CN112865892A - 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; taking a frame of data from each branch signal, and analyzing to obtain partial information of the current frame effective data of each branch signal as prejudgment information; selecting a plurality of branches as a first branch set according to the signal-to-noise ratio sequencing result of the branch in which the previous frame participates in the 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 branch meeting the diversity merging condition into a set according to the signal-to-noise ratios of the branches, and recording the signal-to-noise ratio sequencing result of the branch in which the current frame participates in the merging; and diversity combining is carried out 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 and the complexity and power consumption of 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 a system based on signal-to-noise ratio sequencing.
Background
By using the diversity technology, the influence of noise and interference on the signal can be effectively reduced, thereby improving the signal quality. The transmission and reception diagram is shown in fig. 1.
Different algorithms of diversity combining techniques that can be used at the receiving end include Maximum Ratio Combining (MRC), Equal Gain Combining (EGC), Selective Combining (SC), switch dwell combining (SSC), Generalized Selective Combining (GSC), adaptive generalized selective combining (a-GSC), and the like.
Maximal Ratio Combining (MRC) is an algorithm that performs selection of combining weights with combining signal to noise ratio maximization as a combining criterion. The weight coefficient of Maximal Ratio Combining (MRC) is positively correlated with the signal amplitude and inversely correlated with the noise power, so the essence of Maximal Ratio Combining (MRC) is to give a larger weight coefficient to branches with good channel conditions, and conversely, a smaller weight coefficient. Maximum Ratio Combining (MRC) therefore enhances the desired signal and attenuates noise and interference, thereby enhancing the received signal. Assuming that the noise components are independent of each other, the combined signal-to-noise ratio of Maximum Ratio Combining (MRC) is the sum of the signal-to-noise ratios of all combining branches.
The generalized selection combination selects the L with the best performance from all L branchesCThe strip branches are subjected to Maximum Ratio (MRC) or Equal Gain Combination (EGC), the complexity of the system is reduced, certain performance is ensured, and the hardware complexity is far lower than that of the Maximum Ratio Combination (MRC). But this scheme may be due to merging branches LCToo large to result in merging of weak branches or merging branches LCToo small results in some strong branches being ignored, affecting system performance. For merging branch LCThe selection of the channel is set in advance only, and the channel cannot be flexibly changed according to the channel condition, so that the self-adaptability is lacked.
Adaptive generalized selection combining (a-GSC) adds the strongest finger continuously to achieve a predetermined quality of communication until it is estimated that adding the next finger will not compensate for the difference from the expected performance. Similar to the known Generalized Selective Combining (GSC) reception scheme, the reception scheme periodically inserts short trains into the transmitted signalAnd (5) training mode. During this time, the receiver performs all necessary operations to achieve a proper diversity combining scheme. By employing an adaptive generalized selective combining (A-GSC) scheme, the receiver attempts to combine the signal-to-noise ratios γcUp to threshold gammaTAbove, γTThe overall quality requirements of the communication are determined and typically dynamically adjusted as needed. However, the threshold γTInstead of being the only criterion for determining the number of combined branches, the receiver stops the selection process and combines the selected branches according to the Maximal Ratio Combining (MRC) rule once it estimates that adding the next branch does not compensate for the difference from the expected performance. However, this scheme needs to estimate the snr of all diversity branches in each training mode period, and when the training mode interval is much smaller than the channel variation, a lot of computing resources and power consumption are wasted.
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 provides an adaptive diversity combining method based on snr ranking, which comprises:
receiving a plurality of branch signals;
taking a frame of data from each branch signal, and analyzing to obtain partial information of the current frame effective data of each branch signal as prejudgment information;
selecting a plurality of branches as a first branch set according to the signal-to-noise ratio sequencing result of the branch in which the previous frame participates in the 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 branch meeting the diversity merging condition into a set according to the signal-to-noise ratios of the branches, and recording the signal-to-noise ratio sequencing result of the branch in which the current frame participates in the merging;
and diversity combining is carried out 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, respectively calculating the signal-to-noise ratio of each branch according to the prejudgment 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 ratios of the branches participating in merging of the first frame.
As an improvement of the above method, the signal-to-noise ratio of each branch is calculated by the prediction information of the frame; the method specifically comprises the following steps:
performing time domain-frequency domain transformation on the prejudgment information of the first frame of each branch;
calculating second moment M for transformed prejudgment information2Comprises the following steps:
wherein, ynThe method comprises the steps of obtaining pre-judging information of a frequency domain, wherein n is the length of the pre-judging information, and E is an operator for obtaining an average value;
calculating the fourth moment M4Comprises the following steps:
by a second moment M2And fourth order moment M4Calculating to obtain the pre-judgment information power estimation value s of the branch1Comprises the following steps:
by a second moment M2And the signal power estimate s1Calculating to obtain the pre-judgment information noise power estimation value s of the branch2Comprises the following steps:
s2＝M2-s1
from the signal power estimate s1Sum noise power estimate s2Calculating to obtain a pre-judgment information signal-to-noise ratio estimated value rho of the branch:
as an improvement of the above method, the sorting is performed according to the signal-to-noise ratio, a plurality of branches meeting the preset conditions are selected, the merging is performed according to the adaptive generalized selection merging method, and the sorting result of the signal-to-noise ratio of the branches participating in the merging of the first frame is recorded; the method specifically comprises the following steps:
sorting according to the numerical value of the branch signal-to-noise ratio of each path to obtain a branch signal-to-noise ratio sorting result;
sequentially taking out each branch from the sorting result of the signal-to-noise ratio of the branch for judgment, if the branch meets the preset condition and the signal-to-noise ratio is less than or equal to the preset threshold value gammaTThen merging the first frame of the branch according to a self-adaptive generalized selection merging method;
and when the judgment of each branch in the branch signal-to-noise ratio sequencing results is finished, recording the branch signal-to-noise ratio sequencing results of the first frame participating in the combination.
As an improvement of the above method, according to the sorting result of the signal-to-noise ratio of the branches participating in the merging in the previous frame, selecting a plurality of branches as a first branch set, 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 diversity merging condition into a set according to the signal-to-noise ratios of the branches, and recording the sorting result of the signal-to-noise ratio of the branches participating in the merging in the current frame; the method specifically comprises the following steps:
step 1) selecting a plurality of branches as a first branch set according to the sorting result of the signal-to-noise ratio of the branches participating in merging in the previous frame;
step 2) taking 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 prejudgment information;
step 4) judging whether the signal-to-noise ratio of the current frame of the branch meets a preset condition, if so, storing the frame into a set, and turning to the 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, 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 traversal of the branches of the first branch set is finished, if so, turning to step 7), and if not, turning to step 2);
step 7) judging whether all the branches are screened, if so, turning to step 8); if not, randomly selecting other branches, and turning to the step 3);
and 8) merging the data frames in the set by using a maximum ratio merging rule, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in merging.
As an improvement of the above method, the method determines whether the signal-to-noise ratio of the current frame of the branch meets a preset condition, and if so, stores the frame in a set; the method specifically comprises the following steps:
the SNR of each branch is taken out from the sorting result of the SNR of the branches in turn to be multiplied by a given value mu, which belongs to (0, 1), and whether the product is larger than gamma or not is judged(1)And the signal-to-noise ratio is less than or equal to a set threshold value gammaTIf yes, storing the frame data into a set; wherein, γ(1)And ordering the maximum signal-to-noise ratio in the set for the signal-to-noise ratio of the branch participating in the combination of the previous frame.
An adaptive diversity combining system based on signal-to-noise ratio ordering, the system comprising: the device comprises a receiving module, an obtaining module, a judging module and a diversity combining module; wherein,
the receiving module is used for receiving multi-path branch signals;
the acquisition module is used for acquiring frame data of each branch signal and acquiring partial information of the current frame effective data of each branch signal as prejudgment information through analysis;
the judging module is used for selecting a plurality of branches as a first branch set according to the signal-to-noise ratio sequencing result of the branches participating in the merging of the previous frame, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the prejudging information of the current frame, storing the branch current frames meeting the diversity merging condition into a set according to the signal-to-noise ratios of the branches, and recording the signal-to-noise ratio sequencing result of the branches participating in the merging of the current frame;
and the diversity combining module is used for carrying out 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 has little performance gap with a self-adaptive generalized selection combining (A-GSC) scheme, can reduce unnecessary calculation of the signal-to-noise ratio of the diversity branches, and particularly greatly reduces the processing time required by a system and the complexity and power consumption of system calculation when the interval of a training mode is far smaller than the change of a channel, thereby being simple and effective to realize and having better self-adaptability to the change of the channel environment;
2. in practical application, if a sending end or a receiving end is in a state of moving and being static, a channel is often switched between slow fading and fast fading, the diversity combining method is suitable for both the slow fading environment and the fast fading environment, has better self-adaptability, and particularly under the slow fading environment, the interval of a training mode is far smaller than the change condition of the channel, the diversity branch participating in combining is selected from the frame by referring to the signal-to-noise ratio condition of the time-division diversity branch of the last frame, and the stronger branch of the last frame is preferentially selected, so that the consumption of a large amount of unnecessary computing resources can be reduced while the performance is not influenced greatly.
Drawings
Fig. 1 is a schematic diagram of transmission and reception in embodiment 1 of the present invention;
fig. 2 is a flow chart 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 of a configuration of an adaptive diversity combining system according to embodiment 2 of the present invention.
Reference numerals
201. Receiving unit 202, acquiring unit 203, and calculating unit
204. First judging unit 205, second judging unit 206, third judging unit
207. Fourth judgment unit 208 and diversity combining unit
Detailed Description
The technical solution of the present invention will be 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 snr ranking.
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 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 plurality of diversity signals;
(2) the first frames are combined according to an adaptive generalized selection combining (A-GSC) method;
(3) in the training modes of other frames, the diversity branches participating in the combination of the previous frame are sorted from large to small according to the signal-to-noise ratio of the diversity branches in 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 current frame, and judging whether the signal-to-noise ratio meets the preset condition;
(5) judging whether the output signal-to-noise ratio is larger than a set threshold value gammaTIf the output signal-to-noise ratio is less than the sum of the signal-to-noise ratios of the diversity branches participating in the combination, similar operation is carried out on the subsequent branches in sequence according to the sequencing result until the output signal-to-noise ratio is greater than the threshold value gammaT；
(6) If the signal-to-noise ratio is still less than the threshold value gammaTThen randomly selecting the other diversity branches which are not screened to carry out estimation judgment until the output signal-to-noise ratio is greater than the threshold value gammaTOr all diversity branchesScreening is finished;
data mode:
(7) the diversity branches passed through the screening are combined according to a Maximum Ratio Combining (MRC) criterion.
Wherein the step of judging whether the diversity branch meets the preset condition in (4) comprises:
(a) setting a value of μ, where μ ∈ (0, 1);
(b) setting gamma(1)The value is the maximum value of the signal-to-noise ratio in the diversity branch where the previous frame participates in the combining, and gamma is the maximum value in the first frame(1)The maximum value of signal-to-noise ratio in the diversity branch participating in the combination of the first frame;
(c) judging whether the product of the signal-to-noise ratio of the diversity branch and the given mu value is larger than gamma or not(1)If the branch number is larger than the preset number, the diversity branch meets the preset condition;
the first frames are combined according to an adaptive generalized selection combining (A-GSC) method;
the multi-path diversity signal is composed of a reference signal and a non-reference signal. The reference signal is a sequence for auxiliary reception (channel estimation calculation) and known at both the transmitting and receiving ends, and is also called a training sequence; the non-reference signal is a data sequence which is transmitted by a transmitting end and can be obtained only by the receiving end through the processes of equalization, demodulation, decoding and the like.
The following operations are carried out in the training mode of the rest frames; as shown in fig. 2:
s101, receiving a multi-path diversity signal;
s102, sorting branches participating in merging of the previous frame according to the SNR of the branches participating in merging of the previous frame from large to small;
s103, according to the sorting result, taking out the remaining strongest branches which are not screened;
s104, calculating the signal-to-noise ratio of the branch;
the scheme utilizes the effective data part in the frame as prejudgment information, thereby carrying out signal-to-noise ratio estimation: and calculating the signal-to-noise ratio estimation by using a second-order fourth-order moment method on the received effective data subjected to time domain-frequency domain transformation. First, second moment is calculated:
wherein y isnFor the received effective data part with the cyclic prefix removed and subjected to time domain-frequency domain transformation, the range of n is the length of the effective data part, and E is the average value.
And then calculating fourth moment:
the signal power estimate is:
the noise power estimate is:
s2＝M2-s1
the signal-to-noise ratio estimation value is:
s105, judging whether the calculated branch signal-to-noise ratio meets a preset condition, if so, performing S106; if not, performing S108;
s106, adding the branch into diversity combining;
s107, judging whether the output signal-to-noise ratio is larger than a set threshold value, if so, performing S111; if not, performing S108;
s108, judging whether all the branches participating in sorting are screened completely, and if so, performing S109; if not, S103 is carried out;
s109, judging whether all the branches are screened, if so, performing S111; if not, performing S110;
s110, randomly selecting other branches which are not screened, and carrying out S104;
s111, diversity combining is carried out by using an MRC (maximum likelihood ratio) criterion;
wherein the MRC criterion is a maximum ratio combining criterion.
The method of the invention takes the condition of the diversity branch which is selected by the previous frame and participates in the combination as a reference to screen the diversity branch of the frame, preferentially judges the stronger branch of the previous frame, adds the branch which meets the preset condition into the combination, stops the screening after the output signal-to-noise ratio exceeds the set threshold value, and then carries out the diversity combination according to the MRC criterion.
Calculating the product of the signal-to-noise ratio of the diversity branch and the given value of mu if the product value is larger than gamma(1)If so, judging that the preset condition is met; if the product value is less than or equal to gamma(1)If the preset condition is not met, judging that the preset condition is not met;
specifically, μ is a predetermined value, and μ ∈ (0, 1) is generally set; gamma ray(1)The maximum value of signal-to-noise ratio in the diversity branch of the previous frame participating in the combination, and gamma is the maximum value in the first frame(1)The maximum value of signal-to-noise ratio in the diversity branch participating in the combination of the first frame;
possible alternatives are: setting a value, calculating the SNR of all diversity branches again to screen the branches participating in combination each time when the number of received frames reaches the value, and then continuing to proceed according to the method of the scheme for the next frame, wherein the value can be dynamically changed according to the actual situation. Other methods of calculating the signal-to-noise ratio may be used.
As shown in fig. 3, the frame format of IEEE 802.11a is adopted, where the data field portion of the frame is selected as the anticipation information, and the snr is calculated accordingly.
It should be noted that, in the present embodiment, the method is described by taking the frame format of IEEE 802.11a and the second-order fourth-order moment method as an example for performing the snr estimation, but the method is not limited to this protocol, nor is the snr estimation method of the second-order fourth-order moment method, and as long as the method is satisfied by multi-antenna reception.
Example 2
Based on the above method, embodiment 2 of the present invention provides an adaptive diversity combining system based on signal-to-noise ratio sequencing. The system comprises: the device comprises a receiving module, an obtaining module, a judging module and a diversity combining module; as shown in fig. 4, in which,
the receiving module is used for receiving the multipath branch signals; is composed of a receiving unit 201;
the acquisition module is used for acquiring frame data of each branch signal and acquiring partial information of the current frame effective data of each branch signal as prejudgment information through analysis; 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 sorting result of the signal-to-noise ratios of the branches participating in the merging of the previous frame, sequentially calculating the signal-to-noise ratios of the corresponding branches according to the prejudgment information of the current frame from the first branch set, storing the branch current frames meeting the diversity merging condition into a set according to the signal-to-noise ratios of the branches, and recording the sorting result of the signal-to-noise ratios of the branches participating in the merging of the current frame; specifically, the method comprises a calculating unit 203, a first judging unit 204, a second judging unit 205, a third judging unit 206 and a fourth judging unit 207:
a calculating unit 203, configured to calculate a signal-to-noise ratio of the diversity signal according to the diversity signal; if the frame is the first frame, calculating the signal-to-noise ratios of all diversity branches and sequencing the diversity branches from large to small;
a first determining unit 204, configured to determine whether the signal-to-noise ratio of the diversity branch meets a preset condition; if the judgment result is yes, the branch is added to the diversity combining unit and enters the second judgment unit 205; if the judgment result is no, the branch is not added with the diversity combining unit and enters a third judgment unit 206;
a second determination unit 205 for determining whether the output signal-to-noise ratio exceeds a threshold γTIf yes, the method enters a diversity combining unit 208; if the determination result is negative, the third determination unit 206 is entered;
a third determining unit 206, configured to determine whether all the diversity branches participating in the combining of the previous frame have been screened, and if the determination result is yes, enter a fourth determining unit; if the judgment result is no, entering a calculation unit 203;
a fourth determining unit 207, configured to determine whether all diversity branches have been screened, and if so, enter the diversity combining unit 208; if the judgment result is no, randomly selecting a diversity branch which is not screened yet to enter the calculating unit 203;
the diversity combining module is used for diversity combining the data frames in the set by adopting a maximum ratio combining criterion, and is composed of a diversity combining unit 208.
The channel condition may not change much within several frames of signal transmission, the signal-to-noise ratio condition of the diversity branch of the frame may be the same as or different from the previous frame, the diversity branch participating in the combination may be the same as the previous frame, it is not necessary to recalculate the signal-to-noise ratio of all diversity branches per frame like the a-GSC scheme, especially under the condition that the training mode interval is far smaller than the channel change, the scheme can significantly reduce the number of branches requiring signal-to-noise ratio calculation and the time required for the system to process in the training mode while achieving the small performance gap with the a-GSC scheme, and save the consumption of calculation resources; and the branch with small system performance gain can be removed by using the merging scheme, so that the method has better self-adaptability and reduces the power consumption of the system.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. An adaptive generalized selection diversity combining method based on signal-to-noise ratio sequencing, the method comprising:
receiving a plurality of branch signals;
taking a frame of data from each branch signal, and analyzing to obtain partial information of the current frame effective data of each branch signal as prejudgment information;
selecting a plurality of branches as a first branch set according to the signal-to-noise ratio sequencing result of the branch in which the previous frame participates in the 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 branch meeting the diversity merging condition into a set according to the signal-to-noise ratios of the branches, and recording the signal-to-noise ratio sequencing result of the branch in which the current frame participates in the merging;
and diversity combining is carried out on the data frames in the set by adopting a maximum ratio combining criterion.
2. The adaptive generalized selection diversity combining method based on snr ranking according to claim 1, wherein the method further comprises:
when the received data frame is the first frame, respectively calculating the signal-to-noise ratio of each branch according to the prejudgment 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 ratios of the branches participating in merging of the first frame.
3. The adaptive generalized selection diversity combining method based on snr ranking according to claim 2, wherein the snr of each branch is calculated separately from the prejudged information of the frame; the method specifically comprises the following steps:
performing time domain-frequency domain transformation on the prejudgment information of the first frame of each branch;
calculating second moment M for transformed prejudgment information2Comprises the following steps:
wherein, ynThe method comprises the steps of obtaining pre-judging information of a frequency domain, wherein n is the length of the pre-judging information, and E is an operator for obtaining an average value;
calculating the fourth moment M4Comprises the following steps:
by a second moment M2And fourth order moment M4Calculate to obtain the wayBranch prejudgment information power estimation value s1Comprises the following steps:
by a second moment M2And the signal power estimate s1Calculating to obtain the pre-judgment information noise power estimation value s of the branch2Comprises the following steps:
s2＝M2-s1
from the signal power estimate s1Sum noise power estimate s2And calculating to obtain a pre-judgment information signal-to-noise ratio estimation value p of the branch as follows:
4. the adaptive generalized selection diversity combining method based on signal-to-noise ratio sorting according to claim 3, wherein the sorting is performed according to the signal-to-noise ratio, a plurality of branches meeting preset conditions are selected, the combining is performed according to the adaptive generalized selection combining method, and the signal-to-noise ratio sorting result of the branches of the first frame participating in the combining is recorded; the method specifically comprises the following steps:
sorting according to the numerical value of the branch signal-to-noise ratio of each path to obtain a branch signal-to-noise ratio sorting result;
sequentially taking out each branch from the sorting result of the signal-to-noise ratios of the branches for judgment, and merging the first frames of the branches according to a self-adaptive generalized selection merging method if the branches meet preset conditions and the signal-to-noise ratio is less than or equal to a preset threshold value gamma T;
and when the judgment of each branch in the branch signal-to-noise ratio sequencing results is finished, recording the branch signal-to-noise ratio sequencing results of the first frame participating in the combination.
5. The adaptive generalized selection diversity combining method based on the snr ranking according to claim 1, wherein the method selects a plurality of branches as a first branch set according to the snr ranking results of the branches participating in the combining of the previous frame, sequentially calculates the snrs of the branches in the first branch set according to the prejudgment information of the current frame, stores the current frame of the branches meeting the diversity combining conditions into the set according to the snrs of the branches, and records the snr ranking results of the branches participating in the combining of the current frame; the method specifically comprises the following steps:
step 1) selecting a plurality of branches as a first branch set according to the sorting result of the signal-to-noise ratio of the branches participating in merging in the previous frame;
step 2) taking 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 prejudgment information;
step 4) judging whether the signal-to-noise ratio of the current frame of the branch meets a preset condition, if so, storing the frame into a set, and turning to the 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, 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 traversal of the branches of the first branch set is finished, if so, turning to step 7), and if not, turning to step 2);
step 7) judging whether all the branches are screened, if so, turning to step 8); if not, randomly selecting other branches, and turning to the step 3);
and 8) merging the data frames in the set by using a maximum ratio merging rule, and recording the branch signal-to-noise ratio sequencing result of the current frame participating in merging.
6. The adaptive generalized selection diversity combining method based on SNR sorting according to claim 5, wherein said determining whether the SNR of the current frame of the branch meets a preset condition, if yes, storing the frame in a set; the method specifically comprises the following steps:
sorting from finger signal-to-noise ratioThe result is sequentially taken out of the signal-to-noise ratio of each branch and multiplied by a given value of mu, and the product is determined to be whether more than gamma or not, wherein the product belongs to the field of' 0, 1(1)And the signal-to-noise ratio is less than or equal to a set threshold value gammaTIf yes, storing the frame data into a set; wherein, γ(1)And ordering the maximum signal-to-noise ratio in the set for the signal-to-noise ratio of the branch participating in the combination of the previous frame.
7. An adaptive generalized selection diversity combining system based on signal-to-noise ratio ranking, the system comprising: the device comprises a receiving module, an obtaining module, a judging module and a diversity combining module; wherein,
the receiving module is used for receiving multi-path branch signals;
the acquisition module is used for acquiring frame data of each branch signal and acquiring partial information of the current frame effective data of each branch signal as prejudgment information through analysis;
the judging module is used for selecting a plurality of branches as a first branch set according to the signal-to-noise ratio sequencing result of the branches participating in the merging of the previous frame, sequentially calculating the signal-to-noise ratio of each branch in the first branch set according to the prejudging information of the current frame, storing the branch current frames meeting the diversity merging condition into a set according to the signal-to-noise ratios of the branches, and recording the signal-to-noise ratio sequencing result of the branches participating in the merging of the current frame;
and the diversity combining module is used for carrying out diversity combining on the data frames in the set by adopting a maximum ratio combining criterion.
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