CN114884775A - Deep learning-based large-scale MIMO system channel estimation method - Google Patents
Deep learning-based large-scale MIMO system channel estimation method Download PDFInfo
- Publication number
- CN114884775A CN114884775A CN202210340614.9A CN202210340614A CN114884775A CN 114884775 A CN114884775 A CN 114884775A CN 202210340614 A CN202210340614 A CN 202210340614A CN 114884775 A CN114884775 A CN 114884775A
- Authority
- CN
- China
- Prior art keywords
- neural network
- channel
- channel estimation
- layer
- deep neural
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000013135 deep learning Methods 0.000 title claims abstract description 12
- 238000013528 artificial neural network Methods 0.000 claims abstract description 47
- 230000006870 function Effects 0.000 claims abstract description 18
- 238000004891 communication Methods 0.000 claims abstract description 14
- 230000004913 activation Effects 0.000 claims abstract description 10
- 239000011159 matrix material Substances 0.000 claims description 55
- 238000013527 convolutional neural network Methods 0.000 claims description 20
- 238000012549 training Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 4
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000010267 cellular communication Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Images
Classifications
-
- 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
- H04L25/0204—Channel estimation of multiple channels
-
- 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
-
- 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
- H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
Abstract
The invention discloses a deep learning-based large-scale MIMO system channel estimation method, which comprises the following steps: the pilot signal is transmitted through an MIMO communication system, a receiving end receives the communication signal, preliminary channel estimation is carried out by utilizing a least square algorithm, and preliminary estimation channel state information is input into a pre-trained deep neural network to determine channel estimation. The invention utilizes the point convolution layer, the grouping convolution layer and the depth separable convolution layer to construct the depth neural network, the network reduces the number of parameters to be stored and the convolution times to be calculated, thereby reducing the complexity of the deep learning algorithm in channel estimation; the deep neural network has higher precision and keeps good performance when the signal-to-noise ratio is low; the activation function used in the invention improves the accuracy of the deep neural network for estimating the channel.
Description
Technical Field
The invention relates to the field of channel estimation in a communication system, in particular to a large-scale MIMO system channel estimation method based on deep learning.
Background
The MIMO technology refers to improving communication quality by 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 of the transmitting end and the receiving end. Massive MIMO technology is considered as one of the key technologies of 5G and future cellular communication systems, which utilize much larger spatial degrees of freedom than conventional MIMO systems by deploying massive antennas at base stations in a distributed or centralized manner. Such a large-scale mimo system can multiply channel capacity without increasing spectrum resources and transmission power, and significantly reduce inter-user interference by sufficiently utilizing spatial resources.
In using massive MIMO systems, accurate uplink and downlink channel state information is crucial for signal detection, beamforming, resource allocation, signal preprocessing, etc. In practice, the channel is unknown to the transmitter and must first be estimated from the pilot at the receiver. In Time Division Duplex (TDD) mode, downlink channel state information can be obtained from uplink channel state information using channel reciprocity between uplink and downlink. However, in the time division duplex mode, channel reciprocity does not always exist, and a corresponding calibration process is difficult and complicated, and thus obtaining downlink channel state information through an uplink channel state may be inaccurate.
When the traditional sparsity and compressive sensing method is used for channel estimation, a complex optimization problem is solved at each coherence interval, the complexity of the optimization problem increases along with the increase of the number of antennas, and the optimization problem becomes infeasible along with the use of massive MIMO. Therefore, the channel estimation method based on compressed sensing cannot adapt to the mechanism in the aspects of complexity, power consumption or pilot frequency overhead and the like, needs a large amount of computing time and resources, and cannot meet the real-time processing requirement in actual deployment.
The deep neural network is applied to various communication and signal processing problems by utilizing a deep learning algorithm, shows great potential of an innovative communication system, and is applied to aspects of modulation identification, signal detection, CSI feedback and channel estimation, network routing, service control and the like. When the current model based on the deep learning algorithm executes the channel estimation function of the communication system, a channel matrix is regarded as a two-dimensional image, and the high memory requirement and the computational complexity form the main obstacles of the practical deployment of the neural network in the communication system.
Disclosure of Invention
The purpose of the invention is as follows: in view of the above problems, the present invention aims to provide a large-scale MIMO system channel estimation method based on deep learning.
The technical scheme is as follows: the invention discloses a deep learning-based large-scale MIMO system channel estimation method, which comprises the following steps: transmitting a pilot signal through an MIMO communication system, receiving the communication signal by a receiving end, performing preliminary channel estimation by using a least square algorithm, and inputting preliminary estimation channel state information into a pre-trained deep neural network to determine channel estimation;
the deep neural network comprises a grouped convolutional neural network, a point convolutional neural network and a deep separable convolutional neural network, the point convolutional neural network is adopted in the first layer, the fourth layer, the seventh layer and the ninth layer, the grouped convolutional neural network is adopted in the second layer, and the deep separable convolutional neural network is adopted in the third layer, the fifth layer, the sixth layer and the eighth layer.
Further, activation functions tanh are added to the outputs of the first eight layers in the deep neural network.
Further, the training process of the deep neural network comprises the following steps:
step 1, inputting sample data into a deep neural network, and taking a mean square error function between output estimated channel state information and a real channel matrix as a cost function of a training network;
step 2, updating network parameters of the deep neural network by using an ADAM optimization algorithm, setting an initial learning rate, and adaptively updating the learning rate by using a training process in a subsequent optimization algorithm;
and 3, performing off-line training on the deep neural network by using the sample data, and deploying the trained network at a receiving end.
Further, the sample data comprises original channel matrix data directly generated by using a channel model, the original channel matrix data is preprocessed to generate channel matrix data with the same dimension and relatively low precision, and the original channel matrix data and the preprocessed channel matrix data are marked together to be used as sample data of the training deep neural network.
Further, the relatively low-precision channel matrix data is generated by the following process: and performing discrete Fourier transform on the original channel matrix data to a sparse domain, sampling and interpolating the transformed channel matrix data to restore the original dimension, and then adding noise to the channel matrix data to obtain low-precision channel matrix data.
Further, the preliminary channel estimation is carried out by utilizing a least square algorithm, and the step of inputting the preliminary estimation channel state information into a pre-trained deep neural network to determine the channel estimation comprises the following steps: the receiving end extracts a receiving signal on a pilot frequency position, calculates an initial channel estimation matrix according to a least square algorithm, enables a real part and an imaginary part of the initial channel estimation matrix to form a two-dimensional real number channel matrix, inputs the two-dimensional real number channel matrix into a deep neural network, and outputs the two-dimensional real number matrix formed by the real part and the imaginary part of a channel.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
1. the invention utilizes the point convolution layer, the grouping convolution layer and the depth separable convolution layer to construct the depth neural network, the network reduces the number of parameters to be stored and the convolution times to be calculated, thereby reducing the complexity of the deep learning algorithm in channel estimation;
2. the deep neural network has higher precision and keeps good performance when the signal-to-noise ratio is low; 3. the activation function used in the invention improves the accuracy of the deep neural network for estimating the channel.
Drawings
FIG. 1 is a flow chart of a channel estimation method according to the present invention;
fig. 2 is a normalized mean square error-signal to noise ratio plot of an estimated channel and a true channel using different network frameworks and different activation functions, respectively.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments.
As shown in fig. 1, the method for estimating a channel of a massive MIMO system based on deep learning in this embodiment includes the following steps: the pilot signal is transmitted through an MIMO communication system, a receiving end receives the communication signal, preliminary channel estimation is carried out by utilizing a least square algorithm, and preliminary estimation channel state information is input into a pre-trained deep neural network to determine channel estimation.
The deep neural network comprises a grouped convolutional neural network, a point convolutional neural network and a deep separable convolutional neural network, the point convolutional neural network is adopted in the first layer, the fourth layer, the seventh layer and the ninth layer, the grouped convolutional neural network is adopted in the second layer, and the deep separable convolutional neural network is adopted in the third layer, the fifth layer, the sixth layer and the eighth layer. In this embodiment, the output dimension of the point convolution neural network of the first layer is 96, and the output dimensions of the point convolution neural network of the fourth layer, the seventh layer and the ninth layer are 16, 8 and 2, respectively. The second layer of the packet convolutional neural network groups every two upper layer output features, and the convolutional kernel size is 5 x 5. The depth separable convolutional neural networks of the third layer, the fifth layer, the sixth layer and the eighth layer have the filter bank numbers of 48, 16 and 8 respectively, and the sizes of convolution kernels are all 3 multiplied by 3. The specific parameters of each layer are shown in table 1.
Activation functions tanh are added to the outputs of the first eight layers in the deep neural network, and the output layers of the deep neural network do not adopt the activation functions but directly output the activation functions. In case of large signal-to-noise ratio, other linear activation functions may be selected to speed up the convergence of the network.
TABLE 1 network parameters of each layer of deep neural network
The training process of the deep neural network in this embodiment includes:
step 1, inputting sample data into a deep neural network, taking a mean square error function between output estimated channel state information and a real channel matrix as a cost function of a training network, wherein the expression is as follows:
in the formula H LS The method comprises the steps that initial channel state information estimated by a least square algorithm is obtained, H is training sample data, a function f is a mapping relation from input to output of a deep neural network, T represents the number of training samples, and theta represents weight parameters of the neural network.
And 2, updating network parameters of the deep neural network by using an ADAM optimization algorithm, setting an initial learning rate, and adaptively updating the learning rate by using a subsequent optimization algorithm in a training process. The initial learning rate is set to 0.01 in this embodiment.
And 3, performing off-line training on the deep neural network by using the sample data, and deploying the trained network at a receiving end.
The sample data comprises original channel matrix data directly generated by using a channel model, the original channel matrix data is preprocessed to generate channel matrix data with the same dimension and relatively low precision, and the original channel matrix data and the preprocessed channel matrix data are marked together to be used as the sample data of the training deep neural network.
The generation process of the relatively low-precision channel matrix data comprises the following steps: and performing discrete Fourier transform on the original channel matrix data to a sparse domain, sampling and interpolating the transformed channel matrix data to restore the original dimension, and then adding noise to the channel matrix data to obtain low-precision channel matrix data.
In this embodiment, the COST2100 model is used to generate the original channel matrix data, and an indoor scenario of 5.3GHz is used, in which the base station takes a square area of 20m × 20m as the center, and the ue moves randomly within the square. The base station adopts a uniform linear array, and the number of transmitting antennas is N t 256, the ue has only one receiving antenna, and the system operates in the ofdm mode, and the number of subcarriers is K, where K is 256.
Performing discrete Fourier transform on the original channel matrix data, dividing the channel data obtained after the transform into 5 parts according to the proportion of 1:1:2:3:3, and performing sampling compression, wherein the compression rates are respectively 2 times, 4 times, 8 times, 16 times and 32 times. And recovering the compressed channel data into the dimensionality of the original channel matrix data through interpolation, marking the recovered data together with the original channel matrix data after adding noise to form sample data, and performing deep neural network offline training by using the sample data.
Obtaining approximate sparse channel matrix of angle-delay time domain by carrying out discrete Fourier transform on the channel matrix, and defining channel H of angle-delay time domain a The following were used:
H a ＝F a H s F b
where Fa and Fb are discrete Fourier transform matrices, H S Is a MIMO channel in frequency division duplex mode.
The method comprises the following steps of performing preliminary channel estimation by using a least square algorithm, and inputting preliminary estimation channel state information into a pre-trained deep neural network to determine channel estimation, wherein the channel estimation comprises the following steps: the receiving end extracts a receiving signal on a pilot frequency position, calculates an initial channel estimation matrix according to a least square algorithm, enables a real part and an imaginary part of the initial channel estimation matrix to form a two-dimensional real number channel matrix, inputs the two-dimensional real number channel matrix into a deep neural network, and outputs the two-dimensional real number matrix formed by the real part and the imaginary part of a channel.
In order to further verify the performance of the method of the embodiment, the performance of the channel estimation method of the embodiment is verified by combining simulation. Fig. 2 shows the accuracy of the channel estimation method proposed in this embodiment when estimating a channel, wherein different activation functions are used in the neural network, and the channel estimation method proposed in this embodiment has a relatively close performance under a large signal-to-noise ratio, but has a relatively large difference at a low signal-to-noise ratio, which proves the noise suppression capability between the three different methods.
Claims (6)
1. A large-scale MIMO system channel estimation method based on deep learning is characterized by comprising the following steps: transmitting a pilot signal through an MIMO communication system, receiving the communication signal by a receiving end, performing preliminary channel estimation by using a least square algorithm, and inputting preliminary estimation channel state information into a pre-trained deep neural network to determine channel estimation;
the deep neural network comprises a grouped convolutional neural network, a point convolutional neural network and a deep separable convolutional neural network, the point convolutional neural network is adopted in the first layer, the fourth layer, the seventh layer and the ninth layer, the grouped convolutional neural network is adopted in the second layer, and the deep separable convolutional neural network is adopted in the third layer, the fifth layer, the sixth layer and the eighth layer.
2. The channel estimation method according to claim 1, wherein the activation function tanh is added to the outputs of the first eight layers in the deep neural network.
3. The channel estimation method of claim 1, wherein the training process of the deep neural network comprises:
step 1, inputting sample data into a deep neural network, and taking a mean square error function between output estimated channel state information and a real channel matrix as a cost function of a training network;
step 2, updating network parameters of the deep neural network by using an ADAM optimization algorithm, setting an initial learning rate, and adaptively updating the learning rate by using a training process in a subsequent optimization algorithm;
and 3, performing off-line training on the deep neural network by using the sample data, and deploying the trained network at a receiving end.
4. The channel estimation method according to claim 3, wherein the sample data includes original channel matrix data directly generated by using a channel model, the original channel matrix data is preprocessed to generate channel matrix data with relatively low precision of the same dimension, and the original channel matrix data and the preprocessed channel matrix data are marked together to be used as sample data for training the deep neural network.
5. The channel estimation method of claim 4, wherein the relatively low-precision channel matrix data is generated by: and performing discrete Fourier transform on the original channel matrix data to a sparse domain, sampling and interpolating the transformed channel matrix data to restore the original dimension, and then adding noise to the channel matrix data to obtain low-precision channel matrix data.
6. The channel estimation method of claim 1, wherein the performing the preliminary channel estimation using a least squares algorithm, and inputting the preliminary channel state information into a pre-trained deep neural network to determine the channel estimation comprises: the receiving end extracts a receiving signal on a pilot frequency position, calculates an initial channel estimation matrix according to a least square algorithm, enables a real part and an imaginary part of the initial channel estimation matrix to form a two-dimensional real number channel matrix, inputs the two-dimensional real number channel matrix into a deep neural network, and outputs the two-dimensional real number matrix formed by the real part and the imaginary part of a channel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210340614.9A CN114884775A (en) | 2022-03-31 | 2022-03-31 | Deep learning-based large-scale MIMO system channel estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210340614.9A CN114884775A (en) | 2022-03-31 | 2022-03-31 | Deep learning-based large-scale MIMO system channel estimation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114884775A true CN114884775A (en) | 2022-08-09 |
Family
ID=82668855
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210340614.9A Pending CN114884775A (en) | 2022-03-31 | 2022-03-31 | Deep learning-based large-scale MIMO system channel estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114884775A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115412446A (en) * | 2022-08-10 | 2022-11-29 | 东莞职业技术学院 | Design method for communication model between VAAs of multi-hop virtual MIMO system |
| CN115664898A (en) * | 2022-10-24 | 2023-01-31 | 四川农业大学 | OFDM system channel estimation method and system based on complex convolution neural network |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018028255A1 (en) * | 2016-08-11 | 2018-02-15 | 深圳市未来媒体技术研究院 | Image saliency detection method based on adversarial network |
| CN109450830A (en) * | 2018-12-26 | 2019-03-08 | 重庆大学 | Channel estimation methods based on deep learning under a kind of high-speed mobile environment |
| CN109617847A (en) * | 2018-11-26 | 2019-04-12 | 东南大学 | A kind of non-cycle prefix OFDM method of reseptance based on model-driven deep learning |
| WO2019144575A1 (en) * | 2018-01-24 | 2019-08-01 | 中山大学 | Fast pedestrian detection method and device |
| CN110266620A (en) * | 2019-07-08 | 2019-09-20 | 电子科技大学 | 3D MIMO-OFDM system channel estimation method based on convolutional neural networks |
| CN111555992A (en) * | 2020-05-15 | 2020-08-18 | 厦门大学 | Large-scale multi-antenna channel estimation method based on deep convolutional neural network |
| WO2020253690A1 (en) * | 2019-06-17 | 2020-12-24 | 浙江大学 | Deep learning beam domain channel estimation method based on approximate message passing algorithm |
| CN112153736A (en) * | 2020-09-14 | 2020-12-29 | 南京邮电大学 | Personnel action identification and position estimation method based on channel state information |
| CN112637094A (en) * | 2020-12-17 | 2021-04-09 | 南京爱而赢科技有限公司 | Multi-user MIMO receiving method based on model-driven deep learning |
| CN112737985A (en) * | 2020-12-25 | 2021-04-30 | 东南大学 | Large-scale MIMO channel joint estimation and feedback method based on deep learning |
| US11121788B1 (en) * | 2020-06-08 | 2021-09-14 | Wuhan University | Channel prediction method and system for MIMO wireless communication system |
| CN113472706A (en) * | 2021-07-12 | 2021-10-01 | 南京大学 | MIMO-OFDM system channel estimation method based on deep neural network |
| WO2021203242A1 (en) * | 2020-04-07 | 2021-10-14 | 东莞理工学院 | Deep learning-based mimo multi-antenna signal transmission and detection technologies |
| CN113627376A (en) * | 2021-08-18 | 2021-11-09 | 北京工业大学 | Facial expression recognition method based on multi-scale dense connection depth separable network |
| US11190377B1 (en) * | 2020-05-26 | 2021-11-30 | Wuhan University | Time-frequency block-sparse channel estimation method based on compressed sensing |
-
2022
- 2022-03-31 CN CN202210340614.9A patent/CN114884775A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018028255A1 (en) * | 2016-08-11 | 2018-02-15 | 深圳市未来媒体技术研究院 | Image saliency detection method based on adversarial network |
| WO2019144575A1 (en) * | 2018-01-24 | 2019-08-01 | 中山大学 | Fast pedestrian detection method and device |
| CN109617847A (en) * | 2018-11-26 | 2019-04-12 | 东南大学 | A kind of non-cycle prefix OFDM method of reseptance based on model-driven deep learning |
| CN109450830A (en) * | 2018-12-26 | 2019-03-08 | 重庆大学 | Channel estimation methods based on deep learning under a kind of high-speed mobile environment |
| WO2020253690A1 (en) * | 2019-06-17 | 2020-12-24 | 浙江大学 | Deep learning beam domain channel estimation method based on approximate message passing algorithm |
| CN110266620A (en) * | 2019-07-08 | 2019-09-20 | 电子科技大学 | 3D MIMO-OFDM system channel estimation method based on convolutional neural networks |
| WO2021203242A1 (en) * | 2020-04-07 | 2021-10-14 | 东莞理工学院 | Deep learning-based mimo multi-antenna signal transmission and detection technologies |
| CN111555992A (en) * | 2020-05-15 | 2020-08-18 | 厦门大学 | Large-scale multi-antenna channel estimation method based on deep convolutional neural network |
| US11190377B1 (en) * | 2020-05-26 | 2021-11-30 | Wuhan University | Time-frequency block-sparse channel estimation method based on compressed sensing |
| US11121788B1 (en) * | 2020-06-08 | 2021-09-14 | Wuhan University | Channel prediction method and system for MIMO wireless communication system |
| CN112153736A (en) * | 2020-09-14 | 2020-12-29 | 南京邮电大学 | Personnel action identification and position estimation method based on channel state information |
| CN112637094A (en) * | 2020-12-17 | 2021-04-09 | 南京爱而赢科技有限公司 | Multi-user MIMO receiving method based on model-driven deep learning |
| CN112737985A (en) * | 2020-12-25 | 2021-04-30 | 东南大学 | Large-scale MIMO channel joint estimation and feedback method based on deep learning |
| CN113472706A (en) * | 2021-07-12 | 2021-10-01 | 南京大学 | MIMO-OFDM system channel estimation method based on deep neural network |
| CN113627376A (en) * | 2021-08-18 | 2021-11-09 | 北京工业大学 | Facial expression recognition method based on multi-scale dense connection depth separable network |
Non-Patent Citations (2)
| Title |
|---|
| TING JIANG等: "Estimation of frequency-Selective channels for Millimeter wave MIMO systems based on Compressive sensing", 《2021 INTERNATIONAL CONFERENCE ON COMMUNICATIONS，INFORMATION SYSTEM AND COMPUTER ENGINEERING（CISCE））》 * |
| 王晓天等: "大规模MIMO信号检测算法研究", 《南京邮电大学学报（自然科学版）》, vol. 35, no. 3 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115412446A (en) * | 2022-08-10 | 2022-11-29 | 东莞职业技术学院 | Design method for communication model between VAAs of multi-hop virtual MIMO system |
| CN115664898A (en) * | 2022-10-24 | 2023-01-31 | 四川农业大学 | OFDM system channel estimation method and system based on complex convolution neural network |
| CN115664898B (en) * | 2022-10-24 | 2023-09-08 | 四川农业大学 | OFDM system channel estimation method and system based on complex convolution neural network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112737985B (en) | Large-scale MIMO channel joint estimation and feedback method based on deep learning | |
| CN110099017B (en) | Channel estimation method of hybrid quantization system based on deep neural network | |
| CN107483091B (en) | Channel information feedback algorithm under FDD large-scale MIMO-OFDM system | |
| CN108494449B (en) | Angle domain channel tracking method under large-scale MIMO high-speed mobile scene | |
| CN114884775A (en) | Deep learning-based large-scale MIMO system channel estimation method | |
| CN107332598A (en) | A kind of precoding of mimo system joint and antenna selecting method based on deep learning | |
| CN113748614A (en) | Channel estimation model training method and device | |
| CN111181671B (en) | Deep learning-based downlink channel rapid reconstruction method | |
| CN113472706A (en) | MIMO-OFDM system channel estimation method based on deep neural network | |
| CN109474388B (en) | Low-complexity MIMO-NOMA system signal detection method based on improved gradient projection method | |
| CN114124623B (en) | Wireless communication channel estimation method and device | |
| CN110430150B (en) | Receiver design method of cellular mobile communication system based on neural network | |
| CN108964725B (en) | Sparse estimation method of channel parameters in time-varying large-scale MIMO network | |
| Elbir et al. | Federated learning for physical layer design | |
| Guo et al. | Deep learning for joint channel estimation and feedback in massive MIMO systems | |
| CN106788631B (en) | Large-scale MIMO reciprocity calibration method based on local calibration | |
| CN114567358B (en) | Large-scale MIMO robust WMMSE precoder and deep learning design method thereof | |
| CN114157331B (en) | Large-scale MIMO channel state information feedback method based on pseudo complex value input | |
| CN114268388A (en) | Channel estimation method based on improved GAN network in large-scale MIMO | |
| CN108199990A (en) | A kind of non-Gaussian noise 3D-MIMO channel estimation methods | |
| CN106130938B (en) | Multi-user joint channel estimation method for TDD large-scale MIMO system | |
| Li et al. | Lightweight channel estimation networks for OFDM systems | |
| CN108566227B (en) | Multi-user detection method | |
| CN105375967B (en) | A kind of enhanced maximum-ratio combing detection method and receiver based on statistical method | |
| CN116192209A (en) | Gradient uploading method for air computing federal learning under MIMO channel |
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 |