[go: up one dir, main page]

CN112187375A - 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 PDF

Info

Publication number
CN112187375A
CN112187375A CN202011012684.9A CN202011012684A CN112187375A CN 112187375 A CN112187375 A CN 112187375A CN 202011012684 A CN202011012684 A CN 202011012684A CN 112187375 A CN112187375 A CN 112187375A
Authority
CN
China
Prior art keywords
signal
mppam
modulation
dimensional
signals
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011012684.9A
Other languages
Chinese (zh)
Other versions
CN112187375B (en
Inventor
齐洁
孙海信
简轶
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xiamen University
Original Assignee
Xiamen University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xiamen University filed Critical Xiamen University
Priority to CN202011012684.9A priority Critical patent/CN112187375B/en
Publication of CN112187375A publication Critical patent/CN112187375A/en
Application granted granted Critical
Publication of CN112187375B publication Critical patent/CN112187375B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B14/00—Transmission systems not characterised by the medium used for transmission
    • H04B14/02—Transmission systems not characterised by the medium used for transmission characterised by the use of pulse modulation
    • H04B14/023—Transmission systems not characterised by the medium used for transmission characterised by the use of pulse modulation using pulse amplitude modulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B14/00—Transmission systems not characterised by the medium used for transmission
    • H04B14/02—Transmission systems not characterised by the medium used for transmission characterised by the use of pulse modulation
    • H04B14/026—Transmission systems not characterised by the medium used for transmission characterised by the use of pulse modulation using pulse time characteristics modulation, e.g. width, position, interval
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B14/00—Transmission systems not characterised by the medium used for transmission
    • H04B14/02—Transmission systems not characterised by the medium used for transmission characterised by the use of pulse modulation
    • H04B14/04—Transmission systems not characterised by the medium used for transmission characterised by the use of pulse modulation using pulse code modulation
    • H04B14/046—Systems or methods for reducing noise or bandwidth

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Digital Transmission Methods That Use Modulated Carrier Waves (AREA)

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

MPPAM modulation-based three-dimensional space signal modulation and demodulation method and system
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 transmission efficiency, 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 error rate and the transmission efficiency as indexes, so that the system has lower error rate and higher transmission efficiency.
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 ratio of 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 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 transmission efficiency is set to a low weight value. Setting a high-weight bit error rate and a low-weight transmission efficiency value, training for a certain number of times through machine learning, and when the target function reaches a set threshold or the number of times reaches an upper limit, taking the three-dimensional constellation point which is used most recently as the optimal three-dimensional constellation point.
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 transmission efficiency, 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 transmission efficiency, 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 transmission efficiency, 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 is larger than the minimum distance between the point and the middle point of the traditional two-dimensional constellation point, 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 ratio of 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 in the MPPAM signal training set, performing relevant demodulation on the simulated received signal by using the three-dimensional constellation diagram in the three-dimensional constellation diagram sample set, constructing an objective function by using the error rate and the transmission efficiency, 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 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 transmission efficiency 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 signals in the MPPAM signal training set, perform correlation demodulation on the simulated received signals 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 transmission efficiency, 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 error rate and higher transmission efficiency, the MPPAM signals are subjected to multi-dimensional constellation point mapping, the minimum distance between a point and a middle point of a constellation point diagram is larger than the minimum distance between the point and the middle point of a traditional two-dimensional constellation point diagram, and accordingly MPPAM signals received by a receiving end are easier to distinguish and identify, and the 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 transmission efficiency, 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 transmission efficiency, 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 is larger than the minimum distance between the point and the middle point of the traditional two-dimensional constellation point, 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)

1.一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，所述方法包括：1. a three-dimensional spatial signal modulation and demodulation method based on MPPAM modulation, is characterized in that, described method comprises: S1：对信号源发出的不同时间段内的原始信号分别进行模/数变化和串并变化处理，得到所述不同时间段内的数字信号，通过机器学习算法对所述不同时间段内的数字信号分别进行训练，选取每个数字信号最适合的三维星座点图，构成三维星座点图样本集；S1: Perform analog/digital change and serial-to-parallel change processing on the original signals in different time periods sent by the signal source, to obtain digital signals in the different time periods, and use machine learning algorithms to analyze the digital signals in the different time periods. The signals are trained separately, and the most suitable three-dimensional constellation point diagram for each digital signal is selected to form a three-dimensional constellation point diagram sample set; S2：将不同脉冲位置和脉冲幅度的脉冲信号构成训练集，用不同的MPPAM调制信号对所述训练集中的脉冲信号进行调制后，进行信道传输的模拟，基于模拟得到的接收信噪比与信号功率和噪声功率的函数关系，确定最佳MPPAM调制信号，利用所述最佳MPPAM调制信号对所述训练集中的脉冲信号进行调制，得到相应的MPPAM信号，构成MPPAM信号训练集；S2: The pulse signals with different pulse positions and pulse amplitudes are formed into a training set, and after the pulse signals in the training set are modulated with different MPPAM modulation signals, the simulation of channel transmission is performed, and the received signal-to-noise ratio and the signal based on the simulation are obtained. The functional relationship between power and noise power, determine the optimal MPPAM modulation signal, and use the optimal MPPAM modulation signal to modulate the pulse signal in the training set to obtain the corresponding MPPAM signal, which constitutes the MPPAM signal training set; S3：对所述MPPAM信号训练集中的MPPAM信号进行信道传输的模拟，以所述三维星座点图样本集中的三维星座点图对模拟得到的接收信号进行相关解调，以误码率和传输效率构造目标函数，基于机器学习算法进行训练，确定最佳的三维星座点图；S3: Perform channel transmission simulation on the MPPAM signal in the MPPAM signal training set, and perform correlation demodulation on the simulated received signal with the three-dimensional constellation point diagram in the three-dimensional constellation point diagram sample set, and use the bit error rate and transmission efficiency to perform correlation demodulation on the received signal. Construct the objective function, train based on the machine learning algorithm, and determine the best 3D constellation point map; S4：利用所述最佳MPPAM调制信号对所述不同时间段内的数字信号进行调制后，得到具有三维空间特征的信号并发送，接收端接收到具有三维空间特征的信号后，利用所述最佳的三维星座点图对信号进行解调，最终解调出原始信号。S4: After modulating the digital signals in the different time periods with the optimal MPPAM modulation signal, a signal with three-dimensional spatial characteristics is obtained and sent. After receiving the signal with three-dimensional spatial characteristics, the receiving end uses the most The best three-dimensional constellation point diagram is used to demodulate the signal, and finally the original signal is demodulated. 2.根据权利要求1所述的一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，所述步骤S1中，通过机器学习算法对所述不同时间段内的数字信号分别进行训练，具体包括以下步骤：2. a kind of three-dimensional spatial signal modulation and demodulation method based on MPPAM modulation according to claim 1, is characterized in that, in described step S1, by machine learning algorithm, the digital signal in described different time periods is trained respectively , which includes the following steps: 对所述不同时间段内的数字信号的信号类别个数以及出现的概率进行统计；Statistics on the number of signal categories and the probability of occurrence of the digital signals in the different time periods; 基于所述统计结果，通过机器学习算法对所述不同时间段内的数字信号的正交性和实现复杂性进行分析和训练，筛选出每个数字信号最适合的三维星座点的个数和形式；Based on the statistical results, the orthogonality and implementation complexity of the digital signals in the different time periods are analyzed and trained through a machine learning algorithm, and the number and form of the most suitable three-dimensional constellation points for each digital signal are screened out. ; 根据筛选出的个数和形式生成相应的三维星座点图。The corresponding three-dimensional constellation point map is generated according to the number and form selected. 3.根据权利要求1所述的一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，所述步骤S2中，信噪比与信号功率和噪声功率的函数关系基于公式3. a kind of three-dimensional space signal modulation and demodulation method based on MPPAM modulation according to claim 1, is characterized in that, in described step S2, the functional relationship of signal-to-noise ratio and signal power and noise power is based on formula SNR＝E/N0 SNR=E/N 0 其中式中，E表示每比特的数字波形的平均信号能量，N0表示单位频带内的噪声功率的比值。In the formula, E represents the average signal energy of the digital waveform per bit, and N 0 represents the ratio of the noise power in the unit frequency band. 4.根据权利要求1所述的一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，当所述步骤S2中的接收信噪比达到最大时，对应的MPPAM调制信号为最佳MPPAM调制信号。4. a kind of three-dimensional spatial signal modulation and demodulation method based on MPPAM modulation according to claim 1, is characterized in that, when the received signal-to-noise ratio in the described step S2 reaches the maximum, the corresponding MPPAM modulation signal is the best MPPAM modulated signal. 5.根据权利要求1所述的一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，所述步骤S3中的目标函数表示为：5. a kind of three-dimensional space signal modulation and demodulation method based on MPPAM modulation according to claim 1, is characterized in that, the objective function in described step S3 is expressed as: f(x)＝μ1BER-μ2rb＝μ1ne/n-μ2n/Tf(x)=μ 1 BER-μ 2 rb=μ 1 n e /n-μ 2 n/T 其中式中，μ1表示误码率的权重系数，μ2表示传输速率的权重系数，μ1和μ2可根据需求进行调整，BER是误码率，rb是比特传输速率，ne表示传输错误的比特数，n表示总传输比特数，T表示传输总时长，令f(x)达到最小，此时对应的三维星座点图为所述最佳的三维星座点图。In the formula, μ 1 represents the weight coefficient of the bit error rate, μ 2 represents the weight coefficient of the transmission rate, μ 1 and μ 2 can be adjusted according to requirements, BER is the bit error rate, rb is the bit transmission rate, and ne represents the transmission rate The number of erroneous bits, n represents the total number of transmitted bits, and T represents the total transmission duration, so that f(x) is minimized, and the corresponding three-dimensional constellation point map is the best three-dimensional constellation point map. 6.根据权利要求5所述的一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，所述目标函数中误码率的权重系数设置为高权重值，传输效率的权重系数设置为低权重值。6. a kind of three-dimensional space signal modulation and demodulation method based on MPPAM modulation according to claim 5, is characterized in that, the weight coefficient of bit error rate in the described objective function is set to high weight value, and the weight coefficient of transmission efficiency is set to is a low weight value. 7.根据权利要求1所述的一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，所述步骤S2、S3中对信号进行信道传输的模拟，为基于模拟的信道传输函数，其中包括：基于机器学习对信道传输函数进行拟合。7. a kind of three-dimensional space signal modulation and demodulation method based on MPPAM modulation according to claim 1, is characterized in that, in described steps S2, S3, the simulation of channel transmission is carried out to the signal, for the channel transmission function based on simulation, These include: fitting the channel transfer function based on machine learning. 8.根据权利要求7所述的一种基于MPPAM调制的三维空间信号调制解调方法，其特征在于，基于机器学习对信道传输函数进行拟合具体包括：通过对传输信道长时间的监听，以脉冲信号作为测试信号，将模拟得到的脉冲信号响应和实际接收的脉冲信号响应进行比较，以自适应梯度对信道传输函数进行参数修改，并进行多次迭代，得到最接近真实信道的信道传输函数。8. a kind of three-dimensional spatial signal modulation and demodulation method based on MPPAM modulation according to claim 7, is characterized in that, fitting the channel transfer function based on machine learning specifically comprises: by monitoring the transmission channel for a long time, to The pulse signal is used as the test signal, the simulated pulse signal response is compared with the actual received pulse signal response, the parameters of the channel transfer function are modified with the adaptive gradient, and multiple iterations are performed to obtain the channel transfer function closest to the real channel. . 9.一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该计算机程序被计算机处理器执行时实施权利要求1至8中任一项所述的方法。9. A computer-readable storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a computer processor, the method according to any one of claims 1 to 8 is implemented. 10.一种基于MPPAM调制的三维空间信号调制解调系统，其特征在于，所述系统包括：10. A three-dimensional spatial signal modulation and demodulation system based on MPPAM modulation, wherein the system comprises: 三维星座点图样本集确定单元：配置用于对信号源发出的不同时间段内的原始信号分别进行模/数变化和串并变化处理，得到所述不同时间段内的数字信号，通过机器学习算法对所述不同时间段内的数字信号分别进行训练，选取每个数字信号最适合的三维星座点图，构成三维星座点图样本集；Three-dimensional constellation point diagram sample set determination unit: configured to perform analog/digital change and serial-to-parallel change processing on the original signals in different time periods sent by the signal source, to obtain digital signals in the different time periods, through machine learning The algorithm trains the digital signals in the different time periods respectively, and selects the most suitable three-dimensional constellation point diagram for each digital signal to form a three-dimensional constellation point diagram sample set; MPPAM调制信号优选单元：配置用于将不同脉冲位置和脉冲幅度的脉冲信号构成训练集，用不同的MPPAM调制信号对所述训练集中的脉冲信号进行调制后，进行信道传输的模拟，基于模拟得到的接收信噪比与信号功率和噪声功率的函数关系，确定最佳MPPAM调制信号，利用所述最佳MPPAM调制信号对所述训练集中的脉冲信号进行调制，得到相应的MPPAM信号，构成MPPAM信号训练集；MPPAM modulation signal optimization unit: configured to form a training set of pulse signals with different pulse positions and pulse amplitudes, and after modulating the pulse signals in the training set with different MPPAM modulation signals, simulate the channel transmission, and obtain based on the simulation The function relationship between the received signal-to-noise ratio and the signal power and noise power, determine the optimal MPPAM modulation signal, use the optimal MPPAM modulation signal to modulate the pulse signal in the training set, obtain the corresponding MPPAM signal, and form the MPPAM signal Training set; 三维星座点图优选单元：配置用于对所述MPPAM信号训练集中的MPPAM信号进行信道传输的模拟，以所述三维星座点图样本集中的三维星座点图对模拟得到的接收信号进行相关解调，以误码率和传输效率构造目标函数，基于机器学习算法进行训练，确定最佳的三维星座点图；A three-dimensional constellation point diagram optimization unit: configured to perform channel transmission simulation on the MPPAM signal in the MPPAM signal training set, and perform correlation demodulation on the received signal obtained by the simulation with the three-dimensional constellation point diagram in the three-dimensional constellation point diagram sample set , construct an objective function based on bit error rate and transmission efficiency, and conduct training based on machine learning algorithm to determine the best three-dimensional constellation point map; 原始信号调制解调单元：配置用于利用所述最佳MPPAM调制信号对所述不同时间段内的数字信号进行调制后，得到具有三维空间特征的信号并发送，接收端接收到具有三维空间特征的信号后，利用所述最佳的三维星座点图对信号进行解调，最终解调出原始信号。The original signal modulation and demodulation unit: configured to use the optimal MPPAM modulation signal to modulate the digital signals in the different time periods to obtain a signal with three-dimensional spatial characteristics and send it, and the receiving end receives a signal with three-dimensional spatial characteristics. After the signal is obtained, use the best three-dimensional constellation point diagram to demodulate the signal, and finally demodulate the original signal.
CN202011012684.9A 2020-09-23 2020-09-23 A three-dimensional spatial signal modulation and demodulation method and system based on MPPAM modulation Active CN112187375B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011012684.9A CN112187375B (en) 2020-09-23 2020-09-23 A three-dimensional spatial signal modulation and demodulation method and system based on MPPAM modulation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011012684.9A CN112187375B (en) 2020-09-23 2020-09-23 A three-dimensional spatial signal modulation and demodulation method and system based on MPPAM modulation

Publications (2)

Publication Number Publication Date
CN112187375A true CN112187375A (en) 2021-01-05
CN112187375B CN112187375B (en) 2021-10-22

Family

ID=73956075

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011012684.9A Active CN112187375B (en) 2020-09-23 2020-09-23 A three-dimensional spatial signal modulation and demodulation method and system based on MPPAM modulation

Country Status (1)

Country Link
CN (1) CN112187375B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102075487A (en) * 2009-11-25 2011-05-25 清华大学 Multidimensional constellation mapping based coding and modulating method, demodulating and decoding method and system
CN103259760A (en) * 2013-04-04 2013-08-21 王红星 Impulse waveform modulation method based on multi-dimensional constellation diagram
CN104639254A (en) * 2015-01-27 2015-05-20 华中科技大学 Three-dimensional orthogonal frequency-division multiplexing data modulation method and data demodulation method
US9094125B2 (en) * 2012-05-24 2015-07-28 Nec Laboratories America, Inc. Multidimensional coded-modulation for high-speed optical transport over few-mode fibers
CN105122688A (en) * 2013-03-08 2015-12-02 颖飞公司 Optical communication interface using quadrature amplitude modulation
US10135535B2 (en) * 2012-09-11 2018-11-20 Inphi Corporation Optical communication interface utilizing N-dimensional double square quadrature amplitude modulation
CN111130646A (en) * 2019-12-13 2020-05-08 重庆邮电大学 Time delay jitter resistant high-rate MPPM constellation mapping method
CN111431832A (en) * 2020-03-20 2020-07-17 厦门大学 Signal modulation method and system based on multi-dimensional OFDM and MIMO communication system

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102075487A (en) * 2009-11-25 2011-05-25 清华大学 Multidimensional constellation mapping based coding and modulating method, demodulating and decoding method and system
US9094125B2 (en) * 2012-05-24 2015-07-28 Nec Laboratories America, Inc. Multidimensional coded-modulation for high-speed optical transport over few-mode fibers
US10135535B2 (en) * 2012-09-11 2018-11-20 Inphi Corporation Optical communication interface utilizing N-dimensional double square quadrature amplitude modulation
CN105122688A (en) * 2013-03-08 2015-12-02 颖飞公司 Optical communication interface using quadrature amplitude modulation
CN103259760A (en) * 2013-04-04 2013-08-21 王红星 Impulse waveform modulation method based on multi-dimensional constellation diagram
CN104639254A (en) * 2015-01-27 2015-05-20 华中科技大学 Three-dimensional orthogonal frequency-division multiplexing data modulation method and data demodulation method
CN111130646A (en) * 2019-12-13 2020-05-08 重庆邮电大学 Time delay jitter resistant high-rate MPPM constellation mapping method
CN111431832A (en) * 2020-03-20 2020-07-17 厦门大学 Signal modulation method and system based on multi-dimensional OFDM and MIMO communication system

Also Published As

Publication number Publication date
CN112187375B (en) 2021-10-22

Similar Documents

Publication Publication Date Title
Sen et al. Accurate: Constellation based rate estimation in wireless networks.
CN111431832B (en) Signal modulation method and system based on multi-dimensional OFDM and MIMO communication system
CN112508172A (en) Space flight measurement and control adaptive modulation method based on Q learning and SRNN model
CN111865863B (en) An OFDM signal detection method based on RNN neural network
CN107113263B (en) Systems and methods for designing constellation diagrams and uses thereof
CN106533590A (en) Uplink channel quality measurement method based on receiver EVM
CN113971430A (en) Signal detection and model training method, device, equipment and storage medium
CN117652106A (en) Modeling method for visible cluster and non-stationarity in XL-MIMO system
CN106341216A (en) Wireless communication link adaptive method and uplink and downlink adaptive method
CN106850027A (en) Low complex degree generalized space modulates iteration detection method
CN101562464B (en) Method for detecting spherical decode based on depth-first search
CN114257337B (en) Rate Adaptation
CN112187375B (en) A three-dimensional spatial signal modulation and demodulation method and system based on MPPAM modulation
CN102082619B (en) Transmission adaptive method based on double credible evaluations
Zhao et al. Adaptive power allocation for non-coherent FSK in time-varying underwater acoustic communication channels
CN117614501B (en) A cognitive interference integrated beamforming method and electronic device based on deep reinforcement learning
Wang et al. Radio generation using generative adversarial networks with an unrolled design
CN103152790B (en) Two-graded fusion Modulation Identification method based on dependency sub-clustering
CN115642968A (en) Communication interference signal and spectrum cavity joint cognition method, device and medium based on deep learning
Enhos et al. Modeling and optimization of visible light carrierless amplitude and phase modulation links
Nie et al. Optimization of deterministic pilot pattern placement based on quantum genetic algorithm for sparse channel estimation in OFDM systems
Kim et al. Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications
CN115913836B (en) Adaptive pilot frequency method based on deep reinforcement learning
CN120750495A (en) Adaptive modulation coding method and device for time-varying channel
Qasaymeh et al. Intelligent Receiver for Frequency Hopping Signals Using Deep Learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant