CN112187375B - MPPAM modulation-based three-dimensional space signal modulation and demodulation method and system - Google Patents
MPPAM modulation-based three-dimensional space signal modulation and demodulation method and system Download PDFInfo
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Abstract
The invention provides a three-dimensional space signal modulation and demodulation method and a system based on MPPAM modulation, which comprises the steps of carrying out analog/digital and series-parallel conversion on an original signal, carrying out analysis and training according to the obtained signal, selecting an optimal three-dimensional constellation diagram, forming a training set by pulse signals with different pulse positions and pulse amplitudes, carrying out simulation of channel transmission after modulating the pulse signals in the training set by using different MPPAM modulation signals, determining the optimal MPPAM modulation signal based on the function relation between the received signal-to-noise ratio obtained by simulation and the signal power and the noise power, carrying out related demodulation on the received signal obtained by simulation by using the three-dimensional constellation diagram, constructing a target function, carrying out training based on a machine learning algorithm, determining the optimal three-dimensional constellation diagram and demodulating the actual received signal. The problems of low signal transmission rate, high error rate and low bandwidth utilization rate when MPPAM signals are transmitted and received under a complex environment channel are solved, and the modulation complexity is low.
Description
Technical Field
The invention relates to the technical field of digital communication, in particular to a three-dimensional space signal modulation and demodulation method and system based on MPPAM modulation.
Background
With the rapid development of ultra-wideband communication systems, in order to achieve higher transmission rates from the viewpoint of digital modulation, the order of conventional Quadrature Amplitude Modulation (QAM) is selected to be increased from 64 to 256 or even higher. However, the main problem of the conventional two-dimensional mapping scheme is that the higher the mapping order is, the smaller the Minimum Euclidean Distance (MED) is under the same transmission power constraint. This is a natural consequence of the increased number of constellation points under the same transmit power constraint. This disadvantage significantly reduces the robustness of the transmitted signal in the radio channel and therefore places higher signal-to-noise ratio (SNR) requirements on successful signal demodulation by the receiver. Furthermore, from an implementation point of view, conventional high order two dimensional mappers are also subject to more stringent radio frequency constraints than low order mappers, which necessarily increases cost. In contrast, with three-dimensional (3D) mapping techniques, the constellation point arrangement can be extended from a conventional two-dimensional plane to three-dimensional space, which helps achieve a higher system throughput at the same Bit Error Rate (BER) requirement, and an increased degree of freedom in constellation design.
Signal constellations are one of the important components constituting digital communication systems, and among them, the importance of three-dimensional (3D) signal constellations is increasing, and has been widely studied in the fields of wireless communication and optical communication. Some three-dimensional constellations and their theoretical Symbol Error Probabilities (SEPs) are introduced in Additive White Gaussian Noise (AWGN) channels. The four vertices of the regular tetrahedron are taken as the optimal set of the quaternary signal constellation. The typical structure of the 8-element signal set is a regular hexagon, a twisting structure of the 8-element signal set is introduced to increase the minimum Euclidean distance between symbols, and most of the classical three-dimensional signal constellation structures are not researched on the signal form realized by the signal set after being designed.
After various three-dimensional signal constellation structures are creatively designed, designers do not have much research on how to implement communication systems, and generally transmit three-dimensional signals through different time or different center frequencies, that is, the three-dimensional signals are transmitted through one-dimensional signals without mutual interference, so that not only is the signal rate reduced to a certain extent, but also the implementation complexity of the system is relatively high. Under the condition, the MPPAM modulation mode is used for modulating the three-dimensional signal, the M-PAM and the M-PPM are combined to provide good system performance and lower calculation complexity, and the existing three-dimensional signal transmission system is well improved, so that the error rate is lower than that of the traditional three-dimensional signal transmission system.
Disclosure of Invention
The invention provides a three-dimensional space signal modulation and demodulation method and system based on MPPAM modulation, which aim to overcome the defects of the prior art.
In one aspect, the present invention provides a method for modulating and demodulating a three-dimensional spatial signal based on MPPAM modulation, the method comprising the following steps:
s1: respectively carrying out analog/digital change and serial-parallel change processing on original signals sent by a signal source in different time periods to obtain digital signals in different time periods, respectively training the digital signals in the different time periods through a machine learning algorithm, and selecting a most suitable three-dimensional constellation diagram of each digital signal to form a three-dimensional constellation diagram sample set;
s2: forming a training set by pulse signals with different pulse positions and pulse amplitudes, modulating the pulse signals in the training set by using different MPPAM modulation signals, then simulating channel transmission, determining an optimal MPPAM modulation signal based on a functional relation between a received signal-to-noise ratio obtained by simulation and signal power and noise power, and modulating the pulse signals in the training set by using the optimal MPPAM modulation signal to obtain corresponding MPPAM signals to form an MPPAM signal training set;
s3: carrying out channel transmission simulation on MPPAM signals in the MPPAM signal training set, carrying out relevant demodulation on simulated received signals by using a three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, constructing a target function by using an error rate and a bit transmission rate, training based on a machine learning algorithm, and determining an optimal three-dimensional constellation diagram;
s4: and after receiving the signal with the three-dimensional space characteristic, a receiving end demodulates the signal by using the optimal three-dimensional constellation diagram, and finally, the original signal is demodulated.
The method is based on a machine learning algorithm to optimally select the MPPAM modulated modulation signal, selects the most suitable three-dimensional constellation diagram corresponding to different digital signals, uses a machine learning algorithm self-adaptive simulation channel transmission environment, and determines the optimal three-dimensional constellation diagram by taking the bit error rate and the bit transmission rate as indexes, so that the system has lower bit error rate and higher bit transmission rate.
In a specific embodiment, the training of the digital signals in the different time periods is performed by a machine learning algorithm, which specifically includes the following steps:
counting the signal category number and the occurrence probability of the digital signals in different time periods; analyzing and training the orthogonality and the realization complexity of the digital signals in different time periods through a machine learning algorithm based on the statistical result, and screening out the number and the form of the most suitable three-dimensional constellation points of each digital signal; and generating a corresponding three-dimensional constellation diagram according to the screened number and form. The screened three-dimensional constellation diagram is most suitable for the signal generated after the original signal is processed, and the complexity of hardware implementation is reduced.
In a specific embodiment, in the step S2, the functional relationship between the signal-to-noise ratio and the signal power and the noise power is based on a formula
SNR＝E/N0
Wherein E represents the average signal energy of the digital waveform per bit, N0Representing the noise power within a unit frequency band.
In a specific embodiment, when the received signal-to-noise ratio in step S2 reaches the maximum, the corresponding MPPAM modulated signal is the optimal MPPAM modulated signal.
In a specific embodiment, the objective function in step S3 is expressed as:
f(x)＝μ1BER-μ2rb＝μ1ne/n-μ2n/T
wherein in the formula, mu1Weight coefficient, mu, representing bit error rate2Weight coefficient, mu, representing the bit transmission rate1And mu2Can be adjusted according to requirements, wherein BER is bit error rate, rb is bit transmission rate, and neAnd (3) representing the number of transmission error bits, n representing the total number of transmission bits, T representing the total transmission time length, and enabling f (x) to reach the minimum, wherein the corresponding three-dimensional constellation point diagram is the optimal three-dimensional constellation point diagram.
In a preferred embodiment, the weight coefficient of the bit error rate in the objective function is set to a high weight value, and the weight coefficient of the bit transfer rate is set to a low weight value. Setting a high-weight bit error rate and a low-weight bit transmission rate value, and training for a certain number of times through machine learning, wherein when the target function reaches a set threshold value or the number of times reaches an upper limit, a three-dimensional constellation point diagram used at the last time can be regarded as an optimal three-dimensional constellation point diagram.
In a specific embodiment, the simulation of the channel transmission of the signal in steps S2 and S3 is a channel transmission function based on the simulation, and includes: the channel transfer function is fitted based on machine learning.
In a preferred embodiment, fitting the channel transfer function based on machine learning specifically includes: by monitoring the transmission channel for a long time, taking a pulse signal as a test signal, comparing the pulse signal response obtained by simulation with the actually received pulse signal response, modifying the parameters of the channel transmission function by using the adaptive gradient, and performing multiple iterations to obtain the channel transmission function closest to the real channel.
According to a second aspect of the present invention, a computer-readable storage medium is proposed, on which a computer program is stored, which computer program, when being executed by a computer processor, carries out the above-mentioned method.
According to a third aspect of the present invention, a three-dimensional spatial signal modulation and demodulation system based on MPPAM modulation is provided, the system comprising:
a three-dimensional constellation point diagram sample set determination unit: the method comprises the steps that the method is configured to be used for carrying out analog/digital change and serial-parallel change processing on original signals sent by a signal source in different time periods respectively to obtain digital signals in the different time periods, the digital signals in the different time periods are trained through a machine learning algorithm respectively, and a three-dimensional constellation diagram most suitable for each digital signal is selected to form a three-dimensional constellation diagram sample set;
MPPAM modulation signal optimization unit: configuring pulse signals with different pulse positions and pulse amplitudes to form a training set, modulating the pulse signals in the training set by using different MPPAM modulation signals, then simulating channel transmission, determining an optimal MPPAM modulation signal based on a functional relation between a received signal-to-noise ratio obtained by simulation and signal power and noise power, modulating the pulse signals in the training set by using the optimal MPPAM modulation signal to obtain corresponding MPPAM signals, and forming an MPPAM signal training set;
the three-dimensional constellation diagram optimization unit: configuring simulation for carrying out channel transmission on MPPAM signals in the MPPAM signal training set, carrying out relevant demodulation on simulated received signals by using a three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, constructing an objective function by using an error rate and a bit transmission rate, training based on a machine learning algorithm, and determining an optimal three-dimensional constellation diagram;
original signal modulation-demodulation unit: and the optimal MPPAM modulation signal is configured and used for modulating the digital signals in different time periods to obtain signals with three-dimensional space characteristics and sending the signals, and after a receiving end receives the signals with the three-dimensional space characteristics, the optimal three-dimensional constellation diagram is used for demodulating the signals to finally demodulate the original signals.
The invention respectively carries out analog/digital change and series-parallel change processing on original signals in different time periods sent by a signal source to obtain digital signals in different time periods, respectively trains the digital signals in different time periods through a machine learning algorithm, selects a three-dimensional constellation diagram most suitable for each digital signal to form a three-dimensional constellation diagram sample set, forms pulse signals with different pulse positions and pulse amplitudes into a training set, carries out simulation of channel transmission after modulating the pulse signals in the training set by using different MPPAM modulation signals, determines an optimal MPPAM modulation signal based on a function relation of a received signal-to-noise ratio obtained by simulation and signal power and noise power, modulates the pulse signals in the training set by using the optimal MPPAM modulation signal to obtain corresponding MPPAM signals to form an MPPAM signal training set, carrying out channel transmission simulation on MPPAM signals in the MPPAM signal training set, carrying out relevant demodulation on received signals obtained through simulation through a three-dimensional constellation diagram in a three-dimensional constellation diagram sample set, constructing an objective function through an error rate and a bit transmission rate, training based on a machine learning algorithm, determining an optimal three-dimensional constellation diagram, modulating digital signals in different time periods through the optimal MPPAM modulation signals, obtaining signals with three-dimensional space characteristics and sending the signals, demodulating the signals through the optimal three-dimensional constellation diagram after receiving the signals with the three-dimensional space characteristics by a receiving end, and finally demodulating original signals. The MPPAM signal mapping method can enable the MPPAM signal mapping signal to reach the highest signal-to-noise ratio in the current transmission environment, and utilizes the simulated channel transmission function to distribute the pulse position and the amplitude of the MPPAM signal, so that the MPPAM signal of the distribution principle under the current channel can reach the lowest error rate and higher bit transmission rate, and the MPPAM signal is mapped by the multi-dimensional constellation points, so that the minimum distance between the point and the middle point of the constellation point diagram is larger than the minimum distance between the point and the middle point of the traditional two-dimensional constellation point diagram, thereby the MPPAM signal received by a receiving end is easier to distinguish and identify, and the error rate is lower. Moreover, from the hardware perspective, the modulation complexity of the method is relatively low.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the invention. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a flowchart of a method for modulating and demodulating a three-dimensional spatial signal based on MPPAM modulation according to an embodiment of the present invention;
fig. 2 is a block diagram of a three-dimensional spatial signal modem system based on MPPAM modulation according to an embodiment of the present invention;
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flowchart of a method for modulating and demodulating a three-dimensional spatial signal based on MPPAM modulation according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
s101: the method comprises the steps of respectively carrying out analog/digital change and serial-parallel change processing on original signals sent by a signal source in different time periods to obtain digital signals in different time periods, respectively training the digital signals in the different time periods through a machine learning algorithm, and selecting a most suitable three-dimensional constellation point diagram of each digital signal to form a three-dimensional constellation point diagram sample set.
In a specific embodiment, the training of the digital signals in the different time periods is performed by a machine learning algorithm, which specifically includes the following steps: counting the signal category number and the occurrence probability of the digital signals in different time periods; analyzing and training the orthogonality and the realization complexity of the digital signals in different time periods through a machine learning algorithm based on the statistical result, and screening out the number and the form of the most suitable three-dimensional constellation points of each digital signal; and generating a corresponding three-dimensional constellation diagram according to the screened number and form.
S102: the method comprises the steps of forming a training set by pulse signals with different pulse positions and pulse amplitudes, modulating the pulse signals in the training set by using different MPPAM modulation signals, simulating channel transmission, determining an optimal MPPAM modulation signal based on a functional relation between a received signal-to-noise ratio obtained by simulation and signal power and noise power, modulating the pulse signals in the training set by using the optimal MPPAM modulation signal, obtaining corresponding MPPAM signals, and forming the MPPAM signal training set.
In a specific embodiment, the functional relationship between the signal-to-noise ratio and the signal power and the noise power is based on a formula
SNR＝E/N0
Wherein E represents the average signal energy of the digital waveform per bit, N0Representing the noise power within a unit frequency band.
In a specific embodiment, when the received signal-to-noise ratio reaches the maximum, the corresponding MPPAM modulated signal is the optimal MPPAM modulated signal.
In a preferred embodiment, a machine learning algorithm is used, and training of pulse positions and pulse amplitudes of pulse signals is performed through an environmental channel model, and the most suitable pulse position and pulse amplitude under the current environment are selected, wherein the generation method of the environmental channel model specifically comprises the following steps: by monitoring the transmission channel for a long time, taking a pulse signal as a test signal, comparing the pulse signal response obtained by simulation with the actually received pulse signal response, modifying the parameters of the channel transmission function by using the adaptive gradient, and performing multiple iterations to obtain the channel transmission function closest to the real channel.
In a preferred embodiment, the signal modulated in S102 is subjected to analog transmission by using the channel transmission function obtained in the above process, an objective function is constructed by using the signal-to-noise ratio, and a threshold and an upper limit of iteration times of the objective function are set, when the signal-to-noise ratio reaches a maximum, or the objective function reaches the threshold, or the iteration times reaches the upper limit, the last selected pulse position and pulse amplitude are directly selected to be the optimal pulse position and pulse amplitude in the transmission environment, and the optimal MPPAM modulation signal is determined by the optimal pulse position and pulse amplitude.
S103: and performing channel transmission simulation on the MPPAM signals in the MPPAM signal training set, performing relevant demodulation on the simulated received signals by using the three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, constructing an objective function by using the bit error rate and the bit transmission rate, performing training based on a machine learning algorithm, and determining the optimal three-dimensional constellation diagram.
In a specific embodiment, the objective function in S103 is represented as:
f(x)＝μ1BER-μ2rb＝μ1ne/n-μ2n/T
wherein in the formula, mu1Weight coefficient, mu, representing bit error rate2Weight coefficient, mu, representing the bit transmission rate1And mu2Can be adjusted according to requirements, wherein BER is bit error rate, rb is bit transmission rate, and neAnd (3) representing the number of transmission error bits, n representing the total number of transmission bits, T representing the total transmission time length, and enabling f (x) to reach the minimum, wherein the corresponding three-dimensional constellation point diagram is the optimal three-dimensional constellation point diagram.
In a specific embodiment, the bit error rate weighting factor in the above objective function is set to a high weighting value, and the bit transmission rate weighting factor is set to a low weighting value.
In a preferred embodiment, a machine learning algorithm is used, a three-dimensional constellation point diagram in a three-dimensional constellation point diagram sample set is trained through an environment channel model, and a most suitable three-dimensional constellation point diagram in the three-dimensional constellation point diagram sample set under the current environment is selected, wherein the method for generating the environment channel model specifically comprises the following steps: the transmission function is fitted by a machine learning algorithm by monitoring a transmission channel for a long time and taking a pulse signal as a test signal, the channel transmission function comprises Doppler effect, time delay and attenuation parameters, the simulated pulse signal response is compared with the actual received pulse signal response, the transmission function is subjected to parameter modification by adaptive gradient, and finally the fitting of the transmission function is completed.
In a preferred embodiment, channel transmission simulation is performed on the mppmam signals in the mppmam signal training set according to the obtained transmission function to obtain simulated received signals, the simulated received signals are subjected to correlated demodulation by using the three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, the target function is as described above, the number of training iterations is set, training is performed for a certain number of times through machine learning, and when a set threshold is reached or the number of training times reaches an upper limit, the latest three-dimensional constellation diagram is regarded as the optimal three-dimensional constellation diagram.
S104: and after receiving the signal with the three-dimensional space characteristic, a receiving end demodulates the signal by using the optimal three-dimensional constellation diagram, and finally, the original signal is demodulated.
In a specific embodiment, the modulated signal is transmitted, and when a receiving end receives the signal, the maximum likelihood estimation demodulation is performed on the signal by using the optimal constellation diagram, and the specific steps include: firstly, coherent demodulation is carried out on received three-dimensional space characteristic signals, the demodulated signals are analyzed, mapping is carried out through a set three-dimensional constellation point diagram, a three-dimensional constellation point diagram of a receiving end is constructed, then estimation analysis is carried out on constellation points of the mapped three-dimensional constellation point diagram according to maximum likelihood estimation, so that the constellation points are accurately matched with the constellation points on a standard three-dimensional constellation point diagram, then inverse mapping and inverse modulation are carried out, wherein MPPAM modulation, serial-parallel conversion and analog/digital conversion are included, and therefore original signals are obtained.
Fig. 2 is a block diagram of a three-dimensional spatial signal modem system based on MPPAM modulation according to an embodiment of the present invention. The system comprises a three-dimensional constellation diagram sample set determining unit 201, an MPPAM modulation signal optimizing unit 202, a three-dimensional constellation diagram optimizing unit 203 and an original signal modulation and demodulation unit 204.
In a specific embodiment, the three-dimensional constellation point diagram sample set determining unit 201 is configured to perform analog/digital change and serial/parallel change processing on original signals sent by a signal source in different time periods respectively to obtain digital signals in different time periods, train the digital signals in different time periods respectively through a machine learning algorithm, and select a most suitable three-dimensional constellation point diagram for each digital signal to form a three-dimensional constellation point diagram sample set. The MPPAM modulation signal optimization unit 202 is configured to configure pulse signals with different pulse positions and pulse amplitudes to form a training set, perform simulation of channel transmission after modulating the pulse signals in the training set with different MPPAM modulation signals, determine an optimal MPPAM modulation signal based on a functional relationship between a received signal-to-noise ratio obtained by the simulation and signal power and noise power, and modulate the pulse signals in the training set with the optimal MPPAM modulation signal to obtain corresponding MPPAM signals, thereby forming an MPPAM signal training set. The three-dimensional constellation diagram optimizing unit 203 is configured to perform simulation of channel transmission on the MPPAM signal in the MPPAM signal training set, perform correlation demodulation on a simulated received signal by using the three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, construct an objective function by using the bit error rate and the bit transmission rate, perform training based on a machine learning algorithm, and determine an optimal three-dimensional constellation diagram. The original signal modulation and demodulation unit 204 is configured to modulate the digital signals in different time periods by using the optimal MPPAM modulation signal, obtain and transmit a signal with a three-dimensional spatial feature, and after receiving the signal with the three-dimensional spatial feature, a receiving end demodulates the signal by using the optimal three-dimensional constellation diagram, and finally demodulates the original signal. Through the combined action of the three-dimensional constellation point diagram sample set determining unit 201, the MPPAM modulation signal optimizing unit 202, the three-dimensional constellation point diagram optimizing unit 203 and the original signal modulation and demodulation unit 204, MPPAM signal mapping signals reach the highest signal-to-noise ratio in the current transmission environment, MPPAM signals used in the current channel can reach the lowest bit error rate and higher bit transmission rate, the MPPAM signals are subjected to multi-dimensional constellation point mapping, the minimum distance between a point and a middle point in a constellation point diagram is larger than the minimum distance between the point and the middle point in a traditional two-dimensional constellation point diagram, and accordingly MPPAM signals received by a receiving end are easier to distinguish and identify, and the bit error rate is lower.
Embodiments of the present invention also relate to a computer-readable storage medium having stored thereon a computer program which, when executed by a computer processor, implements the method above. The computer program comprises program code for performing the method illustrated in the flow chart. It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable medium or any combination of the two.
The invention respectively carries out analog/digital change and series-parallel change processing on original signals in different time periods sent by a signal source to obtain digital signals in different time periods, respectively trains the digital signals in different time periods through a machine learning algorithm, selects a three-dimensional constellation diagram most suitable for each digital signal to form a three-dimensional constellation diagram sample set, forms pulse signals with different pulse positions and pulse amplitudes into a training set, carries out simulation of channel transmission after modulating the pulse signals in the training set by using different MPPAM modulation signals, determines an optimal MPPAM modulation signal based on a function relation of a received signal-to-noise ratio obtained by simulation and signal power and noise power, modulates the pulse signals in the training set by using the optimal MPPAM modulation signal to obtain corresponding MPPAM signals to form an MPPAM signal training set, carrying out channel transmission simulation on MPPAM signals in the MPPAM signal training set, carrying out relevant demodulation on received signals obtained through simulation through a three-dimensional constellation diagram in a three-dimensional constellation diagram sample set, constructing an objective function through an error rate and a bit transmission rate, training based on a machine learning algorithm, determining an optimal three-dimensional constellation diagram, modulating digital signals in different time periods through the optimal MPPAM modulation signals, obtaining signals with three-dimensional space characteristics and sending the signals, demodulating the signals through the optimal three-dimensional constellation diagram after receiving the signals with the three-dimensional space characteristics by a receiving end, and finally demodulating original signals. The MPPAM signal mapping method can enable the MPPAM signal mapping signal to reach the highest signal-to-noise ratio in the current transmission environment, and utilizes the simulated channel transmission function to distribute the pulse position and the amplitude of the MPPAM signal, so that the MPPAM signal of the distribution principle under the current channel can reach the lowest error rate and higher bit transmission rate, and the MPPAM signal is mapped by the multi-dimensional constellation points, so that the minimum distance between the point and the middle point of the constellation point diagram is larger than the minimum distance between the point and the middle point of the traditional two-dimensional constellation point diagram, thereby the MPPAM signal received by a receiving end is easier to distinguish and identify, and the error rate is lower.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A three-dimensional space signal modulation and demodulation method based on MPPAM modulation is characterized by comprising the following steps:
s1: respectively carrying out analog/digital change and serial-parallel change processing on original signals sent by a signal source in different time periods to obtain digital signals in different time periods, respectively training the digital signals in the different time periods through a machine learning algorithm, and selecting a most suitable three-dimensional constellation diagram of each digital signal to form a three-dimensional constellation diagram sample set;
s2: forming a training set by pulse signals with different pulse positions and pulse amplitudes, modulating the pulse signals in the training set by using different MPPAM modulation signals, then simulating channel transmission, determining an optimal MPPAM modulation signal based on a functional relation between a received signal-to-noise ratio obtained by simulation and signal power and noise power, and modulating the pulse signals in the training set by using the optimal MPPAM modulation signal to obtain corresponding MPPAM signals to form an MPPAM signal training set;
s3: carrying out channel transmission simulation on MPPAM signals in the MPPAM signal training set, carrying out relevant demodulation on simulated received signals by using a three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, constructing a target function by using an error rate and a bit transmission rate, training based on a machine learning algorithm, and determining an optimal three-dimensional constellation diagram;
s4: and after receiving the signal with the three-dimensional space characteristic, a receiving end demodulates the signal by using the optimal three-dimensional constellation diagram, and finally, the original signal is demodulated.
2. The method as claimed in claim 1, wherein in step S1, the training of the digital signals in the different time periods is performed by a machine learning algorithm, and the method specifically includes the following steps:
counting the signal category number and the occurrence probability of the digital signals in different time periods;
analyzing and training the orthogonality and the realization complexity of the digital signals in different time periods through a machine learning algorithm based on a statistical result, and screening out the number and the form of the most suitable three-dimensional constellation points of each digital signal;
and generating a corresponding three-dimensional constellation diagram according to the screened number and form.
3. The method as claimed in claim 1, wherein the functional relationship between the signal-to-noise ratio and the signal power and the noise power in step S2 is based on a formula
SNR＝E/N0
Wherein E represents the average signal energy of the digital waveform per bit, N0Representing the noise power within a unit frequency band.
4. The method as claimed in claim 1, wherein when the received signal-to-noise ratio in step S2 is maximized, the MPPAM modulated signal is the optimum MPPAM modulated signal.
5. The method as claimed in claim 1, wherein the objective function in step S3 is expressed as:
f(x)＝μ1BER-μ2rb＝μ1ne/n-μ2n/T
wherein in the formula, mu1Weight system for representing bit error rateNumber, mu2Weight coefficient, mu, representing the bit transmission rate1And mu2Can be adjusted according to requirements, wherein BER is bit error rate, rb is bit transmission rate, and neAnd (3) representing the number of transmission error bits, n representing the total number of transmission bits, T representing the total transmission time length, and enabling f (x) to reach the minimum, wherein the corresponding three-dimensional constellation point diagram is the optimal three-dimensional constellation point diagram.
6. The MPPAM modulation-based three-dimensional space signal modulation and demodulation method as claimed in claim 5, wherein the weight coefficient of the bit error rate in the objective function is set as a high weight value, and the weight coefficient of the bit transmission rate is set as a low weight value.
7. The method as claimed in claim 1, wherein the simulation of channel transmission of signals in steps S2 and S3 is a channel transmission function based on simulation, and comprises: the channel transfer function is fitted based on machine learning.
8. The method as claimed in claim 7, wherein fitting the channel transfer function based on machine learning specifically comprises: by monitoring the transmission channel for a long time, taking a pulse signal as a test signal, comparing the pulse signal response obtained by simulation with the actually received pulse signal response, modifying the parameters of the channel transmission function by using the adaptive gradient, and performing multiple iterations to obtain the channel transmission function closest to the real channel.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a computer processor, carries out the method of any one of claims 1 to 8.
10. A system for modulating and demodulating a three-dimensional space signal based on MPPAM modulation, the system comprising:
a three-dimensional constellation point diagram sample set determination unit: the method comprises the steps that the method is configured to be used for carrying out analog/digital change and serial-parallel change processing on original signals sent by a signal source in different time periods respectively to obtain digital signals in the different time periods, the digital signals in the different time periods are trained through a machine learning algorithm respectively, and a three-dimensional constellation diagram most suitable for each digital signal is selected to form a three-dimensional constellation diagram sample set;
MPPAM modulation signal optimization unit: configuring pulse signals with different pulse positions and pulse amplitudes to form a training set, modulating the pulse signals in the training set by using different MPPAM modulation signals, then simulating channel transmission, determining an optimal MPPAM modulation signal based on a functional relation between a received signal-to-noise ratio obtained by simulation and signal power and noise power, modulating the pulse signals in the training set by using the optimal MPPAM modulation signal to obtain corresponding MPPAM signals, and forming an MPPAM signal training set;
the three-dimensional constellation diagram optimization unit: configuring simulation for carrying out channel transmission on MPPAM signals in the MPPAM signal training set, carrying out relevant demodulation on simulated received signals by using a three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, constructing an objective function by using an error rate and a bit transmission rate, training based on a machine learning algorithm, and determining an optimal three-dimensional constellation diagram;
original signal modulation-demodulation unit: and the optimal MPPAM modulation signal is configured and used for modulating the digital signals in different time periods to obtain signals with three-dimensional space characteristics and sending the signals, and after a receiving end receives the signals with the three-dimensional space characteristics, the optimal three-dimensional constellation diagram is used for demodulating the signals to finally demodulate the original signals.
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