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CN-117811624-B - MIMO-OFDM space-frequency coding mode identification method, system, equipment and medium under Alpha stable distributed noise

CN117811624BCN 117811624 BCN117811624 BCN 117811624BCN-117811624-B

Abstract

The method comprises the steps of firstly carrying out space-time frequency coding inter-class identification based on fractional low-order correlation on a received signal, then extracting a cyclic correlation entropy spectrum characteristic diagram of the received signal, finally inputting the extracted characteristic diagram into a trained depth forest network to identify the space-frequency coding mode, carrying out space-time frequency coding inter-class identification based on fractional low-order time lag correlation on the system, the device and the medium, realizing inter-class identification on unknown signals, and determining whether space-frequency coding is used or not, extracting the cyclic correlation entropy spectrum characteristic diagram of the received signal, combining the depth forest network, converting coding identification problem into image identification, effectively realizing the identification of the MIMO-OFDM space-frequency coding mode under the fractional low-order time lag correlation, solving the problem of limited functions of the traditional method in non-cooperative communication, and being applicable to Gaussian noise environments.

Inventors

  • LIU MINGJIAN
  • FAN YAQI
  • ZHANG WEIDONG

Assignees

  • 西安电子科技大学

Dates

Publication Date
20260508
Application Date
20240111

Claims (4)

  1. 1. The method for identifying the spatial coding mode in the MIMO-OFDM system under the Alpha stable distributed noise is characterized by comprising the following steps: step one, carrying out space-time-frequency coding inter-class identification based on fractional low-order time-lag correlation on a received signal of a receiving antenna; 1.1 To the first The signal samples received by the individual receive antennas are represented as: Wherein, the Representing channel parameters between the transmit antenna and the receive antenna, The number of propagation paths is represented and, Stably distributing noise for Alpha; 1.2 Aiming at the influence of Alpha stable distribution noise on signals, carrying out fractional lower-order processing on received signals, inhibiting the influence of non-Gaussian noise on useful signals, and calculating fractional lower-order time lag correlation under different receiving antennas: Wherein, the , Is the block length of the coding mode, Representing the time-lapse of the different times, Representing the length of the OFDM symbol, Representing the received signals of the different receiving antennas; Searching a peak value in the time-lag correlation function, extracting a time-lag value corresponding to the peak value as a peak value characteristic, and drawing a time-lag correlation peak value characteristic diagram; 1.3 After extracting multiple time lags Dividing the feature map into a training set and a testing set, training a dual-channel network DPN by utilizing the feature pattern in the training set to identify the time-lapse correlation peak feature map, dividing the output into two paths in the process of identifying the time-lapse correlation peak feature map by the DPN network, accumulating one path with the original input features to form a residual structure, reducing the redundancy of the original input features, connecting the other path with the original input features in parallel, enabling the current network layer to directly obtain the output of the upper network layer, extracting deeper features from the output, improving the classification accuracy of the model and realizing the identification between space-time-frequency codes; step two, for the received signals of different receiving antennas identified as space-frequency coding in the step one, calculating the cyclic correlation entropy of the received signals, and extracting a cyclic correlation entropy spectrum characteristic diagram of the received signals, wherein the specific process is as follows: For the received signals of different receiving antennas, the calculation of their associated entropy is defined as: writing it in fourier series form, there is a cyclic correlation entropy function: and (3) obtaining Fourier transform for the cyclic correlation entropy, and further obtaining a cyclic correlation entropy spectrum function: taking fixed circulation frequency on the basis As a feature map of the received signal; Step three, inputting the cyclic correlation entropy spectrum feature diagram extracted in the step two into a trained depth forest network to identify the space frequency coding mode, wherein the specific process comprises the following steps: Each layer of the depth forest network consists of a plurality of cascaded random forests, the cyclic correlation entropy spectrum characteristic diagram extracted in the second step is used as input, the cyclic correlation entropy spectrum characteristic diagram is preprocessed by using multi-granularity scanning to obtain characteristic vectors, the obtained characteristic vectors are input into the plurality of cascaded random forests for training, characteristic information of the input characteristic vectors is learned through the random forests and is input into the next layer of the depth forest network, in order to enhance generalization capability of a model, random forests of different types are selected for each layer so as to be suitable for data sets of different sizes, and the cyclic correlation entropy spectrum extracted in the second step is input into the trained depth forest network to obtain a final classification result.
  2. 2. A system for implementing the method for identifying a MIMO-OFDM space-frequency coding scheme under Alpha stable distributed noise according to claim 1, wherein the system for identifying a MIMO-OFDM space-frequency coding scheme under Alpha stable distributed noise comprises: the inter-class identification module is used for carrying out space-time-frequency coding inter-class identification based on fractional low-order correlation on the received signal in the first step, and identifying whether the received signal is in a space-frequency coding mode or not; the feature extraction module is used for calculating the cyclic correlation entropy of the received signal in the second step and extracting a cyclic correlation entropy spectrum feature map; and the space-frequency coding mode identification module is used for inputting the cyclic correlation entropy spectrum characteristic diagram extracted in the step two into a trained depth forest network to realize the classification identification of the space-frequency coding mode.
  3. 3. An apparatus for implementing the method for identifying a MIMO-OFDM space-frequency coding scheme under Alpha stable distributed noise according to claim 1, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, is capable of implementing the method for identifying a MIMO-OFDM space-frequency coding scheme under Alpha stable distributed noise according to claim 1.
  4. 4. A storage medium for receiving a user input program, wherein the computer program stored in the storage medium, when executed by a processor, is capable of performing the identification of the MIMO-OFDM space-frequency coding scheme under Alpha stable distributed noise based on the identification method of the MIMO-OFDM space-frequency coding scheme under Alpha stable distributed noise as set forth in claim 1.

Description

MIMO-OFDM space-frequency coding mode identification method, system, equipment and medium under Alpha stable distributed noise Technical Field The invention belongs to the technical field of communication signal demodulation, and particularly relates to a method, a system, equipment and a medium for identifying MIMO-OFDM space-frequency coding modes under Alpha stable distributed noise. Background The MIMO-OFDM technology is a solution for efficiently solving performance degradation in a new scenario by combining the orthogonal frequency division multiplexing and the MIMO technology, and is a recent research hotspot in the field of wireless communication. The diversity coding scheme employed in the MIMO-OFDM system includes a space-frequency coding scheme, i.e., utilizing space-frequency diversity, in addition to space-time coding. The combination of Space-frequency block codes (SFBC) and MIMO-OFDM can improve the effectiveness and reliability of communication, wherein the identification of the Space-frequency coding mode is one of important contents of deep sensing technology of a non-cooperative MIMO system. Conventional SFBC identification algorithms are designed to extract relevant information from the received signal in a gaussian noise environment. However, gaussian noise is not the only case, such as non-gaussian impulse noise modeled by Alpha stable distributions, and the probability distribution of such noise is often heavy-tailed and cannot be modeled well with gaussian distributions. In the case of non-gaussian noise, its performance may drop significantly due to noise model mismatch. Therefore, the method has important significance for research on the identification aspect of the MIMO-OFDM space-frequency coding mode under the Alpha stable distributed noise. Marey et AL expand the method for detecting the peak value of the specific time-lapse cross-correlation function of two receiving antenna signals, and utilize SFBC-OFDM space domain redundancy to realize identification (Marey M,Dobre O A.Automatic Identification of Space-Frequency Block Coding for OFDM Systems[J].IEEE Transactions on Wireless Communications,2016,16(1):117-128.). of the AL signal and the SM signal, however, the algorithm does not fully utilize the frequency domain redundancy of the SFBC signal, so that when the number of OFDM carriers is increased, the performance is not improved, but the computational complexity is increased exponentially. Gao et al propose constructing detection statistics by utilizing multiple receiving antennas by extracting the space-frequency redundancy of SFBC signals, solving the characteristics by utilizing a random matrix theory by extracting the rank characteristics of adjacent sub-carrier subspaces and distinguishing SFBC-OFDM signals (Gao M,Li Y,Dobre O A,et al.Blind Identification of SFBC-OFDM Signals Using Subspace Decompositions and Random Matrix Theory[J].IEEE Transactions on VehicularTechnology,2018,67(10):9619-9630.). by utilizing a minimum distance criterion, thereby efficiently utilizing the frequency domain redundancy, completing SFBC-OFDM signal identification (Gao M,Li Y,Dobre O A,et al.Blind Identification of SFBC-OFDM Signals Based on the Central Limit Theorem[J].IEEE Transactions on Wireless Communications,2019,18(7):3500-3514.).Kun and the like and proposing a MIMO-SFBC blind identification algorithm based on symbol characteristic values. According to the symbol correlation characteristics of different space-frequency block codes in the frequency domain, deriving the characteristic vector sequences of different space-frequency block codes, utilizing binary hypothesis to test and estimate symbol characteristic values, distinguishing the correlation function characteristic diagrams of different coding types (KunJin,JinKun,Yu Keyuan,Yan Wenjun.Blind recognition of MIMO-SFBC based on Symbolic eigenvalue*[J].Journal ofPhysics:Conference Series,2020,1650(3).). Zhangyuan and the like in the frequency domain of the receiving end by utilizing signals through a decision tree classification recognition algorithm, performing space scale preprocessing conversion to be two-dimensional, and finally adopting an expanded dense convolution network to realize SFBC coding classification recognition (Zhangyuan, zhang Limin, wenjun. SFBC-OFDM recognition method [ J ] based on the cross-correlation characteristic diagrams and the expanded dense convolution network, system engineering and electronic technology, 2021,43 (09): 2657-2664.). Although the algorithm can effectively realize SFBC coding classification, the existing research at present is that the space-frequency coding in-class identification is carried out on the premise of the known diversity coding in-class, and in addition, most of the methods are based on Gaussian noise models, so that the problem of performance degradation of the traditional identification method under non-Gaussian noise is urgently solved under the condition that noise env