CN114900214B - Low-complexity linear precoding algorithm based on OFDM system - Google Patents

Low-complexity linear precoding algorithm based on OFDM system Download PDF

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CN114900214B
CN114900214B CN202210488206.8A CN202210488206A CN114900214B CN 114900214 B CN114900214 B CN 114900214B CN 202210488206 A CN202210488206 A CN 202210488206A CN 114900214 B CN114900214 B CN 114900214B
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matrix
precoding
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operator
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CN114900214A (en
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王闻今
杨辰泓
文简
季姜晗
王亚飞
刘彦浩
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0254—Channel estimation channel estimation algorithms using neural network algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2695—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation with channel estimation, e.g. determination of delay spread, derivative or peak tracking
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a linear pre-coding method based on an OFDM system, which adopts the OFDM system to carry out orthogonal frequency division multiplexing on a channel, converts the channel into a plurality of narrowband sub-channels, considers that parameters such as Lagrangian operators and the like of each sub-channel in the pre-coding matrix solving process are slowly changed in the time and frequency directions, only selects partial sub-carriers in the time and frequency directions to insert pilot frequency, and respectively obtains necessary parameters. And estimating channel parameters of other frequencies by using the acquired parameters of the known frequency points through an interpolation algorithm, so as to directly calculate all the precoding matrixes. The linear precoding algorithm adopts an interpolation method to estimate the channel parameters of unknown subcarriers, so that the complexity of the algorithm is reduced, and the operation efficiency and the practicability are improved. The OFDM technique employed by the linear precoding algorithm has great advantages in combating frequency selective fading or narrowband interference.

Description

Low-complexity linear precoding algorithm based on OFDM system
Technical Field
The invention belongs to the technical field of large-scale MIMO communication, and particularly relates to a low-complexity linear precoding algorithm based on an OFDM system.
Background
With the development of mobile communication technology, mobile intelligent terminals have gradually become an indispensable part of people's daily production and life. Divided by man-centric communication, machine-centric communication is also becoming increasingly important. These newly accessed devices will place a great deal of new traffic demands on the mobile communication system, such as communication cost, complexity, power consumption, data rate, mobility, latency, reliability, etc. In order to meet the development needs of the future human society, the fifth generation mobile communication system for 2020 and later is developed to cope with the appearance of new business and application scenes.
MIMO refers to using a plurality of transmitting antennas and receiving antennas at a transmitting end and a receiving end, respectively, so that signals are transmitted and received through the plurality of antennas at the transmitting end and the receiving end, thereby improving communication quality. The system can fully utilize space resources, realize multiple transmission and multiple reception through a plurality of antennas, can doubly improve the system channel capacity under the condition of not increasing frequency spectrum resources and antenna transmitting power, shows obvious advantages and is regarded as a core technology of next generation mobile communication.
The large-scale multiple input multiple output technology of configuring a large-scale antenna array at the base station side, namely large-scale MIMO, is one of key technologies of a new generation mobile communication network and is also a research hot spot in the field of mobile communication in recent years due to remarkable improvement effect on the capacity of a mobile communication system.
The research of the large-scale MIMO technology with moderate complexity not only has important theoretical significance, but also has important application value in meeting the service requirements of low communication cost, low complexity, low energy consumption, high data rate, low time delay, high reliability and the like proposed by 5G and later 5G.
Massive MIMO generally adopts an Orthogonal Frequency Division Multiplexing (OFDM) technology, i.e., an OFDM system, because it has a plurality of subchannels and requires no interference between the subchannels. The parallel transmission of high-speed serial data is realized through frequency division multiplexing, so that the massive MIMO has better multipath fading resistance and can support multi-user access.
The main idea of OFDM is to divide the channel into several orthogonal sub-channels, convert the high-speed data signal into parallel low-speed sub-data streams, and modulate to transmit on each sub-channel. The orthogonal signals may be separated by employing correlation techniques at the receiving end, which may reduce mutual interference between the sub-channels. The signal bandwidth on each sub-channel is less than the associated bandwidth of the channel, so that each sub-channel can be seen as flat fading, so that inter-symbol interference can be eliminated, and channel equalization is relatively easy since the bandwidth of each sub-channel is only a small fraction of the original channel bandwidth.
As the research emphasis of the current massive MIMO technology, the precoding technology can fully exploit the spatial degrees of freedom to minimize intra-cell and inter-cell interference. If different phase shifts are used in different frequency bands of the system bandwidth to achieve resistance to small-scale fading, the technique is defined as a precoding technique.
Typical precoding techniques mostly assume perfect channel state information, i.e., CSI, can be obtained. However, in a mobile environment, due to processing delay caused by processes such as pilot frequency transmission, channel estimation, precoding matrix calculation and the like, the estimated CSI has larger deviation from a channel in actual downlink transmission. Currently, research on transmission of massive MIMO under imperfect CSI conditions is still in the primary stage. To improve transmission performance in a mobile environment, improvements can be made from two dimensions: on the premise of imperfect CSI, an improved precoding method is designed so as to improve the system performance; and the capability of acquiring the channel information at the base station side is improved, and the accuracy of the CSI is improved by using a channel prediction method.
Whether nonlinear or linear precoding, obtaining accurate channel state information is a precondition for achieving high performance precoding. Aiming at the phenomenon that the large-scale MIMO channel information is imperfect in a mobile scene, the channel information of a plurality of known detection periods can be utilized to predict the future channel information, so that the precoding performance is assisted to be enhanced. In a static channel environment, more accurate channel state information can be obtained by utilizing a channel sounding reference signal and a common channel estimation method, and channel prediction brings a certain degree of gain to system performance no matter for a narrowband system or a broadband system. The actual wireless channel is continuously changing, especially in a mobile scenario, the speed of the channel change is positively related to the relative speed of the mobile device and the base station. Therefore, there is a need to design a low complexity linear precoding algorithm suitable for use under imperfect channel state information conditions.
Disclosure of Invention
The invention aims to provide a low-complexity linear precoding algorithm based on an OFDM system so as to solve the technical problems in the background technology.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
a low complexity linear precoding algorithm based on an OFDM system comprising the steps of:
step 1, pilot frequency is inserted into an OFDM system from time dimension and frequency dimension respectively, and a channel matrix of a pilot frequency signal is obtained;
step 2, calculating Lagrangian operator and user power vector parameters of the pilot channel by using a convolutional neural network;
step 3, estimating Lagrange operator parameters of the rest subcarriers by using an interpolation method through Lagrange operators of known pilot channels and user power vector parameters;
and 4, calculating a precoding matrix by using Lagrange operator parameters of all subcarriers.
Further, in step 1, a known pilot symbol is inserted at the transmitting end, and the receiving end estimates the channel characteristic by using the pilot symbol; consider a single-cell multi-user massive MIMO downlink transmission where the number of base station side antennas is M t The number of users is K, each user is provided with M k A root antenna; setting downlink data sent to kth user asThe precoding matrix for user k is +.>The corresponding frequency domain channel matrix is->The receiving noise is n k The noise power is sigma 2 The method comprises the steps of carrying out a first treatment on the surface of the Then user k receives signal y k Represented as
If the transmitted signals of K users are jointly expressed asThe received signals of all users are expressed as +.>System transmission model rewriting
y＝HPx+n
Extracting the received pilot signal { y from { y (k) } P (k) Estimating therefrom the channel frequency response { H } of the sub-channel into which the pilot is inserted P (k)}。
Further, in step 2, according to the low complexity linear precoding algorithm based on an OFDM system of claim 2, in the step 2, in order to obtain the instantaneous correlation coefficients required by the lagrangian operator in different scenarios, it can be found by simulation of subsequent experiments that the value of the optimal instantaneous correlation coefficient β is mainly related to the user moving speed and the uplink detection period, and on the premise that different users have the same optimal instantaneous correlation coefficient β, we select the user moving speed and the uplink detection period as inputs, and find the optimal instantaneous correlation coefficient β by a table look-up method.
Further, in the step 2, the Lagrangian operator is obtained through a convolutional neural network structure; and constructing a convolutional neural network taking a precoding direction matrix, an instantaneous channel, a channel coupling matrix and a signal-to-noise ratio as inputs and an optimal Lagrange multiplier as output to obtain the optimal Lagrange multiplier. Compared with the traditional algorithm, the method has lower calculation complexity.
Further, step 4 uses a conjugate gradient method, and approaches the minimum generalized eigenvalue through an iterative method, and the optimal precoding direction matrix is the eigenvector corresponding to the minimum generalized eigenvalue.
Further, the convolutional neural network is divided into an input layer, a convolutional layer, a pooling layer, an activation layer, a full connection layer and an output layer; the input layer inputs a data structure to be detected; considering that the instantaneous channel information and the data structure of the channel coupling matrix are different, firstly, normalizing the channel information, and respectively entering the normalized data samples into a neural network from three input layers; then, the three paths of input information are respectively subjected to repeated convolution, pooling and activation; the convolution layer carries out convolution operation on each input sample; the pooling layer reduces the number of features extracted by the convolution layer and passes through the poolRemoving redundant repeated characteristics; the role of the activation layer is to introduce nonlinear factors, and the function of Relu (& gt) is used for activation after each layer is pooled; after completion of multiple convolutions, pooling and activation, three paths of data m having the same data structure 1 、m 2 And m 3 Respectively inputting the full connection layers, and mapping the extracted features into predicted values; meanwhile, the signal to noise ratio is also used as a node to participate in full connection, and the training result is adjusted; the result of the prediction finally output from the output layer is a kx1 vector μ.
Obtaining Lagrange's operator mu with instantaneous correlation coefficient of 1 by using instantaneous channel information 1 The method comprises the steps of carrying out a first treatment on the surface of the Obtaining Lagrange's operator mu with instantaneous correlation coefficient of 0 by using statistical information 0 The method comprises the steps of carrying out a first treatment on the surface of the Using weighted models
Obtaining Lagrange operator corresponding to current channelWhere η represents a correlation factor.
Further, the user power vector in the step 2 is obtained by using a weighted model method; first, solving a neural network similar to the Lagrangian operator mu, and solving a power vector when the instantaneous correlation coefficient is zeroCombining the obtained power vectors ∈ ->Using weighted models
Obtaining a power vector corresponding to a current channelWhere ζ represents the correlation factor.
Further, the channel-based process in step 3 is slowly changed, so the parameter μ,Is also slowly changed, so that the rest sub-carriers are not needed to be analyzed one by one, and only the parameters mu and the parameters mu of the sub-channels of the inserted pilot frequency are needed to be analyzed>Using interpolation method to estimate the parameters mu and +.>And further obtains the channel responses H of the remaining subcarriers.
Further, after the lagrangian μ is obtained in step 4, the direction information of the precoding matrix is obtained by solving the generalized eigenvalue method, and the obtained user power vector is combinedThe parameters quickly obtain the complete precoding matrix.
The low-complexity linear precoding algorithm based on the OFDM system has the following advantages:
1. the linear precoding algorithm adopts an interpolation method to estimate the channel parameters of the unknown subcarriers, reduces the complexity of the algorithm and improves the operation efficiency and the practicability.
2. The invention adopts a pilot frequency inserting method by OFDM technology under the condition of channel slow fading assumption, which is an optimal solution.
3. The OFDM technology adopted by the linear precoding algorithm has great advantages in resisting frequency selective fading or narrowband interference. In this multi-carrier system, only a small portion of the carriers will be interfered with, with a high degree of accuracy.
Drawings
Fig. 1 is a diagram of a massive MIMO system model of the present invention;
fig. 2 (a) is a block pilot insertion diagram of an OFDM system according to the present invention;
fig. 2 (b) is a schematic diagram of comb pilot insertion in an OFDM system according to the present invention;
FIG. 2 (c) is a schematic diagram of two-dimensional extraction pilot insertion of an OFDM system according to the present invention;
FIG. 3 is a block diagram of the workflow of the present invention;
fig. 4 (a) is a graph showing the influence of the linear interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=5 ms;
fig. 4 (b) is a graph showing the influence of the linear interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=10ms;
fig. 4 (c) is a graph showing the influence of the linear interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=20ms;
fig. 4 (d) is a graph showing the influence of the linear interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=40 ms;
fig. 4 (e) is a graph showing the influence of the linear interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=80 ms;
fig. 4 (f) is a graph showing the influence of the linear interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=160 ms;
fig. 5 (a) is a graph showing the influence of the nearest neighbor interpolation method on the performance of the system approaching the optimal machine learning auxiliary robust pre-coding algorithm when the channel detection period t=5 ms;
fig. 5 (b) is a graph showing the influence of the nearest neighbor interpolation method on the performance of the system approaching the optimal machine learning auxiliary robust pre-coding algorithm when the channel detection period t=10ms;
fig. 5 (c) is a graph showing the influence of the nearest neighbor interpolation method on the performance of the system approaching the optimal machine learning auxiliary robust pre-coding algorithm when the channel detection period t=20ms;
fig. 5 (d) is a graph showing the influence of the nearest neighbor interpolation method on the performance of the system approaching the optimal machine learning auxiliary robust pre-coding algorithm when the channel detection period t=40 ms;
fig. 5 (e) is a graph showing the influence of the nearest neighbor interpolation method on the performance of the system approaching the optimal machine learning auxiliary robust pre-coding algorithm when the channel detection period t=80 ms;
fig. 5 (f) is a graph showing the influence of the nearest neighbor interpolation method on the performance of the system approaching the optimal machine learning auxiliary robust pre-coding algorithm when the channel detection period t=160 ms;
fig. 6 (a) is a graph showing the influence of the cubic spline interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=5 ms;
fig. 6 (b) is a graph showing the influence of the cubic spline interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=10ms;
fig. 6 (c) is a graph showing the influence of the cubic spline interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=20ms;
fig. 6 (d) is a graph showing the influence of the cubic spline interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=40 ms;
fig. 6 (e) is a graph showing the influence of the cubic spline interpolation method of the present invention on the performance of the system approaching the optimal machine learning auxiliary robust precoding algorithm when the channel detection period t=80 ms;
fig. 6 (f) is a graph showing the influence of the cubic spline interpolation method of the present invention on the system performance of the near-optimal machine learning aided robust precoding algorithm when the channel detection period t=160 ms
Detailed Description
For better understanding of the purpose, structure and function of the present invention, a low complexity linear precoding algorithm based on an OFDM system is described in further detail below with reference to the accompanying drawings.
The invention provides a low-complexity linear precoding algorithm, which adopts an OFDM system to carry out orthogonal frequency division multiplexing on a channel, converts the channel into a plurality of narrowband subchannels, considers that parameters such as Lagrangian operators and the like of each subchannel in the solving process of a precoding matrix are slowly changed in the time and frequency directions, only selects part of subcarriers in the time and frequency directions to insert pilot frequency, and respectively obtains necessary parameters. And estimating channel parameters of other frequencies by using the acquired parameters of the known frequency points through an interpolation algorithm, so as to directly calculate all the precoding matrixes. Fig. 1 shows a model diagram of a massive MIMO system according to the present invention.
As shown in fig. 3, the present invention includes the steps of:
step 1, as shown in fig. 2, pilot frequencies are inserted into the OFDM system from the time and frequency dimensions, respectively, so as to obtain a channel matrix of the pilot signals.
Pilot frequency is inserted every certain time and frequency in two dimensions of time domain and frequency domain, and channel matrix of pilot frequency signal can be estimated according to received pilot frequency signal.
Considering large-scale MIMO downlink transmission of single-cell multi-user, inserting a known pilot symbol into a base station end by a signal, and estimating channel characteristics by a receiving end by using the pilot symbol; wherein the number of the base station side antennas is M t The number of users is K, each user is provided with M k A root antenna; setting downlink data sent to kth user asThe precoding matrix of user k isThe corresponding frequency domain channel matrix is->The receiving noise is n k The noise power is sigma 2 The method comprises the steps of carrying out a first treatment on the surface of the Then user k receives signal y k Represented as
If the transmitted signals of K users are jointly expressed asThe received signals of all users are expressed as +.>System transmission model rewriting
y＝HPx+n
Wherein H is a channel matrix, P is a precoding matrix, x is a transmission signal, y is a reception signal, and n is noise; extracting received pilot signal { y (k) } from received signal { y (k) } P (k) Estimating therefrom the channel frequency response { H } of the sub-channel into which the pilot is inserted P (k)}。
And step 2, obtaining an instantaneous correlation coefficient through a channel matrix, wherein the instantaneous correlation coefficient is used as input of a convolutional neural network, and Lagrange operator and user power vector parameters of a pilot channel are used as output.
In order to obtain the instantaneous correlation coefficient required by calculating the Lagrangian operator under different scenes, on the premise of setting the same optimal instantaneous correlation coefficient beta of different users, the moving speed of the users and the uplink detection period are selected as inputs, and the optimal beta is found through a table look-up method. The Lagrangian operator is obtained through a convolutional neural network structure; and constructing a convolutional neural network taking a precoding direction matrix, an instantaneous channel, a channel coupling matrix and a signal-to-noise ratio as inputs and an optimal Lagrange multiplier as output to obtain the optimal Lagrange multiplier.
Solving the lagrangian requires solving two problems, including:
1. and constructing a neural network taking a precoding direction matrix, an instantaneous correlation coefficient, an instantaneous channel coupling matrix and a signal-to-noise ratio as inputs and a Lagrange operator as an output, and directly obtaining the Lagrange operator through channel information with lower calculation complexity.
2. The expressions of the channel posterior model degrade when the instantaneous correlation coefficients are 1 and 0. Will beThe process of solving the Lagrangian operator is simplified into a weighted model, and the instantaneous channel is utilized to solve mu 1 Using machine learning, the inputs are the channel coupling matrix and the signal to noise ratio, and the lagrangian multiplier at output β=0 solves μ 0 And obtaining the Lagrange operator mu of the pilot frequency channel after the weighting treatment. The weighting factor is related to the instantaneous correlation coefficient.
In order to reduce the complexity of calculating the user power, a weighting method is used to obtain the user power. The precoding matrix when the instantaneous correlation coefficient is 1 is obtained during solving, and the power vector can be further solved; a power vector with an instantaneous correlation coefficient of 0 can be obtained through the neural network. The user power can be obtained by weighting.
The neural network is divided into an input layer, a convolution layer, a pooling layer, an activation layer, a full connection layer and an output layer. The input layer inputs the data structure to be detected. Considering that the instantaneous channel information and the data structure of the channel coupling matrix are different, the channel information needs to be normalized first, and normalized data samples enter the neural network from three input layers respectively. And then, the three paths of input information are respectively subjected to repeated convolution, pooling and activation. The convolution layer carries out convolution operation on each input sample, and in order to reduce the complexity of the network as much as possible, the number of convolution kernels is as small as possible on the premise of not affecting the performance. The pooling layer can reduce the number of the features extracted by the convolution layer, and redundant repeated features are removed through pooling. The role of the activation layer is to introduce non-linearities, activated after pooling of each layer using the Relu (-) function. After completion of multiple convolutions, pooling and activation, three paths of data m having the same data structure 1 、m 2 And m 3 And respectively inputting the full connection layers, and mapping the extracted features into predicted values. Meanwhile, the signal to noise ratio is also used as a node to participate in full connection, and the training result is adjusted. The result of the prediction finally output from the output layer is a kx1 vector μ.
Obtaining Lagrange's operator mu with instantaneous correlation coefficient of 1 by using instantaneous channel information 1 The method comprises the steps of carrying out a first treatment on the surface of the Obtaining Lagrange's operator mu with instantaneous correlation coefficient of 0 by using statistical information 0 . Using weighted models
And acquiring a Lagrange operator mu corresponding to the current channel. Where η represents a correlation factor.
The user power vector is obtained using a weighted model method. First, solving a neural network similar to the Lagrangian operator mu, and solving a power vector when the instantaneous correlation coefficient is zeroCombining the obtained power vectors ∈ ->Using weighted models
Obtaining a power vector corresponding to a current channelWhere ζ represents the correlation factor.
And 3, estimating Lagrange operator parameters of the rest subcarriers by using an interpolation method through Lagrange operators of known pilot channels and user power vector parameters.
The parameters mu, based on the channel being slowly varying,Is also slowly changed, so that the rest subcarriers are not needed to be analyzed one by one, and only mu and +.>Mu,/of the remaining sub-carriers is estimated using interpolation>And further obtains the channel responses H of the remaining subcarriers.
And 4, forming matrixes Am, bk and m by using Lagrange operator parameters of all subcarriers and instantaneous channel information, wherein a feature vector corresponding to a minimum generalized feature value of a matrix pair (Am; bk and m) is the optimal precoding matrix.
After Lagrange operator mu is obtained, the direction information of the precoding matrix is obtained by using a method for solving generalized eigenvalue, and the obtained user power vector is combinedThe parameters can quickly obtain the complete precoding matrix.
According to the scheme, the algorithm structure method for estimating the channel by using the interpolation method in the linear precoding algorithm is derived from the traditional precoding matrix algorithm, but the characteristic of an OFDM system is utilized, and the interpolation method is combined with the traditional precoding matrix algorithm, so that short-time large-scale data transmission can be realized.
The linear precoding algorithm adopts an interpolation method to estimate the channel parameters of unknown subcarriers, so that the complexity of the algorithm is reduced, and the operation efficiency and the practicability are improved.
Under the assumption of slow fading of the channel, the method of inserting pilot frequency is adopted by the OFDM technology, which is the optimal solution.
The OFDM technique employed by the linear precoding algorithm has great advantages in combating frequency selective fading or narrowband interference. In this multi-carrier system, only a small portion of the carriers will be interfered with, with a high degree of accuracy.
To verify the performance of the optimized linear precoding algorithm, simulations were performed on it. In the simulation, each time slot is set to contain a plurality of blocks, and each block contains one OFDM symbol. The first subframe of each detection period is used for an uplink detection process, and other subframes are used for downlink transmission. And the base station designs a precoding matrix by utilizing the channel information obtained in the uplink detection process and uses the precoding matrix as a transmission process of all downlink blocks in the next time slot. The remaining subframes of each sounding period except the first subframe are used for downlink transmission. In the transmission process, each generated user data passes through a downlink channel after user selection, code modulation and precoding under the guidance of channel information. After the channel estimation, the sum rate of the current transmission can be counted.
The MATLAB software is used for calculating and drawing images, the influence of three interpolation methods, namely linear interpolation (figure 4), nearest neighbor interpolation (figure 5) and cubic spline interpolation (figure 6), on the performance of the system approaching the optimal machine learning auxiliary robust pre-coding algorithm is found out to be not much different from the performance of the algorithm and the rate for obtaining all parameters, and the complexity of the method is effectively reduced on the premise of almost no performance loss.
It will be understood that the invention has been described in terms of several embodiments, and that various changes and equivalents may be made to these features and embodiments by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (6)

1. A low complexity linear precoding algorithm based on an OFDM system, comprising the steps of:
step 1, pilot frequency is inserted into an OFDM system from time dimension and frequency dimension respectively, and a channel matrix of a pilot frequency signal is obtained;
step 2, obtaining an instantaneous correlation coefficient through a channel matrix, wherein the instantaneous correlation coefficient is used as the input of a convolutional neural network, and a Lagrange operator of a pilot channel and a user power vector are used as the output;
step 3, estimating Lagrangian operators of the rest subcarriers through Lagrangian operators of known pilot channels and user power vectors by using an interpolation method;
step 4, forming matrix A by Lagrange's operator and instantaneous channel information of all sub-carriers m And B k,m Matrix pair (A m ；B k,m ) The feature vector corresponding to the minimum generalized feature value of (2) is the maximumOptimizing a precoding direction matrix; where k represents the number of users and m represents the number of probing cycles;
in the step 1, considering the large-scale MIMO downlink transmission of single-cell multi-user, a signal inserts a known pilot symbol in a base station end, and a receiving end estimates channel characteristics by using the pilot symbol; wherein the number of the base station side antennas is M t The number of users is K, each user is provided with M k A root antenna; by C M×N Representing M rows and N columns of complex matrix sets, and setting downlink data sent to kth user asThe precoding matrix for user k is +.>The corresponding frequency domain channel matrix is->The receiving noise is n k The method comprises the steps of carrying out a first treatment on the surface of the Then user k receives signal y k Represented as
If the transmitted signals of K users are jointly expressed asThe received signals of all users are expressed asSystem transmission model rewriting
y＝HPx+n
Wherein H is a channel matrix, P is a precoding matrix, x is a transmission signal, y is a reception signal, and n is noise; extracting the received pilot signal { y (k) } from the received signal { y (k) } P (k) Estimating therefrom the channel frequency response { H } of the sub-channel into which the pilot is inserted P (k)}；
The convolutional neural network is divided into an input layer, a convolutional layer, a pooling layer, an activation layer, a full connection layer and an output layer; the input layer inputs a data structure to be detected; considering that the instantaneous channel information and the data structure of the channel coupling matrix are different, firstly, normalizing the channel information, and respectively entering the normalized data samples into a neural network from three input layers; then, the three paths of input information are respectively subjected to repeated convolution, pooling and activation; the convolution layer carries out convolution operation on each input sample; the pooling layer reduces the number of the features extracted by the convolution layer, and eliminates repeated redundant features through pooling; the role of the activation layer is to introduce nonlinear factors, and the function of Relu (& gt) is used for activation after each layer is pooled; after completion of multiple convolutions, pooling and activation, three paths of data m having the same data structure 1 、m 2 And m 3 Respectively inputting the full connection layers, and mapping the extracted features into predicted values; meanwhile, the signal to noise ratio is also used as a node to participate in full connection, and the training result is adjusted; the final result of the prediction output from the output layer is a Kx1 vector
Obtaining Lagrange operator with instantaneous correlation coefficient of 1 by using instantaneous channel informationLagrange operator with instantaneous correlation coefficient of 0>Using weighted models
Obtaining Lagrange operator corresponding to current channelWherein η represents a correlation factor;
the user power vector in the step 5 is obtained by using a weighted model method; neural network for similarly solving Lagrangian operator mu and solving power vector when instantaneous correlation coefficient is zeroCombining the obtained power vectors ∈ ->Using weighted models
Obtaining a power vector corresponding to a current channelWhere ζ represents the weighting factor.
2. The low-complexity linear precoding algorithm based on the OFDM system according to claim 1, wherein in the step 2, in order to obtain the instantaneous correlation coefficients required by the lagrangian operator under different scenes, on the premise of setting that different users have the same optimal instantaneous correlation coefficient β, the user movement speed and the uplink detection period are selected as inputs, and the optimal β is found by a table look-up method.
3. The low complexity linear precoding algorithm based on the OFDM system as claimed in claim 1, wherein the lagrangian is obtained by a convolutional neural network structure in step 2; and constructing a convolutional neural network taking a precoding direction matrix, an instantaneous channel, a channel coupling matrix and a signal-to-noise ratio as input and an optimal Lagrange operator as output to obtain the optimal Lagrange operator.
4. The low-complexity linear precoding algorithm based on the OFDM system according to claim 1, wherein the step 4 approximates the minimum generalized eigenvalue by an iterative method after the lagrangian is obtained, and the optimal precoding direction matrix is the eigenvector corresponding to the minimum generalized eigenvalue.
5. The low complexity linear precoding algorithm based on OFDM system as claimed in claim 1, wherein the channel-based in said step 3 is slowly varying, so that the Lagrangian operator μ, the power vector corresponding to the current channelIs also slowly changed, so that the rest subcarriers are not needed to be analyzed one by one, and only the Lagrange operator mu of the subchannel inserted with the pilot frequency and the power vector corresponding to the current channel are needed to be used>Estimating Lagrange's operator mu for the remaining subcarriers using interpolation other Power vector corresponding to current channel->
6. The low-complexity linear precoding algorithm based on the OFDM system as claimed in claim 1, wherein after the lagrangian μ is obtained in the step 4, the direction information of the precoding matrix is obtained by using a method of solving generalized eigenvalues, and the obtained power vector corresponding to the current channel is combinedAnd rapidly obtaining a complete precoding matrix.
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