be applied to other any needs and determine in the processing procedure of SINR, and can simplify the complexity of institute's applicable procedures to a great extent.

The above only is preferred embodiment of the present invention, and is in order to restriction the present invention, within the spirit and principles in the present invention not all, any modification of being made, is equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (19)

1, a kind of Signal to Interference plus Noise Ratio SINR determines method, it is characterized in that, this method comprises:

Obtain channel response information, calculate in this channel response information each row vector coefficient correlation and, and utilize this channel response information and this coefficient correlation and determine the SINR of each data flow.

2, the method for claim 1 is characterized in that, coefficient correlation of each row vector and before in this channel response information of described calculating further comprises: to the capable vectorial normalized of this channel response information;

Coefficient correlation of each row vector and be in this channel response information of described calculating: calculate per two vectorial normalization results' of row coefficient correlation, according to the coefficient correlation that obtains, calculate described coefficient correlation and.

3, method as claimed in claim 2, it is characterized in that, describedly be: calculate energy of each row vector in the described channel response information, utilize energy of each row vector, the capable vector of correspondence is carried out normalized to the capable vectorial normalized of this channel response information.

4, method as claimed in claim 3 is characterized in that, described energy that utilizes each row vector carries out normalized to the capable vector of correspondence and is:

Energy to each row vector carries out the evolution operation, obtains the amplitude of corresponding row vector;

With the amplitude of each row vector, obtain the vectorial normalization result of row of each row vector divided by self.

As any described method in the claim 2,3,4, it is characterized in that 5, per two the vectorial normalization results' of row of described calculating coefficient correlation is: pass through formula ${\mathrm{\&rho;}}_{\mathrm{ij}}={|\stackrel{\&OverBar;}{h_i}}^{\&prime;}\&times;\stackrel{\&OverBar;}{h_j}|$ Calculate the coefficient correlation between the vectorial normalization result of per two row, wherein ρ _IjBe the coefficient correlation between i the vectorial normalization result of row and j the vectorial normalization result of row, h _i' be i the vectorial normalization result's of row conjugate transpose vector, h _jBe j the vectorial normalization result of row, i and j are the integer in the closed interval [1, Nt], and i＜j, and Nt is the data flow number, || the expression modulo operation;

The coefficient correlation that described basis obtains is calculated described coefficient correlation and is: passes through formula $\mathrm{Corr}=\underset{i<j}{\mathrm{\&Sigma;}}{\mathrm{\&rho;}}_{\mathrm{ij}}^2$ Calculate described coefficient correlation and, wherein Corr for coefficient correlation of each row vector with, the symbol ∑ is represented sum operation.

6, the method for claim 1 is characterized in that, the coefficient correlation of each row vector and be in this channel response information of described calculating: calculate the coefficient correlations of per two row vectors, according to the coefficient correlation that obtains, calculate described coefficient correlation and.

7, method as claimed in claim 6 is characterized in that, the coefficient correlation of per two the row vectors of described calculating is: pass through formula ${\mathrm{\&rho;}}_{\mathrm{ij}}=\frac{|h_i^{\&prime;}\&times;h_j|}{\left|h_i\right|\&CenterDot;\left|h_j\right|}$ Calculate the coefficient correlation between per two capable vectors, wherein ρ _IjBe the coefficient correlation between i row vector and j the capable vector, h _i' be the conjugate transpose vector of i row vector, h _jBe j row vector, || the expression modulo operation, i and j are the integer in the closed interval [1, Nt], and i＜j, Nt is the data flow number;

The coefficient correlation that described basis obtains is calculated described coefficient correlation and is: passes through formula $\mathrm{Corr}=\underset{i<j}{\mathrm{\&Sigma;}}{\mathrm{\&rho;}}_{\mathrm{ij}}^2$ Calculate described coefficient correlation and, wherein Corr for coefficient correlation of each row vector with, the symbol ∑ is represented sum operation.

8, method as claimed in claim 6 is characterized in that, determines further to comprise before the SINR of each data flow: the energy that calculates each row vector in the described channel response information.

As claim 3 or 8 described methods, it is characterized in that 9, the energy of each row vector is in the described channel response information of described calculating:

According to formula | h _k| ²=| h _K1| ²+ | h _K2| ²+ ... + | h _Ki| ²+ ... + | h _KNt| ²Calculate the energy of described row vector, wherein h _kBe k in described channel response information row vector, h _KiBe i element in k the capable vector of described channel response information, | h _Ki| for to element h _KiCarry out modulo operation, and k and i be the integer in the closed interval [1, Nt], Nt is the data flow number.

10, as claim 3 or 8 described methods, it is characterized in that, before the SINR of described definite each data flow, further comprise: the received power of obtaining each data flow;

Describedly utilize this channel response information and this coefficient correlation and determine that the SINR of each data flow is: according to the coefficient correlation of the energy of the received power of each data flow, corresponding row vector and each row vector and, calculate the SINR of corresponding data stream.

11, method as claimed in claim 10 is characterized in that, the energy of described received power, corresponding row vector and the coefficient correlation of each row vector according to each data flow and, the SINR that calculates corresponding data stream is:

Pass through formula $\mathrm{SINR}\left(k\right)=\frac{P_k}{N_0}{\left|h_k\right|}^2(1-\mathrm{Corr})$ Calculate the SINR of each data flow, wherein SINR (k) is the SINR of k data flow, P _kBe the received power of k data flow, N ₀Be noise, | h _k| ²Be the energy of k row vector of described channel response information, Corr for the coefficient correlation of each row vector and, k is the interior integer in closed interval [1, Nt], Nt is the data flow number.

12, as any described method in the claim 1,2,3,4,7,8,9, it is characterized in that described channel response information is: channel state information matrix, perhaps, equivalent channel state information matrix.

13, a kind of Signal to Interference plus Noise Ratio SINR determines device, it is characterized in that, this device comprises: channel response information acquisition module and computing module, wherein,

Described channel response information acquisition module is used to obtain channel response information;

Described computing module be used to calculate each row vector coefficient correlation and, and utilize this channel response information and this coefficient correlation and determine the SINR of each data flow.

14, device as claimed in claim 13 is characterized in that, described device further comprises: the normalized module is used for the capable vectorial normalized of this channel response information;

Described computing module calculates per two vectorial normalization results' of row coefficient correlation, according to the coefficient correlation that obtains, calculate described coefficient correlation and.

15, device as claimed in claim 14 is characterized in that, energy of each row vector in the further calculating channel response information of described normalized module utilizes energy of each row vector, and the capable vector of correspondence is carried out described normalized.

16, device as claimed in claim 15 is characterized in that, described normalized module comprises: energy meter operator module and processing sub, wherein,

Described energy meter operator module is used for the energy of each row vector of calculating channel response information;

Described processing sub is used to utilize energy of each row vector, and the capable vector of correspondence is carried out normalized.

17, device as claimed in claim 15 is characterized in that, described computing module comprises: correlation calculations submodule and SINR determine submodule, wherein,

Described correlation calculations submodule is used to calculate the vectorial normalization result's of each row coefficient correlation, and obtain coefficient correlation and;

Described SINR determine submodule be used to utilize the energy of row vector of received power, this data flow correspondence of each data flow and coefficient correlation and, calculate the SINR of corresponding data stream.

18, device as claimed in claim 13 is characterized in that, described device further comprises: the received power acquisition module is used to obtain the received power of each data flow;

Described computing module comprises: energy meter operator module, correlation calculations submodule and SINR determine submodule, and wherein, described energy meter operator module is used for the energy of each row vector of calculating channel response information; Described correlation calculations submodule is used to calculate the coefficient correlation of each row vector, and obtain each row vector coefficient correlation and; Described SINR determine submodule be used to utilize the energy of row vector of received power, this data flow correspondence of each data flow and coefficient correlation and, calculate the SINR of corresponding data stream.

19, as claim 17 or 18 described devices, it is characterized in that described device further comprises: the received power acquisition module is used to obtain the received power of each data flow.

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