CN113839744A - Blind detection method of generalized wireless optical MIMO system based on deep learning - Google Patents

Blind detection method of generalized wireless optical MIMO system based on deep learning Download PDF

Info

Publication number
CN113839744A
CN113839744A CN202111108640.0A CN202111108640A CN113839744A CN 113839744 A CN113839744 A CN 113839744A CN 202111108640 A CN202111108640 A CN 202111108640A CN 113839744 A CN113839744 A CN 113839744A
Authority
CN
China
Prior art keywords
layer
blind
deep learning
wireless optical
detection method
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.)
Granted
Application number
CN202111108640.0A
Other languages
Chinese (zh)
Other versions
CN113839744B (en
Inventor
陈晨
钟鑫
曾林
刘敏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University
Original Assignee
Chongqing University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Chongqing University filed Critical Chongqing University
Priority to CN202111108640.0A priority Critical patent/CN113839744B/en
Publication of CN113839744A publication Critical patent/CN113839744A/en
Application granted granted Critical
Publication of CN113839744B publication Critical patent/CN113839744B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0036—Systems modifying transmission characteristics according to link quality, e.g. power backoff arrangements specific to the receiver
    • H04L1/0038—Blind format detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0047—Decoding adapted to other signal detection operation
    • H04L1/0048—Decoding adapted to other signal detection operation in conjunction with detection of multiuser or interfering signals, e.g. iteration between CDMA or MIMO detector and FEC decoder
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0054—Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Quality & Reliability (AREA)
  • Radio Transmission System (AREA)

Abstract

The invention relates to the technical field of wireless optical communication, and particularly discloses a blind detection method of a generalized wireless optical MIMO system based on deep learningrAmplifying and extracting the characteristics of a received signal y output by the PD receiver in parallel to obtain an input signal
Figure DDA0003273305820000011
Then input signal is input through blind detector which is trained in advance
Figure DDA0003273305820000012
And detecting and directly outputting corresponding bit information. The blind detector is built based on a deep learning neural network, and learns correct bit vectors and input signals after training is finished
Figure DDA0003273305820000014
The relationship betweenCan be used for real-time input signal
Figure DDA0003273305820000013
And (6) detecting. The invention can obtain the error code performance equivalent to the Joint-ML detection scheme with low computation complexity equivalent to the ZF-ML detection scheme under the condition of no real-time channel information, and obtain the optimal detection effect identical to the Joint-ML detection scheme.

Description

Blind detection method of generalized wireless optical MIMO system based on deep learning
Technical Field
The invention relates to the technical field of wireless optical communication, in particular to a blind detection method of a generalized wireless optical MIMO system based on deep learning.
Background
In recent years, wireless optical communication (OWC) technology has attracted much attention due to its characteristics of abundant spectrum resources, green and low power consumption. Among them, wireless optical communication using infrared, visible light, or ultraviolet Light Emitting Diodes (LEDs) is considered to be a very potential technology capable of satisfying the demand for high-speed communication. However, due to the limited modulation bandwidth of LEDs, the implementation and development of OWC systems is limited in practical applications. To date, there are many techniques for increasing the capacity of a band-limited OWC system, and multiple-input multiple-output (MIMO) transmission is a technique capable of effectively increasing the system capacity. In order to further exploit the advantages of MIMO systems, researchers have further proposed generalized wireless optical MIMO (gomimo) transmission techniques.
In order to successfully implement the GOMIMO system, an efficient MIMO detection scheme method should be adopted. Similar to the conventional MIMO system, Joint-maximum likelihood (Joint-ML) detection is generally the optimal detection scheme for the GOMIMO system. However, Joint-ML detection has extremely high computational complexity, which makes it limited in practical application. In addition, joint detection based on zero forcing equalization and ML (ZF-ML) is also a practical low complexity detection scheme suitable for the GOMIMO system. However, both Joint-ML and ZF-ML detection schemes require that accurate real-time channel information be known in advance for successful implementation, and therefore the system must perform channel estimation in advance. Real-time channel estimation in the GOMIMO system usually needs to be completed by transmitting additional training symbols, and the use of the additional training symbols will inevitably cause the reduction of the system spectrum efficiency and the increase of the system delay.
Disclosure of Invention
The invention provides a blind detection method of a generalized wireless optical MIMO system based on deep learning, which solves the technical problems that: how to achieve error performance comparable to the Joint-ML detection scheme without the need for real-time channel information.
In order to solve the technical problems, the invention provides a blind detection method of a generalized wireless optical MIMO system based on deep learning, which comprises the following steps:
s1, for NrPreprocessing the received signal y output by the PD receiver in parallel to obtain an input signal
Figure BDA0003273305800000021
S2, input signal pair by adopting trained blind detector
Figure BDA0003273305800000022
And detecting and outputting corresponding bit information.
Preferably, the blind detector comprises 1 input layer, 4 hidden layers, 1 output layer and 1 decision layer which are sequentially arranged;
the input layer is provided with NrA number of neurons, equal to the number of PD receivers, for converting an input signal
Figure BDA0003273305800000023
Inputting the data to the hidden layers one by one; the hidden layer is sequentially provided with four layers, and L is correspondingly arranged1、L2、L3、L4A neuron for learning an input signal by training
Figure BDA0003273305800000024
And statistical characteristics of additive noise; the output layer is provided with S neurons, the S neurons correspond to the total bit number carried by each input vector and are used for generating fuzzy bit information of which the numerical value interval is (0,1) of each input vector; the decision layer is provided with S neurons and is used for judging the S fuzzy bit information generated by the output layer to generate S0 or 1 bit information.
Further, the preprocessing process in step S1 specifically includes:
amplifying the received signal y by alpha times and according to NtGenerating characteristic matrix F by mapping relation of LED transmitter in information transmission, and finally generating input signal
Figure BDA0003273305800000025
Nr≥Nt。
Preferably, the functional relationship among the input layer, the hidden layer and the output layer is expressed as follows:
Figure BDA0003273305800000026
wherein k ═ 1 denotes a first layer, i.e., the input layer; k 2 ≦ k ≦ 5 denotes the second to fifth layers, i.e., the 4-layer hidden layers, and k ≦ 6 denotes the 6-th layer, i.e., the output layer; z is a radical ofkRepresents the output vector of the k-th layer, Wk-1Is a weight matrix of the k-1 th layer and the k layer, bk-1Is the bias vector of the k layer; the activation function of the hidden layer is a ReLU function fReLU(x) Max (0, x); the activation function of the output layer is a Sigmoid function fSigmoid(x)＝1/(1+exp-x)。
Preferably, let z beqIn order to be a vague bit, the bit is,
Figure BDA0003273305800000027
the decision rule of the decision layer is as follows:
Figure BDA0003273305800000031
preferably, it is assumed that N isrA PD receiver and NtIn the mapping process of each LED transmitter, only N is usedaThe LED transmitters are activated for signal transmission, and N is more than or equal to 1a≤NtThen the feature matrix F is represented as Nt×NrWherein each row and each column has NaThe assignment of one element to 1 indicates a signal transmission, and the assignment of the other elements to 0 indicates no signal transmission.
Preferably, the loss function selected by the blind detector is a mean square error:
Figure BDA0003273305800000032
wherein the content of the first and second substances,
Figure BDA0003273305800000033
a bit vector estimated for the decision layer and b is the label of the correct bit vector transmitted, i.e. the received signal y.
Preferably, in the off-line training stage of the blind detector, the blind detection related parameters are tested and determined at different positions in a specific space, then the neural network in the blind detector is trained, and the trained blind detector is stored; when N is presentrWhen the PD receiver is located at a certain position, the feedforward deep neural network module directly calls a trained blind detector corresponding to the position to detect signals.
The invention provides a blind detection method of a generalized wireless optical MIMO system based on deep learning, which is characterized in that a preprocessing module is arranged to carry out N pairs of N pairs according to the mapping relation between an LED transmitter and a PD receiverrAmplifying and extracting the characteristics of a received signal y output by the PD receiver in parallel to obtain an input signal
Figure BDA0003273305800000034
Then the input signal is input through a blind detector in a feedforward deep neural network module which is trained in advance
Figure BDA0003273305800000035
And detecting and directly outputting corresponding bit information. The blind detector is built based on a deep learning neural network, and learns correct bit vectors and input signals after training through an input layer, a hidden layer, an output layer, a decision layer, parameters and function settings
Figure BDA0003273305800000036
The relation between the two can be real-time to the input messageNumber (C)
Figure BDA0003273305800000037
And (6) detecting. The invention solves the problem of dependence of traditional Joint-ML detection and ZF-ML detection on channel information, and can obtain the same optimal detection effect as the Joint-ML detection scheme under the condition of no real-time channel information; meanwhile, the blind detection method of the GOMIMO system based on deep learning solves the problem of high technical complexity of the Joint-ML detection scheme, and can successfully realize the optimal detection of the GOMIMO system with low computation complexity equivalent to that of the ZF-ML detection scheme. The blind detection method of the GOMIMO system based on deep learning provided by the invention can obtain the error code performance equivalent to the Joint-ML detection scheme under the condition of not needing real-time channel information, and has the following main advantages:
1. real-time channel estimation is not needed, so that extra training symbols are not needed for real-time channel estimation, the frequency spectrum efficiency of the GOMIMO system can be improved, and the communication delay is reduced;
2. with a low computational complexity comparable to the ZF-ML detection scheme.
Drawings
Fig. 1 is a schematic block diagram illustrating an application of a generalized wireless optical MIMO system and a blind detection method thereof according to an embodiment of the present invention;
fig. 2(a) and (b) are mapping tables of a PD transmitter and an LED receiver under GOSM (generalized optical spatial modulation) and GOSMP (generalized optical spatial multiplexing), respectively, according to an embodiment of the present invention;
fig. 3 is a flow chart of preprocessing and blind detector detection provided by an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, which are given solely for the purpose of illustration and are not to be construed as limitations of the invention, including the drawings which are incorporated herein by reference and for illustration only and are not to be construed as limitations of the invention, since many variations thereof are possible without departing from the spirit and scope of the invention.
FIG. 1 shows a blind detection method of the present inventionApplication of method to Nr×NtSchematic block diagram on GOMIMO system, and the GOMIMO system comprises NrA PD receiver and NtAn LED transmitter. Wherein, in the GOMIMO mapping process, only N is includedaEach (1 is less than or equal to N)a≤Nt) The LED transmitter is activated for signal transmission. As shown in fig. 1, the input bitstream is first divided into two parts: one part is used for generating a constellation symbol vector c by traditional signal modulation, and the other part is used for generating a space index vector v by LED index selection. Based on the obtained constellation symbol vector c and spatial index vector v, GOMIMO (GOSM or gos mp) mapping is performed to generate a transmission signal vector x. FIGS. 2(a) and (b) show N, respectivelyt4 and NaMapping tables of GOSM (generalized optical spatial modulation) and GOSMP (generalized optical spatial multiplexing) corresponding to 2. The GOMIMO detection in fig. 1 refers to the blind detection method of the present invention.
Specifically, as shown in fig. 3, the blind detection method for the generalized wireless optical MIMO system provided in this embodiment is based on deep learning, and includes the steps of:
s1, for NrPreprocessing the received signal y output by the PD receiver in parallel to obtain an input signal
Figure BDA0003273305800000051
S2, input signal pair by adopting trained blind detector
Figure BDA0003273305800000052
And detecting and outputting corresponding bit information.
As shown in fig. 3, the preprocessing process in step S1 specifically includes:
amplifying the received signal y by alpha times and according to NtGenerating characteristic matrix F by mapping relation of LED transmitter in information transmission, and finally generating input signal
Figure BDA0003273305800000053
Nr≥Nt. The signal amplification factor alpha can be set by preliminary testing, and the feature moments for feature extractionThe array F is determined by the GOMIMO mapping relationship. In NrA PD receiver and NtIn the mapping process of each LED transmitter, only N is usedaThe LED transmitters are activated for signal transmission, and N is more than or equal to 1a≤NtThen the feature matrix F is represented as Nt×NrWherein each row and each column has NaThe assignment of one element to 1 indicates a signal transmission, and the assignment of the other elements to 0 indicates no signal transmission. Taking fig. 2 as an example, the feature matrix corresponding to the two mapping manners of fig. 2 is:
Figure BDA0003273305800000054
more specifically, the blind detector includes 1 input layer, 4 hidden layers, 1 output layer, and 1 decision layer, which are sequentially arranged.
As shown in FIG. 3, the input layer is provided with NrA number of neurons, equal to the number of PD receivers, for converting an input signal
Figure BDA0003273305800000055
Inputting the data into the hidden layers one by one; four layers are sequentially arranged on the hidden layer, and L is correspondingly arranged1、L2、L3、L4A neuron for learning an input signal by training
Figure BDA0003273305800000056
And statistical characteristics of additive noise; the output layer is provided with S neurons, the S neurons correspond to the total bit number carried by each input vector and are used for generating fuzzy bit information of which the numerical value interval is (0,1) of each input vector; the decision layer is provided with S neurons and is used for judging the S fuzzy bit information generated by the output layer to generate S0 or 1 bit information. Each dot in the neural network module stores a scalar value for each neuron, and each neuron has an activation function.
The functional relationship of the input layer, the hidden layer and the output layer is expressed as follows:
Figure BDA0003273305800000061
wherein k ═ 1 denotes a first layer, i.e., an input layer; k is 2 ≦ k ≦ 5 for the second to fifth layers, i.e., the 4-layer hidden layer, and k is 6 for the 6 th layer, i.e., the output layer; z is a radical ofkRepresents the output vector of the k-th layer, Wk-1Is a weight matrix of the k-1 th layer and the k layer, bk-1Is the bias vector of the k layer; the activation function of the hidden layer is a ReLU function fReLU(x) Max (0, x); the activation function of the output layer is Sigmoid function fSigmoid(x)＝1/(1+exp-x)。
Suppose zqIn order to be a vague bit, the bit is,
Figure BDA0003273305800000062
for the decided bits, the decision rule of the decision layer is:
Figure BDA0003273305800000063
the input layer, the hidden layer and the output layer of the decision layer in the blind detector are typical deep neural networks, the back propagation training is carried out by taking a loss function as a standard through pre-generated symbols and corresponding labels, the weight value in the network is iterated, and the process is repeated for a plurality of times until the loss function is converged, namely the training of the neural networks is completed. Under the GOMIMO system, the used training symbols are the direct received signals of the GOMIMO system, and the labels are the corresponding correct bit information. The selected loss function is the Mean Square Error (MSE) and can be expressed as:
Figure BDA0003273305800000064
wherein the content of the first and second substances,
Figure BDA0003273305800000065
bit vector estimated for the decision layer, and b is the correct bit vector transmitted, i.e. of the received signal yAnd (4) a label.
Taking a room as an example, in the off-line training stage of the blind detector, testing and determining blind detection related parameters at different positions in the room, training a neural network in the blind detector, and storing the trained blind detector; when N is presentrWhen a PD receiver is positioned at a certain position, a trained blind detector corresponding to the position is directly called to detect signals.
In summary, in the blind detection method for the generalized wireless optical MIMO system based on deep learning according to the embodiments of the present invention, the preprocessing module is configured to perform N pairs according to the mapping relationship between the LED transmitter and the PD receiverrAmplifying and extracting the characteristics of a received signal y output by the PD receiver in parallel to obtain an input signal
Figure BDA0003273305800000066
Then the input signal is input through a blind detector in a feedforward deep neural network module which is trained in advance
Figure BDA0003273305800000071
And detecting and directly outputting corresponding bit information. The blind detector is built based on a deep learning neural network, and learns correct bit vectors and input signals after training through an input layer, a hidden layer, an output layer, a decision layer, parameters and function settings
Figure BDA0003273305800000072
The relation between the two signals can be real-time compared with the input signal
Figure BDA0003273305800000073
And (6) detecting. The invention solves the problem of dependence of traditional Joint-ML detection and ZF-ML detection on channel information, and can obtain the same optimal detection effect as the Joint-ML detection scheme under the condition of no real-time channel information; meanwhile, the blind detection scheme of the GOMIMO system based on deep learning provided by the invention solves the problem of higher technical complexity of the Joint-ML detection scheme, and can realize successful implementation with low computational complexity equivalent to that of the ZF-ML detection schemeThe optimal detection of the GOMIMO system is realized. The blind detection method of the GOMIMO system based on deep learning provided by the invention can obtain the error code performance equivalent to the Joint-ML detection scheme under the condition of not needing real-time channel information, and has the following main advantages:
1. real-time channel estimation is not needed, so that extra training symbols are not needed for real-time channel estimation, the frequency spectrum efficiency of the GOMIMO system can be improved, and the communication delay is reduced;
2. with a low computational complexity comparable to the ZF-ML detection scheme.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (8)

1. A blind detection method of a generalized wireless optical MIMO system based on deep learning is characterized by comprising the following steps:
s1, for NrPreprocessing the received signal y output by the PD receiver in parallel to obtain an input signal
Figure FDA0003273305790000013
S2, input signal pair by adopting trained blind detector
Figure FDA0003273305790000014
And detecting and outputting corresponding bit information.
2. The blind detection method for the generalized wireless optical MIMO system based on deep learning of claim 1, wherein: the blind detector comprises 1 input layer, 4 hidden layers, 1 output layer and 1 decision layer which are sequentially arranged;
the input layer is provided with NrA number of neurons, equal to the number of PD receivers, for receiving the signalInput signal
Figure FDA0003273305790000015
Inputting the data to the hidden layers one by one; the hidden layer is sequentially provided with four layers, and L is correspondingly arranged1、L2、L3、L4A neuron for learning an input signal by training
Figure FDA0003273305790000016
And statistical characteristics of additive noise; the output layer is provided with S neurons, the S neurons correspond to the total bit number carried by each input vector and are used for generating fuzzy bit information of which the numerical value interval is (0,1) of each input vector; the decision layer is provided with S neurons and is used for judging the S fuzzy bit information generated by the output layer to generate S0 or 1 bit information.
3. The blind detection method for the deep learning-based generalized wireless optical MIMO system according to claim 2, wherein the preprocessing procedure in step S1 specifically includes:
amplifying the received signal y by alpha times and according to NtGenerating characteristic matrix F by mapping relation of LED transmitter in information transmission, and finally generating input signal
Figure FDA0003273305790000011
4. The blind detection method for the deep learning-based generalized wireless optical MIMO system according to claim 3, wherein the functional relationship among the input layer, the hidden layer and the output layer is expressed as follows:
Figure FDA0003273305790000012
wherein k ═ 1 denotes a first layer, i.e., the input layer; k is 2. ltoreq. k.ltoreq.5 denotes the second to fifth layers, i.e. 4 layersA hidden layer, where k 6 denotes the 6 th layer, i.e., the output layer; z is a radical ofkRepresents the output vector of the k-th layer, Wk-1Is a weight matrix of the k-1 th layer and the k layer, bk-1Is the bias vector of the k layer; the activation function of the hidden layer is a ReLU function fReLU(x) Max (0, x); the activation function of the output layer is a Sigmoid function fSigmoid(x)＝1/(1+exp-x)。
5. The blind detection method for the generalized wireless optical MIMO system based on deep learning of claim 4, wherein z is assumedqIn order to be a vague bit, the bit is,
Figure FDA0003273305790000021
the decision rule of the decision layer is as follows:
Figure FDA0003273305790000022
6. the blind detection method for the generalized wireless optical MIMO system based on deep learning according to claim 4, wherein: suppose in NrA PD receiver and NtIn the mapping process of each LED transmitter, only N is usedaThe LED transmitters are activated for signal transmission, and N is more than or equal to 1a≤NtThen the feature matrix F is represented as Nt×NrWherein each row and each column has NaThe assignment of one element to 1 indicates a signal transmission, and the assignment of the other elements to 0 indicates no signal transmission.
7. The blind detection method for the deep learning-based generalized wireless optical MIMO system according to any one of claims 1 to 6, wherein the loss function selected by the blind detector is a mean square error:
Figure FDA0003273305790000023
wherein the content of the first and second substances,
Figure FDA0003273305790000024
a bit vector estimated for the decision layer and b is the label of the correct bit vector transmitted, i.e. the received signal y.
8. The blind detection method for the deep learning-based generalized wireless optical MIMO system according to claim 7, wherein: in the off-line training stage of the blind detector, testing and determining blind detection related parameters at different positions in a specific space, training a neural network in the blind detector, and storing the trained blind detector; when N is presentrWhen the PD receiver is located at a certain position, the feedforward deep neural network module directly calls a trained blind detector corresponding to the position to detect signals.
CN202111108640.0A 2021-09-22 2021-09-22 Blind detection method of generalized wireless optical MIMO system based on deep learning Active CN113839744B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111108640.0A CN113839744B (en) 2021-09-22 2021-09-22 Blind detection method of generalized wireless optical MIMO system based on deep learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111108640.0A CN113839744B (en) 2021-09-22 2021-09-22 Blind detection method of generalized wireless optical MIMO system based on deep learning

Publications (2)

Publication Number Publication Date
CN113839744A true CN113839744A (en) 2021-12-24
CN113839744B CN113839744B (en) 2022-09-02

Family

ID=78960369

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111108640.0A Active CN113839744B (en) 2021-09-22 2021-09-22 Blind detection method of generalized wireless optical MIMO system based on deep learning

Country Status (1)

Country Link
CN (1) CN113839744B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114363218A (en) * 2022-01-07 2022-04-15 合肥工业大学 Communication reachable rate detection method based on end-to-end learning

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102104957A (en) * 2009-12-21 2011-06-22 华为技术有限公司 Method and device for indicating multiple user-multiple input multiple output (MU-MIMO) mode
US20110261833A1 (en) * 2008-09-04 2011-10-27 Yu Yang Method and Device for MIMO Transmission in High Speed Packet Access Evolution System
CN103259631A (en) * 2013-05-13 2013-08-21 杭州电子科技大学 Frequency spectrum blind detecting method based on characteristic functions in MIMO system
CN103763222A (en) * 2014-01-16 2014-04-30 西安电子科技大学 Channel ambiguity removing method in MIMO signal blind detection process
US20140133331A1 (en) * 2012-11-09 2014-05-15 Samsung Electronics Co., Ltd. Method and apparatus for blindly detecting a common search space and a ue specific search space
CN103916344A (en) * 2014-03-07 2014-07-09 南京邮电大学 Virtual multiple input and multiple output system signal blind detection method of wireless sensor network
CN106100789A (en) * 2016-06-13 2016-11-09 南京邮电大学 Multilamellar MIMO wireless sensor network blind checking method based on sub-clustering
CN108390705A (en) * 2018-03-29 2018-08-10 东南大学 The extensive mimo system detection method of deep neural network based on BP algorithm structure
CN109921882A (en) * 2019-02-20 2019-06-21 深圳市宝链人工智能科技有限公司 A kind of MIMO coding/decoding method, device and storage medium based on deep learning
CN110518945A (en) * 2019-08-20 2019-11-29 东南大学 A kind of MIMO detection method and device based on deep learning Yu SDR algorithm
CN111769862A (en) * 2020-06-28 2020-10-13 南京理工大学 Joint detection algorithm in spatial modulation network based on deep learning
CN112821938A (en) * 2021-01-08 2021-05-18 重庆大学 Total throughput and energy consumption optimization method of air-space-ground satellite communication system

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110261833A1 (en) * 2008-09-04 2011-10-27 Yu Yang Method and Device for MIMO Transmission in High Speed Packet Access Evolution System
CN102104957A (en) * 2009-12-21 2011-06-22 华为技术有限公司 Method and device for indicating multiple user-multiple input multiple output (MU-MIMO) mode
US20140133331A1 (en) * 2012-11-09 2014-05-15 Samsung Electronics Co., Ltd. Method and apparatus for blindly detecting a common search space and a ue specific search space
CN103259631A (en) * 2013-05-13 2013-08-21 杭州电子科技大学 Frequency spectrum blind detecting method based on characteristic functions in MIMO system
CN103763222A (en) * 2014-01-16 2014-04-30 西安电子科技大学 Channel ambiguity removing method in MIMO signal blind detection process
CN103916344A (en) * 2014-03-07 2014-07-09 南京邮电大学 Virtual multiple input and multiple output system signal blind detection method of wireless sensor network
CN106100789A (en) * 2016-06-13 2016-11-09 南京邮电大学 Multilamellar MIMO wireless sensor network blind checking method based on sub-clustering
CN108390705A (en) * 2018-03-29 2018-08-10 东南大学 The extensive mimo system detection method of deep neural network based on BP algorithm structure
CN109921882A (en) * 2019-02-20 2019-06-21 深圳市宝链人工智能科技有限公司 A kind of MIMO coding/decoding method, device and storage medium based on deep learning
CN110518945A (en) * 2019-08-20 2019-11-29 东南大学 A kind of MIMO detection method and device based on deep learning Yu SDR algorithm
CN111769862A (en) * 2020-06-28 2020-10-13 南京理工大学 Joint detection algorithm in spatial modulation network based on deep learning
CN112821938A (en) * 2021-01-08 2021-05-18 重庆大学 Total throughput and energy consumption optimization method of air-space-ground satellite communication system

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CHAODI JIN,YUN ZHANG,SHUJUAN YU: ""Virtual MIMO Blind Detection ClusteredWSN System"", 《2015 ASIA-PACIFIC MICROWAVE CONFERENCE (APMC)》 *
XIN ZHONG, CHEN CHEN, LIN ZENG, RUOCHEN ZHANG: ""Joint Detection for Generalized Optical MIMO: A Deep Learning Approach"", 《2021 IEEE 16TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA)》 *
续澄澄: ""空间调制MIMO系统盲检测研究与设计"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *
陈晨: ""基于自适应PI控制方法的海上浮式风机独立变桨控制研究"", 《中国优秀硕士学位论文全文数据库 工程科技辑》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114363218A (en) * 2022-01-07 2022-04-15 合肥工业大学 Communication reachable rate detection method based on end-to-end learning
CN114363218B (en) * 2022-01-07 2023-07-28 合肥工业大学 Communication reachable rate detection method based on end-to-end learning

Also Published As

Publication number Publication date
CN113839744B (en) 2022-09-02

Similar Documents

Publication Publication Date Title
CN108462517B (en) MIMO link self-adaptive transmission method based on machine learning
CN114051701B (en) Apparatus and method for machine learning assisted precoding
CN110336594B (en) Deep learning signal detection method based on conjugate gradient descent method
CN109039534A (en) A kind of sparse CDMA signals detection method based on deep neural network
CN111628833B (en) MIMO antenna number estimation method based on convolutional neural network
CN110098870B (en) Optical generalized spatial modulation method based on OB-MMSE detection algorithm
CN109905170A (en) A kind of nonlinear distortion compensation algorithm and visible light communication device based on K-DNN
Askri et al. DNN assisted sphere decoder
CN106301496B (en) Spatial modulation system based on day line options and precoding
CN113839744B (en) Blind detection method of generalized wireless optical MIMO system based on deep learning
CN116318311B (en) Transmission method based on reconfigurable intelligent surface anti-phase index modulation
CN112242969A (en) Novel single-bit OFDM receiver based on model-driven deep learning
CN114584448A (en) SM-OFDM signal grouping detection method based on deep neural network
Hua et al. Signal detection in uplink pilot-assisted multi-user MIMO systems with deep learning
CN114499601B (en) Large-scale MIMO signal detection method based on deep learning
Ye et al. Bilinear convolutional auto-encoder based pilot-free end-to-end communication systems
CN108880637B (en) Codebook-based multi-symbol joint rotation THP precoding method
CN114598582A (en) Ultra-high order signal modulation format rapid identification method based on transfer learning
KR102064301B1 (en) Signal detection apparatus using ensemble machine learning based on MIMO system and method thereof
CN113225114A (en) Wireless communication signal sending and receiving method based on precoding joint optimization
CN116249128A (en) Joint beam forming method based on end-to-end learning in multi-RIS auxiliary communication system
CN110995330B (en) Antenna selection method based on grouping and EDAS algorithm
CN110365377B (en) Multi-antenna space division multiple access and SCMA non-orthogonal multiple access combined downlink transmission method
Fu et al. Grassmannian constellation design for noncoherent MIMO systems using autoencoders
CN113489545A (en) Light space pulse position modulation step-by-step classification detection method based on K-means clustering

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