WO2021047456A1 - 一种链路自适应调整方法、装置、服务器及存储介质 - Google Patents

一种链路自适应调整方法、装置、服务器及存储介质 Download PDF

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Publication number
WO2021047456A1
WO2021047456A1 PCT/CN2020/113599 CN2020113599W WO2021047456A1 WO 2021047456 A1 WO2021047456 A1 WO 2021047456A1 CN 2020113599 W CN2020113599 W CN 2020113599W WO 2021047456 A1 WO2021047456 A1 WO 2021047456A1
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terminal
mcs index
index value
value
correction value
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PCT/CN2020/113599
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English (en)
French (fr)
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罗泽群
刘巧艳
李建国
史珂
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0002—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission rate
    • H04L1/0003—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission rate by switching between different modulation schemes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received

Definitions

  • the embodiments of the present application relate to the field of communications, and in particular, to a link adaptive adjustment method, device, server, and storage medium.
  • the fourth-generation mobile communication technology 4G has become popular and mature for commercial use
  • the fifth-generation mobile communication technology 5G is about to be commercialized.
  • People have proposed new ideas for high-speed, high-reliability, and low-latency communications. High demands.
  • both 4G and 5G communication systems have introduced hybrid automatic repeat request and link adaptation technologies to achieve the above goals.
  • the channel quality indicator measurement method may be different. Therefore, the current modulation and coding scheme index value determined directly according to the channel quality indicator reported by the user terminal is often not the best matching channel. Modulation and coding scheme index value. Therefore, currently, when communication equipment manufacturers determine the last scheduled modulation and coding scheme index value, they will make corrections according to the modulation and coding scheme index value corresponding to the channel quality indicator fed back by the user terminal.
  • a commonly used classic correction method is to add a fixed modulation and coding scheme index value to the modulation and coding scheme index value corresponding to the channel quality indicator at the beginning of each user terminal scheduling based on the experience of the field test, and In the scheduling process, the correction amount of the modulation and coding scheme index value is adjusted in real time according to the retransmission or confirmation signal fed back by the user terminal.
  • the purpose of the embodiments of the present application is to provide a link adaptive adjustment method, device, server, and storage medium.
  • the embodiment of the present application provides a link adaptive adjustment method, including: acquiring characteristic data of the terminal at the time of link access; acquiring the correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and a preset model; wherein, The preset model is obtained by training the historical feature data of the terminal where the link converges; the MCS index value corrected with the correction value is fed back to the terminal, where the corrected MCS index value is used for link adaptive adjustment.
  • the embodiment of the present application also provides a link adaptive adjustment device, which includes: a characteristic data acquisition module for acquiring characteristic data of the terminal at the time of link access; a correction value acquisition module for acquiring characteristic data and preset data.
  • a characteristic data acquisition module for acquiring characteristic data of the terminal at the time of link access
  • a correction value acquisition module for acquiring characteristic data and preset data.
  • the model obtains the modified value of the MCS index value of the modulation and coding scheme; wherein the preset model is obtained by training the historical feature data of the terminal where the link converges; the feedback module is used to feed back the MCS index value modified with the modified value to the terminal, Among them, the revised MCS index value is used for link adaptive adjustment.
  • the embodiment of the present application also provides a server, including: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor.
  • the processor executes, so that at least one processor can execute the above-mentioned link adaptive adjustment method.
  • the embodiment of the present application also provides a computer-readable storage medium that stores a computer program, and the computer program is executed by a processor to implement the above-mentioned link adaptive adjustment method.
  • Fig. 1 is a flowchart of a link adaptive adjustment method according to the first embodiment of the present application
  • Fig. 2 is a flowchart of a link adaptive adjustment method according to the second embodiment of the present application
  • Fig. 3 is a flowchart of the first mapping function training process in the second embodiment of the present application.
  • Fig. 4 is a flowchart of a link adaptive adjustment method according to the third embodiment of the present application.
  • Fig. 5 is a flowchart of the second mapping function training process in the third embodiment of the present application.
  • Fig. 6 is a flowchart of a link adaptive adjustment method according to the fourth embodiment of the present application.
  • FIG. 7 is a flowchart of the third mapping function training process in the fourth embodiment of the present application.
  • FIG. 8 is a structural diagram of a link adaptive adjustment device in the fifth embodiment of the present application.
  • Fig. 9 is a structural diagram of a server in a sixth embodiment according to the present application.
  • the first implementation manner of the present application relates to a link adaptive adjustment method, and this implementation manner can be applied to a base station.
  • the characteristic data of the terminal at the time of link access is acquired, the correction value of the MCS index value of the modulation and coding scheme is acquired 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.
  • FIG. 1 The flowchart of the link adaptive adjustment method in this embodiment is shown in FIG. 1, and includes:
  • Step 101 Obtain characteristic data of the terminal at the time of link access.
  • the link refers to the path space of electromagnetic waves propagating between the base station and the terminal.
  • the characteristic data acquired by the base station is that the terminal sends its characteristic data at the time of link access to the base station through the link.
  • the base station Before acquiring the characteristic data of the terminal 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. After acquiring the characteristic data of the terminal at the link access time, the base station It is also necessary to input the acquired feature data into the preset model. It can be seen that the historical feature data in the process of training the preset model is consistent with the type of feature data input into the preset model by the terminal.
  • Step 102 Obtain a correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and the preset model.
  • the modulation and coding scheme is one of the link adaptation technologies.
  • the modulation mode and coding rate of the terminal transmission are adjusted according to the change of the channel. When the channel conditions are relatively good, the modulation level and coding rate are increased. When it is worse, reduce the modulation level and coding rate; among them, each MCS index actually corresponds to the physical transmission rate under a set of parameters.
  • the preset model is obtained by the base station through the pre-collected link-converged terminal's historical feature data training.
  • the link-converged terminal is the terminal marked according to the terminal's feedback information. Specifically, the link-converged terminal is based on the pre-collected terminal by the base station. The number of terminals for marking the acknowledgment signal or/and retransmission signal of each large packet service terminal should be large enough.
  • the base station can distinguish between the terminal on which the link converges and the terminal on which the link is not converged through the mark , So that the base station can collect the characteristic data of the terminal whose link converges according to the mark. In this way, the base station can train to obtain the preset model according to the pre-collected historical feature 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 to make the preset model obtained by training more Accurate.
  • the base station After acquiring the characteristic data of the terminal at the time of link access, the base station inputs the acquired characteristic data into the preset model to acquire the correction value of the MCS index value of the modulation and coding scheme.
  • the base station continuously updates the characteristic data of the terminal where the link converges, and retrains the preset model according to the updated characteristic data every other cycle to adapt to changes in the scene, so that the It is assumed that the correction value of the MCS index value obtained by the model is more accurate.
  • Step 103 Feed back the MCS index value corrected by the correction value to the terminal.
  • 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 After obtaining the MCS index value corrected by the correction value, the base station The MCS index value is fed back to the terminal, and the terminal performs link adaptive adjustment according to the MCS index value.
  • the traditional link adaptive adjustment method is to add a fixed modulation and coding scheme index value to the modulation and coding scheme index value corresponding to the channel quality indicator at the start time of each user terminal scheduling.
  • the modulation and coding scheme is corrected by the initial value of the correction value of the fixed modulation and coding scheme index value. It is often not an optimal modulation and coding scheme that can match the current channel.
  • this embodiment can assign an optimal MCS index value correction value to each newly connected terminal in a targeted and intelligent manner, thereby assigning one to each terminal.
  • the MCS index value of the channel is more matched to adjust the link adaptively, so that the link convergence of the terminal is faster, and the throughput in a certain period of time is also higher.
  • the second embodiment of the present application relates to a link adaptive adjustment method.
  • This embodiment is roughly the same as the first embodiment. The difference is that: in this embodiment, the preset model is obtained through classification data training, and the preset model is the first type of mapping function, based on the feature data and the first type of mapping function.
  • the class mapping function determines the category of the terminal, and obtains 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.
  • FIG. 2 The flowchart of the link adaptive adjustment method in this embodiment is shown in Fig. 2, and includes:
  • Step 201 Obtain characteristic data of the terminal at the time of link access. This step is similar to step 101 and will not be repeated here.
  • Step 202 Determine the category to which the terminal belongs based on the feature data and the first-category mapping function.
  • the feature data is classification feature data
  • the preset model is the first type of mapping function.
  • the first type of mapping function is used to determine the category to which the terminal belongs based on the classification feature data, that is, the base station inputs the classification feature data into the first type of mapping function.
  • the category to which the terminal belongs is obtained.
  • the category is obtained by dividing the value range of the correction value of the MCS index value of the terminal where the link converges.
  • the division method can be equal interval division or unequal interval division; and according to each category
  • the value range of the correction value of the MCS index value in is pre-allocated with a correction value of the MCS index value for each category.
  • the value range of the MCS index value correction value of the terminal on which the link converges is [-28,28]
  • the value range is divided into 10 categories, and each category is pre-allocated with a MCS index value correction value . details as follows:
  • Step 2001 Obtain historical feature data of the terminal where the link converges as training data.
  • the base station pre-collects historical feature data of the terminal where the link converges as training data.
  • the historical characteristic data includes at least one of the following or any combination: channel quality indicator CQI, beamforming gain, horizontal angle of arrival, vertical angle of arrival, uplink sounding reference signal, downlink path loss; it is worth noting that the characteristic data can also be For other data, this embodiment does not make specific limitations.
  • Step 2002 Perform training to obtain the first type of mapping function according to the training data and the category to which the terminal corresponding to the training data belongs.
  • 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, and according to the training data
  • the data and the category to which the terminal corresponding to the training data belongs are trained to obtain the first-category 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 Acquire 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 correction value of the MCS index value of the terminal can be obtained according to the value range of the correction value of the MCS index value corresponding to the category. For example: if it is determined that the category to which the terminal belongs is category 10 according to the feature data and the first mapping function, the correction value of the MCS index value of the terminal is 24.
  • Step 204 Feed back the MCS index value corrected by the correction value to the terminal. This step is similar to step 103, and will not be repeated here.
  • the preset model is the first type of mapping function, which is obtained by training based on a large amount of data. Therefore, the accuracy of the category of the terminal determined based on the feature data and the preset model is relatively high, and thus the MCS index value corresponding to the category
  • the correction value of the terminal MCS index value obtained by the value range of the correction value is more accurate, so that the link of the terminal converges faster, and the throughput in a certain period of time is also higher.
  • the third embodiment of the present application relates to a link adaptive adjustment method.
  • This embodiment is roughly the same as the first embodiment, but the difference is that: in this embodiment, the preset model is obtained through regression data training, and the preset model includes N second-type mapping functions, which are judged based on characteristic data The category to which the terminal belongs; the correction value of the MCS index value of the terminal is obtained according to the category to which the terminal belongs and a preset model.
  • FIG. 4 The flowchart of the link adaptive adjustment method in this embodiment is shown in FIG. 4, and includes:
  • Step 301 Obtain characteristic data of the terminal at the time of link access. This step is similar to step 101 and will not be repeated here.
  • Step 302 Determine the category to which the terminal belongs based on the characteristic data.
  • the feature data is regression feature data
  • the base station judges the category to which the terminal belongs based on the regression feature data.
  • the category is obtained by dividing the value range of the characteristic data of the terminal on which the link converges, and is divided into N categories, where N is a positive integer. For example: select the channel quality indicator CQI and downlink path loss in the characteristic data of the terminal where the link converges, the value range of CQI is [0,15], and the value range of downlink path loss is [-120,-90 ], divide these 2 feature data into 6 categories, specifically:
  • Category 1 CQI value [0,5), downlink path loss value [-120,-105);
  • Category 2 CQI value [0,5), downlink path loss value [-105,-90];
  • Category 3 CQI value [5,10), downlink path loss value [-120,-105);
  • Category 4 CQI value [5,10), downlink path loss value [-105,-90];
  • Category 5 CQI value [10,15], downlink path loss value [-120,-105);
  • Category 6 CQI value [10,15], downlink path loss value [-105,-90].
  • Judging the category to which the terminal belongs according to the characteristic data of the regression method is the category to which the terminal belongs according to the value of the characteristic data of the regression method. For example, if the CQI in the characteristic data is 12 and the downlink path loss is -95, the category of the terminal is category 6. .
  • Step 303 Select a second type of mapping function corresponding to the category to which the terminal belongs.
  • the preset model includes N second-type mapping functions; the second-type mapping function is used to obtain the corrected value of the MCS index value of the terminal according to the characteristic data of the regression method; each category is the same as the N second-type mapping functions.
  • the base station determines the second-type mapping function corresponding to the category according to the category to which the terminal belongs, and obtains the correction value of the MCS index value of the terminal according to the corresponding second-type mapping function. For example, if the category to which the terminal belongs is category 6, then the mapping function of the second category corresponding to category 6 is used.
  • Step 3001 Obtain historical feature data of the terminal where the link converges as training data.
  • historical feature data includes at least one or more of channel quality indicator CQI, beamforming gain, horizontal angle of arrival, vertical angle of arrival, uplink sounding reference signal, and downlink path loss; it is worth noting that historical feature data It may also be other data, which is not specifically limited in this embodiment.
  • Step 3002 Perform training to obtain the second type of mapping function according to the training data and the correction value of the MCS index value corresponding to the training data.
  • the initial N second-type mapping functions of the preset model are trained to obtain N second-type mapping functions.
  • Step 304 Obtain a correction value of the MCS index value of the terminal according to the characteristic data and the selected second-type mapping function.
  • the base station inputs the characteristic data of the regression method into the selected second-type mapping function to obtain the output value, and rounds the output value, and uses the rounded value as the correction value of the MCS index value of the terminal. It is worth noting that rounding or rounding can be performed as needed, which is not limited in this embodiment. For example, if the category to which the terminal belongs is category 6, the correction value of the MCS index value of the terminal is obtained according to the second category mapping function corresponding to category 6.
  • Step 305 Feed back the MCS index value corrected by the correction value to the terminal. This step is similar to step 103, and will not be repeated here.
  • the preset model includes N second-type mapping functions, and the N second-type mapping functions are all trained based on a large amount of data. Therefore, the terminal MCS index value obtained according to the category of the terminal and the preset model The correction value of is also more accurate, which makes the link convergence of the terminal faster and the throughput in a certain period of time is also higher.
  • the fourth embodiment of the present application relates to a link adaptive adjustment method.
  • This embodiment is roughly the same as the first embodiment, but the difference is: in this embodiment, the preset model is obtained through logistic regression data training, and the preset model is specifically the third type of mapping function, based on the feature data and the preset The model obtains the probability of the feedback information of the terminal; 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.
  • the initial correction value of the MCS index value is corrected 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 moment of link access.
  • FIG. 6 The flowchart of the link adaptive adjustment method in this embodiment is shown in FIG. 6, and includes:
  • Step 401 Acquire characteristic data of the terminal at the time of link access. This step is similar to step 101 and will not be repeated here.
  • Step 402 Obtain the probability of the feedback information of the terminal according to the characteristic data and the preset model.
  • the characteristic data is the characteristic data of the logistic regression method
  • the base station inputs the acquired characteristic data of the logistic regression method into the third type of mapping function to obtain the probability of the feedback information of the terminal.
  • the preset model is the third type of mapping function; the third type of mapping function is used to obtain the probability of the terminal's feedback information according to the characteristic data of the logistic regression method, that is, to obtain the terminal's feedback information according to the characteristic data of the logistic regression method and the third mapping function The probability.
  • the probability of the feedback information may be the probability of ACK for the acknowledgement information, or the probability of NACK for the retransmission of the information, which is not limited in this embodiment.
  • Step 4001 Obtain historical feature data of the terminal where the link converges as training data.
  • the historical feature data includes the initial correction value of the MCS index value and at least one or any combination of the following: channel quality indicator CQI, beamforming gain, horizontal angle of arrival, vertical angle of arrival, uplink sounding reference signal, downlink Travel loss, MCS index value; it is worth noting that the historical feature data may also be other data, and this embodiment does not make specific limitations.
  • Step 4002 Perform training to obtain the third type of mapping function according to the training data and the probability of the terminal's feedback information corresponding to the training data.
  • Step 403 Determine 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, then step 404 is entered; if the probability of the feedback information is not within the preset range, then step 405 is entered.
  • Step 404 Use the initial correction value of the MCS index value as the correction value of the MCS index value.
  • the initial correction value of the MCS index value is the logistic regression characteristic data acquired by the terminal at the time of link access, and the terminal needs to send the data to the base station. If the probability of the feedback information is within the preset range, the initial correction value of the MCS index value is the correction value of the MCS index value.
  • Step 405 According to the relationship between the probability of the feedback information and the preset range, the initial correction value of the MCS index value is corrected to obtain the correction value of the MCS index value.
  • the characteristic data of the logistic regression method includes the initial correction value of the MCS index value. If the probability of retransmission of information NACK is less than the minimum value of the preset range, it is considered that the current MCS index value is lower than the current channel quality, and the MCS index value should be increased to increase the data transmission rate of the terminal, and the MCS should be adjusted according to the preset correction value.
  • the initial correction value of the index value is adjusted upward; if the probability of retransmission information NACK is greater than the maximum value of the preset range, the current MCS index value is considered to be higher than the current channel quality, which will cause the block error rate to be higher than the standard value, and the MCS should be lowered
  • the index value is used to lower the initial correction value of the MCS index value according to the preset correction value.
  • the preset correction amount may be a first-order MCS index value or an n-order MCS index value, where n is a positive integer, which can be set according to specific conditions, and is not limited in this embodiment.
  • the logistic regression feature data at the modified time includes the initial modified value of the MCS index value
  • the The initial correction value of the MCS index value at the time is correcting the initial correction value of the MCS index value according to the preset correction value in the previous step to obtain the correction value of the MCS index value. If the probability of the re-acquired NACK is within the preset range, the MCS index value corrected according to the preset correction value is the correction value of the MCS index value. If it cannot be within the preset range, continue to adjust according to the above principles until the probability of retransmitting the information NACK meets the preset range.
  • the feedback information is the probability of the confirmation information ACK
  • the probability of the retransmission information NACK can be obtained after the probability of the confirmation information ACK is obtained.
  • the feedback information is the correction method when the retransmission information NACK is corrected.
  • Step 406 Feed back the MCS index value corrected by the correction value to the terminal. This step is similar to step 103, and will not be repeated here.
  • the preset model is the third type of mapping function.
  • the probability of the terminal's feedback information is obtained. If the probability of the feedback information is within the preset range, the MCS index value is used The initial correction value of the MCS index value is the correction value of the MCS index value. If the probability of the feedback information is not within the preset range, the value after the initial correction value of the MCS index value is corrected is the correction value of the MCS index value.
  • the traditional link adaptive adjustment is to correct the correction amount of the modulation and coding scheme index value according to the retransmission or confirmation signal. This correction often takes a long time; however, in this embodiment, the preset model and the correction value are both pre-modified. Existing, so this correction process does not need to take a long time, not only makes the terminal's link convergence faster, but also has a higher throughput within a certain period of time, and it also saves resources.
  • the fifth embodiment of the present application relates to a link adaptive adjustment device, as shown in FIG. 8, including:
  • the characteristic data acquisition module 501 is configured to acquire characteristic data of the terminal at the time of link access.
  • the base station obtains the characteristic data of the terminal at the time of link access and the type of historical characteristic data of the terminal used for link convergence in the process of training the preset model. Unanimous.
  • the correction value obtaining module 502 is configured to obtain the correction value of the MCS index value of the modulation and coding scheme according to the characteristic data and the preset model; wherein, the preset model is obtained by training the historical characteristic data of the terminal where the link converges.
  • the feedback module 503 is 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.
  • the characteristic data acquisition module 501 is used to acquire characteristic data of the terminal at the moment of link access; the preset model is the first type of mapping function; the first type of mapping function is used to determine the The category to which the terminal belongs; the category is obtained by dividing the value range of the correction value of the MCS index value of the terminal where the link converges.
  • the correction value obtaining module 502 is specifically configured to determine the category to which the terminal belongs according to the characteristic data and the preset model; obtain 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; feedback module 503. Used 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.
  • the characteristic data acquisition module 501 is used to acquire characteristic data of the terminal at the moment of link access;
  • the preset model includes N second-type mapping functions; the second-type mapping functions are used to obtain The modified value of the MCS index value of the terminal;
  • N is the number of categories obtained by dividing according to the value range of the characteristic data of the terminal where the link converges; each category corresponds to the N second-type mapping functions one-to-one .
  • the correction value acquisition module 502 is specifically configured to determine the category to which the terminal belongs according to the characteristic data; select the second type of mapping function corresponding to the category to which the terminal belongs according to the category to which the terminal belongs; according to the characteristic data and the selected second type of mapping function and The preset model obtains the correction value of the MCS index value of the terminal; the feedback module 503 is used 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 .
  • the characteristic data acquisition module 501 is used to acquire characteristic data of the terminal at the time of link access; the preset model is the third type of mapping function; the third type of mapping function is used to obtain the terminal’s information according to the characteristic data. Probability of feedback information. According to the characteristic data and the third type of mapping function, the probability of the feedback information of the terminal is obtained; the correction value obtaining module 502 is specifically configured to use the initial value of the MCS index value if the probability of the feedback information is within a preset range The correction value is the correction value of the MCS index value.
  • the initial correction value of the MCS index value is corrected to obtain the correction of the MCS index value
  • the initial correction value of the MCS index value is the characteristic data of the terminal at the time of link access; the feedback module 503 is used to feed back the MCS index value corrected by the correction value to the terminal, where the corrected MCS index
  • the value is used for link adaptive adjustment.
  • this embodiment is a system example corresponding to the first embodiment, the second embodiment, the third embodiment, and the fourth embodiment.
  • This embodiment can be compared with the first embodiment, the second embodiment, and the fourth embodiment.
  • the third embodiment and the fourth embodiment are implemented in cooperation with each other.
  • the related technical details mentioned in the first embodiment, the second embodiment, the third embodiment and the fourth embodiment are still valid in this embodiment. In order to reduce repetition, they will not be repeated here.
  • the related technical details mentioned in this embodiment can also be applied to the first embodiment, the second embodiment, the third embodiment, and the fourth embodiment.
  • modules involved in this embodiment are all logical modules.
  • a logical unit can be a physical unit, a part of a physical unit, or multiple physical units. The combination of units is realized.
  • this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.
  • the sixth implementation manner of the present application relates to a server. As shown in FIG. 9, it includes at least one processor 602; and a memory 601 communicatively connected with the at least one processor; wherein, the memory 601 stores data that can be used by at least one processor 602.
  • the executed instructions are executed by the at least one processor 602, so that the at least one processor 602 can execute the implementation of the foregoing link adaptive adjustment method.
  • the memory 601 and the processor 602 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects one or more various circuits of the processor 602 and the memory 601 together.
  • the bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are all known in the art, and therefore, no further description will be given herein.
  • the bus interface provides an interface between the bus and the transceiver.
  • the transceiver may be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on the transmission medium.
  • the data processed by the processor is transmitted on the wireless medium through the antenna, and further, the antenna also receives the data and transmits the data to the processor.
  • the processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions.
  • the memory can be used to store data used by the processor when performing operations.
  • the seventh embodiment of the present application relates to a computer-readable storage medium that stores a computer program, and the computer program is executed by a processor to implement the foregoing method embodiments.
  • the program is stored in a storage medium and includes several instructions to enable a device ( It may be a single-chip microcomputer, a chip, etc.) or a processor (processor) that executes all or part of the steps of the method described in each embodiment of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .
  • the MCS index value corrected by the correction value is used for linking.
  • Road adaptive adjustment Since it takes into account that the characteristic data of each terminal at the time of link access is different, the optimal modulation and coding scheme for each terminal can be intelligently obtained according to the characteristic data and preset model of each terminal at the time of link access.
  • the modified value of the MCS index value so that each terminal is assigned a more matching channel with the MCS index value modified by the modified value for link adaptive adjustment, so that the terminal's link convergence is faster, and the throughput within a certain period of time is also higher.

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Abstract

本申请实施例公开了一种链路自适应调整方法、装置、服务器及存储介质。本申请中，获取终端在链路接入时刻的特征数据；根据特征数据和预设模型获取调制编码方案MCS索引值的修正值；其中，预设模型通过链路收敛的终端的历史特征数据训练得到；将以修正值修正后的MCS索引值反馈给终端，其中，修正后的MCS索引值用于链路自适应调整。

Description

一种链路自适应调整方法、装置、服务器及存储介质
相关申请的交叉引用
本申请基于申请号为201910851432.6、申请日为2019年9月10日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以引入方式并入本申请。
技术领域
本申请实施例涉及通信领域，特别涉及一种链路自适应调整方法、装置、服务器及存储介质。
背景技术
随着移动通信技术的不断发展，第四代移动通信技术4G已经普及并且成熟商用，第五代移动通信技术5G也即将商用，人们对于通信的高速率、高可靠性以及低时延提出了更高的要求。为了提高传输的可靠性，并使通信系统能够适配当前信道，提高数据传输效率，4G和5G通信系统都引入了混合自动重传请求和链路自适应技术来达到上述目的。
由于不同的用户终端采用不同的基带芯片，对于信道质量指示的测量方式可能有差异，因此直接根据用户终端上报的信道质量指示确定的当前时刻调度的调制编码方案索引值往往不是匹配信道的最优调制编码方案索引值，故目前通信设备厂商在确定最后调度的调制编码方案索引值时，都会根据用户终端反馈的信道质量指示对应的调制编码方案索引值做出修正。一种常用的经典修正方法是根据外场测试的经验，在每个用户终端调度开始时刻，给信道质量指示对应的调制编码方案索引值上加一个固定调制编码方案索引值的修正量初始值，并在调度过程中，根据用户终端反馈的重传或确认信号对调制编码方案索引值的修正量进行实时调节。
然而，发明人发现现有技术中至少存在如下问题：由于同一个小区下不同用户终端的信道状态以及信道质量指示的测量误差可能相差很大，因此对于单个用户终端来说，经过固定调制编码方案索引值的修正量初始值修正后的调制编码方案往往不是能够匹配当前信道的最优调制编码方案。
发明内容
本申请实施方式的目的在于提供一种链路自适应调整方法、装置、服务器及存储介质。
本申请的实施方式提供了一种链路自适应调整方法，包括：获取终端在链路接入时刻的特征数据；根据特征数据和预设模型获取调制编码方案MCS索引值的修正值；其中，预设模型通过链路收敛的终端的历史特征数据训练得到；将以修正值修正后的MCS索引值反馈给终端，其中，修正后的MCS索引值用于链路自适应调整。
本申请的实施方式还提供了一种链路自适应调整装置，包括：特征数据获取模块，用于获取终端在链路接入时刻的特征数据；修正值获取模块，用于根据特征数据和预设模型获取调制编码方案MCS索引值的修正值；其中，预设模型通过链路收敛的终端的历史特征数据训练得到；反馈模块，用于将以修正值修正后的MCS索引值反馈给终端，其中，修正后的MCS索引值用于链路自适应调整。
本申请的实施方式还提供了一种服务器，包括：至少一个处理器；以及，与至少一个处理器通信连接的存储器；其中，存储器存储有可被至少一个处理器执行的指令，指令被至少一个处理器执行，以使至少一个处理器能够执行上述链路自适应调整方法。
本申请的实施方式还提供了一种计算机可读存储介质，存储有计算机程序，计算机程序被处理器执行时实现上述链路自适应调整方法。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1是根据本申请第一实施方式中的链路自适应调整方法的流程图；
图2是根据本申请第二实施方式中的链路自适应调整方法的流程图；
图3是根据本申请第二实施方式中的第一映射函数训练过程的流程图；
图4是根据本申请第三实施方式中的链路自适应调整方法的流程图；
图5是根据本申请第三实施方式中的第二映射函数训练过程的流程图；
图6是根据本申请第四实施方式中的链路自适应调整方法的流程图；
图7是根据本申请第四实施方式中的第三映射函数训练过程的流程图；
图8是根据本申请第五实施方式中的链路自适应调整装置的结构图；
图9是根据本申请第六实施方式中的服务器的结构图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图对本申请的各实施方式进行详细的阐述。然而，本领域的普通技术人员可以理解，在本申请各实施方式中，为了使读者更好地理解本申请而提出了许多技术细节。但是，即使没有这些技术细节和基于以下各实施方式的种种变化和修改，也可以实现本申请所要求保护的技术方案。以下各个实施例的划分是为了描述方便，不应对本申请的具体实现方式构成任何限定，各个实施例在不矛盾的前提下可以相互结合相互引用。
本申请的第一实施方式涉及一种链路自适应调整方法，本实施方式可应用于基站。在本实施方式中，获取终端在链路接入时刻的特征数据，根据特征数据和预设模型获取调制编码方案MCS索引值的修正值，将以修正值修正后的MCS索引值反馈给终端。
下面对本实施方式的一种链路自适应调整方法的实现细节进行具体的说明，以下内容仅为方便理解提供的实现细节，并非实施本方案的必须。
本实施方式中的链路自适应调整方法的流程图如图1所示，包括：
步骤101，获取终端在链路接入时刻的特征数据。
具体地说，链路是指基站和终端之间传播电磁波的路径空间。基站获取的特征数据是终端通过该链路将其在链路接入时刻的特征数据发送给基站的。
由于基站在获取终端在链路接入时刻的特征数据之前，需要通过预先收集的链路收敛终端的特征数据对预设模型进行训练，在获取终端在链路接入时刻的特征数据之后，基站还需要将获取的特征数据输入预设模型中，由此可见，在训练预设模型过程中的历史特征数据与终端输入到预设模型中的特征数据种类一致。
步骤102，根据特征数据和预设模型获取调制编码方案MCS索引值的修正值。
具体地说，调制编码方案为链路自适应技术中的一种，根据信道的变化调整终端传输的调制方式和编码速率，在信道条件比较好的时候，提高调制等级和编码速率，在信道条件比较差的时候，降低调制等级以及编码速率；其中，每一个MCS索引其实对应了一组参数下的物理传输速率。
预设模型是基站通过预先收集的链路收敛的终端的历史特征数据训练得到的，链路收敛的终端为根据终端的反馈信息标记的终端，具体为链路收敛的终端是基站根据预先收集的每个大包业务的终端的确认信号或/和重传信号进行标记的终端，进行标记的终端的数量应该足够大。因为只有链路收敛的终端的特征数据才是对构建预设模型有用的数据，所以通过对链 路收敛的终端进行标记，基站就可以通过标记区分链路收敛的终端和链路未收敛的终端，使得基站可以根据标记去收集链路收敛的终端的特征数据。这样基站就可以根据预先收集的根据终端的反馈信息标记的终端的历史特征数据和对应的标签值，进行训练得到预设模型，且标记的终端的数量足够大，使得训练得到的预设模型更加的准确。
基站在获取终端在链路接入时刻的特征数据之后，将获取的特征数据输入到预设模型中，获取调制编码方案MCS索引值的修正值。
值得注意的是，在具体的应用中，基站不断更新链路收敛的终端的特征数据，并每隔一个周期根据更新的特征数据对预设模型进行重新训练，以适应场景的变化，使得根据预设模型获取的MCS索引值的修正值更加准确。
步骤103，将以修正值修正后的MCS索引值反馈给终端。
具体地说，修正值修正后的MCS索引值为MCS索引值的修正值和终端的信道质量指示CQI对应的MCS索引值之和，基站在得到以修正值修正后的MCS索引值之后，将该MCS索引值反馈给终端，终端根据该MCS索引值进行链路自适应调整。
传统的链路自适应调整方法是在每个用户终端调度开始时刻，给信道质量指示对应的调制编码方案索引值上加一个固定调制编码方案索引值的修正量初始值。但是由于同一个小区下不同用户终端的信道状态以及信道质量指示的测量误差可能相差很大，因此对于单个用户终端来说，经过固定调制编码方案索引值的修正量初始值修正后的调制编码方案往往不是能够匹配当前信道的最优调制编码方案。本实施方式相较于传统的链路自适应调整方法，可以有针对性的、智能的为每个新接入的终端分配一个最优的MCS索引值的修正值，从而为每个终端分配一个更加匹配信道的MCS索引值进行链路自适应调整，使得终端的链路收敛更快，一定时间内的吞吐量也更高。
本申请的第二实施方式涉及一种链路自适应调整方法。本实施方式与第一实施方式大致相同，不同之处在于：在本实施方式中，预设模型是通过分类法数据训练得到的，预设模型为第一类映射函数，根据特征数据和第一类映射函数判断终端的所属类别，并根据类别对应的MCS索引值的修正值的取值范围，获取终端的MCS索引值的修正值。
本实施方式中的链路自适应调整方法的流程图如图2所示，包括：
步骤201，获取终端在链路接入时刻的特征数据。本步骤与步骤101类似，在此不再赘述。
步骤202，根据特征数据和第一类映射函数判断终端所属的类别。
具体地说，特征数据为分类法特征数据，预设模型为第一类映射函数，第一类映射函数 用于根据分类法特征数据判断终端所属的类别，即基站将分类法特征数据输入到第一类映射函数中，得到终端所属的类别。
值得注意的是，类别是通过对链路收敛的终端的MCS索引值的修正值的取值范围进行划分得到，划分方式可以为等间隔划分，也可以为不等间隔划分；并根据每个类别中的MCS索引值的修正值的取值范围为每个类别预先分配一个MCS索引值的修正值。例如：链路收敛的终端的MCS索引值的修正值的取值范围为[-28,28]，将该取值范围划分为10类，并为每个类别预先分配一个MCS索引值的修正值。具体如下：
类别1：[-28,-20)，修正值-24；
类别2：[-20,-15)，修正值-17；
类别3：[-15,-10)，修正值-12；
类别4：[-10,-5)，修正值-7；
类别5：[-5,0)，修正值-2；
类别6：[0,5)，修正值2；
类别7：[5,10)，初始值7；
类别8：[10,15)，修正值12；
类别9：[15,20)，修正值17；
类别10：[20,28]，修正值24。
值得注意的是，第一类映射函数通过以下方式训练得到，流程图如图3所示，包括：
步骤2001，获取链路收敛的终端的历史特征数据作为训练数据。
具体地说，基站预先收集链路收敛的终端的历史特征数据作为训练数据。历史特征数据至少包括以下之一或其任意组合：信道质量指示CQI、波束赋形增益、水平到达角度、垂直到达角度、上行探测参考信号、下行路损；值得注意的是，特征数据还可以为其他数据，本实施方式不做具体的限定。
步骤2002，根据训练数据和训练数据对应的终端所属的类别，进行训练得到第一类映射函数。
具体地说，训练数据对应的终端所属的类别是根据训练数据对应的终端的MCS索引值的修正值确定的，按照MCS索引值的修正值将训练数据对应的终端归入各个类别，并根据训练数据和训练数据对应的终端所属的类别，进行训练得到第一类映射函数。例如：若训练数据对应的终端的MCS索引值的修正值为26，则训练数据对应的终端所属的类别为类别10。
步骤203，根据类别对应的MCS索引值的修正值的取值范围，获取终端的MCS索引值 的修正值。
具体地说，由于已经为每个类别预先分配了一个MCS索引值的修正值，所以根据类别对应的MCS索引值的修正值的取值范围，可以获取终端的MCS索引值的修正值。例如：若根据特征数据和第一映射函数判断终端所属的类别为类别10，则终端的MCS索引值的修正值为24。
步骤204，将以修正值修正后的MCS索引值反馈给终端。本步骤与步骤103类似，在此不再赘述。
本实施方式中，预设模型为第一类映射函数，是根据大量数据训练得到的，因此根据特征数据和预设模型判断的终端的所属类别准确度较高，从而根据类别对应的MCS索引值的修正值的取值范围获取的终端MCS索引值的修正值也更加准确，使得终端的链路收敛更快，一定时间内的吞吐量也更高。
本申请的第三实施方式涉及一种链路自适应调整方法。本实施方式与第一实施方式大致相同，不同之处在于：在本实施方式中，预设模型是通过回归法数据训练得到的，预设模型包括N个第二类映射函数，根据特征数据判断终端所属的类别；根据终端所属的类别和预设模型获取终端的所述MCS索引值的修正值。
本实施方式中的链路自适应调整方法的流程图如图4所示，包括：
步骤301，获取终端在链路接入时刻的特征数据。本步骤与步骤101类似，在此不再赘述。
步骤302，根据特征数据判断终端所属的类别。
具体地说，特征数据为回归法特征数据，基站根据回归法特征数据判断终端所属的类别。类别是通过对所述链路收敛的终端的特征数据的取值范围进行划分得到，划分为N个类别，N为正整数。例如：选取所述链路收敛的终端的特征数据中的信道质量指示CQI和下行路损，CQI的取值范围为[0,15]，下行路损的取值范围为[-120,-90]，将这2个特征数据划分出6个类别，具体为：
类别1：CQI取值[0,5)，下行路损取值[-120,-105)；
类别2：CQI取值[0,5)，下行路损取值[-105,-90]；
类别3：CQI取值[5,10)，下行路损取值[-120,-105)；
类别4：CQI取值[5,10)，下行路损取值[-105,-90]；
类别5：CQI取值[10,15]，下行路损取值[-120,-105)；
类别6：CQI取值[10,15]，下行路损取值[-105,-90]。
根据回归法特征数据判断终端所属的类别为根据回归法特征数据的取值判断终端所属的类别，例如：特征数据中的CQI为12，下行路损为-95，则终端所属的类别为类别6。
步骤303，选择与所述终端所属的类别对应的第二类映射函数。
具体地说，预设模型包括N个第二类映射函数；第二类映射函数用于根据回归法特征数据得到终端的MCS索引值的修正值；各类别与N个第二类映射函数一一对应。基站根据终端所属的类别确定与该类别对应的第二类映射函数，根据对应的第二类映射函数获取终端的所述MCS索引值的修正值。例如：终端所属的类别为类别6，则根据与类别6对应的第二类映射函数。
值得注意的是，第二类映射函数通过以下方式训练得到，流程图如图5所示，包括：
步骤3001，获取链路收敛的终端的历史特征数据作为训练数据。
具体地说，历史特征数据至少包括信道质量指示CQI、波束赋形增益、水平到达角度、垂直到达角度、上行探测参考信号、下行路损的一种或多种；值得注意的是，历史特征数据还可以为其他数据，本实施方式不做具体的限定。
步骤3002，根据训练数据和训练数据对应的MCS索引值的修正值，进行训练得到第二类映射函数。
具体地说，根据各类别下的训练数据和训练数据对应的MCS索引值的修正值对预设模型初始的N个第二类映射函数进行训练得N个述第二类映射函数。
步骤304，根据特征数据和选择的第二类映射函数获取终端的MCS索引值的修正值。
具体地说，基站将回归法特征数据输入到选择的第二类映射函数获取输出值，并对输出值进行取整，并将取整后的值作为终端的所述MCS索引值的修正值。值得注意的是，可以根据需要进行四舍五入取整或舍弃小数点取整，本实施方式不做限定。例如：终端所属的类别为类别6，则根据与类别6对应的第二类映射函数获取终端的所述MCS索引值的修正值。
步骤305，将以修正值修正后的MCS索引值反馈给终端。本步骤与步骤103类似，在此不再赘述。
本实施方式中，预设模型包括N个第二类映射函数，N个第二类映射函数都是根据大量数据训练得到的，因此，根据终端所属的类别和预设模型获取的终端MCS索引值的修正值也更加准确，使得终端的链路收敛更快，一定时间内的吞吐量也更高。
本申请的第四实施方式涉及一种链路自适应调整方法。本实施方式与第一实施方式大致相同，不同之处在于：在本实施方式中，预设模型通过逻辑回归法数据训练得到，预设模型具体为第三类映射函数，根据特征数据和预设模型得到终端的反馈信息的概率；若反馈信息 的概率在预设范围之内，则以MCS索引值的初始修正值为MCS索引值的修正值，若反馈信息的概率不在预设范围之内，根据所述反馈信息的概率与所述预设范围的关系，对所述MCS索引值的初始修正值进行修正得到MCS索引值的修正值，其中，所述MCS索引值的初始修正值为所述终端在链路接入时刻的特征数据。
本实施方式中的链路自适应调整方法的流程图如图6所示，包括：
步骤401，获取终端在链路接入时刻的特征数据。本步骤与步骤101类似，在此不再赘述。
步骤402，根据特征数据和预设模型得到终端的反馈信息的概率。
具体地说，特征数据为逻辑回归法特征数据，基站将获取的逻辑回归法特征数据输入到第三类映射函数中，得到终端的反馈信息的概率。预设模型为第三类映射函数；第三类映射函数用于根据逻辑回归法特征数据得到所述终端的反馈信息的概率，即根据逻辑回归法特征数据和第三映射函数得到终端的反馈信息的概率。反馈信息的概率可以为确认信息ACK的概率，也可以为重传信息NACK的概率，本实施方式不做限定。
值得注意的是，第三类映射函数通过以下方式训练得到，流程图如图7所示，包括：
步骤4001，获取链路收敛的终端的历史特征数据作为训练数据。
具体地说，历史特征数据包括MCS索引值的初始修正值以及至少包括以下之一或其任意组合：信道质量指示CQI、波束赋形增益、水平到达角度、垂直到达角度、上行探测参考信号、下行路损、MCS索引值；值得注意的是，历史特征数据还可以为其他数据，本实施方式不做具体的限定。
步骤4002，根据训练数据和训练数据对应的终端的反馈信息的概率，进行训练得到所述第三类映射函数。
步骤403，判断反馈信息的概率是否在预设范围值之内。若反馈信息的概率在预设范围之内，则进入步骤404，若反馈信息的概率不在预设范围之内，则进入步骤405。
步骤404，以MCS索引值的初始修正值为MCS索引值的修正值。
具体地说，MCS索引值的初始修正值为终端在链路接入时刻获取的逻辑回归法特征数据，且终端需将该数据发送给基站。若反馈信息的概率在预设范围之内，以MCS索引值的初始修正值为MCS索引值的修正值。
步骤405，根据反馈信息的概率与所述预设范围的关系，对MCS索引值的初始修正值进行修正得到MCS索引值的修正值。
具体地说，在反馈信息为重传信息NACK时，根据终端在链路接入时刻的逻辑回归法特 征数据以及第三类映射函数，预测当前时刻该终端反馈的重传信号NACK的概率，值得注意的是，逻辑回归法特征数据中包括MCS索引值的初始修正值。若重传信息NACK的概率小于预设范围的最小值，则认为当前的MCS索引值低于当前信道质量，应该上调MCS索引值以提升终端的数据传输速率，根据预设修正值对所述MCS索引值的初始修正值进行上调；若重传信息NACK的概率大于预设范围的最大值，则认为当前的MCS索引值高于当前信道质量，会造成误块率高于标准值，应该下调MCS索引值，根据预设修正值对所述MCS索引值的初始修正值进行下调。预设修正量可以为1阶MCS索引值或n阶MCS索引值，n为正整数，可以根据具体情况进行设定，本实施方式不做限定。
并再次根据修正后的时刻的逻辑回归法特征数据和预设模型重新预测得到终端的重传信息NACK的概率，修正后的时刻的逻辑回归法特征数据包括MCS索引值的初始修正值，且该时刻的MCS索引值的初始修正值为上一步骤中根据预设修正值对MCS索引值的初始修正值进行修正得到MCS索引值的修正值。若重新获取的NACK的概率在预设范围之内，则以根据预设修正值修正后的MCS索引值为MCS索引值的修正值。若不能预设范围之内，则继续按照上述原则进行调整，直到重传信息NACK的概率满足预设范围。
在反馈信息为确认信息ACK的概率时，由于确认信息ACK的概率与重传信息NACK的概率之和为1，可以在获取确认信息ACK的概率后，得到重传信息NACK的概率，从而可以根据反馈信息为重传信息NACK时的修正方法方法进行修正。
步骤406，将以修正值修正后的MCS索引值反馈给终端。本步骤与步骤103类似，在此不再赘述。
本实施方式中，预设模型为第三类映射函数，根据逻辑回归法特征数据和预设模型得到终端的反馈信息的概率，若反馈信息的概率在预设范围之内，则以MCS索引值的初始修正值为MCS索引值的修正值，若反馈信息的概率不在预设范围之内，以MCS索引值的初始修正值修正后的值为MCS索引值的修正值。传统的链路自适应调整是根据重传或确认信号对调制编码方案索引值的修正量进行修正，这个修正往往需要一段较长的时间；但是本实施方式由于预设模型和修正值都是预先存在的，所以这个修正过程不需要花费很长的时间，不仅使得终端的链路收敛更快，一定时间内的吞吐量也更高，还节约了资源。
本申请第五实施方式涉及一种链路自适应调整装置，如图8所示，包括：
特征数据获取模块501，用于获取终端在链路接入时刻的特征数据。
具体地说，终端在链路接入时刻的特征数据有多种，基站获取终端在链路接入时刻的特征数据与训练预设模型过程中所采用的链路收敛的终端的历史特征数据种类一致。
修正值获取模块502，用于根据特征数据和预设模型获取调制编码方案MCS索引值的修正值；其中，预设模型通过链路收敛的终端的历史特征数据训练得到。
反馈模块503，用于将以修正值修正后的MCS索引值反馈给终端，其中，修正后的MCS索引值用于链路自适应调整。
在一个具体的例子中，特征数据获取模块501，用于获取终端在链路接入时刻的特征数据；预设模型为第一类映射函数；第一类映射函数用于根据特征数据判断所述终端所属的类别；类别通过对链路收敛的终端的MCS索引值的修正值的取值范围进行划分得到。修正值获取模块502具体用于根据特征数据和预设模型判断终端所属的类别；根据类别对应的MCS索引值的修正值的取值范围，获取终端的所述MCS索引值的修正值；反馈模块503，用于将以修正值修正后的MCS索引值反馈给终端，其中，修正后的MCS索引值用于链路自适应调整。
在一个具体的例子中，特征数据获取模块501，用于获取终端在链路接入时刻的特征数据；预设模型包括N个第二类映射函数；第二类映射函数用于根据特征数据得到所述终端的MCS索引值的修正值；N为根据链路收敛的终端的特征数据的取值范围进行划分得到的类别个数；各类别与所述N个第二类映射函数一一对应。修正值获取模块502具体用于根据特征数据判断所述终端所属的类别；根据终端所属的类别选择与终端所属的类别对应的第二类映射函数；根据特征数据和选择的第二类映射函数和预设模型获取终端的所述MCS索引值的修正值；反馈模块503，用于将以修正值修正后的MCS索引值反馈给终端，其中，修正后的MCS索引值用于链路自适应调整。
在一个具体的例子中，特征数据获取模块501，用于获取终端在链路接入时刻的特征数据；预设模型为第三类映射函数；第三类映射函数用于根据特征数据得到终端的反馈信息的概率。根据所述特征数据和所述第三类映射函数得到所述终端的反馈信息的概率；修正值获取模块502具体用于若反馈信息的概率在预设范围之内，则以MCS索引值的初始修正值为MCS索引值的修正值，若反馈信息的概率不在预设范围之内，根据反馈信息的概率与预设范围的关系，对MCS索引值的初始修正值进行修正得到MCS索引值的修正值，其中，MCS索引值的初始修正值为终端在链路接入时刻的特征数据；反馈模块503，用于将以修正值修正后的MCS索引值反馈给终端，其中，修正后的MCS索引值用于链路自适应调整。
不难发现，本实施方式为与第一实施方式、第二实施方式、第三实施方式和第四实施方式相对应的系统实施例，本实施方式可与第一实施方式、第二实施方式、第三实施方式和第四实施方式互相配合实施。第一实施方式、第二实施方式、第三实施方式和第四实施方式中 提到的相关技术细节在本实施方式中依然有效，为了减少重复，这里不再赘述。相应地，本实施方式中提到的相关技术细节也可应用在第一实施方式、第二实施方式、第三实施方式和第四实施方式。
值得一提的是，本实施方式中所涉及到的各模块均为逻辑模块，在实际应用中，一个逻辑单元可以是一个物理单元，也可以是一个物理单元的一部分，还可以以多个物理单元的组合实现。此外，为了突出本申请的创新部分，本实施方式中并没有将与解决本申请所提出的技术问题关系不太密切的单元引入，但这并不表明本实施方式中不存在其它的单元。
本申请第六实施方式涉及一种服务器，如图9所示，包括至少一个处理器602；以及，与至少一个处理器通信连接的存储器601；其中，存储器601存储有可被至少一个处理器602执行的指令，指令被至少一个处理器602执行，以使至少一个处理器602能够执行上述链路自适应调整方法的实施方式。
其中，存储器601和处理器602采用总线方式连接，总线可以包括任意数量的互联的总线和桥，总线将一个或多个处理器602和存储器601的各种电路连接在一起。总线还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路连接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口在总线和收发机之间提供接口。收发机可以是一个元件，也可以是多个元件，比如多个接收器和发送器，提供用于在传输介质上与各种其他装置通信的单元。经处理器处理的数据通过天线在无线介质上进行传输，进一步，天线还接收数据并将数据传送给处理器。
处理器负责管理总线和通常的处理，还可以提供各种功能，包括定时，外围接口，电压调节、电源管理以及其他控制功能。而存储器可以被用于存储处理器在执行操作时所使用的数据。
本申请第七实施方式涉及一种计算机可读存储介质，存储有计算机程序，计算机程序被处理器执行时实现上述方法实施例。
即，本领域技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
在本申请实施方式中，通过获取终端在链路接入时刻的特征数据，并根据特征数据和预设模型获取调制编码方案MCS索引值的修正值，以修正值修正后的MCS索引值进行链路自适应调整。由于考虑到了每个终端在链路接入时刻的特征数据不同，所以根据每个终端在链路接入时刻的特征数据和预设模型可以智能的获取针对每个终端的最优的调制编码方案MCS索引值的修正值，从而为每个终端分配一个更加匹配信道的以修正值修正后的MCS索引值进行链路自适应调整，使得终端的链路收敛更快，一定时间内的吞吐量也更高。
本领域的普通技术人员可以理解，上述各实施方式是实现本申请的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。

Claims (12)

  1. 一种链路自适应调整方法，包括：
    获取终端在链路接入时刻的特征数据；
    根据所述特征数据和预设模型获取调制编码方案MCS索引值的修正值；其中，所述预设模型通过链路收敛的终端的历史特征数据训练得到；
    将以所述修正值修正后的MCS索引值反馈给所述终端，其中，所述修正后的MCS索引值用于链路自适应调整。
  2. 根据权利要求1所述的链路自适应调整方法，其中，所述预设模型为第一类映射函数；所述第一类映射函数用于根据所述特征数据判断所述终端所属的类别；所述类别通过对所述链路收敛的终端的MCS索引值的修正值的取值范围进行划分得到；
    根据所述特征数据和预设模型获取调制编码方案MCS索引值的修正值，包括：
    根据所述特征数据和所述第一类映射函数判断所述终端所属的类别；
    根据所述类别对应的MCS索引值的修正值的取值范围，获取所述终端的所述MCS索引值的修正值。
  3. 根据权利要求2所述的链路自适应调整方法，其中，所述第一类映射函数通过以下方式训练得到，包括：
    获取所述链路收敛的终端的历史特征数据作为训练数据，其中，所述历史特征数据至少包括以下之一或其任意组合：信道质量指示CQI、波束赋形增益、水平到达角度、垂直到达角度、上行探测参考信号、下行路损；
    根据所述训练数据和所述训练数据对应的终端所属的类别，训练得到所述第一类映射函数。
  4. 根据权利要求1所述的链路自适应调整方法，其中，所述预设模型包括N个第二类映射函数；所述第二类映射函数用于根据所述特征数据得到所述终端的MCS索引值的修正值；所述N为根据所述链路收敛的终端的特征数据的取值范围进行划分得到的类别个数；各所述类别与所述N个第二类映射函数一一对应；
    根据所述特征数据和预设模型获取调制编码方案MCS索引值的修正值，包括：
    根据所述特征数据判断所述终端所属的类别；
    选择与所述终端所属的类别对应的第二类映射函数；
    根据所述特征数据和所述选择的第二类映射函数获取所述终端的所述MCS索引值的修 正值。
  5. 根据权利要求4所述的链路自适应调整方法，其中，所述第二类映射函数通过以下方式训练得到：
    获取所述链路收敛的终端的历史特征数据作为训练数据，其中，所述历史特征数据至少包括以下之一或其任意组合：信道质量指示CQI、波束赋形增益、水平到达角度、垂直到达角度、上行探测参考信号、下行路损的；
    根据所述训练数据和所述训练数据对应的MCS索引值的修正值，训练得到所述第二类映射函数。
  6. 根据权利要求1所述的链路自适应调整方法，其中，所述预设模型为第三类映射函数；所述第三类映射函数用于根据所述特征数据得到所述终端的反馈信息的概率，其中，所述反馈信息为重传信息NACK或确认信号ACK；
    根据所述特征数据和预设模型获取调制编码方案MCS索引值的修正值，包括：
    根据所述特征数据和所述第三类映射函数得到所述终端的反馈信息的概率，所述特征数据包括MCS索引值的初始修正值；
    若所述反馈信息的概率在预设范围之内，则以MCS索引值的初始修正值为所述MCS索引值的修正值，若所述反馈信息的概率不在预设范围之内，根据所述反馈信息的概率与所述预设范围的关系，对所述MCS索引值的初始修正值进行修正得到MCS索引值的修正值。
  7. 根据权利要求6所述的链路自适应调整方法，其中，所述若所述反馈信息的概率不在预设范围之内，根据所述反馈信息的概率与所述预设范围的关系，对索引值的初始修正值进行修正得到MCS索引值的修正值，包括：
    步骤1、若所述反馈信息的概率为重传信息NACK的概率，当所述NACK的概率小于预设范围的最小值时，根据预设修正值对所述MCS索引值的初始修正值进行上调；当所述概率大于预设范围的最大值时，根据预设修正值对所述MCS索引值的初始修正值进行下调；
    步骤2、重新获取NACK的概率，若所述重新获取的NACK的概率在预设范围之内，则将根据预设修正值修正后的MCS索引值作为MCS索引值的修正值；
    若所述反馈信息的概率不在预设范围之内，则重新执行步骤1，直至重新获取的NACK的概率在预设范围之内。
  8. 根据权利要求6所述的链路自适应调整方法，其中，所述第三类映射函数通过以下方式训练得到，包括：
    获取所述链路收敛的终端的历史特征数据作为训练数据，其中，所述历史特征数据包括 MCS索引值的初始修正值以及至少包括以下之一或其任意组合：信道质量指示CQI、波束赋形增益、水平到达角度、垂直到达角度、上行探测参考信号、下行路损、MCS索引值；
    根据所述训练数据和所述训练数据对应的终端的反馈信息的概率，训练得到所述第三类映射函数。
  9. 根据权利要求1所述的链路自适应调整方法，其中，所述链路收敛的终端为根据所述终端的反馈信息标记的终端，其中，所述反馈信息为重传信息NACK或确认信号ACK。
  10. 一种链路自适应调整装置，包括：
    特征数据获取模块，用于获取终端在链路接入时刻的特征数据；
    修正值获取模块，用于根据所述特征数据和预设模型获取调制编码方案MCS索引值的修正值；其中，所述预设模型通过链路收敛的终端的历史特征数据训练得到；
    反馈模块，用于将以所述修正值修正后的MCS索引值反馈给所述终端，其中，所述修正后的MCS索引值用于链路自适应调整。
  11. 一种服务器，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1至9中任一所述的链路自适应调整方法。
  12. 一种计算机可读存储介质，存储有计算机程序，其中，所述计算机程序被处理器执行时实现权利要求1至9中任一所述的链路自适应调整方法。
PCT/CN2020/113599 2019-09-10 2020-09-04 一种链路自适应调整方法、装置、服务器及存储介质 WO2021047456A1 (zh)

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