Channel feedback method of large-scale MIMO multi-user system
阅读说明：本技术 一种大规模mimo多用户系统的信道反馈方法 (Channel feedback method of large-scale MIMO multi-user system ) 是由 何学诚 李从改 刘锋 徐艳丽 于 2021-07-22 设计创作，主要内容包括：本发明公开了一种大规模MIMO多用户系统的信道反馈方法,包括：用户使用导频信息进行信道估计,获取下行链路的CSI信息；通过MU-CsiNet模型对CSI信息进行数据压缩,获得压缩数据S-(u)；将压缩数据S-(u)进行编码、调制后进行传输,基站通过信号检测技术将不同用户的上传数据逐一分解,获取用户发射的原始信息；基站侧对每个用户上传的数据进行解压缩后,获得数据下行链路CSI信息；基站利用实时获取的下行链路的CSI信息进行天线选择和预编码操作,基站将待传输的数据发送给接收端,接收端对接收到的数据信道估计后重复上述的过程。应用本发明实施例,有效的改进了系统性能,提高模型的精度。(The invention discloses a channel feedback method of a large-scale MIMO multi-user system, which comprises the following steps: a user uses the pilot frequency information to carry out channel estimation and acquires CSI information of a downlink; performing data compression on CSI information through an MU-CsiNet model to obtain compressed data S u (ii) a Will compress the data S u Coding and modulating the data and then transmitting the data, and decomposing uploaded data of different users one by a base station through a signal detection technology to obtain original information transmitted by the users; decompressing the data uploaded by each user by the base station side to obtain CSI information of a data downlink; the base station utilizes the CSI information of the downlink acquired in real time to carry out antenna selection and precoding operation, and the base station sends the data to be transmittedAnd for the receiving end, the receiving end repeats the process after estimating the received data channel. By applying the embodiment of the invention, the system performance is effectively improved, and the model precision is improved.)
1. A channel feedback method for large scale MIMO multi-user system assumes that the system has K independent users, each user is equipped with a single antenna, and the transmitting end is equipped with NtA root antenna, and using OFDM techniques and having a number of subcarriers ofIt is characterized by comprising:
user u uses pilot frequency information to carry out channel estimation and acquires CSI information of downlinkAssuming that the system obtains perfect estimate information, i.e.
User u performs information on CSI through MU-CsiNet modelPerforming data compression to obtain compressed data suWherein, in the step (A),
fIDFT{. denotes 2D-IDFT operation, fen{·，θ1Denotes the compression operation of MU-CsiNet, θ1Parameters representing a compression module;
user u will compress data suAnd after coding and modulation, transmitting, wherein the transmission formula is as follows:
wherein the content of the first and second substances,indicating the transmission procedure of the user side uplink,representing data acquired by a base station;
the base station decomposes the uploaded data of different users one by one through a signal detection technology to obtain the original information transmitted by the users, and the specific expression is as follows:
wherein the content of the first and second substances,indicating the detection procedure of the base station side uplink and usingRepresenting sets of user information, i.e.
The base station side decompresses the data uploaded by each user to obtain downlink CSI information, and the specific formula is as follows:
wherein f isde{·，θ2Denotes the decompression operation of MU-CsiNet, θ2Parameters representing decompression modules, fDFT{. denotes a 2D-DFT operation,set of CSI information for all users, i.e.
Base station utilizing downlink CSI information acquired in real timePerforming antenna selection and precoding operations;
the base station sends the data X to be transmitted to the receiving end, specifically expressed as:
wherein f isdown{. denotes the downlink operation,indicating the information received by the user side, and the receiving end is used for receiving the received dataThe above process is repeated after channel estimation.
2. The channel feedback method for massive MIMO multi-user system according to claim 1, wherein the method further comprises the steps of:
in the first step, parameter coding module parameters are trained in a noise-free ideal environmentAnd decoding module parametersThe expression is as follows
Wherein f isen{·，θ1Denotes the compression operation of MU-CsiNet, θ1Parameters representing compression modules, fde{·，θ2Denotes the decompression operation of MU-CsiNet, θ2A parameter indicative of a decompression module is provided,indicating the CSI information of the u-th user,andrespectively representing the coding module parameter and the decoding module parameter obtained after training;
second, system fixes coding module parametersThe compressed data obtained by prediction is obtained by a transmission moduleAs shown in the following equation:
wherein the content of the first and second substances,representing the transmission process of the user uplink, where the signal-to-noise ratio is m and the noise is zmTo do soIndicating base station uplinkWherein the number of users is K, system utilizationParameters to decoding moduleFurther training is carried out to obtain
Technical Field
The invention relates to the technical field of wireless communication, in particular to a channel feedback method of a large-scale MIMO multi-user system.
Background
In recent years, with the rapid development of 5G mobile communication technology, massive MIMO, which is one of the key technologies, has received high attention from the academic and industrial circles. Massive MIMO technology can not only provide high data rates without adding extra bandwidth, but it can greatly improve the throughput of the system without losing more transmit power. The most remarkable characteristic is that the system has extremely high spectrum utilization efficiency, because the system utilizes the existing spectrum resources and the space freedom degree provided by the multi-antenna of the receiving end and the transmitting end, the gain maximization of reliability and effectiveness is achieved, and the transmission rate is improved and the communication quality is improved. However, while the massive MIMO system provides services for multiple users, the transmitting end needs to acquire CSI information of the downlink to perform channel adaptive transmission optimization such as precoding and antenna selection. In the TDD transmission mode, the base station only needs to perform channel estimation by using reciprocity of uplink and downlink channels to obtain CSI information. In the FDD transmission mode, since the uplink and downlink links do not have channel diversity, the base station needs to rely on frequent channel feedback to acquire downlink CSI information, which increases the overhead of uplink transmission for the system. However, FDD has the characteristics of strong interference immunity, high spectral efficiency, low delay, etc., making it more widely used in cellular networks. Conventional compression algorithms do not solve the data overhead problem in FDD systems very well and therefore novel solutions are needed.
Various approaches to massive MIMO have been proposed by many researchers, such as using deep learning methods, using compressed sensing methods, and using packet feedback. However, these methods have some disadvantages:
most models only consider the performance indexes of the models, and neglect the application condition of the models in the actual environment. Although these indexes are representative, lack of verification of actual scenes, and too high degree of fitting may result in reduced generalization capability of the model.
Most models only consider single-user scenarios, and do not consider the complex problems generated in multi-user scenarios. The influence of the difference between user CSI information on the model in a multi-user scene is ignored, the influence of an uplink signal detection mode on the model in the multi-user scene is ignored, and the adaptability of precoding of a downlink, an antenna selection algorithm and a compression model is ignored.
As the signal-to-noise ratio of the feedback link decreases, the accuracy of the model is greatly affected, and as the number of users increases, the accuracy of the model gradually decreases. This problem is mainly caused by errors in signal detection, e.g. by estimating user data one by one, in case of OSIC detection. As the overall noise of the user becomes larger, the estimation error of the previous user is propagated, which affects the subsequent users to different degrees, thereby causing the overall performance to decrease. Therefore, the invention carries out joint optimization aiming at the problem so as to improve the accuracy of the model.
Disclosure of Invention
The invention aims to provide a channel feedback method of a large-scale MIMO multi-user system, which aims to overcome the defects in the prior art, effectively improves the system performance and improves the model precision.
In order to achieve the above object, the present invention provides a channel feedback method for a massive MIMO multi-user system, which assumes that the system has K independent users, each user is equipped with a single antenna, and the transmitting end is equipped with NtA root antenna, and using OFDM techniques and having a number of subcarriers ofThe method comprises the following steps:
user u uses pilot frequency information to carry out channel estimation and acquires CSI information of downlink
User u performs information on CSI through MU-CsiNet modelThe data compression is carried out, and the data compression,obtaining compressed data SuWherein, in the step (A),
fIDFT{. denotes 2D-IDFT operation, fen{·，θ1Denotes the compression operation of MU-CsiNet, θ1Parameters representing a compression module;
user u will compress data SuAnd after coding and modulation, transmitting, wherein the transmission formula is as follows:
wherein the content of the first and second substances,indicating the transmission procedure of the user side uplink,data acquired by a base station is represented;
the base station decomposes the uploaded data of different users one by one through a signal detection technology to obtain the original information transmitted by the users, and the specific expression is as follows:
wherein the content of the first and second substances,indicating the detection procedure of the base station side uplink and usingRepresenting sets of user information, i.e.
The base station side decompresses the data uploaded by each user to obtain downlink CSI information, and the specific formula is as follows:
wherein f isde{·，θ2Denotes the decompression operation of MU-CsiNet, θ2Parameters representing decompression modules, fDFT{. denotes a 2D-DFT operation,set of CSI information for all users, i.e.
Base station utilizing downlink CSI information acquired in real timePerforming antenna selection and precoding operations;
the base station sends the data X to be transmitted to the receiving end, specifically expressed as:
wherein f isdown{. denotes the downlink operation,indicating the information received by the user side, and the receiving end is used for receiving the received dataThe above process is repeated after channel estimation.
In one implementation, the method further comprises the steps of:
in the first step, the parameters of the coding module are trained in a noise-free ideal environmentAnd decoding module parametersThe expression is as follows
Wherein f isen{·，θ1Denotes the compression operation of MU-CsiNet, θ1Parameters representing compression modules, fde{·，θ2Denotes the decompression operation of MU-CsiNet, θ2A parameter indicative of a decompression module is provided,indicating the CSI information of the u-th user,andrespectively representing the coding module parameter and the decoding module parameter obtained after training;
second, system fixes coding module parametersThe compressed data S is obtained by prediction and is obtained through a transmission moduleAs shown in the following equation:
wherein the content of the first and second substances,representing the transmission process of the user uplink, where the signal-to-noise ratio is m and the noise is zmTo do soIndicating the detection procedure of the base station uplink, where the number of users is K, system utilizationParameters to decoding moduleFurther training is carried out to obtain
The channel feedback method of the large-scale MIMO multi-user system provided by the embodiment of the invention has the following beneficial effects:
(1) most models only consider the performance indexes of the models, and neglect the application condition of the models in the actual environment. Although these indexes are representative, lack of verification of actual scenes, and too high degree of fitting may result in reduced generalization capability of the model. The invention adapts to the actual simulation scene, and the model performance is verified through an uplink and downlink simulation system.
(2) Most models only consider single-user scenarios, and do not consider the complex problems generated in multi-user scenarios. The influence of the difference between user CSI information on the model in a multi-user scene is ignored, the influence of an uplink signal detection mode on the model in the multi-user scene is ignored, and the adaptability of precoding of a downlink, an antenna selection algorithm and a compression model is ignored. The invention adapts to users with different speeds and positions, and considers the adaptability of precoding and antenna selection methods.
(3) With the continuous improvement of feedback accuracy under ideal environment, attention should be paid to the performance of compression algorithms under complex scenes. The invention performs combined optimization aiming at the condition of poor model performance under low signal-to-noise ratio, effectively improves the system performance and verifies through simulation.
Drawings
Fig. 1 is a schematic structural diagram of a simulation model of a multi-user massive MIMO system.
Fig. 2 is a flow chart of a method of MU-csiant compression and decompression.
FIG. 3 is a process of model joint optimization in a low SNR scenario.
Fig. 4 shows the results of experiments performed by combining the MU-csiant model with the simulation system, wherein linear zero-forcing coding is adopted and 64 antennas are used at the transmitting end.
Fig. 5 shows the results of experiments performed by combining the MU-csiant model with the simulation system, wherein linear zero-forcing coding is adopted and 20 antennas are used at the transmitting end.
Fig. 6 shows the result of an experiment of the optimized MU-csiant model in combination with a simulation system, where the experiment employs linear zero-forcing coding and 64 antennas are used at the transmitting end, and the number of users is 20.
Fig. 7 shows the result of an experiment of the optimized MU-csiant model in combination with a simulation system, where the experiment employs linear zero-forcing coding and 64 antennas are used at the transmitting end, and the number of users is 30.
FIG. 8 shows the result of an experiment of combining an optimized MU-CsiNet model with a simulation system, wherein linear zero forcing coding is adopted in the experiment, 64 antennas are used at a transmitting end, and the number of users is 40
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.
The invention provides a channel feedback method of a large-scale MIMO multi-user system, which comprises the steps of firstly, constructing a simulation system of a multi-user uplink and downlink, secondly, collecting multi-user channel simulation data, carrying out MU-CsiNet model training, thirdly, packaging an MU-CsiNet model, and integrating the MU-CsiNet model into the simulation system.
The overall simulation process and the application process of MU-csiant are described with reference to fig. 1-3, assuming that the system has K independent users and each user is equipped with a single antenna, and the transmitting end is equipped with Nt antennas, and uses OFDM technology and the number of subcarriers isIn the following, the user u is taken as an example to describe the simulation process, which is mainly divided into seven steps.
First, user u performs channel estimation using pilot information to obtain CSI information of downlinkAssuming that the system obtains perfect estimate information, i.e.
Second, user u uses MU-CsiNet model to process CSI informationPerforming data compression to obtain compressed data suAs shown in the following formula:
wherein f isIDFT{. denotes 2D-IDFT operation, fen{·，θ1Denotes the compression operation of MU-CsiNet, θ1Representing parameters of the compression module.
The specific compression process is as follows: first, raw data is processed using 2D-IDFTIs compressed and then depth is usedAnd the compression module for learning is used for compressing, wherein the compression coding module mainly comprises a normalization operation, a convolution layer, a residual error network, a full connection layer and a plurality of activation functions.
Third, user u will suAfter encoding, modulating and the like, the transmission is carried out, as shown in the following formula:
wherein the content of the first and second substances,indicating the transmission procedure of the user side uplink,representing the actual data acquired by the base station.
Fourthly, because the uplink resources are limited, the data received by the base station are mostly interlaced by a plurality of users, and therefore the base station needs to use a signal detection technology to decompose the uploaded data of different users one by one. And the original information transmitted by the user is obtained by using the inverse processes of decoding, demodulation and the like, and the method is shown in the following formula:
wherein the content of the first and second substances,indicating the detection procedure of the base station side uplink and usingRepresenting sets of user information, i.e.
And fifthly, decompressing the data uploaded by each user by the base station side to obtain the CSI information of the data downlink. This process is the inverse of compression, as shown in the following equation:
wherein f isde{·，θ2Denotes the decompression operation of MU-CsiNet, θ2Parameters representing decompression modules, fDFT{. denotes a 2D-DFT operation,set of CSI information for all users, i.e.
The decompression process can be mainly described as: decompression is carried out by using a decompression module, and then data recovery is carried out by using 2D-DFT. The decoding module mainly comprises a full connection layer, two residual error networks, a convolution layer and an inverse normalization operation.
Sixthly, the base station utilizes the CSI information of the downlink acquired in real timeAntenna selection and precoding operations are performed to improve the channel capacity for transmission and to eliminate interference between different users.
Seventhly, the base station sends the data X to be transmitted to the receiving end through a series of operations, and a formula can be usedIs shown in which fdown{. denotes the downlink operation,indicating the information received at the user side. The receiving end pair receives the dataChannel estimationThe above process is repeated after counting.
Secondly, the optimization process aiming at the low signal-to-noise ratio scene comprises two steps: firstly, the whole model is optimized, and then specific data is optimized.
In the first step, parameters are trained in a noise-free ideal environmentAndthe expression is as follows
Wherein f isen{·，θ1Denotes the compression operation of MU-CsiNet, θ1Parameters representing compression modules, fde{·，θ2Denotes the decompression operation of MU-CsiNet, θ2A parameter indicative of a decompression module is provided,indicating the CSI information of the u-th user,andrespectively representing the parameters of the coding module and the parameters of the decoding module obtained after training.
Second, system fixes coding module parametersPredicting to obtain compressed data S, and obtaining the compressed data S through a transmission moduleAs shown in the following equation:
wherein the content of the first and second substances,representing the transmission process of the user uplink, where the signal-to-noise ratio is m and the noise is zmTo do soDenotes the detection procedure of the uplink of the base station, where the number of users is K. System utilizationParameters to decoding moduleFurther training is carried out to obtain
In order to better verify the performance of the MU-CsiNet model, the invention applies CSI to a downlink for model performance verification. The invention respectively adopts two different schemes to verify the model performance. Firstly, the base station does not select antennas and uses 64 antennas to communicate with 20 users, and precoding adopts a linear zero forcing mode; second, the base station selects 20 antennas for communication with 20 users, and precoding adopts a linear zero forcing method.
The result of the first scheme is shown in FIG. 4, and it can be found that the SNR of the uplink signal to noise ratio is higherupThe recovery degree of the CSI has little influence on the downlink at > 4, but has a small gap from the ideal environment.
The result of the second scheme is shown in fig. 5, it can be found that the performance of the scheme in the second scheme is far worse than that of the first scheme, but since the first scheme uses more antennas and consumes more radio frequency resources, different schemes can be selected according to different service scenarios.
In a large-scale MIMO system based on multiple users, with the continuous improvement of feedback accuracy under an ideal environment, attention should be paid to the performance of a compression algorithm under a complex scene. According to the experimental results of fig. 4 and 5, it can be observed that the compression model MU-csiint can have a good effect in an ideal scene, but at the same time, it is found that the model performs poorly at a low signal-to-noise ratio, and the accuracy of the model gradually decreases as the number of users increases. Therefore, the invention establishes a corresponding optimization method aiming at the problems, and provides a combined optimization scheme aiming at different users and different signal-to-noise ratios.
As shown in FIG. 6, for the scenario with the number of users being 20, the SNR which is improved obviously after the optimization is selected by the inventionupThe scenarios of 0 and 2 were simulated. The results show that the optimized models are all improved to a certain degree.
As shown in FIG. 7, the SNR is selected in the present invention for the scenario where the number of users is 30upSimulations were performed for the 2 and 4 scenarios. The result shows that the SNR of the optimized model isupThe improvement is more pronounced at 4, and the SNRupThe effect is not good enough when the dosage is 2.
As shown in FIG. 8, the SNR is selected in the present invention for the scenario where the number of users is 40upSimulations were performed for the 4 and 6 scenarios. The result shows that the SNR of the optimized model isupThe improvement is more pronounced at 6, and the SNRupThe effect is not good enough when the total content is 4.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.