CN108881096B - Spatial modulation MQAM base station based on phase decision - Google Patents
Spatial modulation MQAM base station based on phase decision Download PDFInfo
- Publication number
- CN108881096B CN108881096B CN201810534422.5A CN201810534422A CN108881096B CN 108881096 B CN108881096 B CN 108881096B CN 201810534422 A CN201810534422 A CN 201810534422A CN 108881096 B CN108881096 B CN 108881096B
- Authority
- CN
- China
- Prior art keywords
- module
- data
- mqam
- modulation
- spatial modulation
- 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
- 238000001514 detection method Methods 0.000 claims abstract description 25
- 238000007781 pre-processing Methods 0.000 claims abstract description 25
- 238000004891 communication Methods 0.000 claims abstract description 22
- 238000001914 filtration Methods 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims abstract description 12
- 230000005540 biological transmission Effects 0.000 claims abstract description 9
- 238000009826 distribution Methods 0.000 claims description 41
- 238000012549 training Methods 0.000 claims description 24
- 238000010586 diagram Methods 0.000 claims description 18
- 238000000605 extraction Methods 0.000 claims description 14
- 238000007476 Maximum Likelihood Methods 0.000 claims description 13
- 238000005457 optimization Methods 0.000 claims description 10
- 238000013139 quantization Methods 0.000 claims description 9
- 238000013527 convolutional neural network Methods 0.000 claims description 7
- 239000000284 extract Substances 0.000 claims description 7
- 230000004927 fusion Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- NIXOWILDQLNWCW-UHFFFAOYSA-N Acrylic acid Chemical compound OC(=O)C=C NIXOWILDQLNWCW-UHFFFAOYSA-N 0.000 claims description 3
- 239000000969 carrier Substances 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000011158 quantitative evaluation Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000003321 amplification Effects 0.000 claims description 2
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims 1
- 238000004364 calculation method Methods 0.000 abstract description 6
- 238000000034 method Methods 0.000 description 25
- 238000013461 design Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008054 signal transmission Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
- H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
- H04L27/345—Modifications of the signal space to allow the transmission of additional information
- H04L27/3461—Modifications of the signal space to allow the transmission of additional information in order to transmit a subchannel
- H04L27/3483—Modifications of the signal space to allow the transmission of additional information in order to transmit a subchannel using a modulation of the constellation points
-
- 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
- H04L1/02—Arrangements for detecting or preventing errors in the information received by diversity reception
- H04L1/06—Arrangements for detecting or preventing errors in the information received by diversity reception using space diversity
- H04L1/0612—Space-time modulation
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Digital Transmission Methods That Use Modulated Carrier Waves (AREA)
Abstract
The invention requests to protect a spatial modulation MQAM base station based on phase decision, which comprises a data receiving module, a data transmitting module, a filtering amplifying module, a spatial modulation MQAM module, a data preprocessing module and a data tuning processing module, wherein the data transmitting module and the data receiving module transmit or receive data through a plurality of channels and a plurality of addresses; the data receiving module receives an external communication signal, transmits the external communication signal to the data filtering and amplifying module for filtering and amplifying, and then transmits the external communication signal to the data preprocessing module for data preprocessing steps including denoising and windowing; the data preprocessing module transmits the data to a spatial modulation MQAM module for spatial QAM modulation to obtain a signal detection result of a spatial modulation system, and transmits the signal detection result to a data transmitting module for transmission; the invention can improve the modulation accuracy of the communication base station equipment and reduce the calculation complexity.
Description
Technical Field
The invention belongs to the technical field of communication base stations, and particularly belongs to a spatial modulation MQAM base station based on phase decision.
Background
The base station is a high power multi-channel two-way radio transmitter fixed in one place. A Base Station Subsystem (BSS) is the most direct basic component of a mobile communication system in relation to a wireless cellular network. The base station mainly plays a role of relay in the whole mobile network. The base station is connected with the base station by adopting a wireless channel and is responsible for wireless transmission, reception and wireless resource management. The main base station and the Mobile Switching Center (MSC) are usually connected by wired channels to realize communication between mobile subscribers or between mobile subscribers and fixed subscribers. In daily life, a common communication base station is a wireless communication base station. The base station plays an important role in signal transmission as a relay station for wireless communication. The signals are easily interfered by other signals, and the frequency modulation channels are not sufficiently tuned, so that the signal classification is not accurate.
The spatial modulation technique is an emerging research of the massive MIMO technique in recent years, most of the signal detection, modulation technique and even the channel estimation technique use the related criteria of MIMO, although the system model and principle are about the same, the spatial modulation technique is improved, thus the MIMO technique is usedThe related methods in the art lack the characteristics and attributes of spatial modulation, and the advantages and characteristics of more spatial modulation techniques are still under study. The research trend of the detection method shows how to reduce the calculation complexity of the method and improve the detection performance by improving the research. Therefore, in the spatial modulation MIMO system, the detection method is researched, the detection accuracy of the signal detection method is guaranteed to a certain extent through research and improvement, and meanwhile, the method flow and the calculation process are not complex, so that the method is significant. The principle of maximum likelihood estimation (ML) is to search all the transmitted symbols for the most suitable combination of antenna sequence numbers and modulation symbols, thus obtaining the closest error rate performance, which is called the best performance detection method. However, the method has too many searching targets, and the implementation steps are very complicated, so that the method is difficult to be practically applied to a large-scale antenna system. The invention reduces the complexity of symbol search by researching the characteristics of the MQAM constellation diagram, and has the difficulty of estimating possible constellation points with different amplitudes. Therefore, the invention provides a spatial modulation MQAM base station based on phase decision, which can effectively reduce the computational complexity of a receiving end and has better performance along with the increase of the number of modulation points. The computational complexity of the ML method is 6NrNtM, the complexity of the method provided by the invention is (6N)r+2+5R)Nt. The base station is provided with the signal modulation processing module and the power distributor, so that the problems of power distribution, channel distribution and tuning are effectively solved.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A spatial modulation MQAM base station based on phase decision is provided, which improves the signal transmission efficiency and reduces the calculation complexity.
The technical scheme of the invention is as follows:
a spatial modulation MQAM base station based on phase decision comprises a data receiving module, a data transmitting module, a filtering amplification module, a spatial modulation MQAM module, a data preprocessing module and a data tuning processing module, wherein the data transmitting module and the data receiving module transmit or receive data through a plurality of channels and a plurality of addresses; the data receptionThe module receives an external communication signal, transmits the external communication signal to the data filtering and amplifying module for filtering and amplifying, and then transmits the external communication signal to the data preprocessing module for data preprocessing including denoising and windowing; the data preprocessing module transmits the data to a spatial modulation MQAM module for spatial QAM modulation, and the spatial modulation MQAM module converts the detection problem of symbols sent by a spatial modulation system into a quantization demodulation problem; secondly, quantizing the converted received signals according to the distribution characteristics of the constellation points in the MQAM constellation diagram, estimating the transmitted symbols according to the phase of the quantized signals, and performing maximum likelihood optimal estimation on the activated antenna indexes; finally, a signal detection result of the spatial modulation system is obtained and transmitted to the data sending module for sending; the conversion of the detection problem of symbols sent by the spatial modulation system into the quantization demodulation problem specifically includes: in a spatial modulation system, the ML maximum likelihood estimation can be expressed as 2 nested search problems, i.e. first searching for the transmitted symbol s and then the antenna index l, can be expressed as
Wherein,
indicates the index of the transmit antenna and,
denotes a transmitted symbol, y denotes a received signal vector, hlRepresenting the ith column of the channel matrix. For internal optimization problems
That is, under the condition of giving the activated antenna index l, the transmitted symbol s is solved, and for the MQAM modulated signal, the internal optimization problem is still equivalent to that of the MQAM modulated signal
Wherein,
the detection problem of symbols sent by an SM system can be converted into a quantization demodulation problem;
the data tuning processing module comprises a preprocessing module, a feature extraction module and a training tuning module; the preprocessing module is used for preprocessing the received signals including windowing, and converting the signals into a smooth pseudo Winger-Ville time frequency distribution graph and an optimal time frequency distribution graph by utilizing the smooth pseudo Winger-Ville distribution and the optimal time frequency distribution graph; the characteristic extraction module adopts a convolutional neural network to automatically extract the characteristics of a smooth pseudo Winger-Ville time-frequency distribution graph and an optimal time-frequency distribution graph, and carries out characteristic fusion quantitative evaluation on the characteristics of the two time-frequency images by utilizing a multi-mode fusion model, and the characteristic extraction module specifically comprises the following steps: carrying out time-frequency analysis processing on the characteristics of the collected smooth pseudo Winger-Ville time-frequency distribution diagram and the optimal time-frequency distribution diagram, and calculating a fuzzy function and a fuzzy function mean value of a training set signal; selecting a two-dimensional radial Gaussian kernel function as an optimal kernel function based on classified optimal time frequency distribution; calculating an optimal kernel function through iterative search; performing time-frequency transformation on the training set signals under the optimal kernel function, and extracting characteristic values for classification; designing a classifier of the training set signals, and classifying the characteristic values of the training set signals; the training tuning module takes the fused features as the input of the multilayer perceptron, firstly trains the model by using a training set, and then completes the modulation of the signal by using the trained model.
Further, the model of the data receiving module receiving the external communication signal is as follows:
where r (t) and s (t) denote the received signal and the transmitted signal, respectively, α denotes the channel gain, ω0And theta0Representing frequency offset and phase offset, and n (t) gaussian noise, where when s (t) is ASK, FSK and PSK modulation, the expression:
when s (t) is QAM modulated, two orthogonal carriers cos (2 π f) are used for QAM signalct) and sin (2 π f)ct), the expression is:
Further, the smooth pseudo-Winger-Ville distribution suppresses cross terms by windowing and intercepting in the time delay and frequency offset directions simultaneously, and the expression is as follows:
SPWVDx(t,f)＝∫∫h(τ)g(v)x(t-v+τ/2)x*(t-v-τ/2)e-j2πfτdvdτ
where H (τ) and g (v) are two real even window functions, x (t) ═ r (t) + jH [ r (t) ], H [ · ] denotes hilbert transform, t and f denote time and frequency, respectively, v denotes frequency offset, τ denotes time delay, and x (t) is the conjugate of x (t);
the two-dimensional radial Gaussian kernel function is expressed in a rectangular coordinate system as follows:
wherein σ (ψ) controls the expansion of the radial gaussian kernel function in the direction of the radial angle ψ, referred to as an expansion function; psi is the included angle between the radial direction and the horizontal direction;
the two-dimensional radial Gaussian kernel function is expressed in a polar coordinate system as follows:
further, the feature extraction module automatically extracts image features by using a residual network in a convolutional neural network, and designs the network as h (x) ═ f (x) + x, where x denotes a network input, and h (x) denotes an output after passing through the network, and learns a residual function f (x) ═ h (x) — x, and if f (x) ═ 0, an identity map h (x) ═ x is formed.
Further, the quantizing the converted received signal according to the distribution characteristics of the constellation points in the MQAM constellation diagram, and determining the optimal estimation value of the transmitted signal specifically includes:
calculating corresponding M constellation points with R constellation points with different amplitudes according to MQAM signals with different points, wherein each amplitude is A from small to large1,A2,…,Ar,…,ARI.e. the M constellation points are distributed in R with radius A1,A2,…,Ar,…,AROn the concentric circles of (1), the number of constellation points on each circle is m1,m2,…,mr,…,mRFor the MQAM constellation diagram, assuming that the initial phase is 0, the ith constellation point on the r-th circle can be represented as
Wherein ir is 1,2, …, mr，
For a given antenna/is calculated
Representing the estimated phase of the transmitted symbol, the internal optimization problem in SM systems can be equated with
Wherein 0 is not less than thetal≤2π，θlWhich represents the phase of the received symbol,
further, root ofEstimating the transmitted symbols according to the phase of the received symbols, the estimating of the transmit antennas using maximum likelihood estimation comprising: using formulas
Where A is the signal amplitude, the corresponding transmitted symbol is calculated
Further, the maximum likelihood optimal estimation of the active antenna index includes the steps of: corresponding transmission symbols are calculated
Substituting into ML optimal detection formula, ML search of active antenna index is carried out, namely
Wherein
The invention has the advantages of
The signal tuning device not only effectively solves the problem that the signal is interfered by noise or other useless signals in the transmission process, but also can increase the useful signals, makes up the defect that the signals are weakened in the transmission process, solves the problem of daily communication of people and brings convenience to people; two time-frequency distributions are simultaneously applied to represent signals by two-dimensional images, and the difference between different modulation signals is described from two aspects; the convolutional neural network is utilized to automatically extract the image characteristics of the two time-frequency distribution graphs, the problem that the characteristics need to be manually designed in the traditional modulation classification method is solved, and the characteristics of the two time-frequency distribution graphs are fused by using a multi-mode fusion model so as to further improve the accuracy of signal tuning. Meanwhile, the design and calculation of the optimal kernel function are completed through training set signals, and the optimal kernel function is an optimal value based on data and is beneficial to target classification and identification; the invention provides a searching method and an optimizing process of an optimal kernel function; although the search time of the optimal kernel function is longer, the search time is longer only in the training process, once the training is finished, the search calculation of the optimal kernel function is not needed in the testing and application processes, and therefore the real-time requirement of target classification and identification is not influenced. The invention designs two isolated links of the feature extraction algorithm and the classifier, and realizes organic combination through the optimization process of the optimal kernel function, so that the feature value acquired by the feature extraction algorithm is beneficial to the design of the classifier, and the accuracy of the target recognition system is effectively improved. According to the characteristics of the QAM constellation diagram, the phase of the modulation symbol is used for judgment, the search of a modulation symbol space in an ML joint detection method is avoided, the complexity is greatly reduced, and the method is applied to a base station and reduces the expense of the base station. The method not only approaches the ML performance, but also has lower complexity, and the method carries out windowing processing in the time-frequency domain, thereby preserving effective signals while denoising and improving the practical application effect.
Drawings
Fig. 1 is a schematic diagram of a spatial modulation MQAM base station based on phase decision according to a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
fig. 1 shows a spatial modulation MQAM base station based on phase decision, which includes a data receiving module, a data transmitting module, a filtering and amplifying module, a spatial modulation MQAM module, a data preprocessing module, and a data tuning and processing module, where the data transmitting module and the data receiving module transmit or receive data through multiple channels and multiple addresses; the data receiving module receives an external communication signal, transmits the external communication signal to the data filtering and amplifying module for filtering and amplifying, and then transmits the external communication signal to the data preprocessing module for data preprocessing steps including denoising and windowing; the above-mentionedThe data preprocessing module transmits the data to a spatial modulation MQAM module for spatial QAM modulation, and the spatial modulation MQAM module converts the detection problem of symbols sent by a spatial modulation system into a quantization demodulation problem; secondly, quantizing the converted received signals according to the distribution characteristics of the constellation points in the MQAM constellation diagram, estimating the transmitted symbols according to the phase of the quantized signals, and performing maximum likelihood optimal estimation on the activated antenna indexes; finally, a signal detection result of the spatial modulation system is obtained and transmitted to the data sending module for sending; the conversion of the detection problem of symbols sent by the spatial modulation system into the quantization demodulation problem specifically includes: in a spatial modulation system, the ML maximum likelihood estimation can be expressed as 2 nested search problems, i.e. first searching for the transmitted symbol s and then the antenna index l, can be expressed as
Wherein,
indicates the index of the transmit antenna and,
denotes a transmitted symbol, y denotes a received signal vector, hlRepresenting the ith column of the channel matrix. For internal optimization problems
That is, under the condition of giving the activated antenna index l, the transmitted symbol s is solved, and for the MQAM modulated signal, the internal optimization problem is still equivalent to that of the MQAM modulated signal
Wherein,
the detection problem of symbols sent by an SM system can be converted into a quantization demodulation problem;
the data tuning processing module comprises a preprocessing module, a feature extraction module and a training tuning module; the preprocessing module is used for preprocessing the received signals including windowing, and converting the signals into a smooth pseudo Winger-Ville time frequency distribution graph and an optimal time frequency distribution graph by utilizing the smooth pseudo Winger-Ville distribution and the optimal time frequency distribution graph; the characteristic extraction module adopts a convolutional neural network to automatically extract the characteristics of a smooth pseudo Winger-Ville time-frequency distribution graph and an optimal time-frequency distribution graph, and carries out characteristic fusion quantitative evaluation on the characteristics of the two time-frequency images by utilizing a multi-mode fusion model, and the characteristic extraction module specifically comprises the following steps: carrying out time-frequency analysis processing on the characteristics of the collected smooth pseudo Winger-Ville time-frequency distribution diagram and the optimal time-frequency distribution diagram, and calculating a fuzzy function and a fuzzy function mean value of a training set signal; selecting a two-dimensional radial Gaussian kernel function as an optimal kernel function based on classified optimal time frequency distribution; calculating an optimal kernel function through iterative search; performing time-frequency transformation on the training set signals under the optimal kernel function, and extracting characteristic values for classification; designing a classifier of the training set signals, and classifying the characteristic values of the training set signals; the training tuning module takes the fused features as the input of the multilayer perceptron, firstly trains the model by using a training set, and then completes the modulation of the signal by using the trained model.
Preferably, the model of the data receiving module receiving the external communication signal is:
where r (t) and s (t) denote the received signal and the transmitted signal, respectively, α denotes the channel gain, ω0And theta0Representing frequency offset and phase offset, and n (t) gaussian noise, where when s (t) is ASK, FSK and PSK modulation, the expression:
when s (t) is QAM modulated, two orthogonal carriers cos (2 π f) are used for QAM signalct) and sin (2 π f)ct), the expression is:
Preferably, the smoothed pseudo-Winger-Ville distribution suppresses cross terms by performing windowing interception in the time delay and frequency offset directions, respectively, and the expression is:
SPWVDx(t,f)＝∫∫h(τ)g(v)x(t-v+τ/2)x*(t-v-τ/2)e-j2πfτdvdτ
where H (τ) and g (v) are two real even window functions, x (t) ═ r (t) + jH [ r (t) ], H [ · ] denotes hilbert transform, t and f denote time and frequency, respectively, v denotes frequency offset, τ denotes time delay, and x (t) is the conjugate of x (t);
the two-dimensional radial Gaussian kernel function is expressed in a rectangular coordinate system as follows:
wherein σ (ψ) controls the expansion of the radial gaussian kernel function in the direction of the radial angle ψ, referred to as an expansion function; psi is the included angle between the radial direction and the horizontal direction;
the two-dimensional radial Gaussian kernel function is expressed in a polar coordinate system as follows:
preferably, the feature extraction module automatically extracts image features using a residual network in a convolutional neural network, and designs the network to be h (x) ═ f (x) + x, where x denotes a network input, and h (x) denotes an output after passing through the network, and learns a residual function f (x) ═ h (x) — x, and if f (x) ═ 0, an identity map h (x) ═ x is formed.
Preferably, the quantizing the converted received signal according to the distribution characteristics of the constellation points in the MQAM constellation diagram, and the determining the optimal estimation value of the transmitted signal specifically includes:
calculating corresponding M constellation points with R constellation points with different amplitudes according to MQAM signals with different points, wherein each amplitude is A from small to large1,A2,…,Ar,…,ARI.e. the M constellation points are distributed in R with radius A1,A2,…,Ar,…,AROn the concentric circles of (1), the number of constellation points on each circle is m1,m2,…,mr,…,mRFor the MQAM constellation diagram, assuming that the initial phase is 0, the ith constellation point on the r-th circle can be represented as
Wherein ir is 1,2, …, mr，
For a given antenna/is calculated
Representing the estimated phase of the transmitted symbol, the internal optimization problem in SM systems can be equated with
Wherein 0 is not less than thetal≤2π，θlWhich represents the phase of the received symbol,
preferably, the estimating of the transmission symbol according to the phase magnitude of the received symbol, and the estimating of the transmission antenna using the maximum likelihood estimation includes: using formulas
Where A is the signal amplitude, the corresponding transmitted symbol is calculated
Preferably, the performing the maximum likelihood optimal estimation on the activated antenna index includes the steps of: corresponding transmission symbols are calculated
Substituting into ML optimal detection formula, ML search of active antenna index is carried out, namely
Wherein
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (4)
1. A spatial modulation MQAM base station based on phase decision is characterized by comprising a data receiving module, a data transmitting module, a filtering amplification module, a spatial modulation MQAM module, a data preprocessing module and a data tuning processing module, wherein the data transmitting module and the data receiving module transmit or receive data through a plurality of channels and a plurality of addresses; the data receiving module receives an external communication signal, transmits the external communication signal to the data filtering and amplifying module for filtering and amplifying, and then transmits the external communication signal to the data preprocessing module for data preprocessing steps including denoising and windowing; the data preprocessing module transmits the data to a spatial modulation MQAM module for spatial QAM modulation, and the space isThe modulation MQAM module converts the detection problem of symbols sent by a space modulation system into a quantization demodulation problem; secondly, quantizing the converted received signals according to the distribution characteristics of the constellation points in the MQAM constellation diagram, estimating the transmitted symbols according to the phase of the quantized signals, and performing maximum likelihood optimal estimation on the activated antenna indexes; finally, a signal detection result of the spatial modulation system is obtained and transmitted to the data sending module for sending; the conversion of the detection problem of symbols sent by the spatial modulation system into the quantization demodulation problem specifically includes: in a spatial modulation system, the ML maximum likelihood estimation can be expressed as 2 nested search problems, i.e. first searching for the transmitted symbol s and then the antenna index l, can be expressed as
Wherein,
indicates the index of the transmit antenna and,
denotes a transmitted symbol, y denotes a received signal vector, hlColumn l representing the channel matrix, for the internal optimization problem
That is, under the condition of giving the activated antenna index l, the transmitted symbol s is solved, and for the MQAM modulated signal, the internal optimization problem is still equivalent to that of the MQAM modulated signal
Wherein,
the detection problem of symbols sent by an SM system can be converted into a quantization demodulation problem;
the data tuning processing module comprises a preprocessing module, a feature extraction module and a training tuning module; the preprocessing module is used for preprocessing the received signals including windowing, and converting the signals into a smooth pseudo Winger-Ville time frequency distribution graph and an optimal time frequency distribution graph by utilizing the smooth pseudo Winger-Ville distribution and the optimal time frequency distribution graph; the characteristic extraction module adopts a convolutional neural network to automatically extract the characteristics of a smooth pseudo Winger-Ville time-frequency distribution graph and an optimal time-frequency distribution graph, and carries out characteristic fusion quantitative evaluation on the characteristics of the two time-frequency images by utilizing a multi-mode fusion model, and the characteristic extraction module specifically comprises the following steps: carrying out time-frequency analysis processing on the characteristics of the collected smooth pseudo Winger-Ville time-frequency distribution diagram and the optimal time-frequency distribution diagram, and calculating a fuzzy function and a fuzzy function mean value of a training set signal; selecting a two-dimensional radial Gaussian kernel function as an optimal kernel function based on classified optimal time frequency distribution; calculating an optimal kernel function through iterative search; performing time-frequency transformation on the training set signals under the optimal kernel function, and extracting characteristic values for classification; designing a classifier of the training set signals, and classifying the characteristic values of the training set signals; the training tuning module takes the fused features as the input of the multilayer perceptron, firstly trains a model by using a training set, and then completes the modulation of signals by using the trained model;
the data receiving module receives an external communication signal model as follows:
where r (t) and s (t) denote the received signal and the transmitted signal, respectively, α denotes the channel gain, ω0And theta0Representing frequency offset and phase offset, and n (t) gaussian noise, where when s (t) is ASK, FSK and PSK modulation, the expression:
when s (t) is QAM modulated, two orthogonal carriers cos (2 π f) are used for QAM signalct) and sin (2 π f)ct), the expression is:
the smooth pseudo Winger-Ville distribution suppresses cross terms by windowing and intercepting in the time delay direction and the frequency offset direction respectively, and the expression is as follows:
SPWVDx(t,f)＝∫∫h(τ)g(v)x(t-v+τ/2)x*(t-v-τ/2)e-j2πfτdvdτ
where h (τ) and g (v) are two real even window functions, x (t) ═ r (t) + jH [ r (t)]，H[·]Representing the Hilbert transform, t and f representing time and frequency, respectively, v representing frequency offset, τ representing time delay, x*(t) is the conjugate of x (t);
the two-dimensional radial Gaussian kernel function is expressed in a rectangular coordinate system as follows:
wherein σ (ψ) controls the expansion of the radial gaussian kernel function in the direction of the radial angle ψ, referred to as an expansion function; psi is the included angle between the radial direction and the horizontal direction;
the two-dimensional radial Gaussian kernel function is expressed in a polar coordinate system as follows:
the characteristic extraction module utilizes a residual error network in a convolutional neural network to automatically extract image characteristics, the network is designed to be H (x) ═ f (x) + x, x represents network input, H (x) represents output after the network is passed, and an identity mapping H (x) ═ x is formed by learning a residual error function f (x) ═ H (x) — x as long as f (x) ═ 0.
2. The phase decision-based spatial modulation MQAM base station according to claim 1, wherein the quantizing the transformed received signals according to the distribution characteristics of constellation points in the MQAM constellation diagram, and the determining the optimal estimation value of the transmitted signals specifically comprises:
calculating corresponding M constellation points with R constellation points with different amplitudes according to MQAM signals with different points, wherein each amplitude is A from small to large1,A2,…,Ar,…,ARI.e. the M constellation points are distributed in R with radius A1,A2,…,Ar,…,AROn the concentric circles of (1), the number of constellation points on each circle is m1,m2,…,mr,…,mRFor the MQAM constellation diagram, assuming that the initial phase is 0, the ith constellation point on the r-th circle can be represented as
Wherein ir is 1,2, …, mr，
For a given antenna/is calculated
Representing the estimated phase of the transmitted symbol, the internal optimization problem in SM systems can be equated with
Wherein 0 is not less than thetal≤2π，θlWhich represents the phase of the received symbol,
3. the phase decision based spatial modulation MQAM base station of claim 1, wherein the estimation of the transmitted symbols is performed according to the phase magnitude of the received symbols, and the estimation of the transmit antennas using maximum likelihood estimation comprises: using formulas
Where A is the signal amplitude, the corresponding transmitted symbol is calculated
4. The phase decision based spatial modulation MQAM base station according to claim 3, wherein the maximum likelihood optimal estimation of the active antenna index comprises the steps of: corresponding transmission symbols are calculated
Substituting into ML optimal detection formula, ML search of active antenna index is carried out, namely
Wherein
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810534422.5A CN108881096B (en) | 2018-05-30 | 2018-05-30 | Spatial modulation MQAM base station based on phase decision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810534422.5A CN108881096B (en) | 2018-05-30 | 2018-05-30 | Spatial modulation MQAM base station based on phase decision |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108881096A CN108881096A (en) | 2018-11-23 |
| CN108881096B true CN108881096B (en) | 2021-04-02 |
Family
ID=64335375
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810534422.5A Active CN108881096B (en) | 2018-05-30 | 2018-05-30 | Spatial modulation MQAM base station based on phase decision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108881096B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110942100B (en) * | 2019-11-29 | 2023-04-07 | 山东大学 | Working method of spatial modulation system based on deep denoising neural network |
| CN110855591B (en) * | 2019-12-09 | 2021-10-29 | 山东大学 | QAM and PSK signal intra-class modulation classification method based on convolutional neural network structure |
| CN115378785B (en) * | 2021-05-18 | 2023-08-22 | 中国科学院上海高等研究院 | Detection demodulation method and system, storage medium and terminal |
| CN113411106B (en) * | 2021-05-31 | 2023-03-24 | 海南大学 | Power distribution method based on deep learning in safe space modulation system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1901383A (en) * | 2005-07-19 | 2007-01-24 | 三洋电机株式会社 | Noise canceller |
| CN103051458A (en) * | 2012-12-27 | 2013-04-17 | 南通大学 | Method for obtaining steady radio frequency fingerprints from BPSK (Binary Phase Shift Keying) signal |
| CN105354592A (en) * | 2015-10-22 | 2016-02-24 | 上海无线电设备研究所 | Classification based optimal time-frequency distribution design and target identification method |
| CN107528807A (en) * | 2016-12-13 | 2017-12-29 | 重庆邮电大学 | A kind of spatial modulation MQAM signal detecting methods based on phase judgement |
-
2018
- 2018-05-30 CN CN201810534422.5A patent/CN108881096B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1901383A (en) * | 2005-07-19 | 2007-01-24 | 三洋电机株式会社 | Noise canceller |
| CN103051458A (en) * | 2012-12-27 | 2013-04-17 | 南通大学 | Method for obtaining steady radio frequency fingerprints from BPSK (Binary Phase Shift Keying) signal |
| CN105354592A (en) * | 2015-10-22 | 2016-02-24 | 上海无线电设备研究所 | Classification based optimal time-frequency distribution design and target identification method |
| CN107528807A (en) * | 2016-12-13 | 2017-12-29 | 重庆邮电大学 | A kind of spatial modulation MQAM signal detecting methods based on phase judgement |
Non-Patent Citations (2)
| Title |
|---|
| Textual sentiment analysis via three different attention convolutional neural networks and cross一modality consistent regression;Zufan Zhang;《ScienceDirect》;20180131;第275卷;全文 * |
| 深度学习下的雷达辐射源信号分类识别;赵敏;《中国优秀硕士学位论文全文数据库 信息科技辑》;20180415;正文第4-69页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108881096A (en) | 2018-11-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108881096B (en) | Spatial modulation MQAM base station based on phase decision | |
| CN108234370B (en) | Communication signal modulation mode identification method based on convolutional neural network | |
| CN108764077B (en) | Digital signal modulation classification method based on convolutional neural network | |
| CN101420758B (en) | Method for resisting simulated main customer attack in cognitive radio | |
| CN101764786B (en) | MQAM signal recognition method based on clustering algorithm | |
| CN110636466A (en) | WiFi indoor positioning system based on channel state information under machine learning | |
| CN113259288B (en) | Underwater sound modulation mode identification method based on feature fusion and lightweight hybrid model | |
| CN107332800A (en) | Modulate accurate wireless transmission scheme in a kind of direction selected based on random sub carrier wave | |
| CN114338301B (en) | Channel estimation method of RIS auxiliary millimeter wave system based on compressed sensing | |
| CN110460359A (en) | A kind of mimo system signal acceptance method neural network based | |
| CN111027614B (en) | Noise enhanced radio frequency fingerprint identification method and device | |
| CN111313943A (en) | Three-dimensional positioning method and device under deep learning assisted large-scale antenna array | |
| Liu et al. | Jamming recognition based on feature fusion and convolutional neural network | |
| CN114757224A (en) | Specific radiation source identification method based on continuous learning and combined feature extraction | |
| CN103973621B (en) | A kind of parameter identification method of binary Continuous Phase frequency keying modulated signal | |
| Wang et al. | Underwater communication signal recognition using sequence convolutional network | |
| Jiang et al. | Modulation recognition of underwater acoustic communication signals based on data transfer | |
| Cun et al. | Specific emitter identification based on eye diagram | |
| CN103152790B (en) | Two-graded fusion Modulation Identification method based on dependency sub-clustering | |
| CN113514796B (en) | Passive positioning method, system and medium | |
| CN107528807B (en) | Spatial modulation MQAM signal detection method based on phase decision | |
| CN104467995A (en) | Blind master user detection and transmission level recognition method based on hidden Markov models | |
| CN112232120B (en) | Radar radiation source signal classification system and method based on software radio | |
| CN108832982A (en) | A kind of time-frequency distributions modulator approach for base station signal | |
| Huang et al. | Identification of radio transmitters fingerprint based on curve fitting |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210315 Address after: 130012 room 701, building 9, Nanhu Xuefu classic community, Chaoyang District, Changchun City, Jilin Province Applicant after: JILIN JLU COMMUNICATION DESIGN INSTITUTE Co.,Ltd. Address before: 401121 6-1, building 19, 8 Hongye Road, Yubei District, Chongqing Applicant before: Mao Shuchun |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |