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CN116074168A - A Perception Parameter Estimation Method for Communication Perception Integrated System Based on SS-OTFS - Google Patents

A Perception Parameter Estimation Method for Communication Perception Integrated System Based on SS-OTFS Download PDF

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CN116074168A
CN116074168A CN202211615091.0A CN202211615091A CN116074168A CN 116074168 A CN116074168 A CN 116074168A CN 202211615091 A CN202211615091 A CN 202211615091A CN 116074168 A CN116074168 A CN 116074168A
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perception
parameters
vector
otfs
delay
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李航
杨烯
程知群
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Hangzhou University Of Electronic Science And Technology Fuyang Institute Of Electronic Information Co ltd
Hangzhou Dianzi University
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Hangzhou Dianzi 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
    • H04L27/0014—Carrier regulation
    • 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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0047—Decoding adapted to other signal detection operation
    • H04L1/005—Iterative decoding, including iteration between signal detection and decoding operation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/0014—Carrier regulation
    • H04L2027/0024—Carrier regulation at the receiver end
    • H04L2027/0026—Correction of carrier offset
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a perception parameter estimation method of a communication perception integrated system based on SS-OTFS, which processes radar echo signals by using UAMP-SBL algorithm at a radar receiver and at least comprises the following steps: step A: writing a radar echo signal vector received by a radar receiver into a form of multiplying a high-dimensional matrix by a sparse vector; and (B) step (B): performing unitary transformation on the radar echo signal vector in the step A; step C: b, initializing basic parameters for the unitary transformed signals in the step B, and performing iterative computation through a UAMP-SBL algorithm; step D: c, taking the mean value and the variance of the sparse vector and other parameters obtained by calculation in the step C as return values, and repeating the process in the step C until the circulation is finished; step E: and D, further processing the estimated value obtained in the step D to obtain estimated perception parameters, wherein the perception parameters at least comprise delay and Doppler estimation.

Description

Sensing parameter estimation method of communication sensing integrated system based on SS-OTFS
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a perception parameter estimation method of a communication perception integrated system based on SS-OTFS.
Background
With the continuous development of wireless communication technology, wireless spectrum resources are limited. The next generation wireless communication system needs to multiplex radar frequency bands to realize communication and sensing spectrum sharing so as to improve spectrum utilization rate. In addition, the wireless communication and the perception have similar and common parts in the aspects of hardware architecture, signal processing and the like, and the integrated design can enable the future business use to reduce the reconstruction cost of the existing network to the greatest extent, so that the real reciprocal benefit is realized, for example, the perception auxiliary communication is utilized, and the radar echo is utilized for tracking and the like. Based on the above background, communication awareness Integration (ISAC) systems have been proposed.
In some high mobility scenarios, such as internet of vehicles, high speed rail, unmanned aerial vehicle, etc., the Orthogonal Frequency Division Multiplexing (OFDM) technology breaks the orthogonality of the subcarriers due to the doppler shift being too large, thereby generating serious inter-subcarrier interference. Accordingly, the conventional ISAC system based on OFDM may deteriorate communication performance in a high mobility scenario, and thus an Orthogonal Time Frequency Space (OTFS) modulation technique is proposed. The OTFS modulation technique directly performs data modulation in the delay-doppler domain and spreads over the entire time-frequency domain, so that symbols in the transmission unit experience nearly identical and slowly varying sparse channels, thereby overcoming the high doppler shift caused by the high mobility scenario. The ISAC system using OTFS at present mainly obtains superior performance based on Maximum Likelihood Detection (MLD) when performing perceptual parameter estimation, but the computational complexity of solving the MLD problem is too high. It is therefore desirable to find a perceptual parameter estimation method with low computational complexity, while having high robustness.
Disclosure of Invention
Aiming at the existing technical defects, the invention provides a perception parameter estimation method of a communication perception integrated system based on SS-OTFS, namely, an ISAC transmission framework based on space expansion orthogonal time-frequency space (SS-OTFS) modulation, and a perception parameter estimation method based on unitary approximate message transfer (UAMP) Sparse Bayesian Learning (SBL). Wherein, the signal transmitted to the User Equipment (UE) by the Base Station (BS) is reflected by the UE and scattered by surrounding scatterers, and then received by a radar receiver which is positioned at the same position as the transmitter to obtain an echo signal. The UAMP-SBL-based method of the receiver utilizes prior information and an iterative process to process echo signals for perceptual parameter estimation, including delay and Doppler shift estimation. The method can effectively reduce the computational complexity of the perception parameter estimation.
In order to achieve the above purpose, the specific technical scheme adopted by the invention is as follows:
a perception parameter estimation method of a communication perception integrated system based on SS-OTFS at least comprises the following steps:
step A: writing a radar echo signal vector received by a radar receiver into a form of multiplying a high-dimensional matrix by a sparse vector;
and (B) step (B): performing unitary transformation on the radar echo signal vector in the step A;
step C: b, initializing basic parameters for the unitary transformed signals in the step B, and performing iterative computation through a UAMP-SBL algorithm;
step D: c, taking the mean value and the variance of the sparse vector and other parameters obtained by calculation in the step C as return values, and repeating the process in the step C until the circulation is finished;
step E: and D, further processing the sparse vector estimated value obtained in the step D to obtain estimated perception parameters (delay and Doppler).
As a further improvement, one BS serves the scenario of K UEs, in the downlink, the BS broadcasts a common message to K UEs within the signal coverage area, and perceives the location information of the UEs based on the received echoes; provided with N BS There are P independent separable paths between the BS of the root antenna and each single antenna UE, then the radar return signal is expressed as:
Figure BDA0004000188260000031
wherein ,
Figure BDA0004000188260000032
is a radar echo signal in the time delay angle domain, a>
Figure BDA0004000188260000033
Is the fading coefficient of the radar channel, alpha is N for distributing power to the signal on each antenna BS ×N BS Dimensional matrix, precoding matrix P multiplexes signals onto each antenna, x TD Is a transmitted symbol vector in the delay-doppler domain, < >>
Figure BDA0004000188260000034
Is an additive white gaussian noise column vector,/->
Figure BDA0004000188260000035
Is N for spatially expanding a signal BS ×N BS Is a matrix of the discrete fourier transform of (c),
Figure BDA0004000188260000036
is the steering vector of the antenna array, where θ i,p Is the relative angle (.) T and (·)H The transpose and Hermite transpose are represented respectively.
Figure BDA0004000188260000037
MN x MN dimensional permutation matrices representing the relative delay effects, wherein,
Figure BDA0004000188260000038
Figure BDA0004000188260000039
a diagonal matrix of MN x MN representing the effect of relative doppler shift, wherein,
Figure BDA0004000188260000041
m and N are denoted as the number of subcarriers and the number of slots respectively,
Figure BDA0004000188260000042
and
Figure BDA0004000188260000043
The round trip delay index and the Doppler shift index related to the ith path of the ith UE;
Figure BDA0004000188260000044
wherein ,τi,p and vi,p The p-th path related relative delay and relative doppler shift for the i-th UE are respectively, Δf and T are the subcarrier spacing and the slot duration, Δf=1/T.
As a further refinement, the relative angle of the BS and UE is known or estimated available.
As a further improvement, in step a, the formula (1) is transformed and rewritten as:
Figure BDA0004000188260000045
wherein ,
Figure BDA0004000188260000046
corresponds to a set of delay and doppler shift indices; taking the set of values as a column in a high-dimensional matrix phi, and selecting the round trip delay index to be within the range +.>
Figure BDA0004000188260000047
The Doppler index selection range is +.>
Figure BDA0004000188260000048
For the sense matrix Φ, h has non-zero values +.>
Figure BDA0004000188260000049
Otherwise, zero is set, so that a sparse vector is formed; wherein Φ is MNN BS The dimension matrix of the xLM (2N-1), L is the number of separable relative angles; let phi be +.>
Figure BDA00040001882600000410
A dimension matrix; the perceptual parameters are inferred by estimating the locations of non-zero values in the sparse vector h.
As a further improvement, in step B, Φ is subjected to SVD decomposition Φ=uΛv H And unitary transforming equation (5), i.e
Figure BDA0004000188260000051
The method comprises the following steps:
r＝Ψh+ω (25)
wherein ,
Figure BDA0004000188260000052
and
Figure BDA0004000188260000053
Omega is 0 as mean and beta as covariance matrix -1 I is gaussian noise, β is the accuracy of the noise.
As a further improvement, in step C, the iterative process of information in the UAMP-SBL algorithm includes the following six steps:
step C1: initializing and setting basic parameters: let λ= ΛΛ H 1,1 is a column vector with all elements 1,
Figure BDA0004000188260000054
(0) ＝0.01，
Figure BDA0004000188260000055
s=0, 0 is a column vector sum of all 0 elements and t=0;
step (a)C2: calculating τ p and p：
Figure BDA0004000188260000056
Figure BDA0004000188260000057
wherein·represents the dot product of two vectors;
step C3, calculating v z ，
Figure BDA0004000188260000058
and
Figure BDA0004000188260000059
Figure BDA00040001882600000510
Figure BDA00040001882600000511
Figure BDA00040001882600000512
Where/represents two vector dot divisions;
step C4: calculating τ s and s：
Figure BDA00040001882600000513
s＝τ s ·(r-p) (32)
step C5: calculating τ q and q：
Figure BDA0004000188260000061
Figure BDA0004000188260000062
step C6: calculating the mean of sparse vectors
Figure BDA0004000188260000063
Variance τ h Parameter->
Figure BDA0004000188260000064
and ∈：
Figure BDA0004000188260000065
Figure BDA0004000188260000066
Figure BDA0004000188260000067
Figure BDA0004000188260000068
as a further improvement, in step D, the updated basic parameters, including the mean and variance of the estimated value of the sparse vector h, are returned to step C2 for the next iteration.
In step E, the estimated sparse vector h is identified to obtain a set of delay index and doppler shift index of the corresponding position, and then they are converted to the relative delay and the relative doppler shift by the formula (4), so as to obtain the required perception parameter.
The invention provides a perception parameter estimation method for an ISAC system based on SS-OTFS. After the radar receiver performs unitary transformation processing on the radar echo signals, the UAMP-SBL algorithm is utilized to iterate the echo signals. Compared with the SBL algorithm, the method can obtain lower calculation complexity and higher estimation accuracy.
Drawings
FIG. 1 is a flowchart of a method for estimating perceptual parameters according to an embodiment of the present invention;
fig. 2 is a graph of signal-to-noise ratio (SNR) versus estimated sparse vector h accuracy Normalized Mean Square Error (NMSE) when compared to a conventional SBL algorithm in accordance with the present invention.
Fig. 3 is a graph comparing the estimated perceived parameter (delay and doppler) index with the actual index.
Detailed Description
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
Referring to fig. 1, a flowchart of a method for estimating a perception parameter based on communication perception integration supporting SS-OTFS is provided in an embodiment of the present invention, where a UAMP-SBL algorithm is used at a radar receiver to process radar echo signals, and the method at least includes the following steps:
step A: writing a radar echo signal vector received by a radar receiver into a form of multiplying a high-dimensional matrix by a sparse vector;
and (B) step (B): performing unitary transformation on the radar echo signal vector in the step A;
step C: b, initializing basic parameters for the unitary transformed signals in the step B, and performing iterative computation through a UAMP-SBL algorithm;
step D: c, taking the mean value and the variance of the sparse vector and other parameters obtained by calculation in the step C as return values, and repeating the process in the step C until the circulation is finished;
step E: d, further processing the estimated value obtained in the step D to obtain estimated perception parameters, wherein the perception parameters at least comprise delay and Doppler;
we consider the scenario where one BS serves K UEs. In the downlinkAnd broadcasting public information to K UEs in the signal coverage range by the BS, and sensing the position information of the UEs based on the received echoes. Provided with N BS There are P independent separable paths between the BS of the root antenna and each single antenna UE, then the radar return signal can be expressed as:
Figure BDA0004000188260000081
wherein
Figure BDA0004000188260000082
Is a radar echo signal in the Time Delay Angle (TDA) domain, +>
Figure BDA0004000188260000083
Is N for spatially expanding a signal BS ×N BS Is a Discrete Fourier Transform (DFT) matrix of (a),
Figure BDA0004000188260000084
is the steering vector, θ, of the antenna array i,p Is the relative angle (.) T and (·)H The transpose and Hermite transpose are represented respectively. Alpha is N for power distribution of signals on each antenna BS ×N BS A dimension matrix.
Figure BDA0004000188260000085
MN x MN dimensional permutation matrix representing relative delay effects, wherein
Figure BDA0004000188260000086
Figure BDA0004000188260000087
A diagonal matrix of MN x MN representing the effect of relative doppler shift, wherein,
Figure BDA0004000188260000088
m and N are denoted as the number of subcarriers and the number of slots respectively,
Figure BDA0004000188260000089
and
Figure BDA00040001882600000810
The p-th path related round trip delay index and doppler shift index of the i-th UE,
Figure BDA00040001882600000811
wherein τi,p and vi,p The p-th path related relative delay and relative doppler shift for the i-th UE are respectively, Δf and T are the subcarrier spacing and the slot duration, Δf=1/T. The precoding matrix P multiplexes the signals onto each antenna. X is x TD Is a transmitted symbol vector in the delay-doppler (DD) domain.
Figure BDA0004000188260000091
Is an Additive White Gaussian Noise (AWGN) column vector.
Assuming that the relative angle of the BS and UE is known or an estimate is available, the estimated parameters of the round trip delay index and the doppler shift index may be estimated using the following methods.
In step a, the transformation of equation (1) can be rewritten as:
Figure BDA0004000188260000092
wherein
Figure BDA0004000188260000093
Corresponding to a set of delay and doppler shift indices. We regard this set of values as a column in the high-dimensional matrix Φ, to ensure accuracy of the estimation, as much as possible to ensure that Φ contains all possible valuesDelay and doppler combinations can occur. The round trip delay index is selected to be +.>
Figure BDA0004000188260000094
The Doppler index selection range is +.>
Figure BDA0004000188260000095
For the perceptual matrix Φ, h has a non-zero value h in the corresponding row i,p Otherwise, zero is set, so that a sparse vector is formed. Wherein Φ is MNN BS And (3) a linear matrix of x LM (2N-1), wherein L is the number of separable relative angles. For convenience of presentation we let Φ be +.>
Figure BDA0004000188260000096
A dimension matrix. The perceptual parameters are then inferred by estimating the locations of non-zero values in the sparse vector h.
In step B, Φ is subjected to SVD decomposition Φ=uΛv H And unitary transforming equation (5), i.e
Figure BDA0004000188260000097
Can obtain
Figure BDA0004000188260000098
wherein ,
Figure BDA0004000188260000099
and
Figure BDA00040001882600000910
Omega is 0 as mean and beta as covariance matrix -1 I is gaussian noise, β is the accuracy of the noise.
In step C, the iterative process of information in the UAMP-SBL algorithm can be summarized as the following six steps:
step C1: initializing and setting basic parameters: let λ= ΛΛ H 1 (1 is a column vector with one element all 1),
Figure BDA0004000188260000101
(0) ＝0.01，
Figure BDA0004000188260000102
s=0 (0 is a column vector with one element all being 0) and t=0.
Step C2: calculating τ p and p
Figure BDA0004000188260000103
Figure BDA0004000188260000104
Where·represents the dot product of two vectors.
Step C3, calculating v z ，
Figure BDA0004000188260000105
and
Figure BDA0004000188260000106
Figure BDA0004000188260000107
Figure BDA0004000188260000108
Figure BDA0004000188260000109
Where/represents two vector dot divisions.
Step C4: calculating τ s and s
Figure BDA00040001882600001010
s＝τ s ·(r-p) (51)
Step C5: calculating τ q and q
Figure BDA00040001882600001011
Figure BDA00040001882600001012
Step C6: calculating the mean of sparse vectors
Figure BDA00040001882600001013
Variance τ h Parameter->
Figure BDA00040001882600001014
And
Figure BDA00040001882600001015
Figure BDA00040001882600001016
Figure BDA0004000188260000111
Figure BDA0004000188260000112
in step D, the updated basic parameters, including the mean and variance of the estimated value of the sparse vector h, are returned to step C2 for the next iteration.
In step E, the estimated sparse vector h is subjected to non-zero position identification to obtain a group of delay indexes and doppler shift indexes of the corresponding positions, and then the delay indexes and the doppler shift indexes are converted into relative delay and relative doppler shift through a formula (4), so as to obtain the required perception parameters.
Referring to fig. 2, a performance comparison graph of the method based on the present invention and the recovery of sparse vector h based on the SBL method is shown, wherein m=16, n=8, and the number of antennas N are set in the embodiment of the present invention BS By adopting the QPSK modulation method, the method of the present invention has a large performance gain compared with the conventional SBL, and is always close to the performance lower bound, while the perceptual matrix ψ of the conventional SBL method is a correlation matrix, which causes iterative divergence to cause serious performance degradation. In terms of computational complexity, the complexity of the method of the present invention in each iteration is
Figure BDA0004000188260000113
Whereas the traditional SBL approach is +.>
Figure BDA0004000188260000114
Referring to fig. 3, a comparison of the estimated index of the perceptual parameter with the actual index is shown, from which it can be seen that the estimated index and the actual index are very matched, demonstrating the effectiveness of the method of the present invention.
In summary, in the ISAC wireless communication downlink based on SS-OTFS, the embodiments of the present invention can utilize echo signals to perform effective perceptual parameter estimation. Compared with the existing SBL algorithm, the method has higher estimation precision of the perception parameters and lower calculation complexity.
The last explanation is: the foregoing is merely a preferred embodiment of the present invention, and the present invention is not limited to the embodiments shown herein. Modifications, substitutions, etc. may be made by one skilled in the art in light of the foregoing description and such modifications and substitutions are intended to be included within the scope of the present invention.

Claims (8)

1. A perception parameter estimation method of a communication perception integrated system based on SS-OTFS (SS-OTFS) is characterized by at least comprising the following steps of:
step A: writing a radar echo signal vector received by a radar receiver into a form of multiplying a high-dimensional matrix by a sparse vector;
and (B) step (B): performing unitary transformation on the radar echo signal vector in the step A;
step C: b, initializing basic parameters for the unitary transformed signals in the step B, and performing iterative computation through a UAMP-SBL algorithm;
step D: c, taking the mean value and the variance of the sparse vector and other parameters obtained by calculation in the step C as return values, and repeating the process in the step C until the circulation is finished;
step E: and D, further processing the estimated value obtained in the step D to obtain estimated perception parameters, wherein the perception parameters at least comprise delay and Doppler estimation.
2. The method for estimating sensing parameters of an SS-OTFS based communication sensing integrated system according to claim 1, wherein one BS serves a scenario of K UEs, and in downlink, the BS broadcasts a common message to K UEs within a signal coverage area and senses location information of the UEs based on received echoes; provided with N BS There are P independent separable paths between the BS of the root antenna and each single antenna UE, then the radar return signal is expressed as:
Figure FDA0004000188250000011
wherein ,
Figure FDA0004000188250000012
is a radar echo signal in the time delay angle domain, a>
Figure FDA0004000188250000013
Is the fading coefficient of the radar channel, alpha is N for distributing power to the signal on each antenna BS ×N BS Dimensional matrix, precoding matrix P multiplexes signals into eachOn the antenna, x TD Is a transmitted symbol vector in the delay-doppler domain, < >>
Figure FDA0004000188250000021
Is an additive white gaussian noise column vector,/->
Figure FDA0004000188250000022
Is N for spatially expanding a signal BS ×N BS Is a discrete fourier transform matrix of a (θ) i,p )＝a(θ i,p )a T (θ i,p )，
Figure FDA0004000188250000023
Is the steering vector of the antenna array, where θ i,p Is the relative angle (.) T and (·)H Representing transpose and Hermite transpose, respectively;
Figure FDA0004000188250000024
Figure FDA0004000188250000025
MN x MN dimensional permutation matrices representing the relative delay effects, wherein,
Figure FDA0004000188250000026
Figure FDA0004000188250000027
a diagonal matrix of MN x MN representing the effect of relative doppler shift, wherein +.>
Figure FDA0004000188250000028
M and N are denoted as the number of subcarriers and the number of slots respectively,
Figure FDA0004000188250000029
and
Figure FDA00040001882500000210
The round trip delay index and the Doppler shift index related to the ith path of the ith UE;
Figure FDA00040001882500000211
wherein ,τi,p and vi,p The p-th path related relative delay and relative doppler shift for the i-th UE are respectively, Δf and T are the subcarrier spacing and the slot duration, Δf=1/T.
3. The method for estimating the perception parameters of the SS-OTFS based communication perception integrated system according to claim 2, wherein the relative angle of the BS and the UE is known or estimated to be available.
4. The method for estimating a perception parameter of an SS-OTFS-based communication perception integrated system according to claim 2, wherein in step a, the formula (1) is transformed and rewritten as:
Figure FDA0004000188250000031
wherein ,
Figure FDA0004000188250000032
corresponds to a set of delay and doppler shift indices; taking the set of values as a column in a high-dimensional matrix phi, and selecting the round trip delay index to be within the range +.>
Figure FDA0004000188250000033
The Doppler index selection range is +.>
Figure FDA0004000188250000034
For the sense matrix Φ, h has non-zero values +.>
Figure FDA0004000188250000035
Otherwise, zero is set, so that a sparse vector is formed; wherein Φ is MNN BS The dimension matrix of the xLM (2N-1), L is the number of separable relative angles; let phi be +.>
Figure FDA0004000188250000036
A dimension matrix; the perceptual parameters are inferred by estimating the locations of non-zero values in the sparse vector h.
5. The method for estimating a perception parameter of an SS-OTFS-based communication perception integrated system according to claim 2, wherein in step B, Φ is subjected to SVD decomposition Φ=uΛv H And unitary transforming equation (5), i.e
Figure FDA0004000188250000037
The method comprises the following steps:
r＝Ψh+ω (6)
wherein ,
Figure FDA0004000188250000038
Ψ＝U HΦ and
Figure FDA0004000188250000039
Omega is 0 as mean and beta as covariance matrix -1 I is gaussian noise, β is the accuracy of the noise.
6. The method for estimating sensing parameters of an SS-OTFS-based communication sensing integrated system according to claim 2, wherein in step C, the iterative process of information in the UAMP-SBL algorithm includes six steps of:
step C1: initializing and setting basic parameters: let λ= ΛΛ H 1,1 is an element wholeA column vector of 1 is used for the column,
Figure FDA0004000188250000041
∈ (0) ＝0.01，
Figure FDA0004000188250000042
s=0, 0 is a column vector sum of all 0 elements and t=0;
step C2: calculating τ p and p：
Figure FDA0004000188250000043
Figure FDA0004000188250000044
wherein·represents the dot product of two vectors;
step C3, calculating v z ，
Figure FDA0004000188250000045
and
Figure FDA0004000188250000046
Figure FDA0004000188250000047
Figure FDA0004000188250000048
Figure FDA0004000188250000049
Where/represents two vector dot divisions;
step (a)And C4: calculating τ s and s：
Figure FDA00040001882500000410
Figure FDA00040001882500000411
step C5: calculating τ q and q：
Figure FDA00040001882500000412
Figure FDA00040001882500000413
step C6: calculating the mean of sparse vectors
Figure FDA00040001882500000414
Variance τ h Parameter->
Figure FDA00040001882500000415
and ∈：
Figure FDA00040001882500000416
Figure FDA00040001882500000417
Figure FDA00040001882500000418
Figure FDA0004000188250000051
7. the method for estimating the perception parameters of the SS-OTFS-based communication perception integrated system according to claim 2, wherein in step D, the updated basic parameters including the mean and variance of the estimated values of the sparse vector h are returned to step C2 for the next iteration.
8. The method for estimating the perceptual parameters of the SS-OTFS-based communication perceptual integrated system of claim 2, wherein in step E, the estimated sparse vector h is subjected to non-zero position recognition to obtain a set of delay indexes and doppler shift indexes of corresponding positions, and then they are converted into relative delay and relative doppler shift by the formula (4), so as to obtain the required perceptual parameters.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116709379A (en) * 2023-05-22 2023-09-05 哈尔滨工业大学（深圳） Performance optimization method, device, electronic device and storage medium
CN118300947A (en) * 2023-08-28 2024-07-05 浙江大学 A low-complexity method for signal recovery and sensing positioning estimation in multi-user communication
CN119364525A (en) * 2024-10-24 2025-01-24 西安电子科技大学 An OTFS secure transmission method, system, device and medium based on synaesthesia integration and frequency domain power allocation

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112882005A (en) * 2021-01-14 2021-06-01 东南大学 OTFS radar target parameter estimation method based on Bayesian learning
CN113852579A (en) * 2021-09-10 2021-12-28 电子科技大学 A low-dimensional subspace OTFS channel estimation method
CN115396263A (en) * 2022-07-29 2022-11-25 北京邮电大学 A Parameter Estimation Method for OTFS Communication-Sensing Integrated Signal Target

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112882005A (en) * 2021-01-14 2021-06-01 东南大学 OTFS radar target parameter estimation method based on Bayesian learning
CN113852579A (en) * 2021-09-10 2021-12-28 电子科技大学 A low-dimensional subspace OTFS channel estimation method
CN115396263A (en) * 2022-07-29 2022-11-25 北京邮电大学 A Parameter Estimation Method for OTFS Communication-Sensing Integrated Signal Target

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MAN LUO ET AL: "Unitary Approximate Message Passing for Sparse Bayesian Learning", 《IEEE TRANSACTION》, 24 September 2021 (2021-09-24), pages 3 - 4 *
SHUANGYANG LI ET AL: "A Novel ISAC Transmission Framework Based on Spatially-Spread Orthogonal Time Frequency Space Modulation", 《IEEE JOURNAL》, 2 March 2022 (2022-03-02), pages 2 - 8 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116709379A (en) * 2023-05-22 2023-09-05 哈尔滨工业大学（深圳） Performance optimization method, device, electronic device and storage medium
CN118300947A (en) * 2023-08-28 2024-07-05 浙江大学 A low-complexity method for signal recovery and sensing positioning estimation in multi-user communication
CN118300947B (en) * 2023-08-28 2025-03-07 浙江大学 Low-complexity multi-user communication signal recovery and perception positioning estimation method
CN119364525A (en) * 2024-10-24 2025-01-24 西安电子科技大学 An OTFS secure transmission method, system, device and medium based on synaesthesia integration and frequency domain power allocation

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