CN111901024B - MIMO channel state information feedback method based on fitting depth learning resistance - Google Patents
MIMO channel state information feedback method based on fitting depth learning resistance Download PDFInfo
- Publication number
- CN111901024B CN111901024B CN202010745080.9A CN202010745080A CN111901024B CN 111901024 B CN111901024 B CN 111901024B CN 202010745080 A CN202010745080 A CN 202010745080A CN 111901024 B CN111901024 B CN 111901024B
- Authority
- CN
- China
- Prior art keywords
- layer
- model
- channel matrix
- neurons
- state information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
- H04B7/0621—Feedback content
- H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
- H04B7/0621—Feedback content
- H04B7/0632—Channel quality parameters, e.g. channel quality indicator [CQI]
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Power Engineering (AREA)
- Error Detection And Correction (AREA)
Abstract
The invention discloses a MIMO channel state information feedback method based on fitting depth learning resistance, which belongs to the field of communication and comprises the following steps: firstly, an Anti-overlapping CSI net model is constructed, a channel matrix is divided into a real part and an imaginary part which are respectively input into an encoder of a user end, the encoder comprises a convolution layer and a full connection layer, data passes through a feedback link after being encoded and reaches a receiving end, a decoder at the receiving end comprises an Anti-fitting layer, the full connection layer, a Refinenet layer and the convolution layer, and finally a predicted channel matrix is output. After the model is built, the model is trained off line, firstly, model parameters are initialized, the model is stored after error convergence, and finally, the trained and stored model is used for predicting channel state information on line. The invention can further improve the recovery precision of the information matrix, ensure that the transmitting end of the system obtains accurate channel state information and improve the communication quality of the system.
Description
Technical Field
The invention relates to the field of communication, in particular to a large-scale MIMO channel state information feedback method based on fitting deep learning resistance.
Background
The large-scale multiple-input multiple-output (MIMO) technology is used as a key technology of a fifth generation (5G) communication system, has the advantages of high spectrum efficiency, large system capacity, strong system robustness and the like, and has higher data transmission rate and improved system reliability compared with an OFDM system in order to ensure that channel state information obtained by channel estimation can be accurately fed back to a transmitting end. Therefore, massive MIMO technology is receiving increasing attention from the industry and academia. However, the significant advantages of massive MIMO techniques depend to a large extent on the channel state information available to the transmitter for the downlink. In a fdd MIMO system, a base station needs to obtain downlink CSI through feedback from a receiving end. However, the use of large-scale antenna arrays results in a drastic increase in channel feedback overhead.
In the MIMO wireless communication system, the conventional channel state information feedback method has a serious disadvantage. At present, traditional research methods in MIMO channel state information feedback are widely applied to communication, but have many defects. First, they rely heavily on the assumption that the channel is sparse. However, the channels are not completely sparse on any basis and may even have no interpretable structure. Secondly, the compressed sensing algorithm uses random projections, and the channel structure is not fully utilized. In addition, most of the existing signal reconstruction algorithms are iterative methods, and the reconstruction speed is low.
In order to realize a high-precision and high-efficiency channel feedback method, a feedback scheme based on deep learning is provided, and the application of the deep learning in channel feedback has a good feedback effect, so that the communication has good performance, and the stability of the system is guaranteed. The deep learning theory is adopted to carry out off-line training data on the channel information at the receiving end of the communication system, and the data is recovered on line, so that the load of a feedback link is greatly reduced, and the accuracy of recovering the channel state information is improved. However, at present, the problem of overfitting in channel information feedback of a deep learning method is not considered in the researches, and the overfitting can cause the problems of reduced prediction performance, reduced model training result and the like, so that the accuracy of finally predicted channel state information is insufficient, and the quality of communication cannot be guaranteed. Therefore, how to find a deep learning algorithm which can improve the recovery speed and accuracy is the key of the channel information feedback scheme.
Disclosure of Invention
The invention provides an anti-fitting deep learning MIMO channel state information feedback method, and solves the problem that the accuracy of predicted channel state information is insufficient due to over-fitting in the conventional deep learning method.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the MIMO channel state information feedback method based on fitting deep learning comprises the following steps:
(1) constructing an Anti-overlapping CSI net model, using a convolutional neural network as an encoder and a decoder, wherein the convolutional neural network can use spatial local correlation by reinforcing a local connection mode between neurons of adjacent layers, and a real part and an imaginary part of a channel matrix H are used as the input of the Anti-overlapping CSI net model;
(2) the channel matrix H data enters an encoder, the encoder is positioned at a user side for sending the data and encodes the channel matrix H into low-dimensional data, and the encoder comprises a convolutional layer and a full connection layer;
(3) the data enters a feedback link after being coded and reaches a receiving end;
(4) at a base station of a receiving end, a decoder starts decoding, and an Anti-overlapping CSI net model is newly constructed on low-dimensional data of a coding end; the decoder at the receiving end comprises an anti-fitting layer, a full connection layer, a RefineNet layer and a convolution layer, and outputs a predicted channel matrix;
(5) after the Anti-overlapping CSI net model is built, the model is trained off line, model parameters are initialized, the model is stored after error convergence, and finally the trained and stored Anti-overlapping CSI net model is used for predicting channel state information on line.
The technical scheme of the invention is further improved as follows: the step (2) comprises the following steps:
at the encoder's convolutional layer, this layer uses a kernel of size 3 × 3 to generate two signatures; after convolutional layer, we reshape the eigenmap into a vector and use a fully-connected layer to generate codeword s, which is a vector of size mx 1; the convolutional layer and the fully-connected layer simulate the projection of the compressed sensing and act as encoders.
The technical scheme of the invention is further improved as follows: the decoder workflow in step (4) is expressed as:
obtaining the codeword s at the receiving end, and then mapping it back to the channel matrix H using the neural network layer (as a decoder); the first layer of the decoder is an anti-fitting unit, a random deactivation (Dropout) algorithm is added, and the method is that a certain proportion of node information is lost randomly in each training period, namely, a certain proportion of the output of the previous layer is changed into zero in the training stage, and the node of the next layer determines a value according to the remaining information; the second layer is a layer with s subjected to random inactivation treatment as input and two outputs with the size of Nc×NtThe fully connected layer of the matrix of (a) as an initial estimate of the real and imaginary parts of H; then, the initial estimation number is input into a plurality of subdivided network units which are continuously refined and reconstructed; the reflexet layer includes a plurality of reflexet units, each consisting of four layers, of which the first layer is an input layer and all remaining 3 layers use 3 × 3 kernels; the second layer and the third layer generate 8 and 16 feature maps respectively, and the last layer generates a final reconstruction of H; the profile generated by the three convolutional layers is set to the input channel matrix size N by appropriate zero paddingc×NtThe same size; selecting ReLU (x) max (x,0) as an activation function, and carrying out batch normalization processing on each layer;
after the channel matrix is refined by a series of RefineNet units, the channel matrix is input to the final convolutional layer, and the sigmoid function is used to scale the values to the [0,1] range.
The technical scheme of the invention is further improved as follows: the step (5) comprises the following steps:
to train Anti-overlapping CSI net, we use end-to-end learning for all kernels and bias values of the encoder and decoder; the parameter set is denoted as Θ ═ Θen,Θde}; the input of the Anti-overlapping CSI net is HiThe reconstructed channel matrix is
It is noted that the input and output of the Anti-overlapping CSI net are normalized channel matrices with elements at [0,1]]Zooming in a range; similar to the automatic encoder, Anti-overlapping CSI net is an unsupervised learning algorithm; the loss function is Mean Squared Error (MSE), and is calculated as follows:
wherein | · | purple2Is the euclidean norm and T is the total number of samples in the training set.
The technical scheme of the invention is further improved as follows: the principle data of the random inactivation algorithm passes through an input layer, a plurality of hidden layers and an output layer; in the normal data transmission process, the neurons of each layer are all connected with the neurons of the next layer, so that overfitting is often caused when a large amount of data are trained, therefore, a random inactivation algorithm is provided, the neurons connected with the neurons of each layer and the next layer are randomly inactivated, the neurons are disconnected with the neurons of the next layer, the training data are reduced, and the purpose of preventing overfitting is achieved; the random inactivation algorithm only inactivates the neurons at random during training to obtain a trained model, and then all the neurons are reconnected together during testing, and a high-precision calculation result is finally output through calculation of the neural network, so that complete channel state information is obtained.
Due to the adoption of the technical scheme, the invention has the technical progress that:
the invention provides a large-scale MIMO channel state information feedback method based on fitting-resistant deep learning, which solves the problem of insufficient accuracy of predicted channel state information caused by over-fitting in the conventional deep learning method, further improves the recovery accuracy of an information matrix, ensures that a system transmitting terminal obtains accurate channel state information, and improves the communication quality of the system.
Drawings
FIG. 1 is a flow chart of Anti-overlapping CSI net model construction and training according to the present invention;
FIG. 2 is a network architecture diagram of an Anti-overlapping CSI net model according to the present invention;
FIG. 3 is a schematic diagram of normal training of a neural network according to the present invention;
FIG. 4 is a schematic diagram of training with the addition of an anti-fit unit according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples:
as shown in fig. 1, a MIMO channel state information feedback method based on fitting-resistant deep learning includes the following steps:
(1) constructing an AOCN (Anti-overlapping CSI net, AOCN) model, and using a convolutional neural network as an encoder and a decoder, wherein the convolutional neural network can use spatial local correlation by reinforcing a local connection mode between neurons of adjacent layers, and a real part and an imaginary part of a channel matrix H are used as input of the convolutional neural network;
(2) the channel matrix H data enters an encoder, the encoder is positioned at a user end for sending the data and encodes the channel matrix H into low-dimensional data, and the encoder comprises a convolutional layer and a full connection layer. The method comprises the following steps: at the encoder's convolutional layer, this layer uses a kernel of size 3 × 3 to generate two signatures. After convolutional layers, we reshape the eigenmap into a vector and use a fully-connected layer to generate the codeword s, which is a vector of size mx 1. The first two layers simulate the projection of compressed sensing and act as encoders;
(3) the data enters a feedback link after being coded and reaches a receiving end;
(4) at the base station of the receiving end, the decoder starts decoding, and low-dimensional data of the encoding end is newly constructed. The decoder at the receiving end comprises an anti-fitting layer, a full connection layer, a RefineNet layer and a convolution layer, and outputs a predicted channel matrix. The decoder workflow is represented as: at the receiving end, the codeword s is obtained and then mapped back to the channel matrix H using the neural network layer (as a decoder). The first layer of the decoder we add a random deactivation (Dropout) algorithm.
The principle of the random inactivation algorithm is shown in fig. 3 and 4, and the principle data of the random inactivation algorithm passes through an input layer, a plurality of hidden layers and an output layer. In the normal data transmission process, data is transmitted as shown in fig. 3, all neurons in each layer are connected with neurons in the next layer, so that an overfitting phenomenon is often caused when a large amount of data is trained, therefore, a random inactivation algorithm is provided, which carries out random inactivation on the neurons in each layer connected with the next layer to disconnect the neurons from the neurons in the next layer, so that the training data is reduced, and the purpose of preventing overfitting is achieved. The random inactivation algorithm only inactivates the neurons at random during training to obtain a trained model, and then all the neurons are reconnected together during testing, and a high-precision calculation result is finally output through calculation of the neural network, so that complete channel state information is obtained.
The second layer is a layer with s subjected to random inactivation treatment as input and two outputs with the size of Nc×NtAs an initial estimate of the real and imaginary parts of H. The initial estimated number is then input into several subdivided network elements that are progressively refined and reconstructed. Each reflonenet unit consists of four layers, of which the first layer is the input layer and all the remaining 3 layers use 3 × 3 kernels. The second and third layers generate 8 and 16 feature maps, respectively, and the last layer generates the final reconstruction of H. The profile generated by the three convolutional layers is set to the input channel matrix size N by appropriate zero paddingc×NtThe same size. And selecting ReLU (x) max (x,0) as an activation function, and performing batch normalization processing on each layer.
After the channel matrix is refined by a series of RefineNet units, the channel matrix is input to the final convolutional layer, and the sigmoid function is used to scale the values to the [0,1] range.
(5) The Anti-overlapping CSI net model is constructed, as shown in FIG. 2, and then the model is trained off-line, firstly, the model parameters are initialized, and the model is stored after the error is convergedAnd finally, predicting the channel state information on line by using the trained and stored Anti-overlapping CSI net model. To train Anti-overlapping CSI net, we use end-to-end learning for all kernels and bias values of the encoder and decoder. The parameter set is denoted as Θ ═ Θen，Θde}. The input of the Anti-overlapping CSI net is HiThe reconstructed channel matrix is
It is noted that the input and output of the Anti-overlapping CSI net are normalized channel matrices with elements at [0,1]]And (4) zooming in and out. Similar to the auto-encoder, Anti-overlapping CSI net is an unsupervised learning algorithm. The loss function is Mean Squared Error (MSE), and is calculated as follows:
wherein | · | purple2Is the euclidean norm and T is the total number of samples in the training set.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (3)
1. The MIMO channel state information feedback method based on fitting deep learning is characterized by comprising the following steps:
(1) constructing an Anti-overlapping CSI net model, using a convolutional neural network as an encoder and a decoder, using spatial local correlation by enhancing a local connection mode between neurons of adjacent layers by the convolutional neural network, and using a real part and an imaginary part of a channel matrix H as the input of the Anti-overlapping CSI net model;
(2) the channel matrix H data enters an encoder, the encoder is positioned at a user side for sending the data and encodes the channel matrix H into low-dimensional data, and the encoder comprises a convolutional layer and a full connection layer;
(3) the data enters a feedback link after being coded and reaches a receiving end;
(4) at a base station of a receiving end, a decoder starts decoding, and low-dimensional data of an encoding end is newly constructed; the decoder at the receiving end comprises an anti-fitting layer, a full connection layer, a RefineNet layer and a convolution layer, and outputs a predicted channel matrix;
(5) after the Anti-overlapping CSI net model is constructed, performing offline training on the model, initializing model parameters, storing the model after error convergence, and finally predicting channel state information on line by using the trained and stored Anti-overlapping CSI net model;
the decoder workflow in step (4) is expressed as:
obtaining a code word s at a receiving end, and then mapping the code word s back to a channel matrix H by using a convolutional neural network; the first layer of the decoder is an anti-fitting layer, a random inactivation algorithm is added, and the method is that a certain proportion of node information is lost randomly in each training period, namely, a certain proportion of the output of the previous layer is changed into zero in the training stage, and the node of the next layer determines a numerical value according to the remaining information; the second layer is a layer with s subjected to random inactivation treatment as input and two outputs with the size of Nc×NtAs initial estimates of the real and imaginary parts of H, wherein: n is a radical ofcFor receiving the number of antennas, NtThe number of transmitting antennas; then, the initial estimation number is input into a plurality of subdivided network units which are continuously refined and reconstructed; the reflexet layer includes a plurality of reflexet units, each consisting of four layers, of which the first layer is an input layer and all remaining 3 layers use 3 × 3 kernels; the second layer and the third layer generate 8 and 16 feature maps respectively, and the last layer generates a final reconstruction of H; the profile generated by the three convolutional layers is set to the input channel matrix size N by appropriate zero paddingc×NtThe same size; selecting ReLU (x) max (x,0) as an activation function, and carrying out batch normalization on each layerChemical treatment;
after a channel matrix is refined through a series of RefineNet units, the channel matrix is input into a final convolutional layer, and a sigmoid function is used for scaling the value to a range of [0,1 ];
the step (5) comprises the following steps:
to train Anti-overlapping CSI net, we use end-to-end learning for all kernels and bias values of the encoder and decoder; the parameter set is denoted as Θ ═ Θen,ΘdeIn which ΘenFor encoding parameter sets ΘdeIs a decoding parameter set; the input of the Anti-overlapping CSI net is HiThe reconstructed channel matrix is
Wherein HiIs the true channel matrix, fdeAs a decoding function, fenAs a coding function, siCompressed code words of the ith channel matrix; it is noted that the input and output of the Anti-overlapping CSI net are normalized channel matrices with elements at [0,1]]Zooming in a range; the loss function is Mean Squared Error (MSE), and is calculated as follows:
wherein | · | purple2Is the euclidean norm and T is the total number of samples in the training set.
2. The MIMO channel state information feedback method based on anti-fitting deep learning of claim 1, wherein: the step (2) comprises the following steps:
at the encoder's convolutional layer, this layer uses a kernel of size to generate two signatures; after convolutional layers, we reshape the eigenmap into a vector and use a fully-connected layer to generate the codeword s, which is a vector of size; the convolutional layer and the fully-connected layer simulate the projection of the compressed sensing and act as encoders.
3. The MIMO channel state information feedback method based on anti-fitting deep learning of claim 1, wherein: the principle data of the random inactivation algorithm passes through an input layer, a plurality of hidden layers and an output layer; in the normal data transmission process, all the neurons of each layer are connected with the neurons of the next layer, and the random inactivation algorithm randomly inactivates the neurons connected with the neurons of each layer and the next layer to disconnect the neurons from the neurons of the next layer; the random inactivation algorithm randomly inactivates the neurons during training to obtain a trained model, and then all the neurons are reconnected together during testing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010745080.9A CN111901024B (en) | 2020-07-29 | 2020-07-29 | MIMO channel state information feedback method based on fitting depth learning resistance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010745080.9A CN111901024B (en) | 2020-07-29 | 2020-07-29 | MIMO channel state information feedback method based on fitting depth learning resistance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111901024A CN111901024A (en) | 2020-11-06 |
| CN111901024B true CN111901024B (en) | 2021-11-05 |
Family
ID=73182650
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010745080.9A Active CN111901024B (en) | 2020-07-29 | 2020-07-29 | MIMO channel state information feedback method based on fitting depth learning resistance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111901024B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4346174A1 (en) * | 2022-09-30 | 2024-04-03 | Nokia Technologies Oy | Devices, methods and apparatuses for channel prediction |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4342143A1 (en) * | 2021-06-12 | 2024-03-27 | Huawei Technologies Co., Ltd. | Artificial intelligence-enabled link adaptation |
| CN115604731A (en) * | 2021-07-09 | 2023-01-13 | 华为技术有限公司（Cn） | Communication method and device |
| CN113660015B (en) * | 2021-08-11 | 2022-08-16 | 东南大学 | Online wireless channel acquisition optimization method under assistance of environment knowledge base |
| CN117751559A (en) * | 2021-11-02 | 2024-03-22 | Oppo广东移动通信有限公司 | CSI feedback method, device, equipment and storage medium |
| WO2023123429A1 (en) * | 2021-12-31 | 2023-07-06 | Oppo广东移动通信有限公司 | Method and device for training channel information feedback model, apparatus, and storage medium |
| CN117200838A (en) * | 2022-06-01 | 2023-12-08 | 华为技术有限公司 | Communication method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108390706A (en) * | 2018-01-30 | 2018-08-10 | 东南大学 | A kind of extensive mimo channel state information feedback method based on deep learning |
| CN109474316A (en) * | 2018-11-22 | 2019-03-15 | 东南大学 | A kind of channel information compression feedback method based on deep-cycle neural network |
| CN109525292A (en) * | 2018-12-24 | 2019-03-26 | 东南大学 | A kind of channel information compression feedback method using bit-level optimization network |
| CN110350958A (en) * | 2019-06-13 | 2019-10-18 | 东南大学 | A kind of more multiplying power compressed feedback methods of CSI of extensive MIMO neural network based |
| CN110912598A (en) * | 2019-11-22 | 2020-03-24 | 中原工学院 | Large-scale MIMO system CSI feedback method based on long-time attention mechanism |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11003988B2 (en) * | 2016-11-23 | 2021-05-11 | General Electric Company | Hardware system design improvement using deep learning algorithms |
| CN110311718B (en) * | 2019-07-05 | 2022-06-10 | 东南大学 | Quantization and inverse quantization method in massive MIMO channel state information feedback |
| CN111464220B (en) * | 2020-03-10 | 2021-06-29 | 西安交通大学 | Channel state information reconstruction method based on deep learning |
-
2020
- 2020-07-29 CN CN202010745080.9A patent/CN111901024B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108390706A (en) * | 2018-01-30 | 2018-08-10 | 东南大学 | A kind of extensive mimo channel state information feedback method based on deep learning |
| CN109474316A (en) * | 2018-11-22 | 2019-03-15 | 东南大学 | A kind of channel information compression feedback method based on deep-cycle neural network |
| CN109525292A (en) * | 2018-12-24 | 2019-03-26 | 东南大学 | A kind of channel information compression feedback method using bit-level optimization network |
| CN110350958A (en) * | 2019-06-13 | 2019-10-18 | 东南大学 | A kind of more multiplying power compressed feedback methods of CSI of extensive MIMO neural network based |
| CN110912598A (en) * | 2019-11-22 | 2020-03-24 | 中原工学院 | Large-scale MIMO system CSI feedback method based on long-time attention mechanism |
Non-Patent Citations (3)
| Title |
|---|
| A fast channel state information feedback scheme based on compressive sensing for underwater acoustic MIMO systems;Hui Ma等;《2016 35th Chinese Control Conference (CCC)》;20160729;全文 * |
| Controlled dropout: A different approach to using dropout on deep neural network;ByungSoo Ko等;《2017 IEEE International Conference on Big Data and Smart Computing (BigComp)》;20170216;摘要，第I-II节，图1-图2 * |
| Sequence-Dropout Block for Reducing Overfitting Problem in Image Classification;Ledan Qian等;《IEEE Access》;20200327;第8卷;全文 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4346174A1 (en) * | 2022-09-30 | 2024-04-03 | Nokia Technologies Oy | Devices, methods and apparatuses for channel prediction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111901024A (en) | 2020-11-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111901024B (en) | MIMO channel state information feedback method based on fitting depth learning resistance | |
| CN108462517B (en) | MIMO link self-adaptive transmission method based on machine learning | |
| CN108566257B (en) | Signal recovery method based on back propagation neural network | |
| Liu et al. | A Markovian model-driven deep learning framework for massive MIMO CSI feedback | |
| CN109617584A (en) | A kind of mimo system beamforming matrix design method based on deep learning | |
| CN110336594B (en) | Deep learning signal detection method based on conjugate gradient descent method | |
| CN110445581B (en) | Method for reducing channel decoding error rate based on convolutional neural network | |
| CN111555781B (en) | Large-scale MIMO channel state information compression and reconstruction method based on deep learning attention mechanism | |
| CN110266620A (en) | 3D MIMO-OFDM system channel estimation method based on convolutional neural networks | |
| CN109039534A (en) | A kind of sparse CDMA signals detection method based on deep neural network | |
| CN110932734B (en) | Deep learning channel decoding method based on alternative direction multiplier method | |
| CN103929210A (en) | Hard decision decoding method based on genetic algorithm and neural network | |
| CN115865145A (en) | Large-scale MIMO channel state information feedback method based on Transformer | |
| CN114499601B (en) | Large-scale MIMO signal detection method based on deep learning | |
| CN114282647A (en) | Neural morphology vision sensor target detection method based on pulse neural network | |
| CN113938254A (en) | Attention mechanism-based layered source-channel joint coding transmission system and transmission method thereof | |
| CN107437976A (en) | A kind of data processing method and equipment | |
| CN113570032A (en) | Limited data spectrum sensing method based on semi-supervised deep neural network | |
| CN116170746B (en) | Ultra-wideband indoor positioning method based on depth attention mechanism and geometric information | |
| CN108093455B (en) | High-energy-efficiency wireless sensor network data transmission method based on time-space correlation | |
| CN114630207B (en) | Multi-sensing-node sensing data collection method based on noise reduction self-encoder | |
| CN114387028B (en) | Intelligent analysis method for commodity demand of online shopping platform | |
| CN110113084A (en) | The channel prediction method of MIMO closed loop transmission system | |
| CN110474798A (en) | A method of wireless communication future signal is predicted using echo state network | |
| CN113852434B (en) | LSTM and ResNet-assisted deep learning end-to-end intelligent communication method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |