CN113746512B

CN113746512B - Downlink precoding method, device and base station

Info

Publication number: CN113746512B
Application number: CN202010460800.7A
Authority: CN
Inventors: 郭森宝; 李桂宝; 乐春晖
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-05-27
Filing date: 2020-05-27
Publication date: 2023-04-07
Anticipated expiration: 2040-05-27
Also published as: CN113746512A; WO2021238634A1

Abstract

The embodiment of the application provides a downlink precoding method, a downlink precoding device and a base station. After acquiring the channel information of the downlink channel, the downlink pre-coding method uses an MIMA algorithm to iteratively generate a power distribution weight of the downlink channel, wherein a target function of the MIMA algorithm is a mutual information lower bound function of the downlink channel, and initialization parameters of the MIMA algorithm are an initial weight and a channel feature matrix in the channel information; and then precoding the downlink data according to the power distribution weight value to realize the transmission of the downlink data. The downlink precoding method takes a mutual information lower bound function as a target function, and completes the calculation of power and weight through iteration so as to determine the optimal constellation combination, and under the condition of smaller iteration times, the overall optimal solution when the channel capacity allocation is maintained in a discrete modulation constellation is ensured.

Description

Downlink precoding method, device and base station

Technical Field

The present application relates to the field of digital communication technologies, and in particular, to a downlink precoding method, apparatus, and base station.

Background

The signal processing link of digital communication is to process the transmitted signal in advance at the transmitting end under the condition of known channel state information. The precoding element may be used to transmit multiple data streams in parallel over multiple transmit antennas to increase the peak transmission rate. In a Multiple Input Multiple Output (MIMO) scenario, if the number of antennas and the size of the antennas are not changed, performance of signal transmission in the scenario is constrained, so that capacities of Single Users (SU) and Multiple Users (MU) are limited.

In order to improve the transmission performance of signals, the transmission power of each channel can be distributed by adjusting the downlink weight in the precoding link. In a typical precoding method, downlink weight calculation of single-user multiple-input multiple-output (SU-MIMO) and multi-user multiple-input multiple-output (MU-MIMO) can be realized in a shannon formula maximization-based manner. I.e. signal capacity C = Wlog [1+ Ps/(N) ₀ W)](ii) a Wherein W is the downlink weight, ps is the input signal power, N ₀ Is the bilateral power spectral density of the noise. Need to ensure 1 Ps/(N) ₀ W) is maximized to obtain maximum capacity. After multiple iterations, the weight and the power distribution result are solved and output, so that the optimal spectrum efficiency is achieved. However, the above precoding method requires multiple iterations, which results in high complexity of solving the global optimal solution and requires a large amount of computation resources and time.

Disclosure of Invention

The application provides a downlink precoding method, a downlink precoding device and a base station, which aim to solve the problem that a traditional precoding method needs to carry out multiple iterations.

In a first aspect, an embodiment of the present application provides a downlink precoding method, where the method includes:

acquiring channel information of a downlink channel, wherein the channel information comprises an initial weight of the downlink channel and a channel characteristic matrix of the downlink channel;

iteratively generating a power distribution weight of a downlink channel by using a Mutual Information Maximization (MIMA) algorithm, wherein a target function of the MIMA algorithm is a mutual information lower bound function of the downlink channel, and initialization parameters of the MIMA algorithm are the initial weight and a channel feature matrix;

and performing precoding on downlink data according to the power distribution weight, wherein the downlink data is data transmitted in the downlink channel.

When the base station transmits downlink data to the terminal, the base station may first obtain channel information of a downlink channel corresponding to the terminal, and extract an initial weight and a channel feature matrix from the downlink channel information, so as to input a mutual information maximization algorithm to iteratively generate a power allocation weight of the downlink channel. In MIMA algorithm, the target function can be set as the mutual information lower bound function of the downlink channel, and the initialization parameters are the initial weight and the channel characteristic matrix. And finally obtaining a power distribution weight value through MIMA algorithm iteration based on the initial weight value and the channel characteristic matrix, thereby performing precoding on the downlink data according to the power distribution weight value and sending the downlink data to be transmitted to the terminal.

By adopting the precoding method, the power weight can be determined in real time according to the channel information of the downlink channel before data transmission, the channel power can be distributed, the power waste can be reduced, and the loss of throughput can be relieved. And the mutual information lower bound function of the downlink channel is used as an objective function of the MIMA algorithm for optimization solution, and the iteration step is simplified by combining gradient iteration, so that the optimal channel capacity allocation is effectively ensured.

In the precoding method, the used channel characteristic matrix includes information capable of characterizing the current downlink channel characteristic, and in one implementation, the pairing information may be extracted from the channel information for a multi-user multiplexing scenario to generate the channel characteristic matrix. That is, the step of acquiring the channel information of the downlink channel may include:

acquiring pairing information, wherein the pairing information comprises one or more of modulation and coding strategy information, modulation scheme information and constellation point type information of a target terminal and a pairing terminal;

and generating a channel characteristic matrix, wherein elements in the channel characteristic matrix are one or more of modulation and coding strategy information, modulation scheme information and constellation point type information extracted from the pairing information.

Through the channel characteristic matrix, constraint can be formed on the iteration process in the MIMA algorithm so as to obtain an iteration result which is more in line with the characteristics of the current downlink channel.

With reference to the downlink precoding method, in a possible implementation manner of the first aspect, the iteratively generating the power allocation weight of the downlink channel by using the MIMA algorithm includes:

the iterative gradient values are set as an initial step factor and a minimum step factor. And generating an initial iteration rate according to the initial weight and the channel characteristic matrix.

And if the iteration gradient value is larger than the minimum step factor, updating the weight matrix. And generating an intermediate iteration rate according to the updated weight matrix and the updated channel characteristic matrix. Updating the iteration gradient value until the intermediate iteration rate is greater than or equal to the initial iteration rate; if the iteration gradient value is larger than the minimum step factor, continuing to update the weight matrix, calculating the intermediate iteration rate according to the updated weight matrix, and repeating the iteration; and if the iteration gradient value is less than or equal to the minimum step factor, extracting a weight matrix to obtain a power distribution weight.

The implementation mode provides an iteration mode based on the MIMA algorithm, and through the iteration mode, the initial weight and the channel characteristic matrix can be used as input, and through multiple iterations, the iteration rate is calculated to deduce the target function to the minimum value of the mutual information lower bound function, so that the iterated optimal power distribution weight is obtained, and relevant precoding operation is carried out.

In the iteration process, the intermediate iteration rate of each time can be respectively obtained, and the obtained intermediate iteration rate is compared with the initial iteration rate to determine whether the iteration is finished according to the size relationship of the intermediate iteration rate and the initial iteration rate. If the intermediate iteration rate is less than the initial iteration rate, the initial iteration rate is updated to the intermediate iteration rate, namely, the next iteration process can be carried out according to the updated initial iteration rate until the intermediate iteration rate is greater than or equal to the initial iteration rate, and the iteration gradient value is updated so as to finish iteration and extraction of the weight matrix when the iteration gradient value is less than or equal to the minimum step factor and obtain the power distribution weight.

The initial iteration rate is a target judgment value calculated in each iteration process, and can be calculated and generated according to the following formula:

R _n ＝G(H,W _n )

wherein R is _n Is the initial iteration rate;

g (H, W) is an objective function, namely a mutual information lower bound function:

h is a channel characteristic matrix; w _n Is an initial weight matrix; e.g. of a cylinder _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m is a group of ^Nt The total number of constellation points; sigma ² For making an uproarAn acoustic variance; n is an iteration number index.

According to the above calculation formula, the iteration rate in each iteration process can be respectively solved, and because the target function is a mutual information lower bound function and comprises the current channel characteristic matrix and the power distribution weight, the target function gradually tends to a lower bound value through iterative calculation, and the weight matrix is gradually updated along with the iterative process.

The weight matrix is a matrix for guiding signal power allocation in a downlink channel, and each element in the matrix may correspond to a specific power allocation situation of each signal transmission process, so in one implementation, the weight matrix may be updated by using the following formula:

wherein alpha is a preset influence coefficient; h is a channel characteristic matrix; w is a group of _n Is a weight matrix;

is the first derivative of the lower bound function of the mutual information;

e _ij is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m ^Nt The total number of constellation points; sigma ² Is the variance of the noise; n is an iteration index; μ is the iterative gradient value.

In order to obtain the finally adopted power distribution weight, the iteration gradient value can be updated when the intermediate iteration rate is greater than or equal to the initial iteration rate, iteration is repeated, and the current weight matrix is extracted until the iteration gradient value is less than or equal to the minimum step factor so as to obtain the power distribution weight. And calculating the channel capacity according to the output weight matrix so as to set a signal transmission mode according to the channel capacity. It can be seen that the above iterative process can simplify the complexity of the iterative step and the iterative process by a gradient-like iterative manner.

In one implementation, the iterative gradient values may be updated as follows:

wherein mu is an iterative gradient value; k is a convergence coefficient. By updating the iterative gradient, the purpose of judging the iterative gradient value and the minimum step factor can be repeated, the subsequent iterative process is continued, the intermediate iterative rate is calculated, the initial iterative rate is updated, and the weight matrix is output until the iterative gradient value is less than or equal to the minimum step factor.

In the above-mentioned MIMA algorithm, initial weights obtained from channel information need to be input into the MIMA algorithm to start the first iteration. In one implementation, the initial weight is a single user weight in the downlink channel; or, the estimated weight of the downlink channel. The single user weight and the estimated weight can be obtained through historical information or a channel characteristic matrix of a downlink channel, and can be used as a reference value of first iteration through the single user weight or the estimated weight so as to finally obtain the output of the MIMA algorithm.

After obtaining the output of the MIMA algorithm, the output of the MIMA algorithm may be used as an input of precoding to perform precoding on the downlink data. Therefore, in an implementation manner, performing precoding on downlink data according to the power allocation weight includes: firstly, generating a distribution power value according to the power distribution weight value and the channel capacity of the downlink channel; and then applying the distribution power value to the downlink data to send the downlink data according to the power and the weight value obtained by calculation.

For a multi-user multiplexing scene, the base station can also generate a downlink signaling containing pairing information; and the downlink signaling is sent to the corresponding terminal, so that the pairing information is respectively sent to the target terminal and the pairing terminal, and the terminal can balance, detect and decode the transmitted data according to the pairing information. Wherein the downlink signaling is carried in radio resource control signaling and/or downlink control information signaling.

In a second aspect, an embodiment of the present application further provides a downlink precoding device, which may include an obtaining module, a weight calculating module, and a precoding module, configured to execute the downlink precoding method provided in the first aspect. Modules for performing the method steps in the implementations of the first aspect may also be included.

Specifically, an acquisition module user acquires channel information of a downlink channel, wherein the channel information comprises an initial weight of the downlink channel and a channel characteristic matrix of the downlink channel;

the weight calculation module is used for generating a power distribution weight of a downlink channel by using a MIMA algorithm in an iteration mode, wherein a target function of the MIMA algorithm is a mutual information lower bound function of the downlink channel, and initialization parameters of the MIMA algorithm are an initial weight and a channel feature matrix;

the pre-coding module is configured to perform pre-coding on downlink data according to the power allocation weight, where the downlink data is data transmitted in the downlink channel.

The downlink pre-coding device can obtain the channel information through the obtaining module and input the channel information to the weight calculating module, so that the weight calculating module uses the MIMA algorithm to iteratively solve the optimal solution. After the optimal solution is solved, precoding may be performed on the downlink data by a precoding module. Because the objective function of the MIMA algorithm operated in the weight calculation module is a mutual information lower bound function of the downlink channel, and the initialization parameters are the initial weight and the channel characteristic matrix in the channel information, the downlink pre-coding device can determine and obtain the optimal power distribution scheme according to the channel characteristics, simplify the iteration process and quickly obtain the optimal power distribution weight.

In an implementation manner, in the channel information obtained by the weight calculation module, the initial weight is a weight of a single user in a downlink channel; or, the estimated weight of the downlink channel. Through the initial weight value, the initial weight value can be used as a reference for the first iteration of the MIMA algorithm so as to carry out subsequent iteration.

In an implementation manner, the weight calculation module is specifically configured to set an iteration gradient value as an initial step factor and a minimum step factor; generating an initial iteration rate according to the initial weight and the channel characteristic matrix; if the iteration gradient value is larger than the minimum step factor, updating the weight matrix; generating an intermediate iteration rate according to the updated weight matrix and the updated channel characteristic matrix; and if the intermediate iteration rate is greater than or equal to the initial iteration rate, updating the iteration gradient value, and extracting a weight matrix when the iteration gradient value is less than or equal to the minimum step factor to obtain a power distribution weight.

Therefore, the weight calculation module in the implementation mode can use a gradient iteration algorithm in the MIMA algorithm, so that the iteration times are simplified, and the iteration time of the MIMA algorithm is shortened.

In one implementation, the weight calculation module is specifically configured to update the initial iteration rate to the intermediate iteration rate if the intermediate iteration rate is less than the initial iteration rate. Namely, the weight calculation module can continue to perform the next iteration by updating the initial iteration rate when the intermediate iteration rate is less than the initial iteration rate until the target function reaches the lower bound of mutual information.

In an implementation manner, when the weight calculation module uses the MIMA algorithm to iteratively solve the optimal solution, the initial iteration rate may be calculated and generated according to the following formula:

R _n ＝G(H,W _n )

wherein R is _n Is the initial iteration rate; g (H, W) is a mutual information lower bound function;

h is a channel characteristic matrix; w is an initial weight matrix; e.g. of the type _ij The constellation point code distance; i and j are respectively the serial numbers of the constellation points; m ^Nt The total number of constellation points; sigma ² Is the noise variance; n is an iteration number index.

In one implementation, when the weight calculation module uses the MIMA algorithm to iteratively solve the optimal solution, the weight matrix may be updated according to the following formula:

is the first derivative of the lower bound function of the mutual information; mu is an iterative gradient value;

e _ij is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m is a group of ^Nt The total number of constellation points; sigma ² Is the variance of the noise; n is an iteration index.

In one implementation, the weight calculation module is specifically configured to update the iteration gradient value if the intermediate iteration rate is greater than or equal to the initial iteration rate, so as to perform the next iteration by adjusting the iteration gradient value. If the iteration gradient value is smaller than or equal to the minimum step factor, outputting a weight matrix; and calculating the channel capacity according to the output weight matrix. That is, the weight calculation module completes iteration when determining that the iteration gradient value is less than or equal to the minimum step factor, and calculates the channel capacity according to the output weight matrix, so that the precoding module executes precoding according to the output result.

Wherein, the weight calculation module may update the iterative gradient value according to the following formula:

wherein mu is an iterative gradient value; k is a convergence coefficient.

For a multi-user multiplexing scenario, in an implementation manner, the obtaining module is further configured to obtain pairing information and generate a channel feature matrix according to the pairing information. The pairing information comprises one or more of modulation and coding strategy information, modulation scheme information and constellation point type information of the target terminal and the pairing terminal; the elements in the channel characteristic matrix are one or more of modulation and coding strategy information, modulation scheme information and constellation point type information extracted from the pairing information. The acquisition module can intercommunicate the information sending modes of the target terminal and the paired terminal by adding the paired information in the channel characteristic matrix, so that data transmission is facilitated between the target terminal and the paired terminal.

In an implementation manner, the precoding module is specifically configured to generate a power allocation value according to a power allocation weight and a channel capacity of a downlink channel; and then, applying the distribution power value to the downlink data to send the downlink data according to the power and the weight value obtained by calculation.

For a multi-user multiplexing scenario, the pre-coding device further includes a pairing module, and the pairing module is configured to generate a downlink signaling including pairing information, and send the downlink signaling to the target terminal and the paired terminal. The downlink signaling may be carried in a radio resource control signaling and/or a downlink control information signaling, so as to implement transmission of downlink data.

In a third aspect, an embodiment of the present application further provides a base station, which may include a signal transceiver station and a controller, where the signal transceiver station is connected to the controller; the controller is configured to execute the operation instructions to implement the downlink precoding method provided in the first aspect, for controlling the signal transceiver station to perform precoding on downlink data.

In a fourth aspect, an embodiment of the present application further provides a communication apparatus, where the communication apparatus may be a terminal or a chip in the terminal or a system on a chip. The communication device may implement the functions performed by the terminal in the above aspects or in each possible implementation manner, and the functions may be implemented by hardware. The communication apparatus may include: a processor and a communication interface, where the processor may be configured to support a communication device to implement the downlink precoding method in the first aspect or any one of the possible implementation manners of the first aspect.

In a fifth aspect, an embodiment of the present application further provides a computer-readable storage medium, which may be a readable non-volatile storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is enabled to execute the downlink pre-coding method in the first aspect or any one of the possible implementation manners of the first aspect.

In a sixth aspect, an embodiment of the present application further provides a computer program product containing instructions, which when run on a computer, enables the computer to execute the downlink precoding method described in the first aspect or any one of the possible implementation manners of the foregoing aspects.

In a seventh aspect, this embodiment also provides a communication apparatus, which may be a terminal or a chip in a terminal or a system on a chip, where the communication apparatus includes one or more processors and one or more memories. The one or more memories are coupled to the one or more processors and the one or more memories are configured to store computer program code comprising computer instructions that, when executed by the one or more processors, cause the communication apparatus to perform the downlink precoding method as described in the first aspect or any possible implementation manner of the first aspect.

In order to implement precoding on downlink data, in the embodiment of the application, after channel information of a downlink channel is acquired, a MIMA algorithm is used for iteratively generating a power allocation weight of the downlink channel, wherein a target function of the MIMA algorithm is a mutual information lower bound function of the downlink channel, and initialization parameters of the MIMA algorithm are an initial weight and a channel feature matrix in the channel information; and then precoding the downlink data according to the power distribution weight value to realize the transmission of the downlink data. The downlink precoding method takes a mutual information lower bound function as a target function, and completes the calculation of power and weight through iteration so as to determine the optimal constellation combination, and under the condition of smaller iteration times, the overall optimal solution when the channel capacity allocation is maintained in a discrete modulation constellation is ensured.

Drawings

Fig. 1 is a schematic view of a MIMO system in an embodiment of the present application;

fig. 2 is a flowchart illustrating a downlink precoding method in an embodiment of the present application;

fig. 3 is a schematic diagram of a constellation combination effect after power and weight adjustment in the embodiment of the present application;

FIG. 4 is a diagram illustrating the power distribution contrast effect in the embodiment of the present application;

FIG. 5 is a schematic flow chart of a MIMA algorithm in an embodiment of the present application;

fig. 6 is a schematic flow chart of acquiring channel information in a multi-user multiplexing scenario in the embodiment of the present application;

fig. 7 is a schematic flowchart of performing precoding in a multi-user multiplexing scenario in the embodiment of the present application;

fig. 8 is a flowchart illustrating a process of sending a downlink signaling in an embodiment of the present application;

fig. 9 is a schematic structural diagram of a downlink precoding apparatus in an embodiment of the present application.

Detailed Description

For the purpose of describing the technical solutions of the present application, a brief description of some concepts used in the present application is first provided below.

In each embodiment of the present application, a downlink channel is a channel when a base station transmits data to a terminal, and includes a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Shared Channel (PDSCH) (or referred to as a downlink data channel).

The physical downlink control channel is mainly used for carrying Downlink Control Information (DCI), and is usually represented as DCI signaling. The DCI may include common control information (e.g., system information, etc.) and user-specific information (e.g., downlink resource allocation indication, uplink scheduling, random access response, uplink power control parameter, etc.), etc. The PDCCH may schedule data channels through the DCI it carries, such as: the DCI may be used to indicate transmission parameters of a data channel (e.g., a time domain resource location of the data channel, etc.), before the data channel is transmitted, a base station or other network equipment may send a PDCCH to a terminal, and after the terminal receives the PDCCH, the terminal may demodulate the DCI in the PDCCH first and then receive or send the data channel on the time domain resource location indicated by the DCI. The physical downlink data channel is used to carry data (or referred to as downlink data) transmitted from the base station to the terminal.

The PDCCH may also indicate a time domain resource location of a channel state information reference signal (CSI-RS) through DCI carried by the PDCCH to trigger transmission of an aperiodic (non-periodic) CSI-RS. The CSI-RS is used for the terminal to measure a channel state between the terminal and the base station, and may include one or more channel state measurement resources. For example, the base station may send DCI for indicating a time domain resource position of the CSI-RS and the CSI-RS to the terminal, and the terminal receives the CSI-RS at the time domain resource position indicated by the DCI, measures a channel state measurement resource included in the CSI-RS, and reports Channel State Information (CSI) to the base station according to a measurement result.

The base station may also set a data transmission mode of the downlink channel according to the CSI reported by the terminal, for example, set an optimal constellation combination mode. For example, for a multiple-input multiple-output system (hereinafter referred to as MIMO system), a base station and a terminal both include multiple antennas, and multiple data streams may be formed when downlink data is transmitted. And by setting the transmit power weight of each data stream in the downlink channel, etc., the base station can transmit downlink data to the terminal according to the set constellation combination mode, thereby fully utilizing the channel capacity.

The constellation, i.e., constellation mapping, is a digital modulation technique. The constellation mapping process is to map a finite field "bit" sequence carrying digital information into a "symbol" sequence suitable for transmission. The value space of each symbol may be a one-dimensional real space, a two-dimensional real space (i.e., a complex space). Constellation mapping consists of two elements, namely constellation (constellation) and constellation point mapping (labeling). The constellation diagram represents a set of all values of the constellation mapping output symbol, wherein each point of the constellation diagram corresponds to one value of the output symbol. The constellation point mapping mode represents a specific mapping relationship from input bits (sequence/group) to constellation points or from constellation points to bits (sequence/group). Generally, a constellation mainly includes Pulse Amplitude Modulation (PAM) in a one-dimensional real space, quadrature Amplitude Modulation (QAM) in a two-dimensional real space, phase Shift Keying (PSK) modulation, and the like.

The process of coding and converting downlink data by the base station according to the constellation combination setting mode to realize constellation mapping is called precoding. In order to obtain the optimal precoding manner, the base station may perform optimization solution on data, such as a power allocation matrix, which is used in precoding, so as to obtain a corresponding data transmission manner.

The following describes a flow of setting a data transmission method of a downlink channel by a base station.

The data transmission mode in which the base station sets the downlink channel in the embodiment of the present application may be applied to a communication system in the MIMO mode, for example: any one of a fourth generation (4 th generation,4 g) system, a Long Term Evolution (LTE) system, a fifth generation (5 th generation,5 g) system, a New Radio (NR) system, and a vehicle-to-electronic (V2X) system, which is also applicable to other next-generation communication systems.

As shown in fig. 1, the MIMO system includes a base station 100 and a plurality of terminals 200. In the MIMO system, a plurality of terminals 200 may establish communication connections with the base station 100.

In the embodiments of the present application, the base station 100 is mainly used for implementing the functions of resource scheduling, radio resource management, radio access control, and the like of the terminal 200. The base station 100 may also be other network devices, such as: a small base station, a wireless access point, a TRP transmission point (transmission point, TP), and any of some other access nodes, etc. The base station 100 has an antenna built into it so that the base station 100 can receive and transmit radio signals during operation and form a radio coverage area. The terminals 200 in the coverage area can implement a mobile communication service by connecting to the internet or other terminals through a radio signal transceiving relationship with the base station 100. In the embodiment of the present application, the apparatus for implementing the function of the base station may be the base station, or may be an apparatus or a functional module, such as a chip system, which can support the base station to implement the function.

The terminal 200 may be a terminal equipment (terminal equipment) or a User Equipment (UE) or a Mobile Station (MS) or a Mobile Terminal (MT), etc. Such as: the terminal may be a mobile phone (mobile phone), a tablet computer or a computer with a wireless transceiving function, or may be a Virtual Reality (VR) terminal, an Augmented Reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in telemedicine, a wireless terminal in a smart grid, a wireless terminal in a smart city (smart city), a smart home, a vehicle-mounted terminal, or the like.

After the terminal 200 accesses the base station 100, the base station 100 serves as a transmitting end to transmit data to the terminal 200, the transmitted data is called downlink data, and a channel for correspondingly transmitting the data is called a downlink channel. In the embodiment of the present application, the apparatus for implementing the function of the terminal may be the terminal, or an apparatus capable of supporting the terminal to implement the function, for example, a chip system.

In the MIMO system, both base station 100 and terminal 200 may use a plurality of antennas, and since a plurality of antennas are used, downlink data may be simultaneously transmitted through the plurality of antennas, respectively, to form a plurality of data streams, thereby improving the transmission efficiency of the downlink data. Meanwhile, base station 100 may also establish a connection relationship with multiple terminals 200 to form a multi-user multiplexing mode, so as to implement communication among multiple terminals 200.

The form of signals transmitted in the base station also differs according to different communication standards. For example, the current mobile communication technology mostly uses digital signals for transmission between the base station 100 and the terminal 200, such as LTE technology and NR technology. The LTE technology and the NR technology define signals as discrete uniformly distributed signals, including QPSK, 16QAM, 64QAM, 256QAM, and the like. Generally, in the process of transmitting a digital signal with multiple inputs and multiple outputs, a single code word transmission mode can be adopted. For example, LTE technology uses single codeword to multi-layer mapping for transmission when outputting more than 2 streams. The NR technique also uses single-code transmission for transmission of no more than 4 streams.

Under the condition that the number of antennas is unchanged and the size of a sky is restricted, the capacity of a single user or multiple users in a multi-input multi-output scene is limited, the capacity is already close to the limit value of a theoretical Shannon formula, and if the performance needs to be further improved, the number of antennas or the size of the sky needs to be increased.

However, in some usage scenarios, even if the number of antennas or the size of a sky is increased, since the weight matrix (or the precoding matrix) based on which the downlink data is precoded is still the general weight determined according to the shannon formula, the power allocation does not match the transmission characteristics of each signal in the downlink channel, and therefore, the constellation combination configured for each downlink data stream is not an optimal combination manner. That is, the demodulation performance of part of the transmission layer is limited by the minimum code distance between constellations, and transmission errors occur; while some of the transport layers are allocated higher power and cannot achieve higher throughput.

For example, the number of antennas of the terminal is usually greater than 2, the number of average transmission layers is also greater than 2, and a Multicast Listener Discovery (MLD) receiver or a Turbo receiver is supported in cooperation with a low-density parity-check (LDPC) decoding rule. Therefore, according to the information theory principle, the optimal weight design is related to the characteristics of the input signal, and according to the shannon formula, when the input signal obeys the gaussian distribution, the equivalent channel capacity is maximized.

Based on this, in order to achieve the effect of maximizing the channel capacity, an exemplary embodiment of the method for solving the optimal weight according to the present application is as follows:

s301: an objective function is set.

In order to obtain the effect of maximizing the channel capacity, a function corresponding to the channel capacity may be set as an objective function, and a power weight corresponding to the channel capacity when the channel capacity is maximized is obtained through iterative solution and calculation.

For example, take the mutual information maximum:

since, the maximum channel capacity is:

therefore, the maximum channel capacity can be guaranteed as long as the shannon formula is guaranteed to be maximized, that is:

wherein h (Y) is entropy information of the output signal Y, h (Y | X) is conditional entropy information of the output signal Y under the condition that the input signal is X, and P _Y Is the variance, P, of the output signal Y _X Is the variance, P, of the input signal X _S For input signal power, W is the system bandwidth, N ₀ Is the bilateral power spectral density of white noise, and N is the number of sampling points of the calculated mutual information under the bandwidth W.

Utilizing the following formula:

Y＝X+n

then obtaining the conditional entropy information of which the output signal is Y under the condition that the input signal is X:

h(Y|X)＝h(n)

since the entropy information satisfies the following equation:

if white Gaussian noise is assumed

The expectation and variance satisfy, respectively:

i.e. when the output signal Y follows a gaussian distribution, the following equation is satisfied:

and: p _Y ＝P _X +σ ² ；

Therefore, a power allocation weight calculation of the MIMO system can be realized by adopting a Shannon formula-based maximization mode. For a single user, singular Value Decomposition (SVD) is performed on a channel autocorrelation matrix, and precoding is performed by using a feature vector after SVD decomposition, that is:

[D,V]＝SVD(R)

combining a shannon formula:

it is known that the optimum capacity can be obtained by maximizing the signal to interference plus noise ratio (SINR).

In order to maximize the SINR, in a white noise scenario, it is only necessary to ensure that the signal power is maximized. Therefore, the optimal value of the single-user weight is the feature vector corresponding to the feature vector with the maximum number of layers of feature values.

Similarly, for the weight under the multi-user scenario, in order to ensure the SINR maximization, a zero forcing mode may be adopted to reduce the power of the interference term; alternatively, the optimal combination of signal power and interference power is obtained by maximizing the SINR and signal to noise ratio (SLNR).

For example: zero forcing weight based on SINR maximization:

W _k ＝[h ₀ ,h ₁ ,…,h _K-1 ] ⁺ _(:,k)

wherein h is _k (K =0,1, \ 8230;, K-1) is channel information of user K in a multi-user, [ 2 ]] ⁺ For the pseudo-inverse operation, (: k) represents the kth column of the matrix, W _k Is the calculated weight of the kth user.

Weight calculation based on SLNR maximization:

wherein R is _k For the k-th user channel autocorrelation matrix, R _i Is the autocorrelation matrix of the ith user, W _k Is the calculated weight of the kth user.

It can be seen that ensuring that the output signal Y follows a gaussian distribution requires that the variance of the input signal follows a gaussian distribution. However, in the current LTE or NR communication system, the transmitted signal X does not follow a gaussian distribution, but rather a discrete uniform distribution. Therefore, the maximum channel capacity obtained by the shannon formula is only a sub-optimal solution, not an optimal solution.

S302: and setting initialization iteration parameters.

After the objective function is set, the initialization iteration parameter can be set according to the channel information in the downlink channel. For example, to obtain an optimal solution, an algorithm of power control and spatial rotation may be used to solve the optimal value, that is:

setting a matrix element p _j Belong to a set

Wherein p is _j In total K +1 selectable values, K + 1) ^Rank And (4) combination.

Calculating a singular value decomposition result of the channel information of the target terminal:

h＝u*d*v′

combining the calculation of the modulation diversity matrix:

wherein the content of the first and second substances,

/>

in the collection

In the selection of variance σ _j J =0, \ 8230;, rank-1, performing a power normalization operation, yielding:

suppose sigma _P Each diagonal element p of _j Belong to a set

Where K is the quantization granularity parameter for which (K + 1) ^Nt The combination is normalized to obtain each diagonal element value:

then calculate each

And sigma _P In a Euclidean distance of between, will >>

The most recent set of power allocation matrices is used as an alternative.

By selecting the appropriate q _MOD Value, calculation

Setting W = V _H Σ _P V _MOD . Wherein p is _j And the power value adjusted for the j layer is the Rank number, and h is the channel information of the target terminal.

S303: and generating an iteration result.

After iteration, an iteration result can be generated, and the iteration result can be used as a power distribution weight of downlink data. That is, when the objective function, that is, the channel capacity is maximized, the corresponding power allocation weight is output, and precoding is performed according to the power allocation weight, so that the optimal spectrum efficiency can be achieved.

Through the process of generating the power distribution weights by adopting the iteration method, if the global optimal solution is adopted, the computation complexity of the global optimal solution obtained through the computation is high through iterating the local optimal solution for multiple times, the optimal solution of the weights can be obtained only by long iteration time, and the process is not beneficial to the engineering realization. The simple optimal local optimal solution solving can also be adopted, namely the thinking of power control and space rotation is adopted for solving, so that the complexity in the engineering realization process is reduced, but the solving mode has smaller performance gain on signal transmission and is easy to generate larger negative gain under the condition of low signal-to-noise ratio.

In summary, if the maximum channel capacity obtained by using shannon's formula is only a sub-optimal solution, not an optimal solution; the iteration complexity of global optimal solution through the power control and space rotation algorithm is high, and the iteration times are also high. Therefore, the embodiment of the present application provides a downlink precoding method, which is used for obtaining a global optimal solution through a small number of iterations in a process of precoding downlink data, so as to perform precoding operation on the downlink data according to a power allocation weight.

The following describes the downlink precoding method according to the present application with reference to the accompanying drawings.

As shown in fig. 2, a downlink precoding method provided in an exemplary embodiment of the present application includes the following steps:

s401: and acquiring channel information of a downlink channel.

When establishing a connection with a user terminal, a base station may obtain channel information of a downlink channel. For example, DCI for acquiring channel information is transmitted to a terminal through a PDCCH, so that the terminal measures a channel state measurement resource and feeds back a measurement result to a base station. The channel information includes a channel feature matrix and initial weights. For the MIMO system, because a plurality of terminals are included and each terminal is provided with a plurality of antennas, a channel characteristic matrix can be formed by feeding back the result.

Each element in the channel feature matrix is used to reflect the signal transmission characteristics between the corresponding terminal and the base station, for example, the channel feature matrix may include Modulation and Coding Scheme (MCS) information, modulation scheme information, constellation point type information, and the like of the current downlink channel. The channel characterization data may be converted into specific values using a specific quantization manner to be embodied as each element in the channel characterization matrix.

The initial weight may be a single user weight or an estimated weight. The single-user weight refers to a weight when a signal is transmitted with a terminal through a downlink channel in a non-MIMO scenario. The estimated weight refers to a weight obtained by estimating according to the access characteristics of the terminals in the scene, such as the number of the terminals, the number of antennas and the size of the sky, in combination with the channel capacity.

The channel information obtained in step S401 may be used as an initialization parameter of the MIMA algorithm, so as to iteratively solve the optimal solution of the power allocation weight using the MIMA algorithm.

S402: and (4) taking the channel information as an initialization parameter, and iteratively generating a power distribution weight value of a downlink channel by using an MIMA algorithm.

After acquiring the channel information, the base station can perform solving calculation of the power distribution weight by calling the MIMA algorithm. In order to use the MIMA algorithm to iteratively solve the power distribution weight value, the called target function of the MIMA algorithm is a mutual information lower bound function of a downlink channel, and meanwhile, the initial weight value and the channel characteristic matrix in the channel information are input into the MIMA algorithm so as to execute the MIMA algorithm to perform iterative optimization solution.

The mutual information lower bound function is specifically expressed as follows:

according to a mutual information formula:

wherein the content of the first and second substances,

wherein, the code distance e _ij ＝x _i -x _j . And solving the lower bound of mutual information by using the Jensen (Jensen) inequality, namely:

because:

thus:

substituting f (H, W) into I (y; x) can obtain the lower bound of mutual information:

/>

wherein:

wherein G (H, W) is a mutual information lower bound function; h is a channel characteristic matrix; w _n Is an initial weight matrix; e.g. of a cylinder _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m ^Nt The total number of constellation points; sigma ² Is the variance of the noise; n is an iteration number index.

Therefore, the mutual information maximization problem can be translated into the minimization of G (H, W), i.e.:

accordingly, the mathematical model for the mutual information maximization problem is equivalent to the problem of minimizing the mutual information lower bound function G (H, W), i.e. setting the objective function of the MIMA algorithm as follows:

it can be seen that, in one possible implementation manner described above, the mutual information maximization problem can be converted into a mutual information lower bound problem for power allocation and weight calculation. Because the iterative computation complexity of the mutual information lower bound function G (H, W) is lower than that of the mutual information maximization function, the iterative computation complexity in the iterative process can be simplified, namely, the complexity in engineering implementation is reduced, and the global optimal solution of the channel capacity in the discrete modulation constellation can be rapidly determined.

In the above-mentioned mutual information lower bound function G (H, W), since H is a channel feature matrix, that is, the channel feature matrix obtained in step S401, and W is a weight matrix, it will be continuously updated along with the subsequent iteration process. To complete the first iteration, W may be set as an initial weight as a decision constraint value calculated for the previous iterations. It can be seen that by using the channel feature matrix and the initial weight as the input of the MIMA algorithm, the iterative output result that conforms to the characteristics of the current channel, i.e., the power distribution weight matrix in the optimal solution state, can be obtained.

After the mutual information lower bound function G (H, W) is determined, iterative optimization solution may be performed on the mutual information lower bound function G (H, W). In one implementation, to simplify the subsequent iteration process, the weight matrix may also be updated by using a gradient iteration methodW _n Namely:

wherein alpha is a preset influence coefficient; h is a channel characteristic matrix; w _n Is a weight matrix; mu is the iterative gradient of the gradient,

is the first derivative of the mutual information lower bound function G (H, W) with respect to the weight W, thus:

namely:

wherein e is _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m ^Nt The total number of constellation points; sigma ² Is the variance of the noise; n is an iteration number index.

Based on the formula, a mutual information lower bound function G (H, W) can be used as a target function, and a channel characteristic matrix H and an initial weight W are used ₁ And performing gradient iteration as an iteration initialization parameter to obtain a corresponding weight matrix W under the condition that the objective function G (H, W) is minimized.

S403: and performing precoding on the downlink data according to the power distribution weight.

After obtaining the weight matrix W output by the iterative computation of the MIMA algorithm, the output result of step S402 may be used as an input of precoding to perform precoding on downlink data. Because the data can be sent between the base station and the terminal by using the multi-layer constellation combination mode, the combination of the power distribution weight and the data obtained by iterative solution can be used, the combination of the optimal multi-layer constellation is realized, and the constellation after combination can obtain the optimal throughput.

For example, data transmission may be performed by using a combination of multi-layer constellations, as shown in fig. 3, solution calculation is performed by using the MIMA algorithm provided in the above embodiment, so that it can be ensured that the combined constellation manner can obtain the optimal throughput. As in fig. 3, two QPSK constellations are combined into a discrete gaussian constellation by constellation convergence, which better conforms to the gaussian distribution.

For another example, as shown in fig. 4, for a case, in two-layer transmission, if the base station allocates power P0 to layer 0 and allocates power P1 to layer 1 by using a conventional method, since the transmitted data is discrete data, the demodulation performance is limited by the minimum code distance between constellations, and power P1 allocated to layer 1 is small, thereby easily causing layer 1 transmission errors. After the MIMA algorithm provided in the above embodiment is used, since it is assumed that data is discrete data, power P0+ P1 may be allocated to layer 0, and 0 power may be allocated to layer 1, thereby ensuring optimal throughput performance.

For another case, when two layers are transmitted, the power P0+ P1 is allocated to layer 0 and P2 is allocated to layer 1 by using the conventional method, and due to the data discrete characteristic, higher throughput cannot be obtained by allocating higher power to layer 0, thereby resulting in power waste and throughput loss. After the MIMA algorithm provided in the above embodiment is used, since it is assumed that data is discrete data, power P0 may be allocated to layer 0, and power P0+ P1 may be allocated to L1, thereby ensuring optimal throughput performance.

The MIMA algorithm scheme shown in the previous embodiments is further explained below with reference to the accompanying drawings.

In an implementation manner, as shown in fig. 5, the step of iteratively generating the power allocation weight of the downlink channel by using the MIMA algorithm shown in step 2 may specifically include the following steps:

s4021: setting the iterative gradient value as an initial step factor and setting a minimum step factor.

Before iteration is started, iteration parameters need to be set, and the initial step factor and the minimum step factor can be set according to actual signal transmission needs, namely, an initialization weight matrix W is set ₁ Setting an initial step factor mu = mu for the channel characteristic matrix and simultaneously _init Setting a minimum step factor mu _min . By setting the iteration gradient values, initial values for subsequent iterations may be determined. In the subsequent iteration process, iteration can be started according to the initial step-size factor, and the time for completing the iteration is determined by comparing the initial step-size factor with the minimum step-size factor.

S4022: and generating an initial iteration rate according to the initial weight and the channel characteristic matrix.

After the iteration gradient value is set, the first iteration can be started, namely, the initial iteration rate R is generated according to the initial weight value and the channel characteristic matrix _n ＝G(H,W _n ) Wherein the initial iteration rate is calculated according to the following formula:

R _n ＝G(H,W _n )

in the formula, R _n Is the initial iteration rate; g (H, W) is a mutual information lower bound function, namely:

wherein H is a channel characteristic matrix; w _n Is the initial weight value; e.g. of a cylinder _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m is a group of ^Nt The total number of constellation points; sigma ² Is the noise variance; n is an iteration number index.

For example, in the first iteration process, the initial weight W of the current downlink channel may be directly input in the mutual information lower bound function G (H, W) ₁ And a channel characteristic matrix H, and calculating an initial iteration rate R ₁ ＝G(H,W ₁ ). After the initial iteration rate is calculated, a judgment link in the iteration step is entered, and in the above exemplary embodiment, two judgments need to be performed respectively on the iteration gradient value and the iteration rate.

S4023: and if the iteration gradient value is larger than the minimum step factor, updating the weight matrix.

After the initial iteration rate is generated, the iteration gradient value may be compared to a minimum step factor to determine whether the iteration has been completed. If the iterative gradient value is greater than the minimumStep factor, the iteration process is not completed, so the weight matrix W can be updated _n And continue with the next iteration. If the iteration gradient value is less than or equal to the minimum step factor, the iteration is determined to be completed, so that the weight matrix at the moment can be output, and the channel capacity can be calculated according to the output weight matrix.

For the first iteration process, the initial step factor mu = mu is set _init The set initial step-size factor is necessarily larger than the set minimum step-size factor mu _min Therefore, in the first iteration process, the iteration gradient value is larger than the minimum step factor, namely, the weight matrix is updated.

In one implementation, the weight matrix may be updated according to the following equation:

wherein mu is an iterative gradient value; alpha is a preset influence coefficient;

is the first derivative of the mutual information lower bound function G (H, W) with respect to the weight W, i.e.:

in the formula, H is a channel characteristic matrix; w is an initial weight; e.g. of the type _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m ^Nt The total number of constellation points; sigma ² Is the variance of the noise; n is an iteration index.

For example, for the first iteration process, the weight matrix needs to be updated since the iteration gradient value is determined to be greater than the minimum step factor. So that the first iteration can be calculated

Based on the calculated->

Updating the weight matrix, i.e. < >>

S4024: and generating an intermediate iteration rate according to the updated weight matrix and the channel characteristic matrix.

After updating the weight matrix, the updated weight matrix W is used _n+1 And taking the channel characteristic matrix H of the current downlink channel as input, and calculating the intermediate iteration rate according to the calculation formula of the iteration rate again, namely:

R _n+1 ＝G(H,W _n+1 )

for example, for the updated weight matrix W obtained during the first iteration ₂ The updated weight matrix W can be used ₂ And channel characteristics matrix H input R _n ＝G(H,W _n ) Calculating an intermediate iteration rate R ₂ So as to carry out subsequent judgment on the iteration rate, thereby realizing the iteration process.

After the intermediate iteration rate is calculated, the intermediate iteration rate can be compared with the initial iteration rate, so that whether the iteration is finished or not is determined according to a comparison result.

S4025: if the intermediate iteration rate is greater than or equal to the initial iteration rate, updating the iteration gradient value, and extracting a weight matrix when the iteration gradient value is less than or equal to the minimum step factor to obtain the power distribution weight.

Through the comparison of the intermediate iteration rate and the initial iteration rate, if the intermediate iteration rate is greater than or equal to the initial iteration rate, it is indicated that the current mutual information lower bound function is close to the minimum result, so that the iteration gradient value can be updated, iteration is continued, the mutual information lower bound function gradually approaches to the minimum value, and a corresponding weight matrix is extracted until the iteration gradient value is less than or equal to the minimum step factor, so as to obtain the power distribution weight of each signal in the current downlink channel.

In another comparison result juxtaposed to step S4025, if the intermediate iteration rate is less than the initial iteration rate, it indicates that the current mutual information lower bound function G (H, W) has not yet obtained the minimization result. Therefore, the initial iteration rate can be updated to the intermediate iteration rate, and the process jumps to step S4023 to continue the iteration until the intermediate iteration rate calculated in the iteration process is greater than or equal to the initial iteration rate calculated in the last iteration, and outputs the corresponding weight matrix.

For example, an intermediate iteration rate R is correspondingly calculated for the first iteration process ₂ If the intermediate iteration rate R is determined by comparison ₂ ＜R ₁ Then determining the current mutual information lower bound function R ₁ ＝G(H,W ₁ ) The minimization result is not obtained, and therefore, the initial iteration rate may be set to R ₂ And skipping to execute the step S4023 to continue the iteration.

For step S4025, if the intermediate iteration rate is greater than or equal to the initial iteration rate, the iteration gradient value may be updated in a gradient iteration manner to further determine whether to obtain a minimization result of the mutual information lower bound function G (H, W). Namely, the MIMA algorithm may include: if the intermediate iteration rate is greater than or equal to the initial iteration rate, the iteration gradient values are updated.

The iterative gradient values may be updated as follows:

wherein mu is an iterative gradient value; k is a convergence coefficient.

During the iteration, the iteration gradient value may be updated by a set convergence coefficient k, and typically, a convergence coefficient k =3 may be set. After updating the iterative gradient value, the process may jump to step S4023, and continue to determine the iterative gradient value, so as to perform iteration again according to the determination result, and update the weight matrix.

For example, at an intermediate iteration rate R that is correspondingly calculated for the first iteration process ₂ If the intermediate iteration rate R of the first iteration is determined by comparison ₂ ≥R ₁ The iterative gradient values can then be updated, i.e. μ =1/3 μ _init And correspondingly, the updated iteration gradient value is continuously compared with the set minimum step factor. If the updated iterative gradient value mu is still larger than the minimum step factor mu _min Then, the weight matrix is continuously updated.

Similarly, after the weight matrix is updated, the intermediate iteration rate may also be calculated by executing step S4024, and repeated iteration is performed by executing step S4025 to compare the intermediate iteration rate with the initial iteration rate until the intermediate iteration rate is greater than or equal to the initial iteration rate and the iteration gradient value is less than the set minimum step factor, so as to determine that iteration is completed and output the final weight matrix. And finally, extracting corresponding elements from the weight matrix to serve as the power distribution weight of each signal in the current downlink channel, thereby realizing the pre-coding of the downlink data.

Therefore, in the implementation manner, the target function in the MIMA algorithm is set to adopt the mutual information lower bound function to perform the optimal solution of the power and the weight, so that the computational complexity in each iteration can be simplified. And correspondingly, an iteration mode combining the Jersen inequality, the quasi-gradient iteration and the quasi-Newton iteration is utilized, so that the iteration times are reduced, the complexity of the iteration steps is simplified, and the MIMA algorithm can be applied to engineering.

The following describes an iterative process of the MIMA algorithm with reference to a specific implementation example, for example:

after the channel information is obtained, the initial weight W in the channel information can be extracted ₁ And a channel characteristic matrix H is summed, and the initial weight W is initialized ₁ And the channel characteristic matrix H as initialization parameters of the MIMA algorithm. Setting the initial step factor mu = mu at the same time _init Setting a minimum step factor mu _min 。

According to the initial weight W ₁ Calculating initial iteration rate R by summing channel characteristic matrix H ₁ ＝G(H,W ₁ ) Determining the initial step factor mu by comparison _init Greater than a minimum step factor mu _min Then, thenUpdating the weight matrix

Then according to the updated weight matrix W ₂ Generating an intermediate iteration rate R with the channel characteristic matrix H ₂ ＝G(H,W ₂ )。

Then, by comparison, determining the intermediate iteration rate R ₂ Less than the initial iteration rate R ₁ Then the initial iteration rate is set to an intermediate iteration rate R ₂ Continuing iteration to obtain weight matrix W ₂ As initial weight and as initialization parameter of MIMA algorithm together with channel characteristic matrix H.

At this time, due to the initial step factor μ _init Greater than a minimum step factor mu _min Therefore, the weight matrix needs to be updated

And calculating an intermediate iteration rate R ₃ ＝G(H,W ₃ ). Then, by comparison, determining the intermediate iteration rate R ₃ Less than the initial iteration rate R ₂ Then the initial iteration rate is set to an intermediate iteration rate R ₃ Continuing iteration to obtain weight matrix W ₃ As initial weight and as initialization parameter of MIMA algorithm together with channel characteristic matrix H.

Similarly, a weight matrix W is obtained through iterative computation in sequence ₄ 、W ₅ 、W ₆ \8230;, and intermediate iteration rate R ₄ 、R ₅ 、R ₆ 823070, 8230and comparing up to a middle iteration rate R _n Greater than or equal to the initial iteration rate R _n-1 Then, the iterative gradient value μ' =1/3 μ is updated.

Then, by comparison, the step factor iterative gradient value mu' is determined to be larger than the minimum step factor mu _min And then updating the weight matrix

And calculating an intermediate iteration rate R _n+1 ＝G(H,W _n+1 ) And continuously carrying out comparison and iterative computation to obtain weightMatrix W _n+2 、W _n+3 、W _n+4 823060, 8230and intermediate iteration rate R _n+2 、R _n+3 、R _n+4 82303060, 82303080, and intermediate iteration rate R _n+m Greater than or equal to the initial iteration rate R _n+m-1 Then, the iterative gradient value μ "=1/3 μ' is updated.

By contrast, if it is determined that the step factor iterative gradient value μ' is less than or equal to the minimum step factor μ _min Then, the iteration is completed, and the weight matrix W at the moment is output _n+m . And calculating the channel capacity according to the output weight matrix, thereby determining the weight and power correspondingly distributed by each signal.

As can be known from actual detection and analysis, the implementation mode can solve a global optimal solution after dozens of iterations or dozens of iterations, and compared with thousands of iterations in an academic theory, the implementation mode can greatly simplify the complexity of iteration steps, thereby saving the solution time and being applied to the real-time precoding process of downlink data. The method can carry out targeted optimal solution on the current downlink channel, and carry out precoding on the downlink data according to the solution result, thereby achieving the purposes of not reducing the signal transmission efficiency but obtaining the optimal throughput, improving the downlink data sending efficiency of the base station in the MIMO system scene, and improving the signal transmission quality.

In order to send downlink data to the terminal, it is necessary to perform precoding on the downlink data according to the calculated power allocation weight, as shown in fig. 6, in an implementation manner, the performing precoding on the downlink data according to the power allocation weight shown in step S403 may include:

s4031: and generating a distribution power value according to the power distribution weight value and the channel capacity of the downlink channel.

After obtaining the power distribution weight value through iteration, the base station can calculate the distribution power value of the signal by combining the channel capacity of the current downlink channel. The power value allocation means that for the transmission power of the corresponding digital signal, the transmission power required by different types of signals is different, and the combination mode of the corresponding multilayer constellations is also different.

S4032: and applying the distribution power value to the downlink data.

According to the distribution power value determined by the power distribution weight value, the optimal sending mode of the downlink data can be obtained by utilizing the optimal solution obtained by calculation, so that the channel capacity is reasonably utilized. Therefore, after the allocation power value is generated, the allocation power value can be applied to the downlink data to form a downlink data stream, so that the downlink data can be sent to the terminal according to the optimal power and the weight value obtained by calculation, and the application channel capacity is maximized.

The downlink precoding method provided by the embodiment can be applied to a single-user multiple-input multiple-output system and can also be applied to a multi-user multiple-input multiple-output system, and both the power distribution weight value which accords with the characteristics of the current downlink channel can be obtained before downlink data is transmitted, so that the optimal throughput performance can be obtained.

For a multi-user MIMO system, since the system includes multiple user terminals, and the multiple user terminals can communicate with each other through a base station, when one terminal (target terminal) is paired with another terminal (paired terminal), communication data generated between the two terminals can also generate downlink data in the base station. For example, after the terminal a is paired with the terminal B, the data sent by the terminal a to the terminal B is sent to the base station first; the base station sends the corresponding data as downlink data to the terminal B through a downlink channel.

It should be noted that the downlink data refers to data transmitted from the base station to the terminal, and in a multi-user MIMO scenario, the downlink data includes not only communication data transmitted from the paired terminal to the target terminal, but also other data transmitted to the target terminal, for example, data transmitted to the target terminal by the internet. Also, the pairing relationship is not limited to the pairing relationship between two terminals, and may be a pairing relationship between a plurality of terminals.

When transmitting data corresponding to a terminal having a pairing relationship, the base station may acquire a channel characteristic related to the paired terminal as channel information to form a channel characteristic matrix. Therefore, as shown in fig. 7, in order to adapt to the multi-user multiplexing scenario, the step of acquiring channel information of the downlink channel shown in step S401 may include:

s4011: and acquiring pairing information.

The pairing information is used for representing channel characteristic data corresponding to the terminal having the pairing relationship, and may include one or more of modulation and coding strategy information, modulation scheme information, and constellation point type information of the target terminal and the pairing terminal.

The modulation and coding strategy information is usually expressed as an MCS index value, and can be used to implement rate configuration in LTE. The MCS information may form a rate table with the MCS index values as rows and columns of the table regarding the factors affecting the communication rate concerned. Each MCS index corresponds to a physical transmission rate under a set of parameters.

The modulation scheme information is used for characterizing digital modulation parameters when the current base station transmits digital signals, such as parameters of a carrier signal and related parameters of source coding, encryption, equalization and the like. Different base stations, terminals and even downlink channels can adopt different forms of modulation schemes, and the modulation/demodulation mode of the downlink channel data can be determined through the modulation scheme information, so that the transmitted signals have stronger anti-interference performance and anti-channel loss performance, and better safety.

The constellation point type information is data for representing a data mapping state, and in the field of digital communication, a digital signal is represented on a complex plane to visually represent the signal and a relationship between the signals. Therefore, after channel coding, data is mapped on the constellation diagram, and the different types of constellation points will affect the mapping result of the data on the constellation diagram.

For each terminal, after the terminal is connected to the base station, different channel characteristic data are respectively corresponding to the terminal according to different hardware characteristics. The channel characteristic data may include modulation and coding strategy information, modulation scheme information, constellation point type information, and the like, so that the pairing information may be extracted and obtained from the channel characteristic data corresponding to the target terminal and the pairing terminal, respectively.

S4012: a channel characterization matrix is generated.

After extracting the pairing information, the base station may generate a channel characteristic matrix using the pairing information. Compared with the above embodiments, in this embodiment, on the basis of the original channel feature matrix, corresponding data, such as one or more of modulation and coding strategy information, modulation scheme information, and constellation point type information, may be extracted from the pairing information, and the extracted data is added to the channel feature matrix.

The three kinds of information can be combined to form a channel characteristic matrix for characterizing the characteristics of the downlink channel. Obviously, the channel information is not limited to the above three types, and may also include other types of information, such as the number of antennas, the size of the sky, the channel capacity, and the like, and different channel information may be formed according to important factors considered in the actual signal transmission process.

Therefore, by the above method for generating a channel feature matrix, the channel feature associated with the paired terminal can be added to the generated channel feature matrix. When two paired terminals are communicated with each other, the paired terminal channel characteristics can be obtained in the results of power calculation and weight calculation, and the two paired terminals can utilize the corresponding channel characteristics to perform equalization, detection and decoding, so that the signal transmission efficiency between the two paired terminals is improved.

For the pairing information, on one hand, a channel characteristic matrix can be formed as channel information, and on the other hand, the pairing information can also be sent to each terminal with a pairing relationship through a base station. If multiple users can mutually know information related to data transmission, such as corresponding MCS, modulation scheme, constellation point type and the like, the method can realize that a suitable data processing mode is directly adopted after pairing. For example, the same modulation/demodulation mode and the like can be adopted, so that the problem that the terminal cannot demodulate data or adopts a blind demodulation method for demodulation is reduced, the processes of equalization, detection and decoding of the data by the terminal can be accelerated, and the purpose of achieving the maximum value of the channel capacity is conveniently achieved.

Therefore, in a multi-user MIMO system, a base station may send signaling to a target terminal and a paired terminal respectively to notify the target terminal and the paired terminal to obtain channel characteristics from each other, that is, as shown in fig. 8, in an implementation manner, the downlink precoding method further includes:

s404: and sending downlink signaling.

For a multi-user multiplexing scenario, the base station may generate downlink signaling for the target user and the paired user, and may send the downlink signaling to the target terminal and the paired terminal, respectively, so as to notify the paired information to the target terminal and the paired terminal. Namely, the target terminal obtains the matching information, such as the modulation and coding strategy information, the modulation scheme information, the constellation point type information and the like corresponding to the matching terminal notified by the base station by receiving the downlink signaling, and then performs equalization, detection and decoding by using the received matching information. Similarly, the pairing terminal may also obtain pairing information corresponding to the target terminal notified by the base station by receiving the downlink signaling, and perform equalization, detection, and decoding by using the pairing information. The downlink signaling may include pairing information such as MCS, modulation scheme, constellation point type, and the like.

The downlink signaling can be directly analyzed by the terminal, so that the pairing information such as the modulation and coding strategy information, the modulation scheme information, the constellation point type information and the like of the paired terminal can be extracted from the downlink signaling. In one implementation, the downlink signaling is carried in Radio Resource Control (RRC) signaling and/or DCI signaling.

The RRC is also called Radio Resource Management (RRM) or Radio Resource Allocation (RRA), and refers to performing radio resource management, control, and scheduling by using a certain strategy and means, and under the condition that the requirement of service quality is met, the limited radio network resources are fully utilized, the planned coverage area is ensured, and the service capacity and the resource utilization rate are improved.

The corresponding RRC signaling is signaling when the base station performs radio resource management, control and scheduling, and the signaling can be directly transmitted between the terminal and the base station without being transmitted in a downlink data manner. Similarly, the DCI signaling may be carried by a downlink physical control channel, and is dedicated to sending downlink control information to the terminal, including uplink and downlink resource allocation, harq information, power control, and the like, and is also not required to be sent in a downlink data manner.

The terminal may receive the downlink signaling carried in the RRC signaling or the DCI signaling or through a combination of the RRC signaling and the DCI signaling. The terminal can perform the balancing, detecting and decoding processes by using the corresponding downlink signaling, thereby obtaining the optimal power distribution and throughput performance.

The downlink precoding method provided in the above embodiments and the downlink precoding method provided in various implementation manners or the steps included in the method may be combined with each other to obtain more implementation manners of the precoding method, which is not described herein again.

Based on the downlink precoding method provided in the foregoing embodiment, in an exemplary embodiment of the present application, a downlink precoding device is also provided. The downlink precoding apparatus may be configured to implement the downlink precoding method provided in the foregoing embodiment. As shown in fig. 9, in an implementation manner, the downlink precoding apparatus includes: the obtaining module 1, the weight calculating module 2, and the precoding module 3 are respectively configured to execute step S1, step S2, and step S3 in the above embodiment, so as to perform precoding on downlink data.

For example, the obtaining module 1 is configured to obtain channel information of a downlink channel, and send the channel information to the weight calculating module 2, where the channel information includes an initial weight of the downlink channel and a channel feature matrix of the downlink channel.

The weight calculation module 2 is used for receiving the channel information sent by the acquisition module 1 and generating the power distribution weight of the downlink channel by iteration through the MIMA algorithm. The target function of the MIMA algorithm is a mutual information lower bound function of a downlink channel, and the initialization parameters of the MIMA algorithm are the initial weight and a channel feature matrix. After the weight calculation module 2 calculates the power distribution weight, it may also send the power distribution weight to the precoding module 3.

And the precoding module 3 is used for receiving the power distribution weight and executing precoding on the downlink data according to the power distribution weight.

It can be seen that, the downlink precoding device may obtain channel information through the obtaining module 1 before downlink data is sent. And then the weight calculation module 2 executes the MIMA algorithm, and iteratively calculates and solves the optimal solution of the power and the weight according to the channel information to obtain the power distribution weight. And finally, precoding is carried out on the downlink data according to the power distribution weight through a precoding module 3, and the optimal combination mode of the multilayer constellation is determined, namely the optimal throughput is obtained.

In order to execute the MIMA algorithm, the weight calculation module 2 may also specifically execute the following operations to obtain a power allocation weight: firstly, setting an iteration gradient value as an initial step factor and a minimum step factor; generating an initial iteration rate according to the initial weight and the channel characteristic matrix; if the iteration gradient value is larger than the minimum step factor, updating the weight matrix; then generating an intermediate iteration rate according to the updated weight matrix and the updated channel characteristic matrix; if the intermediate iteration rate is greater than or equal to the initial iteration rate, extracting a weight matrix to obtain a power distribution weight.

To sum up, the embodiments of the present application provide a downlink precoding method, which can use an MIMA algorithm to iteratively generate a power allocation weight of a downlink channel after channel information of the downlink channel is obtained, and then perform precoding on downlink data according to the power allocation weight to implement transmission of the downlink data. The downlink precoding method takes a mutual information lower bound function as a target function, and completes the calculation of power and weight through iteration so as to determine the optimal constellation combination, and under the condition of smaller iteration times, the overall optimal solution when the channel capacity allocation is maintained in a discrete modulation constellation is ensured.

For other implementation manners in the foregoing embodiment, on the basis of the foregoing downlink precoding device, different functional units may be configured to implement the corresponding implementation manners, and details are not described here again.

It should be noted that the division of each module in the downlink precoding apparatus in the foregoing implementation is only a division of a logic function, and all or part of the division may be integrated into one physical entity or may be physically separated in an actual implementation process. For example, the obtaining module 1 may be implemented by a signal transceiver, and the weight calculating module 2 and the pre-coding module 3 may be implemented by a controller, that is, the signal transceiver obtains channel information and sends the channel information to the controller, so that the controller performs an MIMA algorithm according to the channel information to perform iterative solution, performs pre-coding on downlink data, and finally sends the downlink data to the terminal through the signal transceiver.

To this end, in an exemplary embodiment, a base station 100 is further provided, which may include a signal transceiver station 101 and a controller 102, wherein the signal transceiver station 101 may be configured to acquire channel information, and the controller 102 is configured to perform a MIMA algorithm and precoding on downlink data. The signal transceiver station 101 is connected to the controller 102 to transmit the acquired channel information to the controller 102; the controller 102 is configured to execute the operation instructions to implement the downlink pre-coding method, and control the signal transceiver station to perform pre-coding on downlink data.

The controller 102 may have a built-in processor and a memory, wherein the memory may store a control program corresponding to the precoding method, and the processor may call the corresponding control program from the memory and precode downlink data by executing the control program. The processor may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of the CPU and the NP. The processor may also further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof.

The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.

The memory may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as read-only memory (ROM), flash memory, a hard disk, or a solid state drive; the memory may also comprise a combination of the above kinds of memories.

For example, when the base station 100 has the terminal 200 accessed, the transceiver station 101 may acquire channel information corresponding to the terminal 200 and send the channel information to the controller 102. After receiving the channel information, the processor in the controller 102 may first call an application program related to the MIMA algorithm in the memory, and extract an initial weight and a channel feature matrix in the channel information as inputs of the MIMA algorithm program, so as to generate a power allocation weight by executing the MIMA algorithm.

After the power distribution weight is generated, the precoding-related application program can be called from the memory, and the power distribution weight is used as the input of the precoding-related application program to perform precoding on the downlink data. Finally, the precoding result is sent to the terminal 200 through the transceiver station 101.

For other implementations in the above embodiments, only the specific application program needs to be stored in the memory of the base station 100, respectively. When the corresponding condition is reached, the processor directly calls and executes the application program to implement other implementation modes, which is not described herein again.

In one exemplary embodiment, a communication apparatus is also provided, which may be a terminal or a chip in a terminal or a system on a chip. The communication device may implement the functions performed by the terminal in the above aspects or possible implementations, which may be implemented by hardware. The communication apparatus may include: a processor and a communication interface, wherein the processor can be used for supporting the communication device to implement the downlink precoding method.

In an exemplary embodiment, a computer-readable storage medium is also provided, which may be a readable non-volatile storage medium, and the computer-readable storage medium has stored therein instructions, which when executed on a computer, enable the computer to execute the above downlink precoding method.

In an exemplary embodiment, a computer program product containing instructions is also provided, which when run on a computer, causes the computer to perform the above described downlink pre-coding method.

In one exemplary embodiment, there is also provided a communication apparatus, which may be a terminal or a chip or a system on a chip in a terminal, including one or more processors and one or more memories. The one or more memories are coupled to the one or more processors for storing computer program code, the computer program code including computer instructions that, when executed by the one or more processors, cause the communication apparatus to perform the downlink pre-coding method as described above.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to be performed in whole or in part.

The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire, such as coaxial cable, fiber optic cable, digital subscriber line, or wireless, such as infrared, wireless, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), among others.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit and scope of the application. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present application and their equivalents, the present application is also intended to encompass such modifications and variations.

Claims

1. A downlink precoding method is characterized by comprising the following steps:

generating a power distribution weight of a downlink channel by using a mutual information maximization algorithm in an iteration mode, wherein a target function of the mutual information maximization algorithm is a mutual information lower bound function of the downlink channel, and initialization parameters of the mutual information maximization algorithm are the initial weight and a channel characteristic matrix;

performing precoding on downlink data according to the power distribution weight, wherein the downlink data is data transmitted in the downlink channel;

the iterative generation of the power distribution weight of the downlink channel by using the mutual information maximization algorithm comprises the following steps: setting the iteration gradient value as an initial step factor and a minimum step factor; generating an initial iteration rate according to the initial weight and the channel characteristic matrix; and determining the power distribution weight of the downlink channel according to the iteration gradient value, the minimum step factor and the initial iteration rate.

2. The downlink precoding method of claim 1, wherein determining the power allocation weight of the downlink channel according to the iteration gradient value, the minimum step factor and the initial iteration rate comprises:

if the iteration gradient value is larger than the minimum step factor, updating a weight matrix;

generating an intermediate iteration rate according to the updated weight matrix and the channel characteristic matrix;

if the intermediate iteration rate is greater than or equal to the initial iteration rate, updating the iteration gradient value, and extracting a weight matrix when the iteration gradient value is less than or equal to the minimum step factor to obtain the power distribution weight.

3. The downlink precoding method of claim 2, wherein the determining the power allocation weight of the downlink channel according to the iteration gradient value, the minimum step factor, and the initial iteration rate further comprises:

and if the intermediate iteration rate is smaller than the initial iteration rate, updating the initial iteration rate to be the intermediate iteration rate.

4. The downlink precoding method of claim 2, wherein the initial iteration rate is calculated according to the following formula:

R _n ＝G(H,W _n )；

wherein R is _n Is the initial iteration rate; g (H, W) is a mutual information lower bound function,

h is a channel characteristic matrix; w _n Is an initial weight matrix; e.g. of a cylinder _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m ^Nt The total number of constellation points; sigma ² Is the noise variance; n is an iteration number index.

5. The downlink precoding method of claim 4, wherein the weight matrix is updated according to the following formula:

wherein alpha is a preset influence coefficient; h is a channel characteristic matrix; w _n Is a weight matrix;

is the first derivative of the lower bound function of the mutual information; />

e _ij The constellation point code distance; i, j are stars respectivelyNumbering the seat points; m ^Nt The total number of constellation points; sigma ² Is the variance of the noise; n is an iteration index; μ is the iterative gradient value.

6. The downlink precoding method of claim 2, wherein after generating an initial iteration rate according to the initial weight and the channel characteristic matrix, the method comprises:

if the iteration gradient value is smaller than or equal to the minimum step factor, outputting a weight matrix;

and calculating the channel capacity according to the output weight matrix.

7. The downlink precoding method of claim 2, wherein the iterative gradient value is updated according to the following formula:

wherein mu is an iterative gradient value; k is a convergence coefficient; mu.s ₀ Is an initial step factor or an iterative gradient value before update.

8. The downlink precoding method of claim 1, wherein the initial weight is a single user weight in the downlink channel; or, the estimated weight of the downlink channel.

9. The downlink precoding method of claim 1, wherein the obtaining of the channel information of the downlink channel comprises:

10. The downlink precoding method of claim 1, wherein performing precoding on downlink data according to the power allocation weights comprises:

generating a distribution power value according to the power distribution weight value and the channel capacity of the downlink channel;

and applying the allocated power value to the downlink data.

11. The downlink precoding method of claim 9 or 10, wherein the method further comprises:

and sending a downlink signaling, wherein the downlink signaling comprises pairing information.

12. The downlink precoding method of claim 11, wherein the downlink signaling is carried in radio resource control signaling and/or downlink control information signaling.

13. A downlink precoding apparatus, comprising:

an obtaining module, configured to obtain channel information of a downlink channel by a user, where the channel information includes an initial weight of the downlink channel and a channel feature matrix of the downlink channel;

the weight calculation module is used for generating a power distribution weight of a downlink channel by using a mutual information maximization algorithm in an iteration mode, wherein a target function of the mutual information maximization algorithm is a mutual information lower bound function of the downlink channel, and initialization parameters of the mutual information maximization algorithm are the initial weight and a channel characteristic matrix;

a precoding module, configured to perform precoding on downlink data according to the power allocation weight, where the downlink data is data transmitted in the downlink channel;

the weight calculation module is specifically used for setting the iterative gradient value as an initial step factor and a minimum step factor; generating an initial iteration rate according to the initial weight and the channel characteristic matrix; and determining the power distribution weight value of the downlink channel according to the iteration gradient value, the minimum step factor and the initial iteration rate.

14. The downlink precoding apparatus of claim 13,

the weight calculation module is specifically configured to update a weight matrix if the iterative gradient value is greater than the minimum step factor;

15. The downlink precoding device of claim 14,

the weight calculation module is specifically configured to update the initial iteration rate to the intermediate iteration rate if the intermediate iteration rate is less than the initial iteration rate.

16. The downlink precoding apparatus of claim 14, wherein the initial iteration rate is calculated according to the following formula:

R _n ＝G(H,W _n )；

h is a channel characteristic matrix; w _n Is an initial weight matrix; e.g. of a cylinder _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m ^Nt The total number of constellation points; sigma ² Is the noise variance; n is iteration frequency cableAnd (3) introducing.

17. The downlink precoding device of claim 16, wherein the weight matrix is updated according to the following formula:

e _ij Is the constellation point code distance; i and j are respectively the serial numbers of the constellation points; m is a group of ^Nt The total number of constellation points; sigma ² Is the noise variance; n is an iteration index; μ is the iterative gradient value.

18. The downlink precoding device of claim 14,

the weight calculation module is specifically configured to output a weight matrix if the iteration gradient value is less than or equal to the minimum step factor;

and calculating the channel capacity according to the output weight matrix.

19. The downlink precoding apparatus of claim 16, wherein the iterative gradient value is updated according to the following equation:

wherein mu is an iterative gradient value; k is a convergence coefficient; mu.s ₀ Is the initial step factor or the iterative gradient value before updating.

20. The downlink precoding apparatus of claim 13, wherein the initial weight is a single user weight in the downlink channel; or, the estimated weight of the downlink channel.

21. The downlink precoding device of claim 13,

the acquisition module is further configured to acquire pairing information, where the pairing information includes one or more of modulation and coding strategy information, modulation scheme information, and constellation point type information of a target terminal and a paired terminal;

22. The downlink precoding device of claim 13,

the precoding module is specifically configured to generate a distribution power value according to the power distribution weight and the channel capacity of the downlink channel;

and applying the allocated power value to the downlink data.

23. The downlink precoding apparatus of claim 21 or 22, further comprising a pairing module;

the pairing module is configured to send a downlink signaling, where the downlink signaling includes pairing information.

24. The downlink precoding apparatus of claim 23, wherein the downlink signaling is carried in radio resource control signaling and/or downlink control information signaling.

25. A base station is characterized by comprising a signal transceiver station and a controller, wherein the signal transceiver station is connected with the controller; the controller is configured to execute operating instructions to implement the method of any one of claims 1-12 for controlling the signaling station to perform precoding on downlink data.