CN112865892B - Adaptive generalized selection diversity combining method and system based on signal-to-noise ratio sequencing - Google Patents
Adaptive generalized selection diversity combining method and system based on signal-to-noise ratio sequencing Download PDFInfo
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Abstract
The invention discloses a self-adaptive generalized selection diversity combining method and a system based on signal-to-noise ratio sequencing, wherein the method comprises the following steps: receiving a plurality of branch signals; each branch signal is respectively provided with one frame of data, and partial information of the effective data of the current frame of each branch signal is obtained through analysis and is used as pre-judging information; selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in the combination, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the pre-judging information of the current frame, storing the current frame of the branches meeting the diversity combining condition into a set according to the signal-to-noise ratio of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in the combination; and carrying out diversity combining on the data frames in the set by adopting a maximum ratio combining criterion. The invention greatly reduces the processing time required by the system, the complexity and the power consumption of the system calculation, is simple and effective to realize, and has better self-adaptability to the change of the channel environment.
Description
Technical Field
The invention relates to the field of communication systems, in particular to a self-adaptive generalized selection diversity combining method and system based on signal-to-noise ratio sequencing.
Background
By using diversity technology, the influence of noise and interference on signals can be effectively reduced, so that the signal quality is improved. The transmission and reception schematic diagram is shown in fig. 1.
Different algorithms of diversity combining technology that can be used at the receiving end include Maximum Ratio Combining (MRC), equal Gain Combining (EGC), selection Combining (SC), switch dwell combining (SSC), generalized Selection Combining (GSC), adaptive generalized selection combining (a-GSC), and the like.
Maximum Ratio Combining (MRC) is an algorithm that performs the selection of combining weights with the combined signal to noise ratio maximized as a combining criterion. The weight coefficient of the Maximum Ratio Combining (MRC) is positively correlated with the signal amplitude and inversely correlated with the noise power, so that the essence of the Maximum Ratio Combining (MRC) is to give a larger weight coefficient to the branch with good channel conditions, and conversely, a smaller weight coefficient. Maximum Ratio Combining (MRC) thus enhances the useful signal while noise and interference are reduced, enabling the received signal to be enhanced. Assuming that the noise components are independent of each other, the combined signal-to-noise ratio of the Maximum Ratio Combining (MRC) is the sum of the signal-to-noise ratios of all combining branches.
Generalized selection combining is to select the L with the best performance from all L branches C The branches are subjected to Maximum Ratio (MRC) or Equal Gain Combination (EGC), so that the complexity of the system is reduced, and certain performance is ensured, and the hardware complexity is far lower than that of the Maximum Ratio Combination (MRC). But the scheme may be due to merging branches L C Oversized resulting in merging of weak branches or merging of branches L C Too small to cause some strong branches to be ignored, affecting system performance. To merge branch L C The selection of (2) can be set in advance, and can not be flexibly changed according to the channel condition, and the adaptability is lacking.
Adaptive generalized selection combining (a-GSC) will add the strongest branch continuously to achieve a predetermined communication quality until it is estimated that adding the next branch will not compensate for the gap from the expected performance. Similar to known Generalized Selection Combining (GSC) reception schemesThe reception mode periodically inserts a short training pattern into the transmission signal. During this time, the receiver performs all necessary operations to achieve the proper diversity combining scheme. By employing an adaptive generalized selection combining (a-GSC) scheme, the receiver attempts to combine the signal-to-noise ratios γ c Increasing to a threshold gamma T Above, gamma T The overall quality requirements of the communication are determined and typically dynamically adjusted as needed. However, the threshold value gamma T And not the only criterion for deciding the number of combining branches, once the receiver estimates that adding the next branch does not compensate for the gap from the expected performance, it stops the selection process and combines the selected branches according to the Maximum Ratio Combining (MRC) rule. However, this scheme estimates the signal-to-noise ratio of all diversity branches during the training pattern period of each frame, and wastes a lot of computational resources and power consumption when the training pattern interval is much smaller than the channel variation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a self-adaptive diversity combining method and a self-adaptive diversity combining system based on signal-to-noise ratio sequencing.
In order to achieve the above object, the present invention proposes an adaptive diversity combining method based on signal-to-noise ratio ordering, the method comprising:
receiving a plurality of branch signals;
each branch signal is respectively provided with one frame of data, and partial information of the effective data of the current frame of each branch signal is obtained through analysis and is used as pre-judging information;
selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in the combination, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the pre-judging information of the current frame, storing the current frame of the branches meeting the diversity combining condition into a set according to the signal-to-noise ratio of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in the combination;
and carrying out diversity combining on the data frames in the set by adopting a maximum ratio combining criterion.
As an improvement of the above method, the method further comprises:
when the received data frame is the first frame, calculating the signal to noise ratio of each branch by the pre-judging information of the frame;
and sorting according to the signal-to-noise ratio, selecting a plurality of branches meeting preset conditions, merging according to a self-adaptive generalized selection merging method, and recording the sorting result of the signal-to-noise ratio of the branches of the first frame participating in merging.
As an improvement of the method, the signal-to-noise ratio of each branch is calculated according to the pre-judging information of the frame; the method specifically comprises the following steps:
performing time domain-frequency domain transformation on the pre-judgment information of the first frame of each branch;
calculating a second moment M for the transformed prejudgment information 2 The method comprises the following steps:
wherein y is n The method is characterized in that the method is frequency domain pre-judgment information, n is the length of the pre-judgment information, and E is an operator for calculating an average value;
calculating fourth moment M 4 The method comprises the following steps:
from the second moment M 2 And fourth moment M 4 Calculating to obtain the predicted information power estimated value s of the branch 1 The method comprises the following steps:
from the second moment M 2 Sum signal power estimate s 1 Calculating to obtain the estimated value s of the noise power of the pre-judgment information of the branch 2 The method comprises the following steps:
s 2 ＝M 2 -s 1
from the signal power estimate s 1 And noise power estimate s 2 Calculating to obtain the pre-treatment of the branchThe estimated value ρ of the judgment information signal to noise ratio is:
as an improvement of the method, the branches meeting the preset conditions are selected to be combined according to the adaptive generalized selection combining method, and the branch signal-to-noise ratio sequencing result of the first frame participating in the combination is recorded; the method specifically comprises the following steps:
sequencing according to the value of the signal to noise ratio of each branch to obtain a branch signal to noise ratio sequencing result;
sequentially taking out each path of branch from the branch signal-to-noise ratio sequencing result to judge, and if the branch meets the preset condition and the signal-to-noise ratio is less than or equal to the preset threshold gamma T Merging the first frames of the branches according to a self-adaptive generalized selection merging method;
after each path of branch in the branch signal-to-noise ratio sequencing results is judged to be completed, the branch signal-to-noise ratio sequencing results of the first frame participating in merging are recorded.
As an improvement of the method, selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in the combination, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the pre-judging information of the current frame, storing the current frame of the branches meeting the diversity combining condition into a set according to the signal-to-noise ratios of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in the combination; the method specifically comprises the following steps:
step 1), selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in merging;
step 2) taking out the branch with the maximum residual signal-to-noise ratio from the first branch set;
step 3) calculating the signal-to-noise ratio of the current frame of the branch according to the prejudgement information;
step 4) judging whether the signal-to-noise ratio of the current frame of the branch meets the preset condition, if so, storing the frame into a set, and turning to step 5); if not, go to step 6);
step 5) summing the signal-to-noise ratios of the current frames of the branches in the set to obtain an output signal-to-noise ratio, judging whether the output signal-to-noise ratio is greater than a threshold value, if so, turning to step 7), and if not, turning to step 6);
step 6) judging whether the branches of the first branch set are traversed, if yes, turning to step 7), and if no, turning to step 2);
step 7) judging whether all branches are screened, if yes, turning to step 8); if not, randomly selecting other branches, and turning to the step 3);
and 8) combining the data frames in the set by using a maximum ratio combining rule, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in the combination.
As an improvement of the above method, the signal-to-noise ratio of the current frame of the branch is judged whether to meet the preset condition, if yes, the frame is stored in the collection; the method comprises the following steps:
sequentially taking out the signal-to-noise ratio of each branch from the branch signal-to-noise ratio sequencing result, multiplying the signal-to-noise ratio of each branch by a given mu value, and judging whether the product is larger than gamma (1) And the signal-to-noise ratio is less than or equal to a set threshold gamma T If yes, storing the frame data into a set; wherein, gamma (1) The signal-to-noise ratio maximum in the branch signal-to-noise ratio ordered set is merged for the last frame.
An adaptive diversity combining system based on signal-to-noise ordering, the system comprising: the device comprises a receiving module, an acquisition module, a judging module and a diversity combining module; wherein, the liquid crystal display device comprises a liquid crystal display device,
the receiving module is used for receiving the multipath branch signals;
the acquisition module is used for respectively acquiring one frame of data for each path of branch signal, and analyzing and acquiring partial information of the effective data of the current frame of each path of branch signal as pre-judging information;
the judging module is used for selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in the combination, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the pre-judging information of the current frame, storing the current frame of the branches meeting the diversity combining condition into a set according to the signal-to-noise ratio of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in the combination;
the diversity combining module is used for performing diversity combining on the data frames in the set by adopting a maximum ratio combining criterion.
Compared with the prior art, the invention has the advantages that:
1. the invention can reduce unnecessary calculation of signal to noise ratio of the diversity branch when the performance gap between the invention and the adaptive generalized selection combining (A-GSC) scheme is not large, especially when the training mode interval is far smaller than the channel change, the processing time required by the system and the complexity and the power consumption of the system calculation are greatly reduced, the realization is simple and effective, and the invention has better adaptability to the change of the channel environment;
2. in practical application, if the transmitting end or the receiving end is in a state of moving and standing, the channel is often switched between slow fading and fast fading, and the diversity combining method in the invention is suitable for the environment of slow fading and fast fading, has better self-adaptability, especially in the environment of slow fading, the training mode interval is far smaller than the change condition of the channel, the diversity branch which participates in combining is selected by referring to the signal-to-noise ratio condition of the diversity branch of the previous frame, the stronger branch of the previous frame is selected by preferentially selecting, and the consumption of a large amount of unnecessary computing resources can be reduced while the influence on the performance is small.
Drawings
Fig. 1 is a transmission/reception diagram of embodiment 1 of the present invention;
fig. 2 is a flowchart of an adaptive diversity combining method according to embodiment 1 of the present invention;
fig. 3 is an IEEE 802.11a physical layer protocol data unit (PPDU) frame structure used in embodiment 1 of the present invention;
fig. 4 is a diagram showing the configuration of an adaptive diversity combining system according to embodiment 2 of the present invention.
Reference numerals
201. Receiving unit 202, acquiring unit 203, calculating unit
204. First judgment unit 205, second judgment unit 206, and third judgment unit
207. Fourth judgment unit 208, diversity combining unit
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 2, embodiment 1 of the present invention provides an adaptive diversity combining method based on signal-to-noise ratio ordering.
The implementation steps of the adaptive generalized selection diversity combining method are divided into a training mode and a data mode, wherein the training mode and the data mode are not divided by processed data, but are divided by a processing state. The training mode performs operations such as judgment, signal-to-noise ratio estimation, screening of antenna branches participating in combination, and the like, and the data mode performs MRC combination according to information obtained by the training mode. The method comprises the following specific steps:
training mode:
(1) Receiving a multipath diversity signal;
(2) The first frames are combined according to a self-adaptive generalized selection combining (A-GSC) method;
(3) In the training mode of the rest frames, the diversity branches of the previous frame participating in combination are ordered from large to small according to the signal to noise ratio of the diversity branches of the previous frame;
(4) According to the sequencing result, calculating the signal-to-noise ratio of the first branch (namely the strongest branch of the previous frame) in the frame, and judging whether the signal-to-noise ratio meets the preset condition or not;
(5) Judging whether the output signal-to-noise ratio is greater than a set threshold value gamma T Wherein the output signal-to-noise ratio is the sum of the signal-to-noise ratios of the diversity branches participating in the combination, and if the sum is smaller, the subsequent branches are similarly operated in sequence according to the sequencing result until the output signal-to-noise ratio is larger than a threshold gamma T ；
(6) If the signal-to-noise ratio is still less than the threshold gamma T Randomly selecting the rest diversity branches not screened to estimate and judge until outputSignal to noise ratio greater than threshold gamma T Or all diversity branches are screened out;
data mode:
(7) Diversity branches passing through the filtering are combined according to a Maximum Ratio Combining (MRC) criterion.
The step of judging whether the diversity branch meets the preset condition in the step (4) comprises the following steps:
(a) Setting a mu value, wherein mu is E (0, 1);
(b) Setting gamma (1) The value is the maximum value of the signal-to-noise ratio in the diversity branch of the previous frame participating in the combination, and if the signal-to-noise ratio is the first frame, gamma is (1) Maximum value of signal-to-noise ratio in diversity branch of first frame participating in combination;
(c) Determining whether the product of the signal-to-noise ratio of diversity branch and given mu value is greater than gamma (1) If the diversity branch is larger than the preset condition, the diversity branch meets the preset condition;
the first frames are combined according to a self-adaptive generalized selection combining (A-GSC) method;
the multipath diversity signal consists of a reference signal and a non-reference signal. Wherein, the reference signal is a sequence known at both receiving and transmitting ends and used for auxiliary receiving (channel estimation calculation), also called training sequence; the non-reference signal is transmitted by the transmitting end, and the receiving end needs to obtain the data sequence through the processes of equalization, demodulation, decoding and the like.
The following operation is performed in the training mode of the rest frames; as shown in fig. 2:
s101, receiving multipath diversity signals;
s102, sorting branches of the previous frame participating in merging according to the signal-to-noise ratio of the previous frame;
s103, according to the sorting result, taking out the rest strongest branches which are not screened yet;
s104, calculating the signal-to-noise ratio of the branch;
the scheme uses the effective data part in the frame as the pre-judging information, thereby estimating the signal-to-noise ratio: and calculating the signal-to-noise ratio estimation by utilizing a second-order fourth-order moment method on the received effective data subjected to the time domain-frequency domain transformation. First, calculating a second moment:
wherein y is n For the received effective data portion from which the cyclic prefix and the time-frequency domain transform are removed, the n range is the length of the effective data portion, and E is the average value.
And then calculating fourth-order moment:
the signal power estimate is:
the noise power estimate is:
s 2 ＝M 2 -s 1
the signal-to-noise ratio estimate is:
s105, judging whether the calculated branch signal-to-noise ratio meets a preset condition, if so, performing S106; if not, S108 is performed;
s106, adding the branch into diversity combination;
s107, judging whether the output signal to noise ratio is greater than a set threshold value, if so, performing S111; if not, S108 is performed;
s108, judging whether all branches participating in sorting are screened completely, if so, carrying out S109; if not, S103 is performed;
s109, judging whether all branches are screened, if yes, performing S111; if not, S110 is performed;
s110, randomly selecting the rest branches which are not screened, and carrying out S104;
s111, performing diversity combining by using an MRC criterion;
wherein the MRC criterion is a maximum ratio combining criterion.
The method of the invention takes the diversity branch situation of the previous frame selection participating in the combination as a reference to carry out the screening of the diversity branch of the present frame, preferentially judges the stronger branch of the previous frame, adds the branch meeting the preset condition into the combination, stops the screening after the output signal to noise ratio exceeds the set threshold, and then carries out the diversity combination according to the MRC criterion.
Calculating the product of the signal-to-noise ratio of diversity branch and given mu value, if the product value is larger than gamma (1) Judging that the preset condition is met; if the product value is less than or equal to gamma (1) Judging that the preset condition is not met;
specifically, μ is a predetermined value, and μ∈ (0, 1) is generally set; gamma ray (1) For the maximum value of signal-to-noise ratio in the diversity branch of the previous frame participating in combination, if the frame is the first frame, gamma (1) Maximum value of signal-to-noise ratio in diversity branch of first frame participation combination;
possible alternatives: setting a value, calculating the signal-to-noise ratio of all diversity branches again each time when the received frame number reaches the value to screen the branches participating in the combination, and then continuing the next frame according to the method of the scheme, wherein the value can be dynamically changed according to the actual situation. Other methods for calculating the signal-to-noise ratio may also be used.
As shown in fig. 3, for the frame format of IEEE 802.11a, the data field portion of the frame is selected as the pre-judgment information, thereby calculating the signal-to-noise ratio.
The present embodiment describes the method by taking the frame format of IEEE 802.11a and the second-order fourth-order moment method for signal-to-noise ratio estimation as an example, but the present embodiment is not limited to this protocol, and is not limited to the second-order fourth-order moment method, so long as the method is satisfied by multi-antenna reception.
Example 2
Based on the above method, embodiment 2 of the present invention proposes an adaptive diversity combining system based on signal-to-noise ratio ordering. The system comprises: the device comprises a receiving module, an acquisition module, a judging module and a diversity combining module; as shown in fig. 4, wherein,
the receiving module is used for receiving the multipath branch signals; is composed of a receiving unit 201;
the acquisition module is used for respectively acquiring one frame of data for each path of branch signal, and analyzing and acquiring partial information of the effective data of the current frame of each path of branch signal as pre-judging information; is composed of an acquisition unit 202;
the judging module is used for selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in merging, sequentially calculating the signal-to-noise ratio of the corresponding branches from the first branch set according to the pre-judging information of the current frame, storing the current frame of the branches meeting the diversity merging condition into a set according to the signal-to-noise ratio of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in merging; specifically, the method includes a calculation unit 203, a first judgment unit 204, a second judgment unit 205, a third judgment unit 206, and a fourth judgment unit 207:
a calculating unit 203 for calculating a signal-to-noise ratio thereof according to the diversity signal; if the first frame is the first frame, calculating the signal-to-noise ratio of all diversity branches and sequencing the diversity branches according to the sequence from the big to the small;
a first judging unit 204, configured to judge whether the signal-to-noise ratio of the diversity branch meets a preset condition; if the determination result is yes, adding the branch into the diversity combining unit and entering the second determining unit 205; if the judgment result is negative, the branch is not added into the diversity combining unit, and the third judgment unit 206 is entered;
a second judging unit 205 for judging whether the output signal-to-noise ratio exceeds the threshold gamma T If the determination result is yes, entering a diversity combining unit 208; if the determination result is no, entering a third determination unit 206;
a third judging unit 206, configured to judge whether all diversity branches participating in merging of the previous frame are screened, and if yes, enter a fourth judging unit; if the judgment result is negative, entering a computing unit 203;
a fourth judging unit 207, configured to judge whether all diversity branches are screened, and if yes, enter a diversity combining unit 208; if the judgment result is no, randomly selecting one diversity branch which is not screened yet to enter the calculation unit 203;
and a diversity combining module, configured to perform diversity combining on the data frames in the set by using a maximum ratio combining criterion, and composed of a diversity combining unit 208.
The signal-to-noise ratio of the diversity branches of the frame is the same as or different from the previous frame, so that the diversity branches participating in the combination are the same as the previous frame, the signal-to-noise ratio of all diversity branches does not need to be recalculated every frame like the A-GSC scheme, and particularly, under the condition that the interval between training modes is far smaller than the channel variation, the scheme can obviously reduce the number of branches needing to calculate the signal-to-noise ratio and the time needed to be processed by the system in the training mode while reaching a small performance gap with the A-GSC scheme, and save the consumption of calculation resources; and the merging scheme can eliminate branches with small gain on the system performance, has better self-adaptability and reduces the power consumption of the system.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and are not limiting. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the appended claims.
Claims (5)
1. An adaptive generalized selection diversity combining method based on signal-to-noise ratio ordering, the method comprising:
receiving a plurality of branch signals;
each branch signal is respectively provided with one frame of data, and partial information of the effective data of the current frame of each branch signal is obtained through analysis and is used as pre-judging information;
selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in merging, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the prejudgment information of the current frame, storing the current frame of the branches meeting the preset conditions into the set according to the signal-to-noise ratio of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in merging;
diversity combining is carried out on the data frames in the set by adopting a maximum ratio combining criterion;
selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in the combination, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the pre-judging information of the current frame, storing the current frame of the branches meeting the diversity combining condition into a set according to the signal-to-noise ratio of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in the combination; the method specifically comprises the following steps:
step 1), taking branches of the previous frame participating in diversity combination as a first branch set, and sequencing the branches from large to small according to the signal-to-noise ratio of the previous frame;
step 2) taking out the branch with the maximum signal-to-noise ratio from the rest branches from the first branch set;
step 3) calculating the signal-to-noise ratio of the current frame of the branch according to the prejudgement information;
step 4) judging whether the signal-to-noise ratio of the current frame of the branch meets the preset condition, if so, storing the frame into a set, and turning to step 5); if not, go to step 6);
step 5) summing the signal-to-noise ratios of the current frames of the branches in the set to obtain an output signal-to-noise ratio, and judging whether the output signal-to-noise ratio is larger than a preset threshold gamma T If yes, go to step 8), if no, go to step 6);
step 6) judging whether the branches of the first branch set are traversed, if yes, turning to step 7), and if no, turning to step 2);
step 7) judging whether all branches are screened, if yes, turning to step 8); if not, randomly selecting other branches, and turning to the step 3);
step 8) combining the data frames in the set by using a maximum ratio combining rule, and recording branch signal-to-noise ratio sequencing results of the current frame participating in combination;
judging whether the signal-to-noise ratio of the current frame of the branch accords with a preset condition, and storing the frame into a set if so; the method comprises the following steps:
multiplying the signal-to-noise ratio of the branch by a given mu value, mu E (0, 1), and judging whether the product is larger than gamma (1) And the output signal-to-noise ratio is less than or equal to a preset threshold gamma T If yes, storing the frame data into a set; wherein, gamma (1) The signal-to-noise ratio maximum in the branch signal-to-noise ratio ordered set is merged for the last frame.
2. The adaptive generalized selection diversity combining method based on signal-to-noise ratio ordering of claim 1, further comprising:
when the received data frame is the first frame, calculating the signal to noise ratio of each branch by the pre-judging information of the frame;
sorting according to the signal-to-noise ratio, selecting a plurality of branches meeting preset conditions, merging according to a self-adaptive generalized selection merging method, and recording the sorting result of the signal-to-noise ratio of the branches of the first frame participating in merging; wherein, when the received data frame is the first frame, the gamma in the preset condition (1) The signal-to-noise ratio maximum in the result is ordered for the signal-to-noise ratio of the first frame branch.
3. The adaptive generalized selection diversity combining method based on signal-to-noise ratio sequencing of claim 2, wherein the signal-to-noise ratio of each branch is calculated from the pre-judgment information of the frame; the method specifically comprises the following steps:
performing time domain-frequency domain transformation on the pre-judgment information of the first frame of each branch;
calculating a second moment M for the transformed prejudgment information 2 The method comprises the following steps:
wherein y is n The pre-judgment information of the frequency domain is n is the length of the pre-judgment information, E is an operator for calculating the mean value；
Calculating fourth moment M 4 The method comprises the following steps:
from the second moment M 2 And fourth moment M 4 Calculating to obtain the predicted information power estimated value s of the branch 1 The method comprises the following steps:
from the second moment M 2 Sum signal power estimate s 1 Calculating to obtain the estimated value s of the noise power of the pre-judgment information of the branch 2 The method comprises the following steps:
s 2 ＝M 2 -s 1
from the signal power estimate s 1 And noise power estimate s 2 The signal-to-noise ratio estimated value rho of the pre-judgment information of the branch is calculated as follows:
4. the adaptive generalized selection diversity combining method based on signal-to-noise ratio sequencing of claim 3, wherein the sequencing is performed according to signal-to-noise ratio, a plurality of branches meeting preset conditions are selected, the combination is performed according to the adaptive generalized selection combining method, and branch signal-to-noise ratio sequencing results of the first frame participating in the combination are recorded; the method specifically comprises the following steps:
sequencing according to the value of the signal to noise ratio of each branch to obtain a branch signal to noise ratio sequencing result;
sequentially taking out each path of branch from the branch signal-to-noise ratio sequencing result to judge, and if the branch meets the preset condition and the output signal-to-noise ratio is less than or equal to the preset threshold gamma T The first frame of the branch is processed according to the adaptive generalized selection combining methodMerging;
after each path of branch in the branch signal-to-noise ratio sequencing results is judged to be completed, the branch signal-to-noise ratio sequencing results of the first frame participating in merging are recorded.
5. An adaptive generalized selection diversity combining system based on signal-to-noise ratio ordering, the system comprising: the device comprises a receiving module, an acquisition module, a judging module and a diversity combining module; wherein, the liquid crystal display device comprises a liquid crystal display device,
the receiving module is used for receiving the multipath branch signals;
the acquisition module is used for respectively acquiring one frame of data for each path of branch signal, and analyzing and acquiring partial information of the effective data of the current frame of each path of branch signal as pre-judging information;
the judging module is used for selecting a plurality of branches as a first branch set according to the branch signal-to-noise ratio sequencing result of the previous frame participating in merging, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the pre-judging information of the current frame, storing the current frame of the branches meeting the preset conditions into the set according to the signal-to-noise ratio of the branches, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in merging;
the diversity combining module is used for performing diversity combining on the data frames in the set by adopting a maximum ratio combining criterion;
the processing procedure of the judging module specifically comprises the following steps:
step 1), taking branches of the previous frame participating in diversity combination as a first branch set, and sequencing the branches from large to small according to the signal-to-noise ratio of the previous frame;
step 2) taking out the branch with the maximum signal-to-noise ratio from the rest branches from the first branch set;
step 3) calculating the signal-to-noise ratio of the current frame of the branch according to the prejudgement information;
step 4) judging whether the signal-to-noise ratio of the current frame of the branch meets the preset condition, if so, storing the frame into a set, and turning to step 5); if not, go to step 6);
step 5) summing the signal-to-noise ratios of the branch current frames in the set to obtain an output signal-to-noise ratio, and judgingWhether the signal-to-noise ratio of the output is larger than a preset threshold gamma T If yes, go to step 8), if no, go to step 6);
step 6) judging whether the branches of the first branch set are traversed, if yes, turning to step 7), and if no, turning to step 2);
step 7) judging whether all branches are screened, if yes, turning to step 8); if not, randomly selecting other branches, and turning to the step 3);
step 8) combining the data frames in the set by using a maximum ratio combining rule, and recording branch signal-to-noise ratio sequencing results of the current frame participating in combination;
judging whether the signal-to-noise ratio of the current frame of the branch accords with a preset condition, and storing the frame into a set if so; the method comprises the following steps:
multiplying the signal-to-noise ratio of the branch by a given mu value, mu E (0, 1), and judging whether the product is larger than gamma (1) And the output signal-to-noise ratio is less than or equal to a preset threshold gamma T If yes, storing the frame data into a set; wherein, gamma (1) The signal-to-noise ratio maximum in the branch signal-to-noise ratio ordered set is merged for the last frame.
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