CN113726488A - Configuration method of downlink modulation and coding scheme and base station - Google Patents

Configuration method of downlink modulation and coding scheme and base station Download PDF

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Publication number
CN113726488A
CN113726488A CN202010447826.8A CN202010447826A CN113726488A CN 113726488 A CN113726488 A CN 113726488A CN 202010447826 A CN202010447826 A CN 202010447826A CN 113726488 A CN113726488 A CN 113726488A
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reference signal
coding scheme
terminal
base station
neural network
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CN113726488B (en
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王东
刘磊
邓伟
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China Mobile Communications Group Co Ltd
China Mobile Communications Ltd Research Institute
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China Mobile Communications Group Co Ltd
China Mobile Communications Ltd Research Institute
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/21—Control channels or signalling for resource management in the uplink direction of a wireless link, i.e. towards the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/23—Control channels or signalling for resource management in the downlink direction of a wireless link, i.e. towards a terminal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality

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  • Computer Networks & Wireless Communication (AREA)
  • Quality & Reliability (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The application provides a configuration method and a base station of a downlink modulation and coding scheme, wherein the method comprises the following steps: receiving a first uplink reference signal sent by a first terminal, and obtaining a first transmission parameter of the first uplink reference signal; and inputting the first transmission parameter into a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model. The method and the device can improve the MCS decision efficiency and reduce the downlink resources occupied by the downlink reference signal transmission.

Description

Configuration method of downlink modulation and coding scheme and base station
Technical Field
The present invention relates to the field of mobile communication technologies, and in particular, to a method for configuring a downlink Modulation and Coding Scheme (MCS) and a base station.
Background
Currently, as shown in fig. 1, in the fourth generation or fifth generation mobile communication systems (4G to 5G), the link adaptation process, which determines the downlink MCS index value and performs downlink scheduling, is completed by the following steps.
S1, a base station sends a Cell Reference Signal (CRS) in a downlink mode.
And S2, the terminal (UE) obtains a Channel Quality Indicator (CQI) by measuring the CRS.
And S3, reporting the channel quality to the base station by the UE, for example, reporting a CQI index (CQI index) to the eNB. For example, the length of the CQI index can be 4 bits, and the value range is 0-15.
And S4, the base station decides MCS according to the channel quality reported by the UE, specifically, a downlink MCS index (MCS index) can be calculated according to the CQI index, and a specific calculation mode can refer to the related prior art, which is not described in detail herein.
And S5, the base station schedules the UE according to the calculated downlink MCS index.
Disclosure of Invention
At least one embodiment of the present application provides a method and a base station for configuring a downlink modulation and coding scheme, so as to improve MCS decision efficiency and reduce downlink resources occupied by downlink reference signal transmission.
According to an aspect of the present application, at least one embodiment provides a method for configuring a downlink modulation and coding scheme, which is applied to a base station, and includes:
receiving a first uplink reference signal sent by a first terminal, and obtaining a first transmission parameter of the first uplink reference signal;
and inputting the first transmission parameter into a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
Furthermore, according to at least one embodiment of the present application, before the step of receiving the first uplink reference signal transmitted by the first terminal, the method further includes:
acquiring a plurality of training data, wherein each training data comprises a transmission parameter of an uplink reference signal of a terminal and a value of a downlink modulation and coding scheme configured for the terminal by the base station;
and taking the transmission parameters as input characteristics of a neural network model, taking values of a downlink modulation and coding scheme configured for the terminal by the base station as labels corresponding to the input characteristics, and training the neural network model until preset convergence conditions are met to obtain the trained neural network model.
Furthermore, according to at least one embodiment of the present application, the transmission parameters of the uplink reference signal include: the receiving quality of the uplink reference signal, the expected quality of the uplink reference signal and the direction weight corresponding to the uplink reference signal.
In addition, according to at least one embodiment of the present application, the uplink reference signal is a sounding reference signal, the received quality is reference signal received power RSRP, and the desired quality is a desired block error rate BLER.
Further, in accordance with at least one embodiment of the present application, the neural network model includes an output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is connected in sequence until the output processing layer is connected; and the number of the nodes of the intermediate processing layer is greater than the number of the candidate values of the downlink modulation and coding scheme.
Further, in accordance with at least one embodiment of the present application, the method further comprises:
and scheduling the downlink transmission of the first terminal according to the downlink modulation and coding scheme configured by the first terminal.
According to another aspect of the present application, at least one embodiment provides a base station comprising:
a receiving module, configured to receive a first uplink reference signal sent by a first terminal, and obtain a first transmission parameter of the first uplink reference signal;
and the configuration module is used for inputting the first transmission parameter to a neural network model obtained by pre-training, and configuring the downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
Further, according to at least one embodiment of the present application, the base station further includes:
a data acquisition module, configured to acquire multiple sets of training data, where each set of training data includes a transmission parameter of an uplink reference signal of a terminal and a value of a downlink modulation and coding scheme configured for the terminal by the base station;
and the model training module is used for training the neural network model by taking the transmission parameters as input characteristics of the neural network model and taking values of a downlink modulation and coding scheme configured for the terminal by the base station as labels corresponding to the input characteristics until preset convergence conditions are met, so as to obtain the trained neural network model.
Furthermore, according to at least one embodiment of the present application, the transmission parameters of the uplink reference signal include: the receiving quality of the uplink reference signal, the expected quality of the uplink reference signal and the direction weight corresponding to the uplink reference signal.
In addition, according to at least one embodiment of the present application, the uplink reference signal is a sounding reference signal, the received quality is reference signal received power RSRP, and the desired quality is a desired block error rate BLER.
Further, in accordance with at least one embodiment of the present application, the neural network model includes an output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is connected in sequence until the output processing layer is connected; and the number of the nodes of the intermediate processing layer is greater than the number of the candidate values of the downlink modulation and coding scheme.
Further, according to at least one embodiment of the present application, the base station further includes:
and the scheduling module is used for scheduling the downlink transmission of the first terminal according to the downlink modulation and coding scheme configured by the first terminal.
In accordance with another aspect of the present application, at least one embodiment provides a base station comprising a transceiver and a processor, wherein,
the transceiver is configured to receive a first uplink reference signal sent by a first terminal, and obtain a first transmission parameter of the first uplink reference signal;
and the processor is used for inputting the first transmission parameter to a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
According to another aspect of the application, at least one embodiment provides a computer readable storage medium having a program stored thereon, which when executed by a processor, performs the steps of the method as described above.
Compared with the prior art, the configuration method of the downlink modulation and coding scheme and the base station provided by the embodiment of the application acquire the downlink MCS by using the uplink reference signal which is sent to the base station by the existing terminal in the prior art, so that the downlink reference signal sent to the terminal by the base station in the downlink MCS decision process can be reduced, and the downlink resource occupation of the downlink reference signal sending is reduced. In addition, the terminal does not need to measure the downlink reference signal, and the base station does not need to wait for the channel quality measured by the terminal based on the downlink reference signal, so that the downlink MCS decision time delay can be reduced, and the decision efficiency can be improved.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart of a base station deciding a downlink MCS index and performing scheduling in the prior art;
fig. 2 is a schematic view of an application scenario according to an embodiment of the present application;
fig. 3 is a flowchart of a method for configuring a downlink modulation and coding scheme according to an embodiment of the present application;
FIG. 4 is an exemplary diagram of a neural network model for a neural network-based downlink MCS decision of the present application;
FIG. 5 is a schematic diagram of linear computation in a neural network in an example of the present application;
FIG. 6 is a schematic diagram of the design of the W matrix and V matrix in a neural network in an example of the present application;
fig. 7 is a schematic structural diagram of a base station according to an embodiment of the present application;
fig. 8 is another schematic structural diagram of a base station according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. In the description and in the claims "and/or" means at least one of the connected objects.
The techniques described herein are not limited to NR systems and Long Time Evolution (LTE)/LTE Evolution (LTE-a) systems, and may also be used for various wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single carrier Frequency Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" are often used interchangeably. CDMA systems may implement Radio technologies such as CDMA2000, Universal Terrestrial Radio Access (UTRA), and so on. UTRA includes Wideband CDMA (Wideband Code Division Multiple Access, WCDMA) and other CDMA variants. TDMA systems may implement radio technologies such as Global System for Mobile communications (GSM). The OFDMA system may implement radio technologies such as Ultra Mobile Broadband (UMB), evolved-UTRA (E-UTRA), IEEE 802.21(Wi-Fi), IEEE802.16(WiMAX), IEEE 802.20, Flash-OFDM, etc. UTRA and E-UTRA are parts of the Universal Mobile Telecommunications System (UMTS). LTE and higher LTE (e.g., LTE-A) are new UMTS releases that use E-UTRA. UTRA, E-UTRA, UMTS, LTE-A, and GSM are described in documents from an organization named "third Generation Partnership Project" (3 GPP). CDMA2000 and UMB are described in documents from an organization named "third generation partnership project 2" (3GPP 2). The techniques described herein may be used for both the above-mentioned systems and radio technologies, as well as for other systems and radio technologies. However, the following description describes the NR system for purposes of example, and NR terminology is used in much of the description below, although the techniques may also be applied to applications other than NR system applications.
The following description provides examples and does not limit the scope, applicability, or configuration set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the spirit and scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different than described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Referring to fig. 2, fig. 2 is a block diagram of a wireless communication system to which an embodiment of the present application is applicable. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 may also be referred to as a User terminal or a User Equipment (UE), where the terminal 11 may be a Mobile phone, a Tablet Personal Computer (Tablet Personal Computer), a Laptop Computer (Laptop Computer), a Personal Digital Assistant (PDA), a Mobile Internet Device (MID), a Wearable Device (Wearable Device), or a vehicle-mounted Device, and the specific type of the terminal 11 is not limited in this embodiment. The network device 12 may be a Base Station and/or a core network element, wherein the Base Station may be a 5G or later-version Base Station (e.g., a gNB, a 5G NR NB, etc.), or a Base Station in other communication systems (e.g., an eNB, a WLAN access point, or other access points, etc.), wherein the Base Station may be referred to as a node B, an evolved node B, an access point, a Base Transceiver Station (BTS), a radio Base Station, a radio Transceiver, a Basic Service Set (BSS), an Extended Service Set (ESS), a node B, an evolved node B (eNB), a home node B, a home evolved node B, a WLAN access point, a WiFi node, or some other suitable terminology in the field, as long as the same technical effect is achieved, the Base Station is not limited to a specific technical vocabulary, it should be noted that, in the embodiment of the present application, only the Base Station in the NR system is taken as an example, but does not limit the specific type of base station.
The base stations may communicate with the terminals 11 under the control of a base station controller, which may be part of the core network or some of the base stations in various examples. Some base stations may communicate control information or user data with the core network through a backhaul. In some examples, some of the base stations may communicate with each other, directly or indirectly, over backhaul links, which may be wired or wireless communication links. A wireless communication system may support operation on multiple carriers (waveform signals of different frequencies). A multi-carrier transmitter can transmit modulated signals on the multiple carriers simultaneously. For example, each communication link may be a multi-carrier signal modulated according to various radio technologies. Each modulated signal may be transmitted on a different carrier and may carry control information (e.g., reference signals, control channels, etc.), overhead information, data, and so on.
The base station may communicate wirelessly with the terminal 11 via one or more access point antennas. Each base station may provide communication coverage for a respective coverage area. The coverage area of an access point may be divided into sectors that form only a portion of the coverage area. A wireless communication system may include different types of base stations (e.g., macro, micro, or pico base stations). The base stations may also utilize different radio technologies, such as cellular or WLAN radio access technologies. The base stations may be associated with the same or different access networks or operator deployments. The coverage areas of different base stations (including coverage areas of base stations of the same or different types, coverage areas utilizing the same or different radio technologies, or coverage areas belonging to the same or different access networks) may overlap.
The communication links in a wireless communication system may comprise an Uplink for carrying Uplink (UL) transmissions (e.g., from terminal 11 to network device 12) or a Downlink for carrying Downlink (DL) transmissions (e.g., from network device 12 to terminal 11). The UL transmission may also be referred to as reverse link transmission, while the DL transmission may also be referred to as forward link transmission. Downlink transmissions may be made using licensed frequency bands, unlicensed frequency bands, or both. Similarly, uplink transmissions may be made using licensed frequency bands, unlicensed frequency bands, or both.
After analyzing the downlink MCS decision process in the background art, the inventors found that the above MCS decision process has the following disadvantages:
1) after receiving the downlink reference signal, the terminals in S1-S3 need to measure for a period of time to obtain a channel quality indicator (e.g., CQI), and need to wait for the uplink resource to arrive and then send the channel quality indicator to the base station, which takes a long time.
2) In an environment where the channel changes rapidly (e.g., the terminal is moving at a high speed), the accuracy of the channel quality estimation may be degraded, resulting in a degradation in the accuracy of the finally calculated MCS. For example, the base station periodically transmits a downlink Reference Signal, such as a CRS and a Channel State Information Reference Signal (CSI-RS), and if the transmission period configuration of the downlink Reference Signal is too short, the base station may occupy too many downlink resources; if the sending period configuration of the downlink reference signal is too long, timeliness is affected, which is very obvious in a scene of rapid channel change such as a high-speed rail, so that overall measurement and decision time consumption is long, and in addition, the accuracy of channel quality evaluation is reduced, and the accuracy of the calculated MCS is also reduced.
In order to solve at least one of the above problems, an embodiment of the present application provides a method for configuring a downlink modulation and coding scheme, which is applied to a base station, and can improve decision efficiency of a downlink Modulation and Coding Scheme (MCS) and reduce downlink resources occupied by downlink reference signal transmission. As shown in fig. 3, the configuration method includes:
step 31, receiving a first uplink reference signal sent by a first terminal, and obtaining a first transmission parameter of the first uplink reference signal.
Here, the first terminal transmits an uplink reference signal to the base station (for convenience of description, the uplink reference signal transmitted by the first terminal is referred to as a first uplink reference signal), and the base station receives the first uplink reference signal and acquires a transmission parameter of the first uplink reference signal (for convenience of description, the transmission parameter of the first uplink reference signal is referred to as a first transmission parameter).
The first uplink Reference Signal may be a Sounding Reference Signal (SRS), or may be another existing Reference Signal or a self-defined Reference Signal. The transmission parameters of the first uplink reference signal may include: the receiving quality of the first uplink reference signal, the expected quality of the first uplink reference signal, and the direction weight corresponding to the first uplink reference signal. For example, the Received quality may specifically be Reference Signal Received Power (RSRP), and the desired quality is a Block Error Rate (BLER). The base station may obtain the first transmission parameter by measuring the first uplink reference signal or searching for configuration information of the first terminal. For example, the direction weight of the uplink reference signal may be obtained from a module that processes channel information, and the RSRP may be obtained from a module that receives the uplink SRS.
And step 32, inputting the first transmission parameter to a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
Here, the present application has trained a neural network model in advance, and in step 32, by inputting a first transmission parameter to the neural network model, a first value of a downlink MCS output by the neural network model can be obtained, where the first value can indicate a downlink MCS index value, so that the base station can configure a downlink modulation and coding scheme of the first terminal according to the first value of the downlink MCS.
It can be seen from the above steps that since there is usually a certain correlation between the uplink and downlink channels between the terminal and the base station, the present application utilizes the above characteristics, and utilizes the uplink reference signal that is already sent to the base station by the terminal in the prior art to obtain the downlink MCS, so as to reduce the downlink reference signal that is sent to the terminal by the base station in the downlink MCS decision process, thereby reducing the downlink resources occupied by the downlink reference signal transmission. In addition, the decision process does not need the terminal to measure the downlink reference signal, and the base station does not need to wait for the channel quality measured by the terminal based on the downlink reference signal, so that the decision time delay of the downlink MCS can be reduced, and the decision efficiency can be improved.
After step 32, the base station may also schedule downlink transmission of the first terminal according to the downlink modulation and coding scheme configured by the first terminal. The specific scheduling method is not described herein again.
The neural network model and the training process thereof of the present application are further described below.
The neural network model employed in the present application includes an output processing layer and at least one intermediate processing layer. The at least one intermediate processing layer is connected in sequence until the output processing layer is connected. Here, for half-frame learning performance, the number of nodes of the intermediate processing layer is greater than the number of candidate values of the downlink modulation and coding scheme. For example, when the candidate value of the downlink modulation and coding scheme is 29, the number of nodes of the intermediate processing layer is at least 30.
Before the step 31, the present application needs to train the neural network model at the base station side, and the specific training process is as follows:
A) acquiring multiple training data, where each training data includes a transmission parameter of an uplink reference signal of a certain terminal acquired by the base station and a value (e.g., a downlink MCS index value) of a downlink modulation and coding scheme configured for the terminal by the base station.
Here, the uplink reference signal in the training data is the same type of signal as the first uplink reference signal transmitted by the first terminal in step 31, such as SRS; the transmission parameter of the uplink reference signal in the training data is the same as the first parameter of the first uplink reference signal in step 31, and may specifically be reception quality, expected quality, and direction weight.
B) And taking the transmission parameters as input characteristics of a neural network model, taking values of a downlink modulation and coding scheme configured for the terminal by the base station as labels corresponding to the input characteristics, and training the neural network model until preset convergence conditions are met to obtain the trained neural network model.
Here, an input feature is constructed according to a transmission parameter in training data, a value of a downlink modulation and coding scheme configured by the base station for the terminal is used as a label corresponding to the input feature, and supervised training is performed on a neural network model until a preset convergence condition is met (for example, a preset iteration number is reached), so that the trained neural network model is obtained.
The present application is further described below by way of more detailed examples.
Example 1:
and obtaining the characteristic of deciding the downlink MCS based on the uplink reference signal sent by the base station receiving UE. Obtaining RSRP of SRS from uplink SRS receiving module of base station (using SRS)rsrpExpression), the SRS direction weight is obtained from the channel information processing module of the base station, so as to obtain the uplink signal data related to the decision downlink MCS, which is expressed by a vector as follows:
a＝(SRSrsrpbler, direction weight 1, direction weight 2, … …, direction weight 64)T
Where, bler represents an expected block error rate, which can be obtained from the service parameter configuration of the terminal.
And inputting the vector serving as input data into a preset neural network model. As shown in fig. 4, the neural network-based downlink MCS decision network model provided in this example includes at least 1 intermediate processing layer and 1 output processing layer, and the at least 2 processing layers are arranged in sequence. The input data passes through at least 2 processing layers in sequence. In order to ensure the learning performance of the neural network, the number of nodes of each intermediate processing layer is set to be at least 30. The number of intermediate treatment layers is at least 1. And parameter weight transformation is carried out from the input layer to the middle layer and from the middle layer to the output layer, the parameter weight is collected to form two W matrixes and two V matrixes, and the W matrixes and the V matrixes are specifically arranged in a middle layer design part.
Here, the number of output layer nodes of the neural network model is consistent with the type of downlink MCS to be obtained, and for example, when the downlink MCS index value has 29 values, the number of nodes of the bundle layer is also 29. The output MCS value vector is expressed as follows:
z＝(MCS0，MCS1，MCS2，…，MCS28)T
the objective of the neural network algorithm is to optimize the known input uplink signal data and downlink MCS values as training data through an objective function to obtain a linear fitting relationship between the known input uplink signal data and the known input downlink MCS values, wherein the linear relationship can be expressed by forming W and V matrixes through weight parameters. When the model is put into practical application after training is completed, the uplink signal data of the terminal to be processed is input into the model, and the downlink MCS value output by the model can be obtained.
a) The model training process is as follows:
the input part extracts the following uplink signal data as input data of the model based on the downlink MCS characteristics:
1) the signal strength of the SRS may be the RSRP of the SRS or a signal-to-noise and interference ratio (SINR).
2) The target bler (values such as 10%, 0.001%, etc.) corresponds to different MCS tables.
3) The SRS direction weight obtained by the base station may specifically be 64 bits (corresponding to 64 channels), or may have fewer bits.
4) The input vector is thus represented as:
a＝(SRSrsrpbler, direction weight 1, direction weight 2, … …, direction weight 64)T
Designing an intermediate treatment layer:
1) and designing a W matrix and a V matrix of the neural network. Each line in the neural network represents a parameter weight and the nodes represent linear calculations, as shown in FIG. 5, the output y of each node1＝f(w1*x1+w2*x2+…+wn*xn) The activation function f uses a sigmod function.
2) The specific design of the W matrix and the V matrix in this example is shown in fig. 6.
Wherein a ═ a1,a2,…,a66)TIs a 66-dimensional input vector, W1＝(w(1,1),w(1,2),…,w(1,66)) To obtain the output y of the first node of the intermediate processing layer1The required parameter weight value is expressed as follows:
y1＝g(w(1,1)*a1+w(1,2)*a2+…+w(1,66)*a66)
where g is the activation function, which uses the sigmod function.
The number of nodes of the intermediate processing layer is N, wherein N is not less than 30. Extracting a parameter weight matrix W matrix from an input layer to a middle layer
W＝(W1,W2,…,WN)T
Similarly, the output layer result z is g (V)1*y1,V2*y2…,V29*yN)TExtracting a parameter weight matrix V matrix from the middle layer to the output layer
V＝(V1,V2,…,V29)T
3) The objective function is designed as follows:
cost＝(zp-z)2
wherein z ispFor the desired output (e.g., MCS 3) and the target data used in training, z is the output value obtained by the actual neural network (e.g., MCS 6), and the optimal solution of the parameter weights in the neural network is obtained by adjusting W, V the parameter weight minimization objective function in the matrix.
An output section:
1) based on the SRS strength and other input parameters, the target is to obtain the value of the downlink MCS based on the channel quality CQI.
b) The application comprises the following steps: and after the training result is gradually fitted to obtain a stable neural network model, the real-time downlink MCS judgment can be carried out according to the SRS obtained by the current cell.
Various methods of embodiments of the present application are described above. An apparatus for carrying out the above method is further provided below.
Referring to fig. 7, an embodiment of the present application provides a base station 70, including:
a receiving module 71, configured to receive a first uplink reference signal sent by a first terminal, and obtain a first transmission parameter of the first uplink reference signal;
the configuration module 72 is configured to input the first transmission parameter to a neural network model obtained through pre-training, and configure a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
Optionally, the base station further includes:
a data acquisition module, configured to acquire multiple sets of training data, where each set of training data includes a transmission parameter of an uplink reference signal of a terminal and a value of a downlink modulation and coding scheme configured for the terminal by the base station;
and the model training module is used for training the neural network model by taking the transmission parameters as input characteristics of the neural network model and taking values of a downlink modulation and coding scheme configured for the terminal by the base station as labels corresponding to the input characteristics until preset convergence conditions are met, so as to obtain the trained neural network model.
Optionally, the transmission parameters of the uplink reference signal include: the receiving quality of the uplink reference signal, the expected quality of the uplink reference signal and the direction weight corresponding to the uplink reference signal.
Optionally, the uplink reference signal is a sounding reference signal, the reception quality is reference signal received power RSRP, and the expected quality is an expected block error rate BLER.
Optionally, the neural network model comprises an output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is connected in sequence until the output processing layer is connected; and the number of the nodes of the intermediate processing layer is greater than the number of the candidate values of the downlink modulation and coding scheme.
Optionally, the base station further includes:
and the scheduling module is used for scheduling the downlink transmission of the first terminal according to the downlink modulation and coding scheme configured by the first terminal.
Referring to fig. 8, an embodiment of the present application provides a structural diagram of a base station 800, including: a processor 801, a transceiver 802, a memory 803, and a bus interface, wherein:
in this embodiment, the base station 800 further includes: a program stored on the memory 803 and executable on the processor 801, which when executed by the processor 801, performs the steps of:
receiving a first uplink reference signal sent by a first terminal, and obtaining a first transmission parameter of the first uplink reference signal;
and inputting the first transmission parameter into a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
It can be understood that, in the embodiment of the present application, when being executed by the processor 801, the computer program can implement each process of the configuration method embodiment of the downlink modulation and coding scheme shown in fig. 3, and can achieve the same technical effect, and in order to avoid repetition, details are not described here again.
In FIG. 8, the bus architecture may include any number of interconnected buses and bridges, with one or more processors, represented by the processor 801, and various circuits, represented by the memory 803, linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 802 may be a number of elements including a transmitter and a receiver that provide a means for communicating with various other apparatus over a transmission medium.
The processor 801 is responsible for managing the bus architecture and general processing, and the memory 803 may store data used by the processor 801 in performing operations.
In some embodiments of the present application, there is also provided a computer readable storage medium having a program stored thereon, which when executed by a processor, performs the steps of:
receiving a first uplink reference signal sent by a first terminal, and obtaining a first transmission parameter of the first uplink reference signal;
and inputting the first transmission parameter into a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
When executed by the processor, the program can implement all implementation manners in the configuration method of the downlink modulation and coding scheme applied to the base station, and can achieve the same technical effect, and is not described herein again to avoid repetition.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (13)

1. A method for configuring a downlink modulation and coding scheme, applied to a base station, includes:
receiving a first uplink reference signal sent by a first terminal, and obtaining a first transmission parameter of the first uplink reference signal;
and inputting the first transmission parameter into a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
2. The method of claim 1, wherein prior to the step of receiving the first uplink reference signal transmitted by the first terminal, the method further comprises:
acquiring a plurality of training data, wherein each training data comprises a transmission parameter of an uplink reference signal of a terminal and a value of a downlink modulation and coding scheme configured for the terminal by the base station;
and taking the transmission parameters as input characteristics of a neural network model, taking values of a downlink modulation and coding scheme configured for the terminal by the base station as labels corresponding to the input characteristics, and training the neural network model until preset convergence conditions are met to obtain the trained neural network model.
3. The method of claim 2, wherein the transmission parameters of the uplink reference signal comprise: the receiving quality of the uplink reference signal, the expected quality of the uplink reference signal and the direction weight corresponding to the uplink reference signal.
4. The method of claim 3, wherein the uplink reference signal is a sounding reference signal, the received quality is a Reference Signal Received Power (RSRP), and the desired quality is a desired block error rate (BLER).
5. The method of claim 1, wherein the neural network model comprises an output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is connected in sequence until the output processing layer is connected; and the number of the nodes of the intermediate processing layer is greater than the number of the candidate values of the downlink modulation and coding scheme.
6. The method of claim 1, further comprising:
and scheduling the downlink transmission of the first terminal according to the downlink modulation and coding scheme configured by the first terminal.
7. A base station, comprising:
a receiving module, configured to receive a first uplink reference signal sent by a first terminal, and obtain a first transmission parameter of the first uplink reference signal;
and the configuration module is used for inputting the first transmission parameter to a neural network model obtained by pre-training, and configuring the downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
8. The base station of claim 7, further comprising:
a data acquisition module, configured to acquire multiple sets of training data, where each set of training data includes a transmission parameter of an uplink reference signal of a terminal and a value of a downlink modulation and coding scheme configured for the terminal by the base station;
and the model training module is used for training the neural network model by taking the transmission parameters as input characteristics of the neural network model and taking values of a downlink modulation and coding scheme configured for the terminal by the base station as labels corresponding to the input characteristics until preset convergence conditions are met, so as to obtain the trained neural network model.
9. The base station of claim 7, wherein the neural network model comprises an output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is connected in sequence until the output processing layer is connected; and the number of the nodes of the intermediate processing layer is greater than the number of the candidate values of the downlink modulation and coding scheme.
10. The base station of claim 7, further comprising:
and the scheduling module is used for scheduling the downlink transmission of the first terminal according to the downlink modulation and coding scheme configured by the first terminal.
11. A base station comprising a transceiver and a processor, wherein,
the transceiver is configured to receive a first uplink reference signal sent by a first terminal, and obtain a first transmission parameter of the first uplink reference signal;
and the processor is used for inputting the first transmission parameter to a neural network model obtained by pre-training, and configuring a downlink modulation and coding scheme of the first terminal according to a first value of the downlink modulation and coding scheme output by the neural network model.
12. A base station, comprising: processor, memory and program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the method of configuring a downlink modulation and coding scheme according to any one of claims 1 to 6.
13. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program, which when executed by a processor implements the steps of the method for configuring a downlink modulation and coding scheme according to any one of claims 1 to 6.
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