CN108881096A - A kind of base station spatial modulation MQAM based on phase judgement - Google Patents
A kind of base station spatial modulation MQAM based on phase judgement Download PDFInfo
- Publication number
- CN108881096A CN108881096A CN201810534422.5A CN201810534422A CN108881096A CN 108881096 A CN108881096 A CN 108881096A CN 201810534422 A CN201810534422 A CN 201810534422A CN 108881096 A CN108881096 A CN 108881096A
- Authority
- CN
- China
- Prior art keywords
- module
- signal
- data
- mqam
- symbol
- 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
Links
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
A kind of base station spatial modulation MQAM based on phase judgement is claimed in the present invention; it includes data reception module, data transmission blocks, filter and amplification module, spatial modulation MQAM module, data preprocessing module and data reconciliation processing module, and the data transmission blocks and data reception module send or receive data by multiple channels and multiple addresses；The data reception module receives external signal of communication, and is transferred to data filtering amplification module and is filtered enhanced processing, be then transferred to data preprocessing module carry out including denoising, the data prediction step including adding window；The data preprocessing module is transferred to spatial modulation MQAM module and carries out space QAM modulation, obtains the signal detecting result of spatial modulation system, and be transferred to data transmission blocks and sent；The present invention can be improved the modulation accuracy of communication base station equipment and reduce computation complexity.
Description
Technical field
The invention belongs to communication base station technical fields, particularly belong to a kind of spatial modulation MQAM base based on phase judgement
It stands.
Background technique
Base station is to be fixed on a local high power multichannel two-way radio transmitter.Base station sub-system (BSS) is
In mobile communication system with the most direct element of wireless cellular network relationship.Base station is main in entire mobile network
Play relaying action.It is connected between base station and base station using wireless channel, is responsible for wireless transmission, reception and wireless resource management.And
Connect between master base station and mobile switching centre (MSC) frequently with wire channel, realize mobile subscriber between or mobile subscriber with
Communication connection between fixed user.In daily life, our common communication base stations are generally radio communication base station.Base station
As the relay station of wireless communication, play an important role in signals transmission.Since signal is easy to be done by other signals
It disturbs, tuning modulation channel is insufficient, Modulation recognition inaccuracy.
Space-modulation technique is signal detection, modulation technique as the research of extensive MIMO technology is risen in recent years
The even channel estimation technique correlation criterion of all largely continuing to use MIMO, although system model and principle are roughly the same,
Space-modulation technique improves to some extent after all, thus continues to use the correlation technique in MIMO technology and lack spatial modulation own characteristic and category
The advantages of property, more space-modulation techniques itself and characteristic are still to be studied.The research tendency of detection method is rendered as how passing through
Linguistic term reduces method computation complexity, improves detection performance.Therefore, in the mimo system of spatial modulation, to detection side
Method is studied, by studying and improving so that the accuracy in detection of signal detecting method has certain guarantee, while method stream
It is very significant that journey, calculating process are again uncomplicated.The principle of maximal possibility estimation (ML) is to search for all transmission symbols
Number possibility, find the combination of most suitable antenna serial number and modulation symbol, therefore can obtain closest to bit error rate performance, claim
On be best performance detection method.But this method search target is excessive, it is sufficiently complex to implement step, extensive
Practical application is difficult in antenna system.The characteristics of present invention is by research MQAM planisphere, reduces the complexity of symbol search,
Difficult point is to estimate the possible constellation point of different amplitudes.Therefore, the invention proposes a kind of skies based on phase judgement
Between modulate the base station MQAM, the computation complexity of receiving end can be effectively reduced, and as the increase performance of modulation points is got over
It is good.The computation complexity of ML method is 6NrNtM, the complexity of method proposed by the present invention are (6Nr+2+5R)Nt.Base of the present invention
It is provided with signal modulation process module, power divider on standing, effectively solves the problems, such as power distribution and channel distribution, tuning.
Summary of the invention
Present invention seek to address that the above problem of the prior art.It is multiple to propose a kind of raising effectiveness reduction calculating
The base station spatial modulation MQAM based on phase judgement of miscellaneous degree.
Technical scheme is as follows：
A kind of base station spatial modulation MQAM based on phase judgement comprising data reception module, data transmission blocks, filter
Wave amplification module, spatial modulation MQAM module, data preprocessing module and data reconciliation processing module, the data transmission blocks
Data are sent or received by multiple channels and multiple addresses with data reception module；Outside the data reception module receives
Signal of communication, and be transferred to data filtering amplification module and be filtered enhanced processing, be then transferred to data preprocessing module into
Data prediction step of the row including denoising, adding window；The data preprocessing module is transferred to spatial modulation MQAM module
Space QAM modulation is carried out, spatial modulation system is sent the test problems amount of being converted into of symbol by the spatial modulation MQAM module
Dissolve tune problem；Secondly according to the characteristic distributions of constellation point in MQAM planisphere, transformed reception signal is quantified, so
Send according to the phase size of signal after quantization the estimation of symbol afterwards, then optimal to activation antenna index progress maximum likelihood
Estimation；The signal detecting result of spatial modulation system is finally obtained, and is transferred to data transmission blocks and is sent；It is described will be empty
Between modulating system send symbol test problems be converted into quantization demodulation problem be specially：In spatial modulation system, ML is maximum
Possibility predication can be expressed as 2 nested search problems, i.e., first to transmission symbol s search, then search for antenna index l, can
To be expressed asWherein,Indicate transmission antenna index,It indicates to send symbol, y is indicated
Receive signal phasor, hlIndicate the l column of channel matrix.For interior optimization problemIt is i.e. given
Under conditions of activating antenna index l, solves and send symbol s, to MQAM modulated signal, interior optimization problem is still equivalent toWherein,The test problems that SM system sends symbol can be converted to quantization solution
Tune problem；
The data reconciliation processing module includes preprocessing module, characteristic extracting module and training tuner module；It is described pre-
Processing module is used to carry out the signal received the pre-treatment step including adding window, and utilizes Smoothing Pseudo Winger-
Ville distribution and optimum time frequency distribution, convert the signal into Smoothing Pseudo Winger-Ville time frequency distribution map and optimum time frequency point
Butut；The characteristic extracting module automatically extracts Smoothing Pseudo Winger-Ville time frequency distribution map and most using convolutional neural networks
The feature of excellent time frequency distribution map, and two kinds of time-frequency image features are subjected to Fusion Features quantitatively evaluating using multimodality fusion model,
It specifically includes：The frequency division when feature of Smoothing Pseudo Winger-Ville time frequency distribution map and optimum time frequency distribution map to acquisition carries out
Analysis processing, calculates the ambiguity function and ambiguity function mean value of training set signal；Selecting two-dimentional radially Gaussian kernel function is based on dividing
The best kernel function of the optimum time frequency distribution of class；Best kernel function is calculated by iterative search；Training set signal is carried out best
Time-frequency conversion under kernel function, and extract the characteristic value for classification；The classifier of project training collection signal, to training set signal
Characteristic value classify；The trained tuner module is using fused feature as the input of multi-layer perception (MLP), first with instruction
Practice collection and carry out training pattern, the modulation of signal is then completed with trained model.
Further, the signal of communication signal model outside the data reception module reception is：
Wherein r (t) and s (t), which is respectively indicated, receives signal and transmitting signal, and α indicates channel gain, ω0And θ0Indicate frequency
Offset and phase offset, n (t) indicate Gaussian noise, wherein expression formula is when s (t) is that ASK, FSK and PSK are modulated：
AmIndicate modulation amplitude, anIt indicates
Symbol sebolic addressing, TsSymbol period, fcIndicate carrier frequency, fmIndicate modulating frequency, φ0Indicate initial phase, φmIndicate modulation phase
Position, g (t) indicate rectangular pulse；
When s (t) is QAM modulation, since QAM signal uses two orthogonal carrier wave cos (2 π fcAnd sin (2 π f t)cT),
Expression formula is：
anAnd bnRespectively indicate symbol sebolic addressing.
Further, the Smoothing Pseudo Winger-Ville distribution is by the way that in time delay and frequency deviation direction, adding window is cut simultaneously respectively
Suppressing crossterms are fetched, expression formula is：
SPWVDx(t, f)=∫ ∫ h (τ) g (v) x (τ/2 t-v+) x* (τ/2 t-v-) e-j2πfτdvdτ
Wherein h (τ) and g (v) is the even window function of two realities, and x (t)=r (t)+jH [r (t)], H [] indicate Martin Hilb
Spy's transformation, t and f respectively indicate time and frequency, and v indicates frequency deviation, and τ indicates time delay, and x* (t) is the conjugation of x (t)；
The two-dimentional radially Gaussian kernel function is expressed as in rectangular coordinate system：
Wherein, σ (ψ) controls radially Gaussian kernel function in the extension in the direction radial angle ψ, referred to as spread function；ψ be it is radial with
The angle of horizontal direction；
The two-dimentional radially Gaussian kernel function is expressed as in polar coordinate system：
Further, the characteristic extracting module automatically extracts image spy using the residual error network in convolutional neural networks
Sign is H (x)=f (x)+x network design, and x indicates network inputs, and H (x) indicates the output after network, passes through study one
A residual error function f (x)=H (x)-x constitutes identical mapping H (x)=x as long as f (x)=0.
Further, the characteristic distributions according to constellation point in MQAM planisphere carry out transformed reception signal
Quantization judges that the optimal estimation value for sending signal specifically includes：
The constellation point that corresponding M constellation point there are R different amplitudes is calculated according to the MQAM signal of different points, each
Amplitude arranges respectively A from small to large1,A2,…,Ar,…,AR, i.e., it is A that this M constellation point, which is distributed in R radius,1,A2,…,
Ar,…,ARConcentric circles on, number of constellation points on each circle is m1,m2,…,mr,…,mR, for MQAM planisphere, it is assumed that just
Beginning phase is 0, then i-th of constellation point on r-th of circle can be expressed asWherein ir=1,
2,…,mr,For given antenna l, calculateIndicate the phase of transmission symbol estimated,
Interior optimization problem in SM system can be equivalent toWherein 0≤θl≤ 2 π, θlExpression connects
The phase of the symbol received,
Further, send according to the phase size of the symbol received the estimation of symbol, the estimation of transmission antenna
Using maximum likelihood estimate including：Utilize formulaWherein A is signal amplitude, calculates correspondence
Transmission symbol
Further, it includes step that described pair of activation antenna index, which carries out maximum likelihood optimal estimation,：It will calculate
Corresponding transmission symbolIt brings into ML optimal detection formula, carries out the ML search of activation antenna index, that is, haveWherein
Beneficial effects of the present invention
The present invention is not only efficiently solved in signals transmission by signal tuning device by noise or other are useless
The interference of signal, additionally it is possible to increase useful signal, make up the deficiency that signal weakens in the transmission, solve the day normal open of people
Letter problem, the convenience brought；Apply two kinds of time-frequency distributions simultaneously signal showed with two dimensional image, by from
The difference between different modulated signals is described in terms of two；Convolutional neural networks are utilized and automatically extract both time-frequency distributions
The characteristics of image of figure overcomes and the problem of artificial design features is needed to use multimodality fusion model will in conventional modulated classification method
The feature of two kinds of time frequency distribution maps is merged, the accuracy tuned with further promotion signal.Meanwhile passing through training set signal
The design and calculating of best kernel function are completed, which is the optimal value based on data, is conducive to target classification and knowledge
Not；The present invention provides the searching method of best kernel function and searching processes；When the search of the best kernel function in the present invention
Longer between although, only the time is longer in the training process, once completing training, does not need in test and application process
Best kernel function is scanned for calculating, therefore does not influence the requirement of real-time of target classification and identification.The present invention proposes feature
Algorithm and two isolated links of classifier design are taken, is realized by the searching process of best kernel function and is organically combined, so that
The characteristic value that feature extraction algorithm obtains is conducive to the design of classifier, effectively improves the accuracy of target identification system.According to
The characteristic of QAM constellation is judged using the phase of modulation symbol, is avoided in ML associated detecting method to modulation symbol sky
Between search, the low complexity of very big land price is applied in base station, reduces the expense of base station.The present invention is not only proximate to ML's
Performance, and there is lower complexity, the present invention remains effective letter while time-frequency domain progress windowing process, denoising
Number, improve practical application effect.
Detailed description of the invention
Fig. 1 is the base station the spatial modulation MQAM schematic diagram that the present invention provides that preferred embodiment is adjudicated based on phase.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed
Carefully describe.Described embodiment is only a part of the embodiments of the present invention.
The present invention solve above-mentioned technical problem technical solution be：
It is as shown in Figure 1 a kind of base station spatial modulation MQAM based on phase judgement comprising data reception module, data
Sending module, filter and amplification module, spatial modulation MQAM module, data preprocessing module and data reconciliation processing module, it is described
Data transmission blocks and data reception module send or receive data by multiple channels and multiple addresses；The data reception
Signal of communication outside block reception, and be transferred to data filtering amplification module and be filtered enhanced processing, it is then transferred to data
Preprocessing module carries out the data prediction step including denoising, adding window；The data preprocessing module is transferred to space
It modulates MQAM module and carries out space QAM modulation, spatial modulation system is sent the detection of symbol by the spatial modulation MQAM module
Problem is converted into quantization demodulation problem；Secondly according to the characteristic distributions of constellation point in MQAM planisphere, transformed reception is believed
Number quantified, then send according to the phase size of signal after quantization the estimation of symbol, then to activation antenna index into
Row maximum likelihood optimal estimation；Finally obtain the signal detecting result of spatial modulation system, and be transferred to data transmission blocks into
Row is sent；The test problems that spatial modulation system is sent symbol are converted into quantization demodulation problem：In space
In modulating system, ML maximal possibility estimation can be expressed as 2 nested search problems, i.e., first to transmission symbol s search, then
Antenna index l is searched for, can be expressed asWherein,Indicate transmission antenna index,
It indicates to send symbol, y indicates to receive signal phasor, hlIndicate the l column of channel matrix.For interior optimization problemUnder conditions of i.e. given activation antenna index l, solves and send symbol s, to MQAM modulated signal,
Interior optimization problem is still equivalent toWherein,SM system can be sent into symbol
Test problems are converted into quantization demodulation problem；
The data reconciliation processing module includes preprocessing module, characteristic extracting module and training tuner module；It is described pre-
Processing module is used to carry out the signal received the pre-treatment step including adding window, and utilizes Smoothing Pseudo Winger-
Ville distribution and optimum time frequency distribution, convert the signal into Smoothing Pseudo Winger-Ville time frequency distribution map and optimum time frequency point
Butut；The characteristic extracting module automatically extracts Smoothing Pseudo Winger-Ville time frequency distribution map and most using convolutional neural networks
The feature of excellent time frequency distribution map, and two kinds of time-frequency image features are subjected to Fusion Features quantitatively evaluating using multimodality fusion model,
It specifically includes：The frequency division when feature of Smoothing Pseudo Winger-Ville time frequency distribution map and optimum time frequency distribution map to acquisition carries out
Analysis processing, calculates the ambiguity function and ambiguity function mean value of training set signal；Selecting two-dimentional radially Gaussian kernel function is based on dividing
The best kernel function of the optimum time frequency distribution of class；Best kernel function is calculated by iterative search；Training set signal is carried out best
Time-frequency conversion under kernel function, and extract the characteristic value for classification；The classifier of project training collection signal, to training set signal
Characteristic value classify；The trained tuner module is using fused feature as the input of multi-layer perception (MLP), first with instruction
Practice collection and carry out training pattern, the modulation of signal is then completed with trained model.
Preferably, the signal of communication signal model outside the data reception module reception is：
Wherein r (t) and s (t), which is respectively indicated, receives signal and transmitting signal, and α indicates channel gain, ω0And θ0Indicate frequency
Offset and phase offset, n (t) indicate Gaussian noise, wherein expression formula is when s (t) is that ASK, FSK and PSK are modulated：
AmIndicate modulation amplitude, anIt indicates
Symbol sebolic addressing, TsSymbol period, fcIndicate carrier frequency, fmIndicate modulating frequency, φ0Indicate initial phase, φmIndicate modulation phase
Position, g (t) indicate rectangular pulse；
When s (t) is QAM modulation, since QAM signal uses two orthogonal carrier wave cos (2 π fcAnd sin (2 π f t)cT),
Expression formula is：
anAnd bnRespectively indicate symbol sebolic addressing.
Preferably, the Smoothing Pseudo Winger-Ville distribution passes through respectively in the adding window interception simultaneously of time delay and frequency deviation direction
Carry out suppressing crossterms, expression formula is：
SPWVDx(t, f)=∫ ∫ h (τ) g (v) x (τ/2 t-v+) x* (τ/2 t-v-) e-j2πfτdvdτ
Wherein h (τ) and g (v) is the even window function of two realities, and x (t)=r (t)+jH [r (t)], H [] indicate Martin Hilb
Spy's transformation, t and f respectively indicate time and frequency, and v indicates frequency deviation, and τ indicates time delay, and x* (t) is the conjugation of x (t)；
The two-dimentional radially Gaussian kernel function is expressed as in rectangular coordinate system：
Wherein, σ (ψ) controls radially Gaussian kernel function in the extension in the direction radial angle ψ, referred to as spread function；ψ be it is radial with
The angle of horizontal direction；
The two-dimentional radially Gaussian kernel function is expressed as in polar coordinate system：
Preferably, the characteristic extracting module automatically extracts image spy using the residual error network in convolutional neural networks
Sign is H (x)=f (x)+x network design, and x indicates network inputs, and H (x) indicates the output after network, passes through study one
A residual error function f (x)=H (x)-x constitutes identical mapping H (x)=x as long as f (x)=0.
Preferably, the characteristic distributions according to constellation point in MQAM planisphere, to the transformed reception signal amount of progress
Change, judges that the optimal estimation value for sending signal specifically includes：
The constellation point that corresponding M constellation point there are R different amplitudes is calculated according to the MQAM signal of different points, each
Amplitude arranges respectively A from small to large1,A2,…,Ar,…,AR, i.e., it is A that this M constellation point, which is distributed in R radius,1,A2,…,
Ar,…,ARConcentric circles on, number of constellation points on each circle is m1,m2,…,mr,…,mR, for MQAM planisphere, it is assumed that just
Beginning phase is 0, then i-th of constellation point on r-th of circle can be expressed asWherein ir=1,
2,…,mr,For given antenna l, calculateIndicate the phase of transmission symbol estimated,
Interior optimization problem in SM system can be equivalent toWherein 0≤θl≤ 2 π, θlExpression connects
The phase of the symbol received,
Preferably, send according to the phase size of the symbol received the estimation of symbol, the estimation of transmission antenna is adopted
With maximum likelihood estimate including：Utilize formulaWherein A is signal amplitude, calculates correspondence
Transmission symbol
Preferably, it includes step that described pair of activation antenna index, which carries out maximum likelihood optimal estimation,：It will calculate pair
The transmission symbol answeredIt brings into ML optimal detection formula, carries out the ML search of activation antenna index, that is, haveWherein
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (7)
1. a kind of base station spatial modulation MQAM based on phase judgement, which is characterized in that sent including data reception module, data
Module, filter and amplification module, spatial modulation MQAM module, data preprocessing module and data reconciliation processing module, the data
Sending module and data reception module send or receive data by multiple channels and multiple addresses；The data reception module connects
Signal of communication outside receiving, and be transferred to data filtering amplification module and be filtered enhanced processing, it is then transferred to data and locates in advance
Reason module carries out the data prediction step including denoising, adding window；The data preprocessing module is transferred to spatial modulation
MQAM module carries out space QAM modulation, and spatial modulation system is sent the test problems of symbol by the spatial modulation MQAM module
It is converted into quantization demodulation problem；Secondly according to the characteristic distributions of constellation point in MQAM planisphere, to transformed reception signal into
Then row quantization send according to the phase size of signal after quantization the estimation of symbol, then carries out most to activation antenna index
Maximum-likelihood optimal estimation；The signal detecting result of spatial modulation system is finally obtained, and is transferred to data transmission blocks and is sent out
It send；The test problems that spatial modulation system is sent symbol are converted into quantization demodulation problem：In spatial modulation system
In system, ML maximal possibility estimation can be expressed as 2 nested search problems, i.e., first to transmission symbol s search, then to day clue
Draw l search, can be expressed asWherein,Indicate transmission antenna index,It indicates to send symbol
Number, y indicates to receive signal phasor, hlIndicate the l column of channel matrix.For interior optimization problem
Under conditions of i.e. given activation antenna index l, solves and send symbol s, to MQAM modulated signal, interior optimization problem is still of equal value
InWherein,Quantization can be converted by the test problems that SM system sends symbol
Demodulation problem；
The data reconciliation processing module includes preprocessing module, characteristic extracting module and training tuner module；The pretreatment
Module is used to carry out the signal received the pre-treatment step including adding window, and utilizes Smoothing Pseudo Winger-Ville points
Cloth and optimum time frequency distribution, convert the signal into Smoothing Pseudo Winger-Ville time frequency distribution map and optimum time frequency distribution map；Institute
It states characteristic extracting module and automatically extracts Smoothing Pseudo Winger-Ville time frequency distribution map and optimum time frequency using convolutional neural networks
The feature of distribution map, and two kinds of time-frequency image features are subjected to Fusion Features quantitatively evaluating using multimodality fusion model, it is specific to wrap
It includes：The feature of Smoothing Pseudo Winger-Ville time frequency distribution map and optimum time frequency distribution map to acquisition carries out at time frequency analysis
Reason calculates the ambiguity function and ambiguity function mean value of training set signal；Select two-dimentional radially Gaussian kernel function for based on classification
The best kernel function of optimum time frequency distribution；Best kernel function is calculated by iterative search；Best core letter is carried out to training set signal
Time-frequency conversion under several, and extract the characteristic value for classification；The classifier of project training collection signal, to the spy of training set signal
Value indicative is classified；The trained tuner module is using fused feature as the input of multi-layer perception (MLP), first with training set
Carry out training pattern, the modulation of signal is then completed with trained model.
2. the base station spatial modulation MQAM according to claim 1 based on phase judgement, which is characterized in that the data connect
Receiving the signal of communication signal model outside module reception is：
Wherein r (t) and s (t), which is respectively indicated, receives signal and transmitting signal, and α indicates channel gain, ω0And θ0Indicate frequency shift (FS)
And phase offset, n (t) indicate Gaussian noise, wherein expression formula is when s (t) is that ASK, FSK and PSK are modulated：AmIndicate modulation amplitude, anIndicate symbol sebolic addressing,
TsSymbol period, fcIndicate carrier frequency, fmIndicate modulating frequency, φ0Indicate initial phase, φmIndicate phase modulation, g (t) table
Show rectangular pulse；
When s (t) is QAM modulation, since QAM signal uses two orthogonal carrier wave cos (2 π fcAnd sin (2 π f t)cT), it expresses
Formula is：
anAnd bnRespectively indicate symbol sebolic addressing.
3. the base station spatial modulation MQAM according to claim 1 based on phase judgement, which is characterized in that the Smoothing Pseudo
Winger-Ville distribution is by the way that respectively in the adding window interception simultaneously of time delay and frequency deviation direction come suppressing crossterms, expression formula is：
SPWVDx(t, f)=∫ ∫ h (τ) g (v) x (τ/2 t-v+) x*(t-v-τ/2)e-j2πfτdvdτ
Wherein h (τ) and g (v) is the even window function of two realities, and x (t)=r (t)+jH [r (t)], H [] indicate that Hilbert becomes
It changes, t and f respectively indicate time and frequency, and v indicates frequency deviation, and τ indicates time delay, x*(t) conjugation for being x (t)；
The two-dimentional radially Gaussian kernel function is expressed as in rectangular coordinate system：
Wherein, σ (ψ) controls radially Gaussian kernel function in the extension in the direction radial angle ψ, referred to as spread function；ψ is radial and horizontal
The angle in direction；
The two-dimentional radially Gaussian kernel function is expressed as in polar coordinate system：
4. the base station spatial modulation MQAM according to claim 1 based on phase judgement, which is characterized in that the feature mentions
Modulus block automatically extracts characteristics of image using the residual error network in convolutional neural networks, network design be H (x)=f (x)+
X, x indicate network inputs, and H (x) indicates the output after network, by learning a residual error function f (x)=H (x)-x, only
F (x)=0 is wanted, identical mapping H (x)=x is just constituted.
5. the base station spatial modulation MQAM according to claim 1 based on phase judgement, which is characterized in that the basis
The characteristic distributions of constellation point in MQAM planisphere quantify transformed reception signal, judge that sending the optimal of signal estimates
Evaluation specifically includes：
The constellation point that corresponding M constellation point there are R different amplitudes, each amplitude are calculated according to the MQAM signal of different points
Row is respectively A from small to large1,A2,…,Ar,…,AR, i.e., it is A that this M constellation point, which is distributed in R radius,1,A2,…,Ar,…,AR
Concentric circles on, number of constellation points on each circle is m1,m2,…,mr,…,mR, for MQAM planisphere, it is assumed that initial phase is
0, then i-th of constellation point on r-th of circle can be expressed asWherein ir=1,2 ..., mr,For given antenna l, calculate The phase of transmission symbol estimated is indicated, in SM system
Interior optimization problem can be equivalent toWherein 0≤θl≤ 2 π, θlIndicate the symbol received
Phase,
6. the base station spatial modulation MQAM according to claim 1 based on phase judgement, which is characterized in that according to receiving
The phase size of symbol send the estimation of symbol, the estimation of transmission antenna using maximum likelihood estimate including：Utilize formulaWherein A is signal amplitude, calculates corresponding transmission symbol
7. the base station spatial modulation MQAM according to claim 6 based on phase judgement, which is characterized in that described pair of activation
It includes step that antenna index, which carries out maximum likelihood optimal estimation,：Corresponding transmission symbol will be calculatedBring the optimal inspection of ML into
It surveys in formula, carries out the ML search of activation antenna index, that is, haveWherein
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 true CN108881096A (en) | 2018-11-23 |
| CN108881096B 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) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110855591A (en) * | 2019-12-09 | 2020-02-28 | 山东大学 | QAM and PSK signal intra-class modulation classification method based on convolutional neural network structure |
| CN110942100A (en) * | 2019-11-29 | 2020-03-31 | 山东大学 | Working method of spatial modulation system based on deep denoising neural network |
| CN113411106A (en) * | 2021-05-31 | 2021-09-17 | 海南大学 | Power distribution method based on deep learning in safe space modulation system |
| CN115378785A (en) * | 2021-05-18 | 2022-11-22 | 中国科学院上海高等研究院 | Detection demodulation method and system, storage medium and terminal |
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 |
|---|
| ZUFAN ZHANG: "Textual sentiment analysis via three different attention convolutional neural networks and cross一modality consistent regression", 《SCIENCEDIRECT》 * |
| 赵敏: "深度学习下的雷达辐射源信号分类识别", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110942100A (en) * | 2019-11-29 | 2020-03-31 | 山东大学 | Working method of spatial modulation system based on deep denoising neural network |
| CN110942100B (en) * | 2019-11-29 | 2023-04-07 | 山东大学 | Working method of spatial modulation system based on deep denoising neural network |
| CN110855591A (en) * | 2019-12-09 | 2020-02-28 | 山东大学 | QAM and PSK signal intra-class modulation classification method based on convolutional neural network structure |
| 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 |
| CN115378785A (en) * | 2021-05-18 | 2022-11-22 | 中国科学院上海高等研究院 | Detection demodulation method and system, storage medium and terminal |
| 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 |
| CN113411106A (en) * | 2021-05-31 | 2021-09-17 | 海南大学 | Power distribution method based on deep learning in safe space modulation system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108881096B (en) | 2021-04-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108881096A (en) | A kind of base station spatial modulation MQAM based on phase judgement | |
| CN108764077B (en) | Digital signal modulation classification method based on convolutional neural network | |
| CN101764786B (en) | MQAM signal recognition method based on clustering algorithm | |
| CN107332800A (en) | Modulate accurate wireless transmission scheme in a kind of direction selected based on random sub carrier wave | |
| CN108540419A (en) | A kind of OFDM detection methods of the anti-inter-sub-carrier interference based on deep learning | |
| CN111585922A (en) | Modulation mode identification method based on convolutional neural network | |
| CN106357575A (en) | Multi-parameter jointly-estimated interference type identification method | |
| CN106027438A (en) | Anti-aliasing modulation demodulator for analog-digital hybrid amplitude modulation broadcasting system | |
| CN110460359A (en) | A kind of mimo system signal acceptance method neural network based | |
| Chen et al. | Automatic modulation classification using multi-scale convolutional neural network | |
| CN106230544A (en) | The monitoring identification of a kind of automobile remote-control interference signal and localization method | |
| CN100499611C (en) | Inspection of blank field maximum rear-proving probability in wireless communication system | |
| CN108880650A (en) | A kind of communication base station for signal tuning | |
| CN103152790B (en) | Two-graded fusion Modulation Identification method based on dependency sub-clustering | |
| CN108832982A (en) | A kind of time-frequency distributions modulator approach for base station signal | |
| CN108770082A (en) | Communication base station based on Smoothing Pseudo Winger-Ville distributions and optimum time frequency distribution | |
| CN108242957B (en) | Detection system for identifying OAM light beam topology charge number based on signal crosstalk distribution characteristics | |
| Chen et al. | Unlocking signal processing with image detection: a frequency hopping detection scheme for complex EMI environments using STFT and CenterNet | |
| Ma et al. | Deep learning based cognitive radio modulation parameter estimation | |
| CN104467995A (en) | Blind master user detection and transmission level recognition method based on hidden Markov models | |
| Cun et al. | Specific emitter identification based on eye diagram | |
| CN109347571A (en) | Wireless broadcast communication method and system based on ultrasound | |
| CN110011744A (en) | Port detecting method, system and terminal | |
| CN111917674A (en) | Modulation identification method based on deep learning | |
| CN113242201A (en) | Wireless signal enhanced demodulation method and system based on generation classification network |
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 |
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 |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |