CN113726488B - 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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CN113726488B
CN113726488B CN202010447826.8A CN202010447826A CN113726488B CN 113726488 B CN113726488 B CN 113726488B CN 202010447826 A CN202010447826 A CN 202010447826A CN 113726488 B CN113726488 B CN 113726488B
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reference signal
uplink reference
coding scheme
terminal
neural network
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CN113726488A (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)
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  • Mobile Radio Communication Systems (AREA)

Abstract

The application provides a configuration method of a downlink modulation and coding scheme and a base station, 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 parameters into a pre-trained neural network model, and configuring the downlink modulation and coding scheme of the first terminal according to the 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 communications technologies, and in particular, to a method and a base station for configuring a downlink modulation and coding scheme (Modulation and Coding Scheme, MCS).
Background
Currently, as shown in fig. 1, in the fourth generation or the fifth generation mobile communication systems (4G to 5G), the link adaptation process, the decision of the downlink MCS index value and the downlink scheduling are completed by the following steps.
S1, a base station transmits a cell reference signal (Cell Reference Signal, CRS) in a downlink mode.
S2, the terminal (UE) obtains channel quality indication (Channel quality indicator, CQI) by measuring the CRS.
S3, the UE reports channel quality to the base station, for example, CQI index (CQI index) is reported to the eNB. For example, the CQI index length may be 4 bits, ranging from 0 to 15.
S4, the base station decides MCS according to the channel quality reported by the UE, specifically, the downlink MCS index (MCS index) can be calculated according to CQI index, and the specific calculation mode can refer to the related prior art, which is not described in detail herein.
S5, the base station schedules the UE according to the calculated downlink MCS index.
Disclosure of Invention
At least one embodiment of the application provides a configuration method of a downlink modulation and coding scheme and a base station, which improve MCS decision efficiency and reduce downlink resource occupation of downlink reference signal transmission.
According to one 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 parameters into a pre-trained neural network model, and configuring the downlink modulation and coding scheme of the first terminal according to the 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 sent by the first terminal, the method further includes:
acquiring a plurality of pieces of training data, wherein each piece of training data comprises transmission parameters of an uplink reference signal of a terminal and values of a downlink modulation and coding scheme configured by the base station for the terminal;
and training the neural network model by taking the transmission parameters as input characteristics of the neural network model and taking the values of the downlink modulation and coding scheme configured by the base station for the terminal as labels corresponding to the input characteristics until the preset convergence condition is 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.
Furthermore, in accordance with at least one embodiment of the present application, the uplink reference signal is a sounding reference signal, the reception quality is a reference signal received power RSRP, and the desired quality is a desired block error rate BLER.
Furthermore, in accordance with at least one embodiment of the present application, the neural network model includes one output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is sequentially connected until the output processing layer is connected; the number of nodes of the intermediate processing layer is larger than the number of candidate values of the downlink modulation and coding scheme.
Furthermore, in accordance with at least one embodiment of the present application, the method further comprises:
and scheduling downlink transmission of the first terminal according to a 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, including:
the receiving module is used for receiving a first uplink reference signal sent by a first terminal and obtaining a first transmission parameter of the first uplink reference signal;
the configuration module is used for inputting the first transmission parameters into a neural network model which is obtained through training in advance, and configuring the downlink modulation and coding scheme of the first terminal according to the first value of the downlink modulation and coding scheme which is output by the neural network model.
Furthermore, according to at least one embodiment of the present application, the base station further comprises:
the data acquisition module is used for acquiring a plurality of training data, wherein each training data comprises transmission parameters of an uplink reference signal of a terminal and values of a downlink modulation and coding scheme configured by the base station for the terminal;
and the model training module is used for taking the transmission parameters as input characteristics of a neural network model, taking the values of the downlink modulation and coding schemes configured by the base station for the terminal as labels corresponding to the input characteristics, training the neural network model until the preset convergence condition is met, and obtaining 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.
Furthermore, in accordance with at least one embodiment of the present application, the uplink reference signal is a sounding reference signal, the reception quality is a reference signal received power RSRP, and the desired quality is a desired block error rate BLER.
Furthermore, in accordance with at least one embodiment of the present application, the neural network model includes one output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is sequentially connected until the output processing layer is connected; the number of nodes of the intermediate processing layer is larger than the number of candidate values of the downlink modulation and coding scheme.
Furthermore, according to at least one embodiment of the present application, the base station further comprises:
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.
According to 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;
the processor is configured to input the first transmission parameter to a neural network model obtained by training in advance, 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.
According to another aspect of the present application, at least one embodiment provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the steps of the method as described above.
Compared with the prior art, the configuration method and the base station of the downlink modulation and coding scheme provided by the embodiment of the invention acquire the downlink MCS by utilizing the uplink reference signal sent to the base station by the existing terminal in the prior art, and can reduce the downlink reference signal sent to the terminal by the base station in the downlink MCS decision process, thereby reducing downlink resource occupation of downlink reference signal transmission. In addition, the method does not need a 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 delay of the downlink MCS 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 designate like parts throughout the figures. 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 in an embodiment of the present application;
fig. 3 is a flowchart of a configuration method of 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 of the neural network-based downlink MCS decisions of the present application;
FIG. 5 is a schematic illustration of linear computation in a neural network in an example of the present application;
FIG. 6 is a schematic diagram of W matrix and V matrix designs in a neural network in examples 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, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be capable of operation in sequences other than those illustrated or described herein, for example. 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. "and/or" in the specification and claims means at least one of the connected objects.
The techniques described herein are not limited to NR systems and long term evolution (Long Time Evolution, LTE)/LTE evolution (LTE-Advanced, LTE-a) systems and may also be used for various wireless communication systems such as code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), single-carrier frequency division multiple access (Single-carrier Frequency-Division Multiple Access, SC-FDMA), and other systems. The terms "system" and "network" are often used interchangeably. A CDMA system may implement radio technologies such as CDMA2000, universal terrestrial radio access (Universal Terrestrial Radio Access, UTRA), and the like. UTRA includes wideband CDMA (Wideband Code Division Multiple Access, WCDMA) and other CDMA variants. TDMA systems may implement radio technologies such as the global system for mobile communications (Global System for Mobile Communication, GSM). OFDMA systems may implement radio technologies such as ultra mobile broadband (UltraMobile Broadband, UMB), evolved UTRA (E-UTRA), IEEE 802.21 (Wi-Fi), IEEE802.16 (WiMAX), IEEE 802.20, flash-OFDM, and the like. UTRA and E-UTRA are parts of the universal mobile telecommunications system (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, LTE-a and GSM are described in the literature from an organization named "third generation partnership project" (3rd Generation Partnership Project,3GPP). CDMA2000 and UMB are described in the literature from an organization named "third generation partnership project 2" (3 GPP 2). The techniques described herein may be used for the systems and radio technologies mentioned above as well as for other systems and radio technologies. However, the following description describes an 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 as 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. Additionally, features described with reference to certain examples may be combined in other examples.
Referring to fig. 2, fig. 2 shows a block diagram of a wireless communication system to which embodiments of the present application are 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), and the terminal 11 may be a terminal-side Device such as a mobile phone, a tablet Computer (Tablet Personal Computer), a Laptop (Laptop Computer), a personal digital assistant (Personal Digital Assistant, PDA), a mobile internet Device (Mobile Internet Device, MID), a Wearable Device (Wearable Device), or a vehicle-mounted Device, which is not limited to a specific type of the terminal 11 in the embodiments of the present application. The network device 12 may be a base station and/or a core network element, where 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 another communication system (e.g., an eNB, a WLAN access point, or other access points, etc.), where the base station may be referred to as a node B, an evolved node B, an access point, a base transceiver station (Base Transceiver Station, a BTS), a radio base station, a radio transceiver, a basic service set (Basic Service Set, BSS), an extended service set (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, and the base station is not limited to a specific technical vocabulary, and in the embodiment of the present application, the base station in the NR system is merely an example, but is not limited to a specific type of the base station.
The base stations may communicate with the terminal 11 under the control of a base station controller, which may be part of the core network or some base stations in various examples. Some base stations may communicate control information or user data with the core network over a backhaul. In some examples, some of these base stations may communicate with each other directly or indirectly over a backhaul link, which may be a wired or wireless communication link. A wireless communication system may support operation on multiple carriers (waveform signals of different frequencies). A multicarrier transmitter may transmit modulated signals on the multiple carriers simultaneously. For example, each communication link may be a multicarrier 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 corresponding coverage area. The coverage area of an access point may be partitioned into sectors that form only a portion of that coverage area. A wireless communication system may include different types of base stations (e.g., macro base stations, micro base stations, or pico base stations). The base station 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 the same or different types of base stations, coverage areas utilizing the same or different radio technologies, or coverage areas belonging to the same or different access networks, may overlap.
The communication link in the wireless communication system may include an Uplink for carrying Uplink (UL) transmissions (e.g., from the terminal 11 to the network device 12) or a Downlink for carrying Downlink (DL) transmissions (e.g., from the network device 12 to the terminal 11). UL transmissions may also be referred to as reverse link transmissions, while DL transmissions may also be referred to as forward link transmissions. Downlink transmissions may be made using licensed bands, unlicensed bands, or both. Similarly, uplink transmissions may be made using licensed bands, unlicensed 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 drawbacks:
1) After receiving the downlink reference signal, the terminal in S1-S3 needs to measure for a period of time to obtain a channel quality index (such as CQI), and needs to wait for the arrival of the uplink resource and then send the channel quality index to the base station, which takes a long time.
2) In environments where the channel changes rapidly (e.g., the terminal is moving at high speed), the accuracy of the channel quality estimate may decrease, resulting in a decrease in the accuracy of the final calculated MCS. For example, the base station periodically transmits downlink reference signals, such as CRS, channel state information reference signals (Channel State Information Reference Signal, CSI-RS), and if the transmission period of the downlink reference signals is configured to be too short, excessive downlink resources are occupied; if the configuration of the transmission period of the downlink reference signal is too long, the timeliness is affected, which is very obvious in the scene of rapid channel change such as high-speed rail, and the time consumption of overall measurement and decision is long, 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, the embodiments of the present application provide a configuration method of a downlink modulation and coding scheme, which is applied to a base station, and can improve the decision efficiency of the downlink Modulation and Coding Scheme (MCS), and reduce downlink resource occupation caused 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 transmission parameters of the first uplink reference signal (for convenience of description, the transmission parameters of the first uplink reference signal are referred to as first transmission parameters).
The first uplink reference signal may be a sounding reference signal (Sounding Reference Signal, SRS), and may also be other existing reference signals or custom reference signals. 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 reception quality may specifically be a reference signal reception power (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 a first uplink reference signal or looking up configuration information of the first terminal. For example, the direction weight of the uplink reference signal may be obtained from a module for processing channel information, and the RSRP may be obtained from a module for receiving the uplink SRS.
And step 32, inputting the first transmission parameters into a neural network model obtained by training in advance, and configuring the downlink modulation and coding scheme of the first terminal according to the first value of the downlink modulation and coding scheme output by the neural network model.
Here, a neural network model has been trained in advance, and in step 32, a first value of a downlink MCS output by the neural network model may be obtained by inputting a first transmission parameter into the neural network model, where the first value may indicate a downlink MCS index value, so that the base station may configure a downlink modulation and coding scheme of the first terminal according to the first value of the downlink MCS.
According to the above steps, because a certain correlation exists between uplink and downlink channels between the terminal and the base station, the downlink MCS is obtained by utilizing the characteristics and using the uplink reference signals sent to the base station by the terminal existing in the prior art, and the downlink reference signals sent to the terminal by the base station in the downlink MCS decision process can be reduced, so that the downlink resource occupied by the downlink reference signal transmission is reduced. 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 delay of the downlink MCS can be reduced, and the decision efficiency can be improved.
After the step 32, the base station may further schedule downlink transmission of the first terminal according to a downlink modulation and coding scheme configured by the first terminal. The specific scheduling method is not described herein.
The neural network model and the training process of the neural network model are further described below.
The neural network model employed in the present application includes one 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 values of the downlink modulation and coding scheme are 29, the number of nodes of the intermediate processing layer is at least 30.
Before the step 31, the application needs to train the neural network model at the base station side, and the specific training process is as follows:
a) And acquiring a plurality of pieces of training data, wherein each piece of training data comprises transmission parameters of an uplink reference signal of a certain terminal acquired by the base station and values (such as a downlink MCS index value) of a downlink modulation and coding scheme configured by the base station for the terminal.
Here, the uplink reference signal in the training data and the first uplink reference signal sent by the first terminal in step 31 are the same type of signal, such as SRS; the transmission parameters of the uplink reference signal in the training data are the same as the first parameters of the first uplink reference signal in step 31, and may specifically be the reception quality, the expected quality and the direction weight.
B) And training the neural network model by taking the transmission parameters as input characteristics of the neural network model and taking the values of the downlink modulation and coding scheme configured by the base station for the terminal as labels corresponding to the input characteristics until the preset convergence condition is met, so as to obtain the trained neural network model.
Here, the input feature is constructed according to the transmission parameters in the training data, the value of the downlink modulation and coding scheme configured by the base station for the terminal is used as the label corresponding to the input feature, and the neural network model is subjected to supervised training until a preset convergence condition (for example, a preset iteration number) is met, so that the trained neural network model is obtained.
The present application is further described by way of more detailed examples below.
Example 1:
and based on the uplink reference signal sent by the base station receiving UE, acquiring the characteristic of the decision downlink MCS. RSRP (for SRS) for acquiring SRS from uplink SRS reception module of base station rsrp Representation) of the downlink MCS, and obtaining the SRS direction weight from the channel information processing module of the base station, thereby obtaining uplink signal data related to the decision downlink MCS, the uplink signal data being represented by vectors as follows:
a＝(SRS rsrp bler, direction weight 1, direction weight 2, … …, direction weight 64) T
Wherein, the bler represents the expected block error rate, and can be obtained from the service parameter configuration of the terminal.
And taking the vector as input data, and inputting the input data into a preset neural network model. As shown in fig. 4, the downlink MCS decision network model based on the neural network 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. To ensure 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. The input layer, the middle layer and the output layer are subjected to parameter weight transformation, the parameter weight sets form two W matrixes and V matrixes, and the W matrixes and the V matrixes are specifically arranged in the middle layer design part.
Here, the number of output layer nodes of the neural network model is consistent with the downlink MCS type to be obtained, for example, when the downlink MCS index value has 29 values, the number of nodes of the bundle layer is 29. The output MCS value vector is expressed as follows:
z＝(MCS 0 ，MCS 1 ，MCS 2 ，…，MCS 28 ) T
the neural network algorithm aims at optimizing the known input uplink signal data and the known input downlink MCS value serving as training data through an objective function to obtain a linear fitting relation between the uplink signal data and the downlink MCS value, wherein the linear fitting relation can be represented by a W matrix and a V matrix through weight parameters. When the model training is put into practical application, the downlink MCS value output by the model can be obtained by inputting the uplink signal data of the terminal to be processed into the model.
a) The model training process comprises the following steps:
an input section extracting downstream uplink signal data based on the downstream MCS characteristics as input data of a model:
1) The signal strength of the SRS may be RSRP or signal-to-interference plus noise ratio (SINR) of the SRS.
2) Target blers (values such as 10%, 0.001%, etc.), different blers correspond to different MCS tables.
3) The SRS direction weight obtained by the base station may be specifically 64 bits (corresponding to 64 channels), or may be fewer bits.
4) The input vector is thus expressed as:
a＝(SRS rsrp bler, direction weight 1, direction weight 2, … …, direction weight 64) T
Intermediate treatment layer design:
1) The W matrix and the V matrix of the neural network are designed. 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 node 1 ＝f(w 1 *x 1 +w 2 *x 2 +…+w n *x n ) 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= (a) 1 ,a 2 ,…,a 66 ) T For 66-dimensional input vector, W 1 ＝(w (1,1) ,w (1,2) ,…,w (1,66) ) To obtain the first node output y of the intermediate processing layer 1 The required parameter weight is as follows:
y 1 ＝g(w (1,1) *a 1 +w (1,2) *a 2 +…+w (1,66) *a 66 )
where g is the activation function, which uses the sigmod function.
The number of intermediate processing layer nodes is N, wherein N is not less than 30. Extracting a parameter weight matrix W matrix from an input layer to an intermediate layer
W＝(W 1 ,W 2 ,…,W N ) T
The same goes for output layer result z=g (V 1 *y 1 ,V 2 *y 2 …,V 29 *y N ) T Extracting parameter weight matrix V matrix from middle layer to output layer
V＝(V 1 ,V 2 ,…,V 29 ) T
3) The objective function is designed as follows:
cost＝(z p -z) 2
wherein z is p For the expected output (for example, mcs=3), which is also the target data used in training, z is the output value obtained by the actual neural network (for example, mcs=6), and the optimal solution of the parameter weight in the neural network is obtained by adjusting the parameter weight minimization objective function in the W, V matrix.
An output section:
1) Based on input parameters such as SRS strength, the goal is to obtain a downlink MCS value based on channel quality CQI.
b) Application: and when training results are gradually fitted, after a stable neural network model is obtained, performing real-time downlink MCS judgment according to the SRS obtained from 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 by training in advance, and configure 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.
Optionally, the base station further includes:
the data acquisition module is used for acquiring a plurality of training data, wherein each training data comprises transmission parameters of an uplink reference signal of a terminal and values of a downlink modulation and coding scheme configured by the base station for the terminal;
and the model training module is used for taking the transmission parameters as input characteristics of a neural network model, taking the values of the downlink modulation and coding schemes configured by the base station for the terminal as labels corresponding to the input characteristics, training the neural network model until the preset convergence condition is met, and obtaining 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 receiving quality is a reference signal receiving power RSRP, and the expected quality is an expected block error rate BLER.
Optionally, the neural network model includes an output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is sequentially connected until the output processing layer is connected; the number of nodes of the intermediate processing layer is larger than the number of 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 schematic structure of a base station 800, which includes: a processor 801, a transceiver 802, a memory 803, and a bus interface, wherein:
in the embodiment of the present application, 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 parameters into a pre-trained neural network model, and configuring the downlink modulation and coding scheme of the first terminal according to the 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, the computer program, when executed by the processor 801, may implement the respective processes of the configuration method embodiment of the downlink modulation and coding scheme shown in fig. 3, and achieve the same technical effects, so that repetition is avoided and no further description is given here.
In fig. 8, a bus architecture may be comprised of any number of interconnected buses and bridges, and in particular, one or more processors represented by the processor 801 and various circuits of the memory represented by the memory 803. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 802 may be a number of elements, i.e., including a transmitter and a receiver, providing 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 stored thereon a program 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 parameters into a pre-trained neural network model, and configuring the downlink modulation and coding scheme of the first terminal according to the first value of the downlink modulation and coding scheme output by the neural network model.
When the program is executed by the processor, all the implementation modes in the configuration method of the downlink modulation and coding scheme applied to the base station can be realized, the same technical effects can be achieved, and the repetition is avoided, so that the description is omitted.
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 solution. 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 will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purposes of the embodiments of the present application.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in 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 may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The foregoing is merely 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 think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to 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, comprising:
receiving a first uplink reference signal sent by a first terminal, and obtaining a first transmission parameter of the first uplink reference signal, wherein the first transmission parameter of the first uplink reference signal comprises: 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;
and inputting the first transmission parameters into a pre-trained neural network model, and configuring the downlink modulation and coding scheme of the first terminal according to the 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 pieces of training data, wherein each piece of training data comprises transmission parameters of an uplink reference signal of a terminal and values of a downlink modulation and coding scheme configured by the base station for the terminal;
and training the neural network model by taking the transmission parameters as input characteristics of the neural network model and taking the values of the downlink modulation and coding scheme configured by the base station for the terminal as labels corresponding to the input characteristics until the preset convergence condition is met, so as 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 reception 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 one output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is sequentially connected until the output processing layer is connected; the number of nodes of the intermediate processing layer is larger than the number of candidate values of the downlink modulation and coding scheme.
6. The method as recited in claim 1, further comprising:
and scheduling downlink transmission of the first terminal according to a downlink modulation and coding scheme configured by the first terminal.
7. A base station, comprising:
the receiving module 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, where the first transmission parameter of the first uplink reference signal includes: 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;
the configuration module is used for inputting the first transmission parameters into a neural network model which is obtained through training in advance, and configuring the downlink modulation and coding scheme of the first terminal according to the first value of the downlink modulation and coding scheme which is output by the neural network model.
8. The base station of claim 7, further comprising:
the data acquisition module is used for acquiring a plurality of training data, wherein each training data comprises transmission parameters of an uplink reference signal of a terminal and values of a downlink modulation and coding scheme configured by the base station for the terminal;
and the model training module is used for taking the transmission parameters as input characteristics of a neural network model, taking the values of the downlink modulation and coding schemes configured by the base station for the terminal as labels corresponding to the input characteristics, training the neural network model until the preset convergence condition is met, and obtaining the trained neural network model.
9. The base station of claim 7, wherein the neural network model comprises one output processing layer and at least one intermediate processing layer; the at least one intermediate processing layer is sequentially connected until the output processing layer is connected; the number of nodes of the intermediate processing layer is larger than the number of 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, where the first transmission parameter of the first uplink reference signal includes: 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;
the processor is configured to input the first transmission parameter to a neural network model obtained by training in advance, 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.
12. A base station, comprising: a processor, a memory and a 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 as claimed in 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 of configuring a downstream modulation and coding scheme according to any of claims 1 to 6.
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