CN112583519B - Link self-adaptive adjustment method, device, server and storage medium - Google Patents
Link self-adaptive adjustment method, device, server and storage medium Download PDFInfo
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- H04L1/0002—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission rate
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Abstract
The embodiment of the invention relates to the field of communication and discloses a link self-adaptive adjustment method, a device, a server and a storage medium. In the invention, the characteristic data of the terminal at the link access time is obtained; acquiring a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model; the method comprises the steps that a preset model is obtained through training of historical characteristic data of a terminal with 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 self-adaptive adjustment of the link. The base station can intelligently allocate a correction value of an optimal modulation and coding scheme index value for each newly accessed terminal in a targeted manner, so that a modulation and coding scheme index value of a more matched channel is allocated for each terminal to carry out link self-adaptive adjustment, 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 adjustment 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 been popular and mature for business, and the fifth generation mobile communication technology 5G is about to be commercialized, and people have put higher demands on high speed, high reliability and low time delay of communication. In order to improve the reliability of transmission and enable a communication system to adapt to a current channel and improve the data transmission efficiency, both the 4G communication system and the 5G communication system introduce hybrid automatic repeat request and link self-adaption technology to achieve the aim.
Because different user terminals adopt different baseband chips, the measurement modes of the channel quality indication may be different, so that the modulation coding scheme index value which is directly scheduled at the current moment and is determined according to the channel quality indication reported by the user terminal is often not the optimal modulation coding scheme index value of the matched channel, and the manufacturer of the communication equipment can make correction 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 finally scheduled at present. A common classical correction method is to add a correction initial value of a fixed modulation coding scheme index value to a modulation coding scheme index value corresponding to channel quality indication at the scheduling start time of each user terminal according to experience of a outfield test, and adjust the correction of the modulation coding scheme index value in real time according to retransmission or acknowledgement signals fed back by the user terminal in the scheduling process.
However, the inventors found that there are at least the following problems in the prior art: because the channel states and measurement errors of channel quality indications of different ues in the same cell may be quite different, for a single ue, the modulation and coding scheme modified by the correction amount initial value of the index value of the fixed modulation and coding scheme is often not the optimal modulation and coding scheme capable of matching the current channel.
Disclosure of Invention
The embodiment of the invention aims to provide a link self-adaptive adjustment method, a device, a server and a storage medium, so that a base station can allocate a correction value of an optimal modulation coding scheme index value for each newly accessed terminal in a targeted and intelligent manner, thereby allocating a modulation coding scheme index value of a more matched channel for each terminal to carry out link self-adaptive adjustment, leading the link convergence of the terminal to be faster and leading the throughput in a certain time to be higher.
In order to solve the above technical problems, 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 an MCS index value of a modulation coding scheme according to the characteristic data and a preset model; the method comprises the steps that a preset model is obtained through training of historical characteristic data of a terminal with 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 self-adaptive adjustment of the link.
The embodiment of the invention also provides a link self-adaptive adjusting device, which comprises: the characteristic data acquisition module is used for acquiring characteristic data of the terminal at the link access moment; the correction value acquisition module is used for acquiring the correction value of the Modulation Coding Scheme (MCS) index value according to the characteristic data and the preset model; the method comprises the steps that a preset model is obtained through training of historical characteristic data of a terminal with 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 self-adaptive adjustment of the link.
The embodiment of the invention also provides a server, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the link adaptation method.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program, and the computer program realizes the link self-adaptive adjustment method when being executed by a processor.
Compared with the prior art, the embodiment of the invention acquires the characteristic data of the terminal at the link access time, acquires the corrected value of the Modulation and Coding Scheme (MCS) index value according to the characteristic data and the preset model, and carries out link self-adaptive adjustment by the MCS index value corrected by the corrected value. Because the characteristic data of each terminal at the link access time is considered to be different, the correction value of the MCS index value of the optimal modulation and coding scheme for each terminal can be intelligently obtained according to the characteristic data of each terminal at the link access time and the preset model, so that a more matched channel is allocated to each terminal, the MCS index value corrected by the correction value is subjected to link self-adaptive adjustment, 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 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 converged link; obtaining a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following components: judging the category of the terminal according to the characteristic data and the first category mapping function; and acquiring the corrected value of the MCS index value of the terminal according to the value range of the corrected value of the MCS index value corresponding to the category. The preset model is obtained through training according to a large amount of data, so that the accuracy of the category of the terminal judged according to the characteristic data and the preset model is high, 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 category 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 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 with the converged link; each category corresponds to N second category mapping functions one by one; obtaining a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following components: judging the category of the terminal according to the characteristic data; selecting a second class mapping function corresponding to the class to which the terminal belongs; and acquiring a correction value of the MCS index value of the terminal according to the characteristic data and the selected second class mapping function. The preset model is obtained through training according to a large amount of data, so that the correction value of the terminal MCS index value obtained according to the category of the terminal and the preset model 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 is a third type mapping function; the third 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 acknowledgement signal ACK; obtaining a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following components: obtaining the probability of 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; if the probability of the feedback information is within the 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 initial correction value of the MCS index value is corrected according to the relation between the probability of the feedback information and the preset range, so as 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, so that the link convergence of the terminal is faster, the throughput within a certain time is higher, and the resource is saved.
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One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which the figures of the drawings are not to be taken in a limiting sense, unless otherwise indicated.
Fig. 1 is a flowchart of a link adaptation method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a link adaptation 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 adaptation method according to a third embodiment of the present invention;
FIG. 5 is a flow chart of a second mapping function training process in a third embodiment in accordance with the present invention;
fig. 6 is a flowchart of a link adaptation method according to a fourth embodiment of the present invention;
FIG. 7 is a flow chart of a third mapping function training process in a fourth embodiment in accordance with the present invention;
fig. 8 is a block diagram of a link adaptation device according to a fifth embodiment of the present invention;
fig. 9 is a structural diagram of a server according to a sixth embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the following detailed description of the embodiments of the present invention will be given with reference to the accompanying drawings. However, those of ordinary skill in the art will understand that in various embodiments of the present invention, numerous technical details have been set forth in order to provide a better understanding of the present application. However, the technical solutions claimed in the present application can be implemented without these technical details and with various changes and modifications based on the following embodiments. The following embodiments are divided for convenience of description, and should not be construed as limiting the specific implementation of the present invention, and the embodiments can be mutually combined 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 this embodiment, the characteristic data of the terminal at the link access time is obtained, a correction value of the modulation and coding scheme MCS index value 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 implementation details of a link adaptive adjustment method according to this embodiment are specifically described below, and the following description is merely provided for understanding the implementation details, and is not necessary to implement this embodiment.
As shown in fig. 1, a flowchart of a link adaptation adjustment method in this embodiment includes:
step 101, obtaining characteristic data of a terminal at a link access time.
Specifically, the link refers to a path space in which electromagnetic waves propagate between the base station and the terminal. The characteristic data acquired by the base station is that the terminal transmits the characteristic data of the terminal at the link access time to the base station through the link.
The base station is required 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 the base station is required 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 characteristic data types input into the preset model by the terminal.
And 102, acquiring a correction value of the Modulation Coding Scheme (MCS) index value according to the characteristic data and a preset model.
Specifically, the modulation coding scheme is one of link self-adaptive technologies, 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 grade and the coding rate are improved, and when the channel condition is poor, the modulation grade and the coding rate are reduced; wherein each MCS index corresponds to a physical transmission rate for a set of parameters.
The preset model is obtained by training the base station through the pre-collected historical characteristic data of the terminal with the converged link, the terminal with the converged link is a terminal marked according to feedback information of the terminal, specifically the terminal with the converged link is a terminal marked by the base station according to the pre-collected acknowledgement signal or/and retransmission signal of each terminal with the large packet service, and the number of the terminals marked should be large enough. Because only the characteristic data of the link-converged terminal is the data useful for constructing the preset model, by marking the link-converged terminal, the base station can distinguish the link-converged terminal from the link-non-converged terminal through the marking, so that the base station can collect the characteristic data of the link-converged terminal according to the marking. Therefore, the base station can train to obtain the preset model 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, and the number of 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 to acquire a correction value of the Modulation Coding Scheme (MCS) index value.
It should be noted that, in a specific application, the base station continuously updates the feature data of the terminal with the converged link, 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.
And step 103, feeding back the MCS index value corrected by the correction value to the terminal.
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, and after obtaining the MCS index value corrected by the correction value, the base station feeds back the MCS index value to the terminal, and the terminal performs link adaptation according to the MCS index value.
The conventional link adaptive adjustment method is to add a correction initial value of a fixed modulation coding scheme index value to a modulation coding scheme index value corresponding to a channel quality indication at a scheduling start time of each user terminal. However, since the channel states and measurement errors of the channel quality indications of different ues in the same cell may be very different, for a single ue, the modulation and coding scheme modified by the initial value of the correction amount of the index value of the fixed modulation and coding scheme 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 intelligently allocate the correction value of the optimal MCS index value for each newly accessed terminal in a targeted way, so that the MCS index value of a more matched channel is allocated for each terminal to carry out the link self-adaptive adjustment, 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 adaptation method. The present embodiment is substantially the same as the first embodiment except that: in this embodiment, the preset model is obtained through data training by a classification method, the preset model is a first type mapping function, the category of the terminal is judged 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 category.
As shown in fig. 2, a flowchart of the link adaptation adjustment method in the present embodiment includes:
step 201, obtaining characteristic data of a terminal at a link access time. This step is similar to step 101 and will not be described again.
And 202, judging the category of the terminal according to the characteristic data and the first category mapping function.
Specifically, the feature data is classified 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 classified feature data, namely, the base station inputs the classified feature data into the first class mapping function to obtain the class to which the terminal belongs.
It is 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 mode may be equal interval division or unequal interval division; and pre-distributing 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 modification value range of the MCS index value of the terminal with the converged link is [ -28,28], the modification value range is divided into 10 classes, and the modification value of the MCS index value is pre-allocated for each class. The method comprises the following steps:
Category 1: [ -28, -20), correction value-24;
category 2: [ -20, -15), correction value-17;
category 3: [ -15, -10), correction value-12;
category 4: [ -10, -5), correction value-7;
category 5: [ -5, 0), correction value-2;
category 6: [0, 5), correction value 2;
category 7: [5, 10), initial value 7;
category 8: [10, 15), correction value 12;
category 9: [15, 20), correction value 17;
category 10: 20,28, correction value 24.
Notably, the first type of mapping function is trained by the following manner, and the flowchart is shown in fig. 3, and includes:
in step 2001, the history feature data of the terminal having the link converged is acquired as training data.
Specifically, the base station collects in advance, as training data, historical feature data of a terminal where a link converges. The historical characteristic data includes at least one of or any combination of the following: 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 feature data may be other data, and the embodiment is not limited specifically.
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 category 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 category according to the correction value of the MCS index value, and training is performed according to the training data and the category 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 category to which the terminal corresponding to the training data belongs is category 10.
Step 203, obtaining 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.
Specifically, since the correction value of one MCS index value has been allocated in advance for each category, the correction value of the MCS index value of the terminal can be acquired from the value range of the correction value of the MCS index value corresponding to the category. For example: if the category to which the terminal belongs is determined to be category 10 based on the feature data and the first mapping function, the correction value of the MCS index value of the terminal is 24.
And 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 again.
In this embodiment, the preset model is a first type mapping function and is obtained through training according to a large amount of data, so that accuracy of the category of the terminal determined according to the feature data and the preset model is high, and therefore the correction value of the terminal MCS index value obtained according to the value range of the correction value of the MCS index value corresponding to the category is also more accurate, so that the link convergence of the terminal is faster, and throughput within a certain time is also higher.
A third embodiment of the present invention relates to a link adaptation method. The present embodiment is substantially the same as the first embodiment except that: in this embodiment, the preset model is obtained through data training by a regression method, and the preset model includes N second class mapping functions, and determines a class to which the terminal belongs according to the feature data; and acquiring a corrected value of the MCS index value of the terminal according to the category to which the terminal belongs and a preset model.
As shown in fig. 4, a flowchart of the link adaptation adjustment method in the present embodiment includes:
step 301, obtaining characteristic data of a terminal at a link access time. This step is similar to step 101 and will not be described again.
And step 302, judging the category of the terminal according to the characteristic data.
Specifically, the feature data is regression feature data, and the base station determines the category to which the terminal belongs according to the regression feature data. The categories are obtained by dividing the value range of the characteristic data of the terminal with the converged link into N categories, wherein N is a positive integer. For example: selecting channel quality indication CQI and downlink loss in the characteristic data of the terminal with the converged link, wherein the value range of the CQI is [0,15], the value range of the downlink loss is [ -120, -90], and dividing the 2 characteristic data into 6 categories, namely:
category 1: CQI value [0, 5), downlink loss value [ -120, -105);
category 2: CQI value [0, 5], downlink loss value [ -105, -90];
category 3: CQI value [5, 10), downlink loss value [ -120, -105);
category 4: CQI value [5,10 ], downlink loss value [ -105, -90];
category 5: CQI value [10,15], downlink loss value [ -120, -105 ];
category 6: CQI value [10,15], downlink loss value [ -105, -90].
The determining of the category to which the terminal belongs according to the regression characteristic data is determining of the category to which the terminal belongs according to the value of the regression characteristic data, for example: and if the CQI in the characteristic data is 12 and the downlink loss is-95, the category of the terminal is category 6.
Step 303, selecting a second class mapping function corresponding to the class to which the terminal belongs.
Specifically, the preset model includes N second class mapping functions; the second type mapping function is used for obtaining a correction value of the MCS index value of the terminal according to the regression characteristic data; each category corresponds to N second category mapping functions one by one. The base station determines a second class mapping function corresponding to the class according to the class of the terminal, and acquires the corrected value of the MCS index value of the terminal according to the corresponding second class mapping function. For example: and if the class to which the terminal belongs is class 6, mapping functions according to a second class corresponding to the class 6.
Notably, the second class of mapping functions is trained by a flowchart as shown in fig. 5, comprising:
step 3001, obtaining historical feature data of the terminal with the converged link as training data.
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 be other data, and the embodiment is not limited specifically.
Step 3002, training to obtain a second type mapping function according to the training data and the correction value of the MCS index value corresponding to the training data.
Specifically, training the N second class mapping functions of the preset model according to training data in each class and correction values of MCS index values corresponding to the training data to obtain 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 class mapping function.
Specifically, the base station inputs the regression characteristic data into the selected second-class mapping function to obtain an output value, rounds the output value, and takes the rounded value as a correction value of the MCS index value of the terminal. Note that rounding or rounding of the decimal point may be performed as needed, and the present embodiment is not limited thereto. For example: and if the category to which the terminal belongs is category 6, acquiring a correction value of the MCS index value of the terminal according to a second category mapping function corresponding to the category 6.
And step 305, 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 again.
In this embodiment, the preset model includes N second class mapping functions, where the N second class mapping functions are all obtained by training according to a large amount of data, so that the correction value of the terminal MCS index value obtained according to the class to which the terminal belongs and the preset model is also more accurate, so that the link convergence of the terminal is faster, and the throughput in a certain time is also higher.
A fourth embodiment of the present invention relates to a link adaptation method. The present embodiment is substantially the same as the first embodiment except that: in the embodiment, the preset model is obtained through data training by a logistic regression method, the preset model is specifically a third type mapping function, and the probability of feedback information of the terminal is obtained according to the characteristic data and the preset model; 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, wherein the initial correction value of the MCS index value is the characteristic data of the terminal at the link access moment.
As shown in fig. 6, a flowchart of the link adaptation adjustment method in the present embodiment includes:
step 401, obtaining characteristic data of a terminal at a link access time. This step is similar to step 101 and will not be described again.
And step 402, obtaining the probability of feedback information of the terminal according to the characteristic data and a preset model.
Specifically, the feature data is logistic regression feature data, and the base station inputs the acquired logistic regression feature data into a third type mapping function to obtain the probability of feedback information of the terminal. The preset model is 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 logistic regression method characteristic data, namely obtaining the probability of the feedback information of the terminal according to the logistic regression method characteristic data and the third mapping function. The probability of the feedback information may be the probability of the acknowledgement information ACK or the probability of the retransmission information NACK, and the present embodiment is not limited.
Notably, the third class of mapping functions is trained by a flowchart as shown in fig. 7, comprising:
in step 4001, historical feature data of a terminal with converged link is obtained as training data.
Specifically, the historical feature data includes an initial correction value for the MCS index value and includes 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 be other data, and the embodiment is not limited specifically.
Step 4002, training according to the training data and the probability of the feedback information of the terminal corresponding to the training data to obtain the third type mapping function.
Step 403, determining 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, step 404 is performed, and if the probability of the feedback information is not within the preset range, step 405 is performed.
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 logistic regression method feature data acquired by the terminal at the link access time, and the terminal needs to send the data to the base station. 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 the retransmission information NACK, the probability of the retransmission signal NACK fed back by the terminal at the current moment is predicted according to the logistic regression characteristic data of the terminal at the link access moment and the third type mapping function, and it is noted that the logistic regression characteristic data includes the initial correction value of the MCS index value. If the probability of retransmitting the 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 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 retransmitting the 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 caused to be higher than the standard value, the MCS index value is required to be adjusted down, and the initial correction value of the MCS index value is adjusted down 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, and n is a positive integer, and may be set according to the specific situation, which is not limited in this embodiment.
And predicting again according to the corrected logistic regression characteristic data and the preset model to obtain the probability of the retransmission information NACK of the terminal, wherein the corrected logistic regression characteristic data comprises the initial correction value of the MCS index value, and the initial correction value of the MCS index value at the moment is the correction value of the MCS index value 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 within the preset range, taking the MCS index value corrected according to the preset correction value as the correction value of the MCS index value. If the information NACK is not within the preset range, continuing to adjust according to the principle until the probability of retransmitting the information NACK meets the preset range.
When the feedback information is the probability of the acknowledgement information ACK, the sum of the probability of the acknowledgement information ACK and the probability of the retransmission information NACK is 1, so that the probability of the retransmission information NACK can be obtained after the probability of the acknowledgement information ACK is obtained, and the correction can be performed according to the correction method when the feedback information is the retransmission information NACK.
And step 406, 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 again.
In this embodiment, the preset model is a third type mapping function, and the probability of feedback information of the terminal is obtained according to the logistic regression method feature data and the preset model, if the probability of the feedback information is within the 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. The conventional link adaptation is to correct the correction amount of the index value of the modulation coding scheme according to the retransmission or the acknowledgement signal, and the correction usually needs a long period of time; however, in this embodiment, since the preset model and the correction value are both pre-existing, the correction process does not need to take a long time, so that the link of the terminal converges faster, the throughput in 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:
the feature data obtaining module 501 is configured to obtain feature data of a terminal at a link access time.
Specifically, the characteristic data of the terminal at the link access time is multiple, and the characteristic data of the terminal at the link access time obtained by the base station is consistent with the historical characteristic data of the terminal with the link convergence adopted in the process of training the preset model.
The correction value obtaining module 502 is configured to obtain a correction value of an MCS index value according to the feature data and a preset model; the preset model is obtained through training historical characteristic data of a terminal with link convergence.
And 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 feature data obtaining module 501 is configured to obtain feature data of a terminal at a link access time; 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 converged link. The correction value obtaining module 502 is specifically configured to determine, according to the feature data and a preset model, a category to which the terminal belongs; acquiring the corrected value of the MCS index value of the terminal according to the value range of the corrected value of the MCS index value corresponding to the category; and 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 feature data obtaining module 501 is configured to obtain feature data of a terminal at a link access time; the preset model comprises N second class mapping functions; the second type 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 with the converged link; each category corresponds to the N second category mapping functions one by one. The correction value obtaining module 502 is specifically configured to determine, according to the feature data, a class to which the terminal belongs; selecting a second class mapping function corresponding to the class to which the terminal belongs according to the class to which the terminal belongs; acquiring a correction value of the MCS index value of the terminal according to the characteristic data, the selected second type mapping function and the preset model; and 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 feature data obtaining module 501 is configured to obtain feature data of a terminal at a link access time; the preset model is a third type mapping function; and the third class 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 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 take an initial correction value of the MCS index value as a correction value of the MCS index value if the probability of the feedback information is within a preset range, and 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 if the probability of the feedback information is not within the preset range, so as to obtain the correction value of the MCS index value, where the initial correction value of the MCS index value is characteristic data of the terminal at the link access time; and 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 is to be noted that this embodiment is a system example corresponding to the first, second, third, and fourth embodiments, and is implemented in cooperation with the first, second, third, and fourth embodiments. 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 in order to reduce repetition, a detailed description is omitted here. Accordingly, the related technical 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 in this embodiment is a logic module, and in practical application, one logic unit may be one physical unit, or may be a part of one physical unit, or may be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, units that are not so close to solving the technical problem presented by the present invention are not introduced in the present embodiment, but this does not indicate that other units 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 perform the embodiments of the link adaptation method.
Where the memory 601 and the processor 602 are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the buses connecting the various circuits of the one or more processors 602 and the memory 601 together. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be 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 the wireless medium via the 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 memory may be used to store data used by the processor in performing operations.
A seventh embodiment of the present invention relates to a computer readable storage medium storing a computer program which, when executed by a processor, implements the above-described method embodiments.
That is, it will be understood by those skilled in the art that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, where the program includes several instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps in the methods of the embodiments described herein. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or 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 of 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.
Claims (11)
1. A method for adaptively adjusting a link, comprising:
acquiring characteristic data of a terminal at a link access moment;
acquiring a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model; the preset model is obtained through training historical characteristic data of a terminal with link convergence;
feeding back the MCS index value corrected by the correction value to the terminal, wherein the corrected MCS index value is used for self-adaptive adjustment of a link;
the preset model is a third type mapping function; the third 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 acknowledgement signal ACK;
obtaining a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following components:
obtaining the probability of 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.
2. The link adaptation 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 converged link;
acquiring a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model, and further comprising:
judging the category of the terminal according to the characteristic data and the first category mapping function;
and acquiring the corrected value of the MCS index value of the terminal according to the value range of the corrected value of the MCS index value corresponding to the category.
3. The link adaptation method according to claim 2, wherein the first class 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 or any combination of the following: 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 of the terminal corresponding to the training data.
4. The link adaptation method according to claim 1, wherein the preset model comprises N second class mapping functions; the second class 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 the value range of the characteristic data of the terminal with the converged link; each category corresponds to the N second category mapping functions one by one;
acquiring a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model, and further comprising:
judging the category of the terminal according to the characteristic data;
selecting a second class mapping function corresponding to the class to which the terminal belongs;
and acquiring a corrected value of the MCS index value of the terminal according to the characteristic data and the selected second class mapping function.
5. The link adaptation method according to claim 4, wherein the second class 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 or any combination of the following: channel quality indicator CQI, beam forming gain, horizontal arrival angle, vertical arrival angle, uplink sounding reference signal and downlink path loss;
And training according to the training data and the correction value of the MCS index value corresponding to the training data to obtain the second type mapping function.
6. The link adaptive adjustment method according to claim 1, wherein if the probability of the feedback information is not within a preset range, correcting the initial correction value of the index value to obtain the correction value of the MCS index value according to the relation between the probability of the feedback information and the preset range, includes:
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 the preset range, the initial correction value of the MCS index value is adjusted downwards according to the preset correction value;
step 2, re-acquiring the NACK probability, and if the re-acquired NACK probability is within a 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 probability of the feedback information is not within the preset range, re-executing the step 1 until the probability of the re-acquired NACK is within the preset range.
7. The link adaptation method according to claim 1, wherein the third class mapping function 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 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;
and training to obtain the third type mapping function according to the training data and the probability of feedback information of the terminal corresponding to the training data.
8. The link adaptation method according to claim 1, wherein the terminal with link convergence is a terminal marked according to feedback information of the terminal, wherein the feedback information is retransmission information NACK or acknowledgement signal ACK.
9. A link adaptation adjustment device, comprising:
the characteristic data acquisition module is used for acquiring characteristic data of the terminal at the link access moment;
the correction value acquisition module is used for acquiring a correction value of the Modulation Coding Scheme (MCS) index value according to the characteristic data and a preset model; the preset model is obtained through training historical characteristic data of a terminal with link convergence;
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 self-adaptive adjustment of a link;
the preset model is a third type mapping function; the third 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 acknowledgement signal ACK;
obtaining a correction value of an MCS index value of a modulation coding scheme according to the characteristic data and a preset model, wherein the correction value comprises the following components:
obtaining the probability of 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.
10. A server, comprising:
at least one processor; the method comprises the steps of,
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 of any one of claims 1 to 8.
11. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the link adaptation method of any one of claims 1 to 8.
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| PCT/CN2020/113599 WO2021047456A1 (en) | 2019-09-10 | 2020-09-04 | Link adaptation adjustment method, device, server and storage medium |
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