CN111431831B - Multi-dimensional OFDM environment-based adaptive modulation method and system - Google Patents

Multi-dimensional OFDM environment-based adaptive modulation method and system Download PDF

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CN111431831B
CN111431831B CN202010200532.5A CN202010200532A CN111431831B CN 111431831 B CN111431831 B CN 111431831B CN 202010200532 A CN202010200532 A CN 202010200532A CN 111431831 B CN111431831 B CN 111431831B
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CN111431831A (en
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孙海信
周明章
简轶
苗永春
齐洁
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Xiamen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2695—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation with channel estimation, e.g. determination of delay spread, derivative or peak tracking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03159—Arrangements for removing intersymbol interference operating in the frequency domain
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2668—Details of algorithms
    • H04L27/2681—Details of algorithms characterised by constraints
    • H04L27/2688—Resistance to perturbation, e.g. noise, interference or fading
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2691—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation involving interference determination or cancellation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
    • H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
    • H04L27/345—Modifications of the signal space to allow the transmission of additional information
    • H04L27/3461—Modifications of the signal space to allow the transmission of additional information in order to transmit a subchannel
    • H04L27/3483—Modifications of the signal space to allow the transmission of additional information in order to transmit a subchannel using a modulation of the constellation points

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Abstract

The invention provides a multidimensional OFDM environment-based adaptive modulation method, which comprises the steps of collecting noise environments in different scenes to form noise environment models in different scenes; extracting a pseudo noise sequence from a signal received by a receiving end, and reconstructing a signal channel transmission function after performing lower acquisition, Fourier transform and data segmentation processing; respectively carrying out multi-dimensional OFDM modulation on the pseudo noise sequence, mapping the modulated pseudo noise sequence to each subcarrier, transmitting the subcarrier in a noise environment model to obtain a certain number of environment channel transfer function samples, and establishing an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples; and finally, carrying out channel estimation network training on the signal channel transmission function by using a neural network to obtain different types of optimal channel estimation network transmission functions. The invention effectively solves the problems of poor channel estimation precision, more pilot frequency resource occupation, poor constellation point selection and the like in the prior art.

Description

Multi-dimensional OFDM environment-based adaptive modulation method and system
Technical Field
The invention relates to the technical field of wireless communication, in particular to a multidimensional OFDM environment-based adaptive modulation method and system.
Background
Orthogonal Frequency Division multiplexing (ofdm) (orthogonal Frequency Division multiplexing) technology is a well-known multi-carrier modulation technology, and its main principle is: the channel is divided into a plurality of orthogonal sub-channels, high-speed data is converted into parallel low-speed sub-data streams, and the parallel low-speed sub-data streams are modulated to each sub-channel for transmission. The orthogonal signals can be separated by using correlation techniques at the receiving end, which can reduce mutual interference between the sub-channels. The signal bandwidth on each subchannel is less than the associated bandwidth of the channel, so each subchannel can be viewed as flat fading, and intersymbol interference can be eliminated.
In the OFDM system, since the wireless channel has variable characteristics, the performance of the system is greatly affected, and therefore, the design of the receiving end is a critical task. The accuracy of channel estimation is directly related to the performance of the system, and to combat the influence of multipath fading on the system, a channel estimation technique is adopted at the receiving end to improve the accuracy of the transmitted signal.
Channel estimation is a process of estimating model parameters of a certain assumed channel model from received data, and the accuracy of channel estimation directly affects the performance of the whole system. The existing OFDM system expands the vertical dimension of the channel on the original OFDM system, which causes the wireless channel to be more complex than other situations, if the existing conventional Least Square (LS) channel estimation is adopted, the LS algorithm obtains the response H of the channel at the pilot frequency position through certain calculation, and interpolates the response H to obtain a complete channel response value, and performs channel estimation on the channel response value, but the defects of poor estimation precision, more pilot frequency resource occupation, poor constellation point selection and the like exist.
Disclosure of Invention
The invention provides a multidimensional OFDM environment-based adaptive modulation method and system, aiming at solving the defects of poor channel estimation precision, more occupied pilot frequency resources, poor constellation point selection and the like in an LS channel estimation algorithm adopted in the prior art.
In one aspect, the present invention provides a multidimensional OFDM environment-based adaptive modulation method, including the following steps:
s1: collecting noise environments under different scenes to form noise environment models under different scenes;
s2: extracting a pseudo noise sequence from a signal received by a receiving end, and reconstructing a signal channel transmission function after performing lower acquisition, Fourier transform and data segmentation processing;
s3: respectively carrying out multi-dimensional OFDM modulation on the pseudo noise sequence, mapping the modulated pseudo noise sequence to each subcarrier, transmitting the subcarrier in a noise environment model to obtain a certain number of environment channel transfer function samples, and establishing an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples; and;
s4: and based on an environment mixed sample database, performing channel estimation network training on the signal channel transmission function by using a neural network to obtain different types of optimal channel estimation network transmission functions.
Preferably, the expression of the optimal channel estimation network transfer function in step S4 is as follows:
Figure GDA0002980654780000021
wherein F (-) denotes a signal channel transmission network, srIs the signal received by the receiving end. The trained optimal channel estimation network transfer function can effectively transfer useful signals and eliminate interferers in a target environment.
Preferably, the neural network in step S4 has a structure including an input layer, a plurality of connection layers, an activation layer, and an output layer, and the number of neurons in each of the connection layers, the activation layer, and the output layer is larger than the number of neurons in the input layer. The number of neurons of the connection layer, the activation layer and the output layer in the neural network is larger than that of neurons of the input layer, so that signal channels can be fully extracted.
Preferably, the connection layers are connected through a leakage linear rectification unit, and the output layer is output through a hyperbolic function. The selection of the linear rectification function with leakage and the hyperbolic function has better effect than other common activation functions.
Preferably, after the step S4, the method further includes performing type selection of constellation mapping on the transmitting end based on the characteristics of the environmental channel transfer function samples, and performing signal equalization on the receiving end by using a minimum mean square error equalizer to eliminate channel interference. The constellation mapping type is selected based on the environment channel transfer function, so that the mapping efficiency is higher, and the signal-to-noise ratio is better.
Further preferably, the feature extraction includes that the system extracts tap coefficients and delay features of the transmission channel, and the specific extraction mode is expressed as follows:
tap coefficient: n istap＝n(a≥0.1)
Maximum time delay: delaymax＝max(ntap)
Wherein n (a is more than or equal to 0.1) represents the tap position of which the normalized optimal channel amplitude is more than or equal to 0.1, and the position of the tap with the maximum time delay is extracted according to the extracted main taps of the channel and is marked as the maximum time delay.
Preferably, the multidimensional OFDM modulation adopts a three-dimensional MQAM modulation method, and the specific expression is as follows:
Figure GDA0002980654780000031
wherein g (t-mT)s) Is a width of TsA rectangular pulse having an amplitude of 1,
Figure GDA0002980654780000032
is the frequency omegacThe amplitude of the signal at (a) is,
Figure GDA0002980654780000033
is a frequency of 2 omegacThe amplitude of the signal at, here, the level state value at a certain moment, omegacAnd 2 omegacFor the two carrier frequencies it is possible to have,
Figure GDA0002980654780000034
and betamRepresenting the phase value.
Further preferably, the three-dimensional MQAM modulation may also be expressed as a cubic mapping, and the specific mapping coordinates are expressed as:
Figure GDA0002980654780000035
Figure GDA0002980654780000036
Figure GDA0002980654780000037
wherein, Xm，YmAnd ZmRespectively mapped to the X axis, the Y axis and the Z axis of a three-dimensional rectangular coordinate system,
Figure GDA0002980654780000038
and betamRepresenting the phase value. Based on three-dimensional cube mapping, three independent orthogonal carriers are convenient to carry binary bit information to complete parallel transmission.
In a second aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method of the above embodiments.
According to a third aspect of the present invention, there is provided a system for adaptive modulation based on a multidimensional OFDM environment, the system comprising:
noise environment model unit: the method comprises the steps of configuring noise environments for acquiring different scenes to form noise environment models in different scenes;
a receiving end signal processing unit: the method comprises the steps that a pseudo noise sequence is extracted from a signal received by a receiving end, and a signal channel transmission function is reconstructed after lower acquisition, Fourier transform and data segmentation processing;
ambient mix sample unit: the system comprises a configuration unit, a noise environment model, a pseudo noise sequence generation unit, a transmission unit and a data processing unit, wherein the configuration unit is used for respectively carrying out multi-dimensional OFDM modulation on the pseudo noise sequence, mapping the modulated pseudo noise sequence to each subcarrier, transmitting in the noise environment model to obtain a certain number of environment channel transfer function samples, and establishing an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples;
an optimal channel estimation unit: the configuration is used for carrying out channel estimation network training on the signal channel transmission function by utilizing a neural network based on an environment mixed sample database to obtain the optimal channel estimation network transmission functions of different types.
The invention firstly constructs noise environment models under different scenes, extracts a pseudo noise sequence by using a signal received by a receiving end, reconstructs a signal channel transmission function, maps the pseudo noise sequence to each subcarrier by using the pseudo noise sequence and transmits the pseudo noise sequence on the constructed noise environment models to obtain an environment mixed sample database. And finally, based on the environment mixed sample database, utilizing a neural network to carry out continuous estimation network training on the reconstructed signal channel transmission function to obtain the optimal channel estimation network transmission functions of different types. In the process, the transmitting end selects the optimal constellation mapping from the known constellation map for modulation by utilizing the environmental channel transfer function sample, the optimal channel estimation network transmission function is input to the next stage, and the received signal is equalized by utilizing the minimum mean square error equalizer to eliminate the channel interference, thereby effectively solving the problems of poor channel estimation precision, more occupied pilot frequency resources, poor constellation point selection and the like in the prior art.
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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 multidimensional OFDM environment-based adaptive modulation method according to a specific embodiment of the present application;
fig. 2 is a logic framework diagram of a multi-dimensional OFDM environment-based adaptive modulation receiving end according to a specific embodiment of the present application;
FIG. 3 is a schematic diagram of a multi-dimensional OFDM-based neural network channel estimation network structure according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a multi-dimensional higher order OFDM-MIMO communication system in accordance with a specific embodiment of the present application;
FIG. 5 is a schematic diagram of a logical model of three-dimensional MQAM modulation in a specific embodiment of the present application;
FIG. 6 is a block diagram of an adaptive modulation system based on a multidimensional OFDM environment according to the present application;
FIG. 7 is a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
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 adaptive modulation based on a multi-dimensional OFDM environment according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
s101: and collecting noise environments under different scenes to form noise environment models under different scenes.
In a specific embodiment, long-term monitoring and detection are carried out on the transmission environment in different time periods and seasonality through machine learning, and a noise environment model containing different scenes is built according to the characteristics of noise signals of the transmission environment.
S102: and extracting a pseudo noise sequence from the signal received by the receiving end, and reconstructing a signal channel transmission function after performing lower acquisition, Fourier transform and data segmentation processing.
In a specific embodiment, a signal received by a receiving end is subjected to a Pseudo-noise (PN) Sequence extraction process, and the PN Sequence is a Pseudo-noise Sequence that has some statistical properties similar to random noise but can be repeatedly generated and processed unlike a true random signal, and is called a Pseudo-noise Sequence. On the other hand, comprehensive processing such as down-sampling, Fourier transformation, data segmentation and the like is carried out, wherein the down-sampling is carried out for sampling a sample sequence at intervals of several samples, so that the obtained new sequence is the down-sampling of the original sequence, the Fourier transformation is to convert a signal from a time domain into a frequency domain, and finally, a real value is obtained through data imaginary part and real part segmentation, and finally, a channel transmission function is reconstructed, so that the neural network channel estimation is conveniently carried out on the channel transmission function by combining an environment signal transfer function.
S103: and respectively carrying out multi-dimensional OFDM modulation on the pseudo noise sequence, mapping the modulated pseudo noise sequence to each subcarrier, transmitting in a noise environment model to obtain a certain number of environment channel transfer function samples, and establishing an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples.
In a specific embodiment, a PN sequence is generated, is subjected to high-order modulation and is mapped on a carrier, and is transmitted in a target sea area of a noise environment model under different known scenes to obtain a certain number of environment channel transfer function samples, tap coefficients and time delays of the environment channel transfer function samples are extracted to generate a large number of analog channels, and an environment mixed sample database is established to be used for training a channel estimation network.
In the preferred embodiment, the environmental channel transfer function samples are mainly tapped, and the position of the tap with the largest time delay is taken and recorded as the maximum time delay. The transmitting terminal selects a proper constellation mapping from the known constellation map for modulation according to the positions of the main tap and the maximum time delay tap, so that the mapping efficiency is higher, and the signal-to-noise ratio is better.
S104: and based on an environment mixed sample database, performing channel estimation network training on the signal channel transmission function by using a neural network to obtain different types of optimal channel estimation network transmission functions.
In a specific embodiment, the channel estimation network comprises an input layer, a plurality of connection layers, an active layer and an output layer, the number of neurons of the connection layers, the number of neurons of the active layer and the number of neurons of the output layer are all larger than the number of neurons of the input layer, hidden layers among the connection layers are connected through a leakage linear rectifying unit, and the output layer is output through a hyperbolic function. The selection of the leaky linear rectification function and the hyperbolic function is better than other commonly used activation functions (such as logic functions).
Fig. 2 illustrates a logic framework diagram of an adaptive modulation receiving end based on a multi-dimensional OFDM environment according to an embodiment of the present application. As shown in fig. 2, the receiving end uses a deep neural network to perform channel estimation from both pilot frequency and data, and the deep neural network system at the receiving end includes two stages, i.e., an online training stage and an offline application stage. Wherein, the online training is mainly concentrated in step 201, the offline application comprises step 202-:
step 201: firstly, carrying out actual environment signal transmission by using a modulated PN sequence code, extracting the PN sequence code in a receiving signal of a receiving end, demodulating the PN sequence code to a baseband, and solving an actual environment channel transfer function as a sample by using a least square channel estimation algorithm after down-conversion; secondly, extracting the characteristics of the actual environment channel transfer function, and generating a large number of samples simulating the actual environment channel transfer function by using simulation software for the next training.
In a specific embodiment, MATLAB software is used to generate a convolution of a known transmitted signal s (t) through a sample channel h (t), and random noise w (t) is added to form a signal sample h (t) for training. The corresponding label is the corresponding channel in the channel sample set. And inputting the signal sample subjected to the analog interference into a neural network for training, outputting a channel estimated by the network, setting a loss function, and performing calculation and comparison in each iteration until the network converges.
Step 202: and (3) carrying out Fast Fourier Transform (FFT) on a random signal r (T) received by a receiving end, wherein the transformed signal consists of known pilot frequency and actual transmission information, and finally carrying out data segmentation and transformation T.
Step 203: and separating the real part and the imaginary part of the frequency domain receiving signal R (f) to form an R I R.. R I pattern, wherein R represents the symbol real part, I represents the symbol imaginary part, and the real part is input into a neural network for training operation.
Step 204: obtaining the estimated optimal channel estimation network transmission function through the trained network operation and output
Figure GDA0002980654780000071
For the next Minimum Mean Square Error (MMSE) equalizer channel equalization.
In a specific embodiment, the pilot acts in the channel estimation as a Least squares estimation (LS) method, providing a reference for the channel transfer function estimation experienced by the adjacent data. The difference is, however, that in the present invention, the reference channel is not specifically determined for the pilot, but rather the channel transfer function experienced by the pilot and the data as a whole is determined using the network. The optimization problem of the channel estimator is the choice of the present channel estimation network parameters. A particular deep neural network is used to reconstruct the channel transfer function in a mixture of useful signals and interference. Is represented as follows:
Figure GDA0002980654780000072
wherein F (-) denotes a signal channel transmission network, srIs the signal received by the receiving end.
Step 205: and accessing the equalized signal to a high-dimensional constellation mapping demodulation network, thereby demodulating to obtain a target bit sequence.
In a specific embodiment, the actual processing flow of the multidimensional OFDM environment adaptive modulation receiving end in the present application is as follows:
(1) generating PN sequence from the signal received by the receiving end, modulating by multidimensional high-order OFDM and mapping on the carrier, and transmitting in the target sea area under the known noise environment model to obtain a certain amount of environment channel transfer function samples.
(2) And extracting tap coefficients and time delay of the environmental channel transfer function samples, generating a large number of analog channels, and establishing a mixed sample database for training the channel estimation network. On the other hand, the channel transmission function is input into the next stage, the received signal is equalized by using a minimum mean square error equalizer, and the input detector demodulates the signal to eliminate the channel interference. And after the estimated channel information is used for constellation mapping selection of a transmitting end and signal equalization of a receiving end, the system demodulates the signals by adopting a three-dimensional spherical decoding mode.
In a specific embodiment, the environment channel transfer function is sent to a transmitting end of the system, and the system extracts tap and delay characteristics of the environment channel transfer function for determining which constellation mapping is adopted. The extracted features are represented as follows:
tap coefficient: n istap＝n(a≥0.1)
Maximum time delay: delaymax＝max(ntap)
Wherein n (a is more than or equal to 0.1) represents the tap position of which the normalized optimal channel amplitude is more than or equal to 0.1, and the position of the tap with the maximum time delay is extracted according to the extracted main taps of the channel and is marked as the maximum time delay. The transmitting end selects a proper constellation mapping from the known constellation map for modulation according to the indexes.
(3) And applying the receiving end carrying the trained network to the actual underwater acoustic environment. When moving to another sea area or the channel changes over time, the channel will be collected again and the training process restarted. Since the network has been trained before, for slowly varying channels, the existing parameters can be used as a pre-training result to cope with the new channel, thereby accelerating the convergence of the network.
FIG. 3 is a schematic diagram illustrating a structure of a multidimensional OFDM-based neural network channel estimation network according to an embodiment of the present application, where a received signal is first multiplied by a carrier as shown in FIG. 3
Figure GDA0002980654780000091
The baseband signal is obtained, and is subjected to receiving end comprehensive processing such as filtering and down-sampling in step 301, and then input into the trained channel estimation neural network 302. The channel estimation neural network 302 is composed of three full connection layers (FC), each layer of activation function adopts linear rectification function (RELU) transition, and the output layer adopts hyperbolic tangent function to obtain positive and negative distribution values. The processed baseband receiving signal R (t) and the output estimation channel
Figure GDA0002980654780000092
The input signal is a Minimum Mean Square Error (MMSE) equalizer 303 to obtain an equalized signal. In addition, a feedforward neural network structure is proposed, the number of neurons in each layer is comparedThe number of inputs is large so that the channel can be sufficiently extracted. The activation function for each layer is selected for a different target. In order to achieve stable and rapid convergence, a leakage linear unit (leakage ReLU) is adopted between the connection layers, and the tanh processing output layer is selected according to the large range of positive and negative values of the complex signal.
Fig. 4 is a schematic diagram of a multi-dimensional higher order OFDM-MIMO communication system according to an embodiment of the present application. As shown in fig. 4, the logic for operating the multi-dimensional higher-order OFDM-MIMO communication system comprises:
401: and measuring the noise signals in different environments for a long time, and analyzing the characteristics of the noise signals to form a noise signal database under various complex environmental conditions.
402: sending simple signals such as pulse signals or single-frequency signals and the like in different environments for a long time, receiving actually received signals, continuously simulating, training and iterating through a deep reinforcement learning estimation network, obtaining a simulation channel by changing a channel transmission function and parameters thereof, and constructing a channel function database in different environments.
403: and (3) fusing a mixed channel sample library formed by the noise signal database in the step 401 and the channel function database in the step 404, and building a self-adaptive noise channel environment model containing noise and channels.
404: and designing pulse shaping of the signal according to a channel model containing signal transmission characteristics and time-varying characteristics, and optimizing the amplitude of each symbol and the inter-symbol interval.
405: and performing multi-dimensional high-order orthogonal space modulation and multi-dimensional orthogonal frequency modulation on the signal in the step 404, and modulating the signal into a three-dimensional signal.
406: and reconstructing the three-dimensional signal in the step 405, and performing PAPR reduction operation on the spatial signal in the step 405 through the noise channel environment in the step 403, so that the system is more convenient to transmit data and has effectiveness and low error rate.
407: and performing spatial modulation on the reconstructed signal in the step 406, performing space division multiplexing to form an MIMO signal, transmitting through different user channels, and achieving concurrent transmission of the signal through channels specific to different users.
408: and the receiving end receives the transmitted data from the step 407, and detects, identifies and matches the received signals by using the adaptive noise channel environment model in the step 403 and by using deep reinforcement learning, so as to demodulate and recover the transmitted data.
Firstly, according to the channel state information of the transmission environment, the selection of the mapping point of the high-order modulation constellation is carried out, so as to reduce the peak value to average power ratio (PAPR) and reduce the probability of environmental interference. A multidimensional constellation point selection method based on machine learning environment self-adaptation utilizes an OFDM signal sending model which is constructed in advance based on a deep learning network to train and optimize pulse shaping modulation, multidimensional high-order orthogonal space modulation and multidimensional orthogonal frequency modulation in the step of modulating OFDM signals in sequence, so that the signals are continuously reconstructed with the aim of reducing PAPR, and finally the OFDM signals with environment self-adaptation, which are low in peak-to-average ratio and high in efficiency, can be modulated. The original signal is directly input into an OFDM transmission signal model, and is modulated by directly utilizing the OFDM transmission signal model, wherein the modulation comprises integrated modulation based on environment self-adaptation, such as analog/digital conversion, serial-parallel conversion, pulse position selection, multi-dimensional constellation point selection, signal mapping, signal reconstruction and the like, so that the whole signal modulation process is fully automatic, and the manual processing time and the complexity are reduced.
In some specific embodiments, the estimated channel based on the multidimensional OFDM environment adaptive modulation method of the present invention is used for constellation mapping selection at the transmitting end and signal equalization at the receiving end, and after that, the system demodulates the signal by using a three-dimensional sphere decoding method. Three-dimension is that on the basis of two-dimension modulation, a carrier wave with orthogonality independence is searched, and signal transmission is carried through three paths of orthogonal independence carrier waves.
In a preferred embodiment, continuing to refer to fig. 5, a schematic diagram of a logical model of three-dimensional MQAM modulation in one embodiment of the present application is shown. As shown in FIG. 5, the three-dimensional MQAM modulation uses the principle of quadrature amplitude modulation and adopts cos (ω)Ct)，sin(ωCt)，sin(2ωCt) three not only phasesCarriers that are orthogonal and have zero correlation with each other modulate the signal to a high frequency transmission. The specific three-dimensional MQAM modulation logic comprises the following steps:
step 501: acquiring a binary sequence X (n) signal to be transmitted;
step 502: performing serial-to-parallel conversion (n/3) processing on the binary sequence X (n) signal;
step 503: carrying out level conversion, Gray code conversion and interpolation molding processing on each path of subcarriers after serial-parallel conversion in sequence;
step 504: each sub-carrier processed in step 503 is processed by cos (ω) separatelyCt)，sin(-ωCt)，sin(-2ωCt) one of the carrier modulations;
step 505: and summing the subcarriers processed in the step 504, and performing next transmission preparation.
Derived according to the theory of the conventional two-dimensional MQAM modulation, the general form of the three-dimensional modulation signal expression of MQAM is as follows:
Figure GDA0002980654780000111
wherein g (t-mT)s) Is a width of TsA rectangular pulse having an amplitude of 1,
Figure GDA0002980654780000112
is the frequency omegacThe amplitude of the signal at (a) is,
Figure GDA0002980654780000113
is a frequency of 2 omegacThe signal amplitude can be a plurality of amplitude values, here the level state value, omega, at a certain momentcAnd 2 omegacFor the two carrier frequencies it is possible to have,
Figure GDA0002980654780000114
and betamRepresenting phase values, a number of different phase values may be taken.
An expansion formula:
Figure GDA0002980654780000115
suppose that:
Figure GDA0002980654780000116
Figure GDA0002980654780000117
Figure GDA0002980654780000118
then, Xm，YmAnd ZmThe formulas are respectively mapped into a three-dimensional rectangular coordinate system, namely three-dimensional MQAM modulation is based on cube mapping, and three paths of unrelated orthogonal carriers carry binary bit information to complete parallel transmission.
Fig. 6 shows a frame diagram of a multi-dimensional OFDM environment-based adaptive modulation system according to an embodiment of the present application, and the system 600 includes a noise environment model unit 601, a receiving-end signal processing unit 602, an environment mixed sample unit 603, and an optimal channel estimation unit 606.
In a specific embodiment, the noise environment model unit 601: the method comprises the steps of configuring noise environments for acquiring different scenes to form noise environment models in different scenes; receiving-end signal processing unit 602: the method comprises the steps that a pseudo noise sequence is extracted from a signal received by a receiving end, and a signal channel transmission function is reconstructed after the pseudo noise sequence is subjected to lower acquisition, Fourier transform and data segmentation; environment mixed sample unit 603: the system is configured to respectively carry out multi-dimensional OFDM modulation on pseudo noise sequences, map the modulated pseudo noise sequences to each subcarrier and transmit the pseudo noise sequences in a noise environment model to obtain a certain number of environment channel transfer function samples, and establish an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples; optimal channel estimation unit 606: the configuration is used for carrying out channel estimation network training on the signal channel transmission function by utilizing a neural network based on an environment mixed sample database to obtain the optimal channel estimation network transmission functions of different types.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use in implementing the electronic device of an embodiment of the present application. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 may also be connected to the I/O interface 705 as desired. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by a Central Processing Unit (CPU)701, performs the above-described functions defined in the method of the present application.
The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a noise environment model unit, a receiving-end signal processing unit, an environment mixed sample unit, and an optimal channel estimation unit. The names of the modules do not form a limitation on the modules themselves in some cases, for example, the noise environment model unit can also be described as "acquiring noise environments in different scenes, and forming a noise environment model in different scenes".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: collecting noise environments under different scenes to form noise environment models under different scenes; simultaneously extracting a pseudo noise sequence from a signal received by a receiving end and reconstructing a signal channel transmission function after lower acquisition, Fourier transform and data segmentation processing; respectively carrying out multi-dimensional OFDM modulation on the pseudo noise sequence, mapping the modulated pseudo noise sequence to each subcarrier, transmitting the subcarrier in a noise environment model to obtain a certain number of environment channel transfer function samples, and establishing an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples; and performing channel estimation network training on the signal channel transmission function by using a neural network based on the environment mixed sample database to obtain different types of optimal channel estimation network transmission functions.
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 multidimensional OFDM environment-based adaptive modulation method is characterized by comprising the following steps:
s1: collecting noise environments under different scenes to form noise environment models under the different scenes;
s2: extracting a pseudo noise sequence from a signal received by a receiving end, and reconstructing a signal channel transmission function after performing lower acquisition, Fourier transform and data segmentation processing;
s3: respectively carrying out multi-dimensional OFDM modulation on the pseudo noise sequence, mapping the modulated pseudo noise sequence to each subcarrier, transmitting in the noise environment model to obtain a certain number of environment channel transfer function samples, and establishing an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples; and
s4: and performing channel estimation network training on the signal channel transmission function by using a neural network based on the environment mixed sample database to obtain different types of optimal channel estimation network transmission functions.
2. The adaptive modulation method according to claim 1, wherein the optimal channel estimation network transfer function expression in the step S4 is as follows:
Figure FDA0002980654770000011
wherein F (-) denotes a signal channel transmission network, srFor the receiving endThe received signal.
3. The multi-dimensional OFDM environment-based adaptive modulation method according to claim 1, wherein the neural network in step S4 has a structure including an input layer, a plurality of connection layers, an activation layer, and an output layer, and the numbers of neurons in the connection layers, the activation layer, and the output layer are all larger than the number of neurons in the input layer.
4. The adaptive modulation method based on the multi-dimensional OFDM environment as claimed in claim 3, wherein said connection layers are connected by a leaky linear rectifying unit, and said output layer outputs hyperbolic function.
5. The adaptive modulation method according to claim 1, further comprising the step of performing constellation mapping type selection on the transmitting end based on the characteristics of the environmental channel transfer function samples after the step of S4, and performing signal equalization at the receiving end by using a minimum mean square error equalizer to eliminate channel interference.
6. The adaptive modulation method based on the multi-dimensional OFDM environment as claimed in claim 5, wherein said feature extraction includes extracting tap coefficients and delay features of samples of the transfer function of the environmental channel, and the specific extraction manner is expressed as follows:
tap coefficient: n istap＝n(a≥0.1)
Maximum time delay: delaymax＝max(ntap)
Wherein n (a is more than or equal to 0.1) represents the tap position of which the normalized optimal channel amplitude is more than or equal to 0.1, and the position of the tap with the maximum time delay is extracted according to the extracted main taps of the channel and is marked as the maximum time delay.
7. The adaptive modulation method based on the multi-dimensional OFDM environment as claimed in claim 1, wherein said multi-dimensional OFDM modulation adopts a three-dimensional MQAM modulation method, and the specific expression is as follows:
Figure FDA0002980654770000021
wherein g (t-mT)s) Is a width of TsA rectangular pulse having an amplitude of 1,
Figure FDA0002980654770000022
is the frequency omegacThe amplitude of the signal at (a) is,
Figure FDA0002980654770000023
is a frequency of 2 omegacThe amplitude of the signal at, here, the level state value at a certain moment, omegacAnd 2 omegacFor the two carrier frequencies it is possible to have,
Figure FDA0002980654770000024
and betamRepresenting the phase value.
8. The adaptive modulation method based on the multi-dimensional OFDM environment as claimed in claim 7, wherein said three-dimensional MQAM modulation can also be expressed as cube mapping, and the specific mapping coordinates are expressed as:
Figure FDA0002980654770000025
Figure FDA0002980654770000026
Figure FDA0002980654770000027
wherein, Xm，YmAnd ZmRespectively mapping to X axis, Y axis and Y axis of three-dimensional rectangular coordinate systemThe Z-axis is a direction perpendicular to the Z-axis,
Figure FDA0002980654770000028
and betamRepresenting the phase value.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 8.
10. An adaptive modulation system based on a multidimensional OFDM environment, the system comprising:
noise environment model unit: the method comprises the steps of configuring noise environments for acquiring different scenes to form noise environment models in the different scenes;
a receiving end signal processing unit: the method comprises the steps that a pseudo noise sequence is extracted from a signal received by a receiving end, and a signal channel transmission function is reconstructed after lower acquisition, Fourier transform and data segmentation processing;
ambient mix sample unit: the system is configured to respectively carry out multi-dimensional OFDM modulation on the pseudo noise sequence, map the modulated pseudo noise sequence to each subcarrier, transmit the pseudo noise sequence in the noise environment model to obtain a certain number of environment channel transfer function samples, and establish an environment mixed sample database by extracting the characteristics of the environment channel transfer function samples; and
an optimal channel estimation unit: and the configuration is used for carrying out channel estimation network training on the signal channel transmission function by utilizing a neural network based on the environment mixed sample database to obtain the optimal channel estimation network transmission functions of different types.
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