CN112583519A - Link self-adaptive adjustment method, device, server and storage medium - Google Patents
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Abstract
The embodiment of the invention relates to the field of communication, and discloses a link self-adaptive adjusting method, a link self-adaptive adjusting device, a server and a storage medium. In the invention, the characteristic data of the terminal at the link access moment is obtained; acquiring a correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model; the preset model is obtained by training historical characteristic data of a terminal with a link convergence; and feeding back the MCS index value corrected by the correction value to the terminal, wherein the corrected MCS index value is used for link self-adaptive adjustment. The base station can pertinently and intelligently allocate an optimal modification value of the modulation and coding scheme index value to each newly accessed terminal, and accordingly, each terminal is allocated with a modulation and coding scheme index value which is more matched with a channel to perform link self-adaptive adjustment, so that the link convergence of the terminal is faster, and the throughput in a certain time is higher.
Description
Technical Field
The embodiment of the invention relates to the field of communication, in particular to a link self-adaptive adjusting method, a device, a server and a storage medium.
Background
With the continuous development of mobile communication technology, the fourth generation mobile communication technology 4G has become popular and mature for commercial use, and the fifth generation mobile communication technology 5G will be commercial use, which puts higher demands on high speed, high reliability and low time delay of communication. In order to improve the reliability of transmission, and enable the communication system to adapt to the current channel, and improve the data transmission efficiency, the 4G and 5G communication systems have introduced hybrid automatic repeat request and link adaptation techniques to achieve the above-mentioned objectives.
Because different user terminals adopt different baseband chips and have different measurement modes for the channel quality indication, the modulation coding scheme index value which is directly determined according to the channel quality indication reported by the user terminal and is scheduled at the current moment is often not the optimal modulation coding scheme index value of the matched channel, so that at present, communication equipment manufacturers can make corrections according to the modulation coding scheme index value corresponding to the channel quality indication fed back by the user terminal when determining the modulation coding scheme index value which is scheduled at the last time. A commonly used classical correction method is to add an initial correction value of a fixed modulation coding scheme index value to a modulation coding scheme index value corresponding to a channel quality indicator at the scheduling start time of each user terminal according to experience of an external field test, and to adjust the correction value of the modulation coding scheme index value in real time according to a retransmission or acknowledgement signal fed back by the user terminal during the scheduling process.
However, the inventors found that at least the following problems exist in the prior art: since the channel states of different user terminals in the same cell and the measurement errors of the channel quality indications may be very different, for a single user terminal, the modulation and coding scheme modified by the initial value of the correction amount of the fixed modulation and coding scheme index value is often not the optimal modulation and coding scheme capable of matching the current channel.
Disclosure of Invention
The embodiments of the present invention provide a link adaptive adjustment method, apparatus, server and storage medium, so that a base station can allocate an optimal modulation and coding scheme index value correction value to each newly accessed terminal in a targeted and intelligent manner, and thus allocate a modulation and coding scheme index value more matching with a channel to each terminal for link adaptive adjustment, so that link convergence of the terminal is faster and throughput within a certain time is higher.
In order to solve the above technical problem, an embodiment of the present invention provides a link adaptive adjustment method, including: acquiring characteristic data of a terminal at a link access moment; acquiring a correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model; the preset model is obtained by training historical characteristic data of a terminal with a link convergence; and feeding back the MCS index value corrected by the correction value to the terminal, wherein the corrected MCS index value is used for link self-adaptive adjustment.
The embodiment of the present invention further provides a link adaptive adjusting apparatus, including: the characteristic data acquisition module is used for acquiring the characteristic data of the terminal at the link access moment; the correction value acquisition module is used for acquiring the correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and the preset model; the preset model is obtained by training historical characteristic data of a terminal with a link convergence; and the feedback module is used for feeding back the MCS index value corrected by the correction value to the terminal, wherein the corrected MCS index value is used for link self-adaptive adjustment.
An embodiment of the present invention further provides a server, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the link adaptation method described above.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the link adaptive adjustment method.
Compared with the prior art, the embodiment of the invention obtains the characteristic data of the terminal at the link access time, obtains the correction value of the modulation and coding scheme MCS index value according to the characteristic data and the preset model, and performs link self-adaptive adjustment according to the MCS index value after correction of the correction value. The characteristic data of each terminal at the link access time are different, so that the correction value of the optimal modulation and coding scheme MCS index value for each terminal can be intelligently obtained according to the characteristic data of each terminal at the link access time and the preset model, and each terminal is allocated with a more matched channel and is subjected to link self-adaptive adjustment by the corrected MCS index value, so that the link convergence of the terminal is faster, and the throughput in a certain time is higher.
In addition, the preset model is a first type of mapping function; the first type mapping function is used for judging the type of the terminal according to the characteristic data; the category is obtained by dividing the value range of the correction value of the MCS index value of the terminal with the link convergence; acquiring a correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following steps: judging the class of the terminal according to the characteristic data and the first class mapping function; and acquiring the correction value of the MCS index value of the terminal according to the value range of the correction value of the MCS index value corresponding to the category. The preset model is obtained by training according to a large amount of data, so that the accuracy of the class of the terminal judged according to the characteristic data and the preset model is higher, the correction value of the MCS index value of the terminal obtained according to the value range of the correction value of the MCS index value corresponding to the class is more accurate, the link convergence of the terminal is faster, and the throughput in a certain time is higher.
In addition, the preset model comprises N second-class mapping functions; the second type of mapping function is used for obtaining a correction value of the MCS index value of the terminal according to the characteristic data; n is the number of categories obtained by dividing the value range of the characteristic data of the terminal converged by the link; each category is in one-to-one correspondence with N second-category mapping functions; acquiring a correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following steps: judging the category of the terminal according to the characteristic data; selecting a second type mapping function corresponding to the type of the terminal; and acquiring the correction value of the MCS index value of the terminal according to the characteristic data and the selected second type mapping function. The preset model is obtained by training according to a large amount of data, so that the correction value of the MCS index value of the terminal, which is obtained according to the class of the terminal and the preset model, is more accurate, the link convergence of the terminal is faster, and the throughput within a certain time is higher.
In addition, the preset model is a third type of mapping function; the third type of mapping function is used for obtaining the probability of feedback information of the terminal according to the characteristic data, wherein the feedback information is retransmission information NACK or an acknowledgement signal ACK; acquiring a correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following steps: obtaining the probability of the feedback information of the terminal according to the characteristic data and the third type mapping function, wherein the characteristic data comprises an initial correction value of the MCS index value; and if the probability of the feedback information is within the preset range, taking the initial correction value of the MCS index value as the correction value of the MCS index value, and if the probability of the feedback information is not within the preset range, correcting the initial correction value of the MCS index value according to the relation between the probability of the feedback information and the preset range to obtain the correction value of the MCS index value. Because the preset model and the correction value are pre-existing, the correction process does not need to take a long time, the link convergence of the terminal is faster, the throughput in a certain time is higher, and resources are saved.
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One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
Fig. 1 is a flowchart of a link adaptive adjustment method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a link adaptive adjustment method according to a second embodiment of the present invention;
FIG. 3 is a flow chart of a first mapping function training process in accordance with a second embodiment of the present invention;
fig. 4 is a flowchart of a link adaptive adjustment method according to a third embodiment of the present invention;
FIG. 5 is a flowchart of a second mapping function training process according to a third embodiment of the present invention;
fig. 6 is a flowchart of a link adaptive adjustment method according to a fourth embodiment of the present invention;
FIG. 7 is a flowchart of a third mapping function training process according to a fourth embodiment of the present invention;
fig. 8 is a structural diagram of a link adaptation adjusting apparatus according to a fifth embodiment of the present invention;
fig. 9 is a structural diagram of a server in a sixth embodiment according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments. The following embodiments are divided for convenience of description, and should not constitute any limitation to the specific implementation manner of the present invention, and the embodiments may be mutually incorporated and referred to without contradiction.
The first embodiment of the present invention relates to a link adaptive adjustment method, and the present embodiment is applicable to a base station. In the embodiment, the characteristic data of the terminal at the link access time is obtained, the correction value of the MCS index value of the modulation and coding scheme is obtained according to the characteristic data and the preset model, and the MCS index value corrected by the correction value is fed back to the terminal.
The following describes implementation details of the link adaptive adjustment method according to the present embodiment in detail, and the following is only provided for facilitating understanding of the implementation details and is not necessary for implementing the present solution.
Fig. 1 shows a flowchart of a link adaptive adjustment method in the present embodiment, which includes:
Specifically, a link refers to a path space in which electromagnetic waves propagate between a base station and a terminal. The characteristic data acquired by the base station is sent to the base station by the terminal through the link at the link access time.
The base station needs to train the preset model through the pre-collected characteristic data of the link convergence terminal before acquiring the characteristic data of the terminal at the link access time, and needs to input the acquired characteristic data into the preset model after acquiring the characteristic data of the terminal at the link access time, so that the historical characteristic data in the process of training the preset model is consistent with the type of the characteristic data input into the preset model by the terminal.
And 102, acquiring a correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model.
Specifically, the modulation and coding scheme is one of link adaptation techniques, the modulation mode and the coding rate of terminal transmission are adjusted according to the change of a channel, when the channel condition is good, the modulation level and the coding rate are improved, and when the channel condition is poor, the modulation level and the coding rate are reduced; wherein each MCS index corresponds to a physical transmission rate under a set of parameters.
The preset model is obtained by training a base station through pre-collected historical characteristic data of a link convergence terminal, the link convergence terminal is a terminal marked according to feedback information of the terminal, specifically, the link convergence terminal is a terminal marked by the base station according to pre-collected acknowledgement signals or/and retransmission signals of terminals of each large packet service, and the number of the marked terminals should be large enough. Since only the characteristic data of the terminal with the converged link is the data useful for constructing the preset model, by marking the terminal with the converged link, the base station can distinguish the terminal with the converged link from the terminal without the converged link through the marking, so that the base station can collect the characteristic data of the terminal with the converged link according to the marking. Therefore, the base station can train according to the pre-collected historical characteristic data of the terminal marked according to the feedback information of the terminal and the corresponding label value to obtain the preset model, and the number of the marked terminals is large enough, so that the preset model obtained by training is more accurate.
After acquiring the characteristic data of the terminal at the link access time, the base station inputs the acquired characteristic data into a preset model, and acquires the correction value of the MCS index value of the modulation and coding scheme.
It should be noted that, in a specific application, the base station continuously updates the feature data of the terminal with the link convergence, and retrains the preset model according to the updated feature data every other period to adapt to the change of the scene, so that the correction value of the MCS index value obtained according to the preset model is more accurate.
Specifically, the MCS index value corrected by the correction value is the sum of the correction value of the MCS index value and the MCS index value corresponding to the channel quality indicator CQI of the terminal, the base station feeds back the MCS index value corrected by the correction value to the terminal after obtaining the MCS index value, and the terminal performs link adaptive adjustment according to the MCS index value.
In the conventional link adaptive adjustment method, at the scheduling start time of each user terminal, a correction initial value of a fixed modulation and coding scheme index value is added to a modulation and coding scheme index value corresponding to a channel quality indication. However, since the channel states of different ues in the same cell and the measurement errors of the channel quality indicator may be very different, for a single ue, the modulation and coding scheme modified by the initial value of the correction amount of the fixed modulation and coding scheme index value is often not the optimal modulation and coding scheme capable of matching the current channel. Compared with the traditional link self-adaptive adjustment method, the method can pointedly and intelligently allocate the optimal correction value of the MCS index value to each newly accessed terminal, thereby allocating the MCS index value which is more matched with the channel to each terminal for link self-adaptive adjustment, so that the link convergence of the terminal is faster, and the throughput in a certain time is higher.
A second embodiment of the present invention relates to a link adaptive adjustment method. This embodiment is substantially the same as the first embodiment, except that: in this embodiment, the preset model is obtained by training classification data, the preset model is a first type mapping function, the class to which the terminal belongs is determined according to the feature data and the first type mapping function, and the correction value of the MCS index value of the terminal is obtained according to the value range of the correction value of the MCS index value corresponding to the class.
Fig. 2 shows a flow chart of a link adaptive adjustment method in the present embodiment, which includes:
Step 202, judging the category of the terminal according to the feature data and the first type mapping function.
Specifically, the feature data is classification method feature data, the preset model is a first class mapping function, and the first class mapping function is used for judging the class to which the terminal belongs according to the classification method feature data, that is, the base station inputs the classification method feature data into the first class mapping function to obtain the class to which the terminal belongs.
It should be noted that the category is obtained by dividing the value range of the correction value of the MCS index value of the terminal with link convergence, and the dividing manner may be equal interval division or unequal interval division; and pre-allocating a correction value of the MCS index value for each category according to the value range of the correction value of the MCS index value in each category. For example: the value range of the correction value of the MCS index value of the terminal with the link convergence is [ -28,28], the value range is divided into 10 classes, and one correction value of the MCS index value is pre-allocated to each class. The method comprises the following specific steps:
class 1: [ -28, -20), correction value-24;
class 2: [ -20, -15), correction value-17;
class 3: [ -15, -10), correction value-12;
class 4: [ -10, -5), correction value-7;
class 5: [ -5,0), correction value-2;
category 6: [0,5), correction value 2;
class 7: [5,10), initial value 7;
class 8: [10,15), correction value 12;
class 9: [15,20), correction value 17;
category 10: [20,28], correction value 24.
It should be noted that the first type of mapping function is obtained by training in the following manner, and the flowchart is shown in fig. 3 and includes:
in step 2001, the historical feature data of the terminal with the converged link is acquired as training data.
Specifically, the base station collects history feature data of the terminal where the link converges in advance as training data. The historical characteristic data comprises at least one of the following or any combination thereof: channel quality indicator CQI, beam forming gain, horizontal arrival angle, vertical arrival angle, uplink sounding reference signal and downlink path loss; note that the feature data may be other data, and the present embodiment is not particularly limited.
And step 2002, training according to the training data and the class to which the terminal corresponding to the training data belongs to obtain a first class mapping function.
Specifically, the class to which the terminal corresponding to the training data belongs is determined according to the correction value of the MCS index value of the terminal corresponding to the training data, the terminal corresponding to the training data is classified into each class according to the correction value of the MCS index value, and training is performed according to the training data and the class to which the terminal corresponding to the training data belongs to obtain the first class mapping function. For example: if the correction value of the MCS index value of the terminal corresponding to the training data is 26, the class to which the terminal corresponding to the training data belongs is class 10.
Specifically, since one correction value of the MCS index value has been allocated in advance for each class, the correction value of the MCS index value of the terminal can be acquired according to the value range of the correction value of the MCS index value corresponding to the class. For example: if the class to which the terminal belongs is judged to be the class 10 according to the characteristic data and the first mapping function, the correction value of the MCS index value of the terminal is 24.
And step 204, feeding back the MCS index value corrected by the correction value to the terminal. This step is similar to step 103 and will not be described herein again.
In this embodiment, the preset model is a first type of mapping function and is obtained by training according to a large amount of data, so that the accuracy of the class to which the terminal is judged according to the feature data and the preset model is high, and the correction value of the MCS index value of the terminal, which is obtained according to the value range of the correction value of the MCS index value corresponding to the class, is also more accurate, so that the link convergence of the terminal is faster, and the throughput within a certain time is also higher.
A third embodiment of the present invention relates to a link adaptive adjustment method. This embodiment is substantially the same as the first embodiment, except that: in the embodiment, the preset model is obtained by regression method data training, the preset model comprises N second-class mapping functions, and the class to which the terminal belongs is judged according to the feature data; and acquiring the correction value of the MCS index value of the terminal according to the category of the terminal and a preset model.
Fig. 4 shows a flowchart of a link adaptive adjustment method in the present embodiment, which includes:
And step 302, judging the category of the terminal according to the characteristic data.
Specifically, the feature data is regression method feature data, and the base station determines the category to which the terminal belongs according to the regression method feature data. The categories are obtained by dividing the value range of the characteristic data of the terminal with the link convergence into N categories, wherein N is a positive integer. For example: selecting a Channel Quality Indicator (CQI) and a downlink path loss in the characteristic data of the terminal with the link convergence, wherein the value range of the CQI is [0,15], the value range of the downlink path loss is [ -120, -90], and dividing the 2 characteristic data into 6 categories, specifically:
class 1: CQI value [0,5), downlink path loss value [ -120, -105);
class 2: CQI takes the value of [0, 5], the downlink path loss takes the value of [ -105, -90 ];
class 3: CQI value [5,10), downlink path loss value [ -120, -105);
class 4: CQI value [5,10), downlink path loss value [ -105, -90 ];
class 5: CQI value [10,15], downlink path loss value [ -120, -105);
category 6: the CQI takes on the value [10,15], and the downlink path loss takes on the value [ -105, -90 ].
Judging the category to which the terminal belongs according to the regression method feature data is judging the category to which the terminal belongs according to the value of the regression method feature data, for example: the CQI in the feature data is 12, and the downlink path loss is-95, and the category to which the terminal belongs is category 6.
Specifically, the preset model comprises N second-class mapping functions; the second type of mapping function is used for obtaining the correction value of the MCS index value of the terminal according to the regression method characteristic data; each category corresponds to N second-class mapping functions one to one. And the base station determines a second type of mapping function corresponding to the type of the terminal according to the type of the terminal, and acquires the correction value of the MCS index value of the terminal according to the corresponding second type of mapping function. For example: and if the class to which the terminal belongs is the class 6, according to a second class mapping function corresponding to the class 6.
It should be noted that the mapping function of the second type is obtained by training in the following manner, and the flowchart is shown in fig. 5 and includes:
Specifically, the historical characteristic data at least comprises one or more of Channel Quality Indicator (CQI), beam forming gain, horizontal arrival angle, vertical arrival angle, uplink sounding reference signal and downlink path loss; it should be noted that the historical feature data may also be other data, and the embodiment is not particularly limited.
Specifically, N second-class mapping functions of the initial N preset models are trained according to training data under each class and correction values of MCS index values corresponding to the training data to obtain the N second-class mapping functions.
And step 304, acquiring a correction value of the MCS index value of the terminal according to the characteristic data and the selected second type mapping function.
Specifically, the base station inputs regression method feature data into the selected second type mapping function to obtain an output value, rounds the output value, and uses the rounded value as a correction value of the MCS index value of the terminal. Note that rounding may be performed as necessary or rounding may be performed without limitation in the present embodiment. For example: and if the class to which the terminal belongs is class 6, acquiring the correction value of the MCS index value of the terminal according to a second class mapping function corresponding to the class 6.
In step 305, the MCS index value corrected by the correction value is fed back to the terminal. This step is similar to step 103 and will not be described herein again.
In this embodiment, the preset model includes N second-type mapping functions, and the N second-type mapping functions are obtained by training according to a large amount of data, so that the correction value of the MCS index value of the terminal obtained according to the class to which the terminal belongs and the preset model is more accurate, the link convergence of the terminal is faster, and the throughput within a certain time is higher.
A fourth embodiment of the present invention relates to a link adaptive adjustment method. This embodiment is substantially the same as the first embodiment, except that: in the embodiment, the preset model is obtained by training data through a logistic regression method, the preset model is specifically a third-class mapping function, and the probability of the feedback information of the terminal is obtained according to the characteristic data and the preset model; if the probability of the feedback information is within a preset range, taking the initial correction value of the MCS index value as the correction value of the MCS index value, and if the probability of the feedback information is not within the preset range, correcting the initial correction value of the MCS index value according to the relation between the probability of the feedback information and the preset range to obtain the correction value of the MCS index value, wherein the initial correction value of the MCS index value is the characteristic data of the terminal at the link access time.
Fig. 6 shows a flowchart of a link adaptive adjustment method according to this embodiment, which includes:
And 402, obtaining the probability of the feedback information of the terminal according to the characteristic data and the preset model.
Specifically, the characteristic data is logistic regression method characteristic data, and the base station inputs the acquired logistic regression method characteristic data into a third type mapping function to obtain the probability of the feedback information of the terminal. Presetting a model as a third type mapping function; the third mapping function is used for obtaining the probability of the feedback information of the terminal according to the characteristic data of the logistic regression method, namely obtaining the probability of the feedback information of the terminal according to the characteristic data of the logistic regression method and the third mapping function. The probability of the feedback information may be the probability of acknowledgement information ACK or the probability of retransmission information NACK, and the present embodiment is not limited thereto.
It should be noted that the mapping function of the third type is obtained by training in the following manner, and the flowchart is shown in fig. 7 and includes:
Specifically, the historical characterization data includes an initial correction value of the MCS index value and at least one or any combination of the following: channel quality indicator CQI, beam forming gain, horizontal arrival angle, vertical arrival angle, uplink sounding reference signal, downlink path loss and MCS index value; it should be noted that the historical feature data may also be other data, and the embodiment is not particularly limited.
And 403, judging whether the probability of the feedback information is within a preset range value. If the probability of the feedback information is within the preset range, go to step 404, and if the probability of the feedback information is not within the preset range, go to step 405.
In step 404, the initial correction value of the MCS index value is used as the correction value of the MCS index value.
Specifically, the initial correction value of the MCS index value is the characteristic data of the logistic regression method acquired by the terminal at the link access time, and the terminal needs to send the data to the base station. And if the probability of the feedback information is within the preset range, taking the initial correction value of the MCS index value as the correction value of the MCS index value.
And step 405, correcting the initial correction value of the MCS index value according to the relation between the probability of the feedback information and the preset range to obtain the correction value of the MCS index value.
Specifically, when the feedback information is retransmission information NACK, the probability of the retransmission signal NACK fed back by the terminal at the current time is predicted according to the logistic regression method feature data of the terminal at the link access time and the third type mapping function, and it is noted that the logistic regression method feature data includes an initial correction value of the MCS index value. If the probability of the retransmission information NACK is smaller than the minimum value of the preset range, the current MCS index value is considered to be lower than the current channel quality, the MCS index value is required to be adjusted upwards to improve the data transmission rate of the terminal, and the initial correction value of the MCS index value is adjusted upwards according to the preset correction value; if the probability of the retransmission information NACK is larger than the maximum value of the preset range, the current MCS index value is considered to be higher than the current channel quality, the block error rate is higher than the standard value, the MCS index value is required to be adjusted downwards, and the initial correction value of the MCS index value is adjusted downwards according to the preset correction value. The preset correction amount may be a 1-order MCS index value or an n-order MCS index value, where n is a positive integer and may be set according to specific situations, and the embodiment is not limited.
And predicting again according to the corrected characteristic data of the logistic regression method and the preset model to obtain the probability of the retransmission information NACK of the terminal, wherein the corrected characteristic data of the logistic regression method at the moment comprises the initial correction value of the MCS index value, and the initial correction value of the MCS index value at the moment is obtained by correcting the initial correction value of the MCS index value according to the preset correction value in the last step. And if the probability of the re-acquired NACK is in the preset range, taking the MCS index value corrected according to the preset correction value as the correction value of the MCS index value. And if the NACK can not be within the preset range, continuing to adjust according to the principle until the probability of the retransmission information NACK meets the preset range.
When the feedback information is the probability of the acknowledgement information ACK, since the sum of the probability of the acknowledgement information ACK and the probability of the retransmission information NACK is 1, the probability of the retransmission information NACK can be obtained after the probability of the acknowledgement information ACK is obtained, so that the correction can be performed according to the correction method when the feedback information is the retransmission information NACK.
In step 406, the corrected MCS index value is fed back to the terminal. This step is similar to step 103 and will not be described herein again.
In this embodiment, the preset model is a third type mapping function, the probability of the feedback information of the terminal is obtained according to the characteristic data of the logistic regression method and the preset model, if the probability of the feedback information is within a preset range, the initial correction value of the MCS index value is used as the correction value of the MCS index value, and if the probability of the feedback information is not within the preset range, the value corrected by the initial correction value of the MCS index value is used as the correction value of the MCS index value. In the traditional link adaptive adjustment, the correction quantity of the index value of the modulation and coding scheme is corrected according to a retransmission or confirmation signal, and the correction usually needs a long time; however, in the embodiment, because the preset model and the correction value are pre-existing, the correction process does not need to take a long time, so that the link convergence of the terminal is faster, the throughput within a certain time is higher, and resources are saved.
A fifth embodiment of the present invention relates to a link adaptive adjustment device, as shown in fig. 8, including:
a characteristic data obtaining module 501, configured to obtain characteristic data of the terminal at the link access time.
Specifically, the characteristic data of the terminal at the link access time are various, and the base station acquires the characteristic data of the terminal at the link access time and the historical characteristic data of the terminal with the link convergence adopted in the process of training the preset model are consistent in type.
A correction value obtaining module 502, configured to obtain a correction value of the MCS index value according to the characteristic data and a preset model; the preset model is obtained through training of historical characteristic data of the terminal with the link convergence.
A feedback module 503, configured to feed back the MCS index value corrected by the correction value to the terminal, where the corrected MCS index value is used for link adaptive adjustment.
In a specific example, the characteristic data obtaining module 501 is configured to obtain characteristic data of a terminal at a link access time; presetting a model as a first type mapping function; the first type of mapping function is used for judging the type of the terminal according to the characteristic data; the category is obtained by dividing the value range of the correction value of the MCS index value of the terminal whose link is converged. The correction value obtaining module 502 is specifically configured to determine the category to which the terminal belongs according to the feature data and a preset model; acquiring the correction value of the MCS index value of the terminal according to the value range of the correction value of the MCS index value corresponding to the category; a feedback module 503, configured to feed back the MCS index value corrected by the correction value to the terminal, where the corrected MCS index value is used for link adaptive adjustment.
In a specific example, the characteristic data obtaining module 501 is configured to obtain characteristic data of a terminal at a link access time; the preset model comprises N second-class mapping functions; the second type of mapping function is used for obtaining a correction value of the MCS index value of the terminal according to the characteristic data; n is the number of categories obtained by dividing the value range of the characteristic data of the terminal converged by the link; each category corresponds to the N second-category mapping functions one to one. The correction value obtaining module 502 is specifically configured to determine a category to which the terminal belongs according to the feature data; selecting a second type mapping function corresponding to the type of the terminal according to the type of the terminal; acquiring a correction value of the MCS index value of the terminal according to the characteristic data, the selected second type mapping function and a preset model; a feedback module 503, configured to feed back the MCS index value corrected by the correction value to the terminal, where the corrected MCS index value is used for link adaptive adjustment.
In a specific example, the characteristic data obtaining module 501 is configured to obtain characteristic data of a terminal at a link access time; presetting a model as a third type mapping function; and the third type of mapping function is used for obtaining the probability of the feedback information of the terminal according to the characteristic data. Obtaining the probability of the feedback information of the terminal according to the characteristic data and the third type mapping function; the correction value obtaining module 502 is specifically configured to, if the probability of the feedback information is within a preset range, use an initial correction value of the MCS index value as the correction value of the MCS index value, and if the probability of the feedback information is not within the preset range, correct the initial correction value of the MCS index value according to a relationship between the probability of the feedback information and the preset range to obtain a correction value of the MCS index value, where the initial correction value of the MCS index value is characteristic data of the terminal at a link access time; a feedback module 503, configured to feed back the MCS index value corrected by the correction value to the terminal, where the corrected MCS index value is used for link adaptive adjustment.
It should be noted that this embodiment is a system example corresponding to the first embodiment, the second embodiment, the third embodiment, and the fourth embodiment, and may be implemented in cooperation with the first embodiment, the second embodiment, the third embodiment, and the fourth embodiment. The related technical details mentioned in the first embodiment, the second embodiment, the third embodiment and the fourth embodiment are still valid in the present embodiment, and are not repeated here for reducing the repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first embodiment, the second embodiment, the third embodiment, and the fourth embodiment.
It should be noted that each module referred to in this embodiment is a logical module, and in practical applications, one logical unit may be one physical unit, may be a part of one physical unit, and may be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, elements that are not so closely related to solving the technical problems proposed by the present invention are not introduced in the present embodiment, but this does not indicate that other elements are not present in the present embodiment.
A sixth embodiment of the invention is directed to a server, as shown in fig. 9, comprising at least one processor 602; and, a memory 601 communicatively coupled to the at least one processor; the memory 601 stores instructions executable by the at least one processor 602, and the instructions are executed by the at least one processor 602 to enable the at least one processor 602 to execute the embodiments of the link adaptive adjustment method.
Where the memory 601 and the processor 602 are coupled by a bus, the bus may comprise any number of interconnected buses and bridges that couple one or more of the various circuits of the processor 602 and the memory 601 together. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory may be used to store data used by the processor in performing operations.
A seventh embodiment of the invention relates to a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the above-mentioned method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (12)
1. A link adaptive adjustment method is characterized by comprising the following steps:
acquiring characteristic data of a terminal at a link access moment;
acquiring a correction value of a Modulation and Coding Scheme (MCS) index value according to the characteristic data and a preset model; the preset model is obtained by training historical characteristic data of a terminal with a link convergence;
and feeding back the MCS index value corrected by the correction value to the terminal, wherein the corrected MCS index value is used for link self-adaptive adjustment.
2. The link adaptive adjustment method according to claim 1, wherein the preset model is a first type mapping function; the first class mapping function is used for judging the class of the terminal according to the characteristic data; the category is obtained by dividing the value range of the correction value of the MCS index value of the terminal with the link convergence;
obtaining a correction value of a Modulation and Coding Scheme (MCS) index value according to the characteristic data and a preset model, wherein the correction value comprises:
judging the class of the terminal according to the feature data and the first class mapping function;
and acquiring the correction value of the MCS index value of the terminal according to the value range of the correction value of the MCS index value corresponding to the category.
3. The link adaptive adjustment method according to claim 2, wherein the first type mapping function is trained by:
acquiring historical characteristic data of the terminal with the converged link as training data, wherein the historical characteristic data at least comprises one of the following data or any combination thereof: channel quality indicator CQI, beam forming gain, horizontal arrival angle, vertical arrival angle, uplink sounding reference signal and downlink path loss;
and training to obtain the first class mapping function according to the training data and the class to which the terminal corresponding to the training data belongs.
4. The link adaptive adjustment method according to claim 1, wherein the preset model includes N second-class mapping functions; the second type of mapping function is used for obtaining a correction value of the MCS index value of the terminal according to the characteristic data; the N is the number of categories obtained by dividing according to the value range of the characteristic data of the terminal with the link convergence; each category corresponds to the N second-type mapping functions one to one;
obtaining a correction value of a Modulation and Coding Scheme (MCS) index value according to the characteristic data and a preset model, wherein the correction value comprises:
judging the category of the terminal according to the characteristic data;
selecting a second type mapping function corresponding to the type of the terminal;
and acquiring a correction value of the MCS index value of the terminal according to the characteristic data and the selected second type mapping function.
5. The link adaptive adjustment method according to claim 4, wherein the second type of mapping function is trained by:
acquiring historical characteristic data of the terminal with the converged link as training data, wherein the historical characteristic data at least comprises one of the following data or any combination thereof: channel quality indicator CQI, beam forming gain, horizontal arrival angle, vertical arrival angle, uplink sounding reference signal and downlink path loss;
and training to obtain the second type mapping function according to the training data and the correction value of the MCS index value corresponding to the training data.
6. The link adaptive adjustment method according to claim 1, wherein the preset model is a mapping function of a third type; the third type of mapping function is used for obtaining the probability of the feedback information of the terminal according to the characteristic data, wherein the feedback information is retransmission information NACK or an acknowledgement signal ACK;
obtaining a correction value of a Modulation and Coding Scheme (MCS) index value according to the characteristic data and a preset model, wherein the correction value comprises:
obtaining the probability of the feedback information of the terminal according to the characteristic data and the third type mapping function, wherein the characteristic data comprises an initial correction value of an MCS index value;
and if the probability of the feedback information is within a preset range, taking the initial correction value of the MCS index value as the correction value of the MCS index value, and if the probability of the feedback information is not within the preset range, correcting the initial correction value of the MCS index value according to the relation between the probability of the feedback information and the preset range to obtain the correction value of the MCS index value.
7. The link adaptive adjustment method according to claim 6, wherein if the probability of the feedback information is not within a preset range, the correcting the initial correction value of the index value according to the relationship between the probability of the feedback information and the preset range to obtain the correction value of the MCS index value comprises:
step 1, if the probability of the feedback information is the probability of retransmission information NACK, when the probability of NACK is smaller than the minimum value of a preset range, the initial correction value of the MCS index value is adjusted upwards according to a preset correction value; when the probability is larger than the maximum value of a preset range, adjusting the initial correction value of the MCS index value downwards according to a preset correction value;
step 2, the probability of NACK is obtained again, and if the probability of NACK is in a preset range, the MCS index value corrected according to a preset correction value is used as the correction value of the MCS index value;
and if the probability of the feedback information is not in the preset range, re-executing the step 1 until the re-acquired NACK probability is in the preset range.
8. The link adaptive adjustment method according to claim 6, wherein the mapping function of the third type is trained by:
acquiring historical characteristic data of the terminal with the converged link as training data, wherein the historical characteristic data comprises an initial correction value of an MCS index value and at least one of the following or any combination thereof: channel quality indicator CQI, beam forming gain, horizontal arrival angle, vertical arrival angle, uplink sounding reference signal, downlink path loss and MCS index value;
and training to obtain the third type mapping function according to the training data and the probability of the feedback information of the terminal corresponding to the training data.
9. The link adaptive adjustment method according to claim 1, wherein the terminal where the link converges is a terminal marked according to feedback information of the terminal, and the feedback information is retransmission information NACK or acknowledgement signal ACK.
10. A link adaptive adjustment apparatus, comprising:
the characteristic data acquisition module is used for acquiring the characteristic data of the terminal at the link access moment;
the correction value acquisition module is used for acquiring the correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model; the preset model is obtained by training historical characteristic data of a terminal with a link convergence;
and the feedback module is used for feeding back the MCS index value corrected by the correction value to the terminal, wherein the corrected MCS index value is used for link self-adaptive adjustment.
11. A server, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the link adaptation method of any of claims 1 to 9.
12. A computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the link adaptive adjustment method according to any one of claims 1 to 9 when executed by a processor.
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