CN111314255B - Low-complexity SISO and MIMO receiver generation method - Google Patents

Low-complexity SISO and MIMO receiver generation method Download PDF

Info

Publication number
CN111314255B
CN111314255B CN202010090637.XA CN202010090637A CN111314255B CN 111314255 B CN111314255 B CN 111314255B CN 202010090637 A CN202010090637 A CN 202010090637A CN 111314255 B CN111314255 B CN 111314255B
Authority
CN
China
Prior art keywords
layer
receiver
nodes
siso
mimo
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.)
Expired - Fee Related
Application number
CN202010090637.XA
Other languages
Chinese (zh)
Other versions
CN111314255A (en
Inventor
郝崇正
党小宇
虞湘宾
王萌
黎宁
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.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
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 Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN202010090637.XA priority Critical patent/CN111314255B/en
Publication of CN111314255A publication Critical patent/CN111314255A/en
Application granted granted Critical
Publication of CN111314255B publication Critical patent/CN111314255B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/0008—Modulated-carrier systems arrangements for allowing a transmitter or receiver to use more than one type of modulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station

Abstract

The invention discloses a method for generating low-complexity SISO and MIMO receivers, which comprises the following steps: s1, sampling SISO and MIMO signals at a receiving end; s2, finding an optimal discriminant function, and converting the signal detection problem of different modulation modes in the SISO and MIMO system into the waveform identification problem; s3, training the receiver to minimize the loss function corresponding to the discriminant function of the hidden layer and the output layer, and further obtaining the optimal parameters of the hidden layer and the output layer of the compression stack type self-encoder receiver; and S4, selecting a strategy based on the number of nodes and the number of layers of the bit error rate measurement, and obtaining the low-complexity compression stack type self-encoder receiver structure based on the selection strategy of the number of nodes and the number of layers. The invention can simplify the structure of the receiver, and under the conditions of SISO and MIMO, the error rate of signal detection of different modulation modes under different channels reaches or exceeds the optimal detection theoretical value, and meanwhile, the invention has stronger robustness for CFO and phase deviation.

Description

Low-complexity SISO and MIMO receiver generation method
Technical Field
The invention relates to the technical field of digital communication, in particular to a method for generating low-complexity SISO and MIMO receivers.
Background
With the application of Deep Learning (DL) in the fields of computer vision, natural language processing, automatic driving, and the like, more and more learners apply DL in the field of communication signal processing, such as radar signal processing, signal arrival angle estimation, signal modulation identification, and the like, so as to solve the complex problem that a satisfactory result cannot be obtained by using a conventional statistical method.
At present, based on the research result of DL signal detection, such as the joint optimal transmitting and receiving based on the deep self-encoder, in the structure, the channel is considered as a layer of the neural network of the self-encoder, and is described by using an accurate conditional probability density function, however, in the actual communication system, the information of the channel can not be obtained before the link is established; a BPSK demodulator based on a Convolutional Neural Network (CNN) detects a phase of a BPSK signal by using the CNN, a low-pass filter, a converter, and a synchronization module, but the CNN has a complex structure and includes many optimization parameters, and training and offline deployment of the Network are difficult. The structure of the known neural network receiver in the correlation work is complex, the neural network receiver comprises a large number of layers and nodes, the training difficulty of the network is large, and meanwhile, the known signal demodulation is performed under ideal conditions, namely Carrier Frequency Offset (CFO) and phase Offset (phase Offset) are assumed to be absent.
It is the focus of the present invention to design a low complexity compressed Stacked self-encoder (CSAE) receiver that does not rely on channel models and noise assumptions and has strong robustness to CFO and phase offsets in practical communication systems.
Disclosure of Invention
The invention aims to provide a low-complexity CSAE (continuous adaptive echo cancellation) neural network receiver which is not dependent on a channel model and a noise hypothesis and is suitable for non-ideal Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) conditions, and the number of nodes and the number of layers of each layer of the deep neural network receiver are designed by using Bit Error Rate (BER) as a measurement, so that the structure of the receiver is simplified. The CSAE receiver has a simple structure, only comprises 2 hidden layers, and can reach or exceed the optimal detection theoretical value for the signal detection error rate of different modulation modes under an Additive White Gaussian Noise channel (AWGN) and a Rayleigh fading channel (Rayleigh) under the conditions of SISO and MIMO, and has stronger robustness for CFO and phase offset.
To achieve the above object, with reference to fig. 1, the present invention provides a method for generating a low-complexity SISO and MIMO receiver, where the method includes:
s1, sampling SISO and MIMO signals at a receiving end, wherein a sampling data set comprises signal data of multiple modulation modes of different channels under ideal conditions and non-ideal conditions, and the non-ideal conditions mean that the received signals contain signal defects including channel damage, CFO and phase offset;
s2, finding the optimal discriminant function
Figure BDA0002383590490000011
The problem of detecting signals of different modulation modes in SISO and MIMO systems is converted into the problem of identifying waveforms, and the problem of detecting a compressed stack type self-encoder receiver is described as follows:
Figure BDA0002383590490000021
in the formula, r is a matrix formed by sampling signals of a receiving end, H is a channel response matrix, and theta is a parameter in a discriminant function;
s3, dividing the discriminant function f (r, H) in the step S2 into hidden layer discriminant functions fh(. DEG) and output layer discriminant function foTwo parts, training the receiver by using the sampling data set in step S1 to minimize the loss function corresponding to the discriminant function of the hidden layer and the output layer, and further obtaining the optimal parameters of the hidden layer and the output layer of the compressed stacked self-encoder receiver as follows:
Figure BDA0002383590490000022
Figure BDA0002383590490000023
in the formula, thetah-optAnd thetao-optOptimal parameters for the implicit layer and the output layer of the compressed stack type self-encoder receiver respectively,
Figure BDA0002383590490000024
and
Figure BDA0002383590490000025
the loss functions of the hidden layer and the output layer respectively,
Figure BDA0002383590490000026
is the output characteristic matrix of the hidden layer,
Figure BDA0002383590490000027
the method comprises the steps that an output matrix of a compression stack type self-encoder receiver after signal detection is carried out;
s4, according to the parameter optimization process in the step S3, based on the node number and the layer number selection strategy of the error rate measurement, selecting a few nodes with less node number and higher detection precision layer by layer as the candidate node number of the layer, and simultaneously selecting the nodes with least node number and higher precision of all layers, and minimizing the node number and the layer number of the compression stack type self-encoder receiver, so as to obtain the low-complexity compression stack type self-encoder receiver structure based on the node number and the layer number selection strategy.
In a further embodiment, in step S1, the sampling SISO and MIMO signals at the receiving end means:
s11, creating a signal sending and receiving model, wherein, for the MIMO system, the numbers of the sending antennas and the receiving antennas are respectively NtAnd NrIn which N isr≥NtFor SISO system, it is equivalent to Nr＝N t1 and H-I1×1The MIMO system of (1); the transmission signal adopts four different modulation modes of BPSK, QPSK, 4PAM and 16QAM, and each transmission symbol adopts a rectangular pulse shaping function
Figure BDA0002383590490000028
Wherein T issIs a symbol period;
s12, L samples, i.e., r, are taken of the received signal in each symbol periodi＝[ri(0) ri(1) ... ri(L-1)]TMeanwhile, the number of symbols sampled is M, and the size r of the data set formed after sampling is ═ r0 r1 ... rM-1]L×M。
In a further embodiment, in step S2, the finding of the optimal discriminant function
Figure BDA0002383590490000029
The process of converting the signal detection problem of different modulation modes in SISO and MIMO systems into the waveform identification problem comprises the following steps:
s21, extracting features from the input sample data;
s22, training a classifier;
s23, finding out the optimal discriminant function under different modulation waveforms, and identifying and demodulating the input waveform through the optimal discriminant function; the optimal discriminant function comprises a hidden layer discriminant function and an output layer discriminant function, and each part contains a parameter theta related to the layer structurehAnd thetao。
In a further embodiment, in step S3, when the optimal parameter of the discriminant function is found, the loss function is lost through the hidden layer
Figure BDA0002383590490000031
And output layer loss function
Figure BDA0002383590490000032
Minimization is performed to achieve optimization of the parameters, wherein the property requirements of the sampled data set in step S1 are determined according to the convergence speed requirements of the aforementioned loss function and the accuracy requirements of the signal demodulation.
In a further embodiment, in step S4, the strategy for selecting the number of nodes and the number of layers based on the bit error rate metric is to simplify the number of nodes and the number of layers by using a sampled data set of BPSK in an AWGN channel;
and (3) fine-tuning the number and the layer number of the simplified nodes by adopting QPSK, 4PAM and 16QAM sampling data sets under AWGN and Rayleigh channels respectively to obtain a stable low-complexity compression stack type self-encoder receiver structure capable of realizing signal detection under various modulation modes and different channels.
In a further embodiment, in step S4, the process of obtaining a low-complexity compressed stacked self-encoder receiver structure based on the node number and layer number selection policy includes the following steps:
s41, setting NiRepresenting the number of neurons on the i-th hidden layer, k being the number of sampling points per symbol, and Delta being used as a measure for determining the theoretical bit error rate Delta under the modulation modetBit error rate delta detected by a compression stack self-encoder receivercThe error between;
s42, adopting BPSK sampling data set under AWGN channel as training data of network, and counting N nodes on the first hidden layer1Iteration is respectively carried out from 1 to k, network output detection accuracy corresponding to different node numbers is recorded, the detection accuracy is selected from the detection performances of the different node numbers to meet the requirement of a preset accuracy threshold, a plurality of nodes with the minimum node number are used as the candidate node number of the first hidden layer, and delta is judgedc-δtIf yes, go to step S44, otherwise, go to step S43;
s43, setting the number of nodes in the current hidden layer as the number of candidate nodes obtained in the step S42, adding the next hidden layer, respectively changing the number of nodes in the next hidden layer, carrying out iterative selection according to the strategy in the step S42, determining the number of candidate nodes in the next layer, and repeating the operation until a given condition delta is metc-δt＜Δ；
S44, selecting candidate nodes with the highest output detection accuracy and the least number of nodes in each hidden layer as the nodes of the network and the configuration of the layer number;
s45, fine adjustment of network configuration is carried out by adopting different modulation modes and sampling data sets under channels, and the optimal node number and layer number configuration of the compression stack type self-encoder receiver are determined;
and S46, under SISO and MIMO conditions, respectively, adopting four different modulation modes of BPSK, QPSK, 4PAM and 16QAM to perform signal detection on the generated low-complexity compression stacked self-encoder receiver under the AWGN and Rayleigh channels under ideal conditions and non-ideal conditions.
Compared with the prior art, the technical scheme of the invention has the following remarkable beneficial effects:
(1) the CSAE receiver provided by the invention does not depend on a channel model and a noise hypothesis, and meanwhile, the NLNS strategy designs the number of nodes and the number of layers of each layer of the deep neural network receiver by using a Bit Error Rate (BER) as a measurement, so that the structure and the complexity of the receiver can be simplified.
(2) The CSAE receiver has a simple structure, only comprises 2 hidden layers, and can reach or exceed the optimal detection theoretical value for the signal detection error rate of different modulation modes under an Additive White Gaussian Noise channel (AWGN) and a Rayleigh fading channel (Rayleigh) under the conditions of SISO and MIMO, and has stronger robustness for CFO and phase offset.
(3) The invention simulates SISO, MIMO and signals of various modulation modes under different channel models under ideal and non-ideal conditions, and carries out comparative analysis with the traditional optimal detection performance, the detection performance of the CSAE receiver under the ideal condition reaches or exceeds the optimal theoretical value, and the CSAE receiver has stronger robustness under the non-ideal condition.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
Drawings
The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
fig. 1 is a flow chart of the low complexity SISO and MIMO receiver generation method based on a compression stacked self-encoder of the present invention.
Fig. 2 is a block diagram of a receiver structure of a compression-stacked self-encoder according to the present invention.
Fig. 3 is a graph illustrating the effect of SNR of the training set on detection performance of a CSAE receiver.
FIG. 4 is a graph of the error rate performance of a SISO-CSAE receiver over an AWGN channel.
Fig. 5 is a diagram illustrating the error rate performance of a SISO-CSAE receiver in a Rayleigh channel.
Fig. 6 is a schematic diagram of the error rate performance of the MIMO-CSAE receiver under the Rayleigh channel.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
The design and signal detection of the low-complexity CSAE receiver provided by the invention adopt the following steps:
step 1: sampling SISO and MIMO signals at a receiving end, wherein for the MIMO communication system, the numbers of transmitting antennas and receiving antennas are respectively NtAnd NrIn which N isr≥NtFor SISO system, N is the equivalentr＝N t1 and H-I1×1The MIMO system adopts four different modulation modes of BPSK, QPSK, 4PAM and 16QAM as the transmission signals, and each transmission symbol adopts a rectangular pulse shaping function
Figure BDA0002383590490000051
Wherein T issFor a symbol period, the received signal typically contains channel impairments, CFO and phase offset;
step 2: the receiver finds the optimal discriminant function through a large amount of sampling data in step 1
Figure BDA0002383590490000052
Therefore, the problem of signal detection of different modulation modes in SISO and MIMO systems is converted into the problem of waveform identification, so the detection problem of the CSAE receiver can be described as
Figure BDA0002383590490000053
In the formula, r is a matrix formed by sampling signals of a receiving end, H is a channel response matrix, and theta is a parameter in a discriminant function;
and step 3: the discriminant function f (r, H) in step 2 can be divided into hidden layer discriminant functions fh(. DEG) and output layer discriminant function foTraining the receiver by using the sampling data set in the step 1 to minimize the loss function corresponding to the discriminant functions of the hidden layer and the output layer, and further obtaining the optimal parameters of the hidden layer and the output layer of the CSAE receiver
Figure BDA0002383590490000054
Figure BDA0002383590490000055
In the formula, thetah-optAnd thetao-optOptimal parameters for the hidden layer and the output layer of the CSAE receiver respectively,
Figure BDA0002383590490000056
and
Figure BDA0002383590490000057
the loss functions of the hidden layer and the output layer respectively,
Figure BDA0002383590490000058
is the output characteristic matrix of the hidden layer,
Figure BDA0002383590490000059
is an output matrix of the CSAE receiver after signal detection;
and 4, step 4: according to the parameter optimization process in the step 3, a node Number and layer Number Selection strategy (NLNS) based on BER measurement is designed, a few node numbers with small node numbers and high detection precision are selected layer by layer to serve as candidate node numbers of the layer, and simultaneously, nodes with minimum node numbers and high precision of all Layers are selected, so that the node Number and the layer Number of the CSAE receiver are minimized, and the low-complexity CSAE receiver structure based on the NLNS strategy is obtained.
The CSAE receiver provided by the invention does not depend on a channel model and a noise hypothesis, and meanwhile, the structure and the complexity of the receiver can be simplified by the provided NLNS strategy.
The low complexity compressed stacked self-encoder receiver proposed by the present invention has been validated on Matlab platform. The simulation result shows that the detection performance of the receiver under the ideal condition can reach or exceed the optimal theoretical value, and the receiver has stronger robustness to CFO and phase offset. Fig. 2 is a block diagram of a compressed stacked self-encoder receiver designed based on NLNS strategy.
The following gives specific steps implemented in one example of the present invention:
(1) firstly, the signals of different modulation modes sent by a receiving end are sampled, and the received signals under SISO and MIMO conditions can be expressed as:
Figure BDA0002383590490000061
in the formula, riIs the signal on the ith receiving antenna, alphanIs a baseband transmission data sequence adopting a certain modulation mode, wherein TsFor the symbol period, f is the carrier frequency offset, ζ is the phase offset, hi,jIs the channel response between the jth transmit antenna and the ith receive antenna, Z ═ Z1,z2…zNr]TIs a mean of 0 and a variance of σ2Independent and equally distributed white gaussian noise. The sampling value of the formula (4) under a certain modulation mode can be expressed as
Figure BDA0002383590490000062
Wherein r isi＝[ri(0) ri(1) ... ri(L-1)]TIs the number of sample points within each symbol and M represents the number of cycles of sampling.
(2) The CSAE receiver trains the network according to the signals sampled by the formula (5) and finds the optimal discriminant function
Figure BDA0002383590490000063
Thereby minimizing the error probability function at the receiving end
Figure BDA0002383590490000064
The transmitted signal is reconstructed. The detection problem of the CSAE receiver under the optimal discriminant function can be further expressed as:
Figure BDA0002383590490000065
in the formula (I), the compound is shown in the specification,
Figure BDA0002383590490000066
is the overall loss function of CSAE, which is used to measure the reconstructed signal
Figure BDA0002383590490000067
And a transmission signal sjThe error between. Total loss function
Figure BDA0002383590490000068
Including a hidden layer loss function
Figure BDA0002383590490000069
And output layer loss function
Figure BDA00023835904900000610
(3) The hidden layer loss function uses Euclidean square norm, KL divergence is used as a regularization item to avoid overfitting in training, the output layer uses a Softmax classifier to estimate each input data, and the maximum likelihood criterion is used for waveform prediction of the input data after the hidden layer characteristic extraction, namely:
Figure BDA0002383590490000071
Figure BDA0002383590490000072
where Q is the size of the sampled data set and θhIs a parameter of the hidden layer, lambda is a weight factor, m is the number of neurons of the hidden layer, chi is a sparse coefficient,
Figure BDA0002383590490000073
is the mean activation degree, theta, of neurons m in the hidden layergIs the parameter of the output layer, 1 {. is the indication function, N is the level kind of the signal under a certain modulation mode.
(4) And (3) training and iterating the CSAE receiver according to the loss function expression in the step (3), and obtaining the optimal parameters of the receiver by minimizing the loss function, namely the formula (2) and the formula (3).
(5) According to the CSAE receiver training method in (4), the number of nodes and the number of layers of each layer of the compression stack type self-encoder receiver are determined by BER measurement, thereby simplifying the structure of the receiver. The number of nodes and tier number selection strategy based on the BER metric is as follows, where N isiIndicates the ith hiddenThe number of neurons on the hidden layer, k is the number of sampling points of each symbol (in this example, 8 sampling points are set), and Δ is used for measuring and determining the theoretical bit error rate δ under the modulation modetBit error rate delta with CSAE receiver detectioncThe error between.
Firstly, using a sampling data set of BPSK under an AWGN channel as training data of a network, and counting N nodes on a first hidden layer1And respectively iterating from 1 to 8, and recording the network output detection accuracy corresponding to different node numbers. From the detection performances of the different node numbers, 4 nodes with higher detection accuracy and fewer node numbers are selected as the candidate node number of the first hidden layer. If deltac-δtA further hidden layer needs to be added > delta.
Secondly, the number of the nodes in the first hidden layer is the number of the candidate nodes in the S1, the number of the nodes in the second hidden layer is changed respectively, iterative selection is carried out according to the strategy in the S1, and the number of the candidate nodes in the second layer is further determined.
Thirdly, repeating the operation until the given condition delta is metc-δtAnd delta, selecting the candidate node with the highest output detection accuracy and the least number of nodes in each hidden layer as the node of the network and the configuration of the layer number.
And fourthly, fine adjustment of network configuration is carried out by using sampling data sets under different modulation modes and channels, and the optimal node number and layer number configuration of the CSAE receiver are further determined.
(6) And (3) under SISO and MIMO conditions, performing signal detection on the low-complexity CSAE receiver designed in the step (5) under ideal conditions and non-ideal conditions by adopting four different modulation modes of BPSK, QPSK, 4PAM and 16QAM under AWGN and Rayleigh channels.
Fig. 3 shows the effect of Signal to Noise Ratio (SNR) settings of the training set on detection performance of a CSAE receiver. Under an 8 × 20K data set, a network is trained by using a data set with SNR (signal to noise ratio) of 1dB, and the error code performance of a CSAE receiver reaches the theoretical performance; when the network is trained using SNR over the entire training set, the detection performance of the CSAE receiver is substantially the same as the result of the training network when SNR is 1dB, with a gain of about 0.1dB at only 7 dB. Fig. 4, 5 and 6 use CSAE receivers to demodulate different modulated signals under SISO and MIMO conditions, respectively. According to simulation results, under ideal conditions, the detection performances of BPSK, QPSK and 4PAM under AWGN and Rayleigh channels reach the optimal detection theoretical value, the detection performance of 16QAM under AWGN channel with low SNR exceeds the theoretical value by about 1dB, and under non-ideal SISO and MIMO conditions, the detection of the CSAE receiver has strong robustness.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims (6)

1. A method for generating a low complexity SISO and MIMO receiver, the method comprising:
s1, sampling SISO and MIMO signals at a receiving end, wherein a sampling data set comprises signal data of multiple modulation modes of different channels under ideal conditions and non-ideal conditions, and the non-ideal conditions mean that the received signals contain signal defects including channel damage, CFO and phase offset;
s2, finding the optimal discriminant function
Figure FDA0002949600400000011
The problem of detecting signals of different modulation modes in SISO and MIMO systems is converted into the problem of identifying waveforms, and the problem of detecting a compressed stack type self-encoder receiver is described as follows:
Figure FDA0002949600400000012
in the formula, r is a matrix formed by sampling signals of a receiving end, H is a channel response matrix, and theta is a parameter in a discriminant function;
s3, dividing the discriminant function f (r, H) in the step S2 into hidden layer discriminant functions fh(.) and output layer discriminant function fo(.), training the receiver by using the sampling data set in step S1, so that the loss function corresponding to the discriminant function of the hidden layer and the output layer is the minimum, and further obtaining the optimal parameters of the hidden layer and the output layer of the compressed stacked self-encoder receiver as follows:
Figure FDA0002949600400000015
Figure FDA0002949600400000016
in the formula, thetah-optAnd thetao-optOptimum parameters, L, for the implicit and the output layers of a compression-stacked autoencoder receiver, respectivelyhAnd LoLoss functions of the hidden and output layers, respectively, D ═ D(1),D(2),...D(l)Is the output characteristic matrix of the hidden layer, C ═ C(1),C(2)...C(l)The output matrix of the compression stack type self-encoder receiver after signal detection is carried out;
s4, according to the parameter optimization process in the step S3, based on the node number and the layer number selection strategy of the error rate measurement, selecting a few nodes with less node number and higher detection precision layer by layer as the candidate node number of the layer, and simultaneously selecting the nodes with least node number and higher precision of all layers, and minimizing the node number and the layer number of the compression stack type self-encoder receiver, so as to obtain the low-complexity compression stack type self-encoder receiver structure based on the node number and the layer number selection strategy.
2. The method for generating a low complexity SISO and MIMO receiver of claim 1, wherein in step S1, said sampling SISO and MIMO signals at the receiving end means:
s11, creating a signal sending and receiving model, wherein, for the MIMO system, the numbers of the sending antennas and the receiving antennas are respectively NtAnd NrIn which N isr≥NtFor SISO system, it is equivalent to Nr＝Nt1 and H-I1×1The MIMO system of (1); the transmission signal adopts four different modulation modes of BPSK, QPSK, 4PAM and 16QAM, and each transmission symbol adopts a rectangular pulse shaping function
Figure FDA0002949600400000017
Wherein T issIs a symbol period;
s12, L samples, i.e., r, are taken of the received signal in each symbol periodi＝[ri(0) ri(1)...ri(L-1)]TMeanwhile, the number of symbols sampled is M, and the size r of the data set formed after sampling is ═ r0 r1...rM-1]L×M。
3. The method of claim 1, wherein in step S2, the finding of the optimal discriminant function is performed
Figure FDA0002949600400000021
The process of converting the signal detection problem of different modulation modes in SISO and MIMO systems into the waveform identification problem comprises the following steps:
s21, extracting features from the input sample data;
s22, training a classifier;
s23, finding out the optimal discriminant function under different modulation waveforms, and identifying and demodulating the input waveform through the optimal discriminant function; the optimal discriminant function comprises a hidden layer discriminant function and an output layer discriminant function, and each part contains a parameter theta related to the layer structurehAnd thetao。
4. The method of claim 1, wherein in step S3, the discriminant function is optimized by a hidden layer loss function LhAnd output layer loss function LoMinimization is performed to achieve optimization of the parameters, wherein the property requirements of the sampled data set in step S1 are determined according to the convergence speed requirements of the aforementioned loss function and the accuracy requirements of the signal demodulation.
5. The method for generating low complexity SISO and MIMO receiver according to claim 2, wherein in step S4, the strategy for selecting the number of nodes and the number of layers based on the bit error rate measure is,
simplifying the number of nodes and the number of layers by adopting a sampling data set of BPSK (binary phase shift keying) under an AWGN (AWGN) channel;
and (3) fine-tuning the number and the layer number of the simplified nodes by adopting QPSK, 4PAM and 16QAM sampling data sets under AWGN and Rayleigh channels respectively to obtain a stable low-complexity compression stack type self-encoder receiver structure capable of realizing signal detection under various modulation modes and different channels.
6. The method of claim 1, wherein in step S4, the step of obtaining a low complexity compressed stacked self-encoder receiver structure based on node number and layer number selection strategy comprises the steps of:
s41, setting NiRepresenting the number of neurons in the i-th hidden layer, k being the number of samples per symbol, and Δ being measuredTheoretical bit error rate delta under fixed modulation modetBit error rate delta detected by a compression stack self-encoder receivercThe error between;
s42, adopting BPSK sampling data set under AWGN channel as training data of network, and counting N nodes on the first hidden layer1Iteration is respectively carried out from 1 to k, network output detection accuracy corresponding to different node numbers is recorded, the detection accuracy is selected from the detection performances of the different node numbers to meet the requirement of a preset accuracy threshold, a plurality of nodes with the minimum node number are used as the candidate node number of the first hidden layer, and delta is judgedc-δtIf yes, go to step S44, otherwise, go to step S43;
s43, repeating the following steps until the given condition delta is metc-δt＜Δ：
Setting the number of nodes in the current hidden layer to the number of candidate nodes obtained in the step S42, adding the next hidden layer, respectively changing the number of nodes in the next hidden layer, performing iterative selection according to the strategy in the step S42, and determining the number of candidate nodes in the next layer;
s44, selecting candidate nodes with the highest output detection accuracy and the least number of nodes in each hidden layer as the nodes of the network and the configuration of the layer number;
s45, fine adjustment of network configuration is carried out by adopting different modulation modes and sampling data sets under channels, and the optimal node number and layer number configuration of the compression stack type self-encoder receiver are determined;
and S46, under SISO and MIMO conditions, respectively, adopting four different modulation modes of BPSK, QPSK, 4PAM and 16QAM to perform signal detection on the generated low-complexity compression stacked self-encoder receiver under the AWGN and Rayleigh channels under ideal conditions and non-ideal conditions.
CN202010090637.XA 2020-02-13 2020-02-13 Low-complexity SISO and MIMO receiver generation method Expired - Fee Related CN111314255B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010090637.XA CN111314255B (en) 2020-02-13 2020-02-13 Low-complexity SISO and MIMO receiver generation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010090637.XA CN111314255B (en) 2020-02-13 2020-02-13 Low-complexity SISO and MIMO receiver generation method

Publications (2)

Publication Number Publication Date
CN111314255A CN111314255A (en) 2020-06-19
CN111314255B true CN111314255B (en) 2021-06-08

Family

ID=71147034

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010090637.XA Expired - Fee Related CN111314255B (en) 2020-02-13 2020-02-13 Low-complexity SISO and MIMO receiver generation method

Country Status (1)

Country Link
CN (1) CN111314255B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114124630B (en) * 2021-11-12 2023-10-24 中国电子科技集团公司第二十八研究所 Symbol detection and modulation recognition method based on compact self-encoder

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105142177A (en) * 2015-08-05 2015-12-09 西安电子科技大学 Complex neural network channel prediction method
CN109714086A (en) * 2019-01-23 2019-05-03 上海大学 Optimization MIMO detection method based on deep learning
CN110191073A (en) * 2019-06-28 2019-08-30 华侨大学 A kind of Modulation Mode Recognition method based on deep learning suitable for scene change
CN110719239A (en) * 2019-09-29 2020-01-21 东南大学 Data model dual-drive combined MIMO channel estimation and signal detection method

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101442388B (en) * 2008-12-29 2012-07-04 北京邮电大学 Precoding method and apparatus for multi-input multi-output system
US10445658B2 (en) * 2015-09-14 2019-10-15 Thomson Reuters Global Resources Unlimited Company Docket search and analytics engine
CN109688076B (en) * 2018-04-19 2021-06-11 南京邮电大学 Blind detection method of noise chaotic neural network based on discrete multi-level hysteresis
CN109831400A (en) * 2019-03-06 2019-05-31 深兰科技（上海）有限公司 A kind of received method and apparatus of OFDM signal
CN110061946B (en) * 2019-03-28 2021-11-16 南京邮电大学 Depth signal detection method for high-speed rail

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105142177A (en) * 2015-08-05 2015-12-09 西安电子科技大学 Complex neural network channel prediction method
CN109714086A (en) * 2019-01-23 2019-05-03 上海大学 Optimization MIMO detection method based on deep learning
CN110191073A (en) * 2019-06-28 2019-08-30 华侨大学 A kind of Modulation Mode Recognition method based on deep learning suitable for scene change
CN110719239A (en) * 2019-09-29 2020-01-21 东南大学 Data model dual-drive combined MIMO channel estimation and signal detection method

Also Published As

Publication number Publication date
CN111314255A (en) 2020-06-19

Similar Documents

Publication Publication Date Title
US7817740B2 (en) Method and system for minimum mean squared error soft interference cancellation (MMSE-SIC) based suboptimal maximum likelihood (ML) detection for multiple input multiple output (MIMO) wireless system
KR20070081786A (en) An apparatus and method for multiple input multiple output in communication
CN108989262B (en) Low-complexity incoherent spatial modulation detection method based on APSK modulation
CN1116476A (en) Reception method and CDMA receiver
CN111628833B (en) MIMO antenna number estimation method based on convolutional neural network
CN107835068B (en) Low-complexity orthogonal space modulation spherical decoding detection algorithm with transmit diversity
CN112215335B (en) System detection method based on deep learning
CN101026435A (en) Low-complexity maximum likelihood detecting method and device for communication system
US20070206697A1 (en) Signal receiving method and signal receiving equipment for multiple input multiple output wireless communication system
CN114745248B (en) DM-GSM signal detection method based on convolutional neural network
CN111314255B (en) Low-complexity SISO and MIMO receiver generation method
CN109286587B (en) Multi-active generalized spatial modulation detection method
CN109818891B (en) Lattice reduction assisted low-complexity greedy sphere decoding detection method
CN112636855A (en) OFDM signal detection method
Li et al. Model-driven deep learning scheme for adaptive transmission in MIMO-SCFDE system
CN109818663A (en) A kind of low complex degree difference quadrature spatial modulation detection method
KR101911168B1 (en) Adaptive signal detection method using MIMO-OFDM system and apparatus thereof
CN109167648B (en) Candidate constellation point set generation method and MIMO spatial multiplexing detection method
US8477880B2 (en) Method and apparatus for signal detection based on MMSE in MIMO communication system
US20080232491A1 (en) Systems and methods for low-complexity mimo detection with analytical leaf-node prediction
KR101937559B1 (en) Linear approximation signal detection apparatus using MIMO-OFDM system and method thereof
US8081577B2 (en) Method of calculating soft value and method of detecting transmission signal
CN112187332A (en) Large-scale multi-input multi-output soft detection system and method
CN111313974A (en) Modulation format identification method for signal amplitude sequencing and ratio calculation
GB2439770A (en) Decision error compensation in wireless MIMO receivers

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
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20210608