Communication-aware channel construction method, apparatus, device, storage medium, and program

Abstract

The application discloses a kind of communication perception channel construction method, device, equipment, storage medium and program, applied to wireless communication technical field, wherein, the method comprises: according to the scattering signal of acquisition, constructs quasi-static multipath channel model, and determines the prior distribution of the frequency domain channel gain vector of quasi-static multipath channel model;Perception channel model and communication channel model are established based on the quasi-static multipath channel model;The posterior probability density function of the channel gain of the perception channel model is determined based on the prior distribution;Based on the posterior probability density function and the communication channel model are soft fused, obtain the communication perception channel.The embodiment of the application can realize the fusion of communication stage and perception stage, reduce signal loss, can improve communication quality, enhance perception accuracy, can improve the satisfaction degree of user to system.

Description

Methods, apparatus, devices, storage media, and programs for constructing communication sensing channels Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method, apparatus, device, storage medium, and program for constructing a communication sensing channel. Background Technology

[0002] The main challenges facing mobile communication are the frequency and time selectivity caused by multipath fading channels. Orthogonal Frequency Division Multiplexing (OFDM) technology, with its superior resistance to frequency fading, has become a core technology in mobile communication systems. Information transmission in OFDM systems is divided into two stages: sensing (channel estimation) and communication (signal detection). Currently, both stages employ a "hard fusion" method, where the channel estimator directly delivers the estimated channel state information to the communication detector, which then treats the estimate as the actual value for symbol detection. Like traditional hard-decision loss information, hard fusion fails to fully utilize the soft information from channel estimation and the statistical characteristics of the communication channel model, resulting in reduced communication quality and low sensing accuracy. Summary of the Invention

[0003] This invention provides a method, apparatus, device, storage medium, and program for constructing a communication sensing channel, so as to realize the integration of the communication stage and the sensing stage, reduce signal loss, improve communication quality, and enhance sensing accuracy.

[0004] According to one aspect of the present invention, a method for constructing a communication-aware channel is provided, wherein the method includes:

[0005] A quasi-static multipath channel model is constructed based on the acquired scattering signal, and the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model is determined.

[0006] Based on the aforementioned quasi-static multipath channel model, a sensing channel model and a communication channel model are established.

[0007] Determine the posterior probability density function of the channel gain of the sensing channel model;

[0008] The communication sensing channel is obtained by soft fusion of the posterior probability density function and the communication channel model.

[0009] According to another aspect of the present invention, a communication-aware channel construction apparatus is provided, wherein the apparatus comprises:

[0010] The signal acquisition module is used to construct a quasi-static multipath channel model based on the acquired scattered signal, and to determine the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model.

[0011] The model determination module is used to establish a sensing channel model and a communication channel model based on the quasi-static multipath channel model.

[0012] The perception probability module is used to determine the posterior probability density function of the channel gain of the perception channel model;

[0013] The soft fusion module is used to perform soft fusion based on the posterior probability density function and the communication channel model to obtain the communication sensing channel.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the communication-aware channel construction method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the communication-aware channel construction method according to any embodiment of the present invention.

[0019] The technical solution of this invention constructs a quasi-static multipath channel model based on the received scattered signal, determines the frequency domain channel gain vector according to the quasi-static multipath channel model, constructs the prior distribution of the frequency domain channel gain vector, establishes a sensing channel model and a communication channel model for the quasi-static multipath channel model, establishes the posterior probability density function of the channel gain of the sensing channel model, and obtains the communication sensing channel based on the soft fusion of the posterior probability density function and the communication channel model. This can realize the fusion of the communication stage and the sensing stage, reduce signal loss, improve communication quality, enhance sensing accuracy, and improve user satisfaction with the system.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 is a flowchart of a communication-aware channel construction method according to an embodiment of the present invention;

[0023] Figure 2 is a flowchart of another communication-aware channel construction method provided according to an embodiment of the present invention;

[0024] Figure 3 is a flowchart of another communication-aware channel construction method provided according to an embodiment of the present invention;

[0025] Figure 4 is a diagram illustrating an implementation example of constructing a communication-aware channel according to an embodiment of the present invention.

[0026] Figure 5 is an example diagram of the Gaussian capacity variation of a communication sensing channel according to an embodiment of the present invention;

[0027] Figure 6 is a schematic diagram of a communication sensing channel construction device according to an embodiment of the present invention;

[0028] Figure 7 is a schematic diagram of the structure of an electronic device that implements the communication-aware channel construction method of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Figure 1 is a flowchart of a communication sensing channel construction method according to an embodiment of the present invention. This embodiment is applicable to the fusion of communication channels and sensing channels. The method can be executed by a communication sensing channel construction device, which can be implemented in hardware and / or software. As shown in Figure 1, the method includes:

[0032] Step 110: Construct a quasi-static multipath channel model based on the acquired scattering signal, and determine the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model.

[0033] Scattered signals can be formed when communication signals encounter inhomogeneous media, obstacles, or particles during propagation, causing them to change their propagation direction and scatter in various directions. The quasi-static multipath channel model is a model describing the characteristics of wireless communication channels. In this model, there are multiple propagation paths from the transmitter to the receiver, and each propagation path can correspond to a scattered signal. This quasi-static multipath multichannel model can be represented as follows:

[0034]

[0035] In the above formula, t is time, and τ is... l It is the time delay of the l-th path, c l Let L be the scattered signal of the l-th path, L be the multipath number, and δ() represent the Dirac function. This model can represent the superposition of signals from multiple paths.

[0036] Specifically, the frequency domain channel gain vector can describe the signal gain characteristics in the channel in vector form. The frequency domain channel gain vector can be determined based on the quasi-static multipath channel model. The k-th component of the frequency domain channel gain vector h can be in the following form:

[0037]

[0038] Converting the above form to a vector gives:

[0039] h=Ψ(τ)c

[0040] Among them, c T =(…,c l ,…), Ψ(τ)=(…,ψ l ,…),

[0041] τ represents the time delay vector; τ l The time delay of the l-th path is represented by ; k represents the k-th component of h; T represents the duration of the OFDM symbol excluding the prefix.

[0042] Based on the above embodiments, a prior distribution is constructed based on the frequency domain channel gain vector. The function of this prior distribution can be expressed as follows:

[0043]

[0044] Wherein, the covariance matrix Π depends on the time delay power spectrum of the channel of the scattered signal; N represents the dimension of the frequency domain channel gain vector h, and N can be the number of subcarriers of the frequency domain channel;

[0045] If Π is not of rank, the above formula is modified to:

[0046]

[0047] In the formula This represents the pseudo-reversal of Π.

[0048] Step 120: Establish a sensing channel model and a communication channel model based on the quasi-static multipath channel model.

[0049] The sensing channel model can be a model that senses information through signals under a quasi-static multipath channel model. This sensing channel model is determined by information such as pilot signals and noise signals. The communication channel model can be a communication model constructed based on signals under a quasi-static multipath channel model.

[0050] In this embodiment of the invention, a sensing channel model and a communication channel model can be constructed based on the signal characteristics of a quasi-static multipath channel model. The sensing channel model and the communication channel model can be represented as follows:

[0051] y s =diag(x s h+w s

[0052] y c =diag(x c h+w c

[0053] Where y s and y c These represent the received signal vectors during the sensing and communication phases, respectively. s Represents the pilot signal vector, diag(x) s ) is x s The diagonalized matrix. x c It is the transmitted signal vector during the communication phase, diag(x) c ) is x c The diagonalized matrix. Assuming the channel coherence time is much longer than the duration of a transmission block, the channel gain h is the same for the sensing and communication phases. s and w c Let N and Y represent the noise vectors in the sensing and communication phases, respectively. Assume the noise is a complex additive white Gaussian noise (CAWGN) channel with a total noise power spectral density of N0.

[0054] Step 130: Determine the posterior probability density function of the channel gain of the sensing channel model based on the prior distribution.

[0055] In this embodiment of the invention, the channel gain can be the change in amplitude of the signal corresponding to the sensing channel model after passing through the sensing signal. The channel gain can be determined based on the sensing channel model, and the channel gain can be affected by the path loss, shadowing fading, and multipath effect of the sensing channel model. The posterior probability density function of the channel gain can be described by the probability distribution of the channel gain based on the collected scattered signal using Bayes' theorem. This posterior probability density function can be calculated by Bayes' theorem based on the probability density function of the prior distribution (the probability estimate of the channel gain when there is no observation data), the likelihood function (the probability of receiving the signal given the channel gain), and the evidence factor (the marginal probability of receiving the signal). The posterior probability density function can be expressed as follows:

[0056]

[0057] Where ∑ is the covariance matrix of the posterior probability density function, and h LS =diag -1 (x s )y s It is the frequency domain least squares estimate of the channel gain, γ s This represents the perceived signal-to-noise ratio. Step 140: Softly fuse the posterior probability density function with the communication channel model to obtain the communication sensing channel.

[0058] Soft fusion can refer to the process of probabilistically fusing the perception channel model and the communication information model through a posterior probability density function. This soft fusion can be achieved by substituting the variables in the communication channel model into the posterior probability density function.

[0059] Specifically, using the posterior probability density function p(h|y) s Eliminate the communication channel model p(y) c |x c The variable h in h) is obtained as follows:

[0060] p(y c |x c y s )=∮p(y c |x c h)p(h|y s )dh

[0061] Where p(y) c |x c y s (x) is the signal vector transmitted through a given communication channel. c and the received signal vector y of the sensing channel s The communication sensing channel is obtained after probabilistic fusion of sensing and communication.

[0062] In this embodiment of the invention, a quasi-static multipath channel model is constructed using the received scattered signal. A frequency domain channel gain vector is determined according to the quasi-static multipath channel model, and a prior distribution of this frequency domain channel gain vector is constructed. A sensing channel model and a communication channel model are established for the quasi-static multipath channel model. A posterior probability density function of the channel gain of the sensing channel model is established. Based on the soft fusion of the posterior probability density function and the communication channel model, a communication-sensing channel is obtained. This enables the fusion of the communication and sensing stages, reduces signal loss, improves communication quality, enhances sensing accuracy, and increases user satisfaction with the system.

[0063] Based on the above embodiments of the invention, the method further includes: determining the Gaussian capacity of the communication sensing channel, and transmitting the sensing signal vector X of the communication sensing channel. s Satisfying E[‖X s || 2 ]≤NE s Constraints, and in all In the case of mutual information I(X) c ;Y c |Y s The maximum value obtained is taken as the Gaussian capacity of the communication sensing channel, wherein the X c Y represents the communication transmission signal vector of the communication sensing channel. sY represents the sensing and receiving signal vector of the communication sensing channel. c π(x) represents the communication received signal vector of the communication sensing channel. c ) represents the prior distribution function of the communication transmission signal of the communication sensing channel. Let N be the set of signals on each subcarrier in the communication sensing channel that satisfy a zero-mean Gaussian vector distribution with zero energy, and let E be the number of subcarriers in the communication sensing channel. s E[] represents the energy of the signal on each subcarrier of the communication sensing channel, and E[] represents the expectation.

[0064] In this embodiment of the invention, Gaussian capacity can be a parameter that measures the channel capacity of a communication sensing channel. Gaussian capacity can be the mutual information when the input distribution is Gaussian. Specifically, the communication sensing channel is a vector Gaussian distribution, where both the mean vector and the covariance matrix are x. c The function. The Shannon channel capacity of this channel is defined as:

[0065]

[0066] in, It represents the supremum of all input distributions that satisfy the power constraint.

[0067] E[] represents the expectation; E c E represents the maximum energy of the signal on each subcarrier of the communication channel. s I(X) represents the maximum signal capability on each subcarrier of the sensing channel; c ;Y c |Y s ) indicates that given Y s X below c and Y c Mutual information, mutual information represents X c and Y c The degree of information correlation; X c Y represents the transmitted signal vector during the communication phase. s Y represents the received signal vector during the sensing phase. c Let represent the received signal vector during the communication phase, π() represent the prior distribution function, and N represent the number of subcarriers in the communication sensing channel.

[0068] make Let represent the set of all zero-mean Gaussian vector distributions that satisfy the energy distribution. Then, the Gaussian capacity of the communication sensing channel generated by soft fusion is defined as:

[0069]

[0070] The derivation process of the Gaussian capacity of the aforementioned communication sensing channel includes:

[0071] First find I(X) c ;Y c |y s ), and then Y s Seeking expectations.

[0072] Given mutual information I(X) c ;Y c |y s The calculation process of ) is as follows:

[0073] I(X c ;Y c |y s )=h(Y c |y s )-h(Y c |X c y s )in,

[0074]

[0075] τ represents the time delay vector; τ l denoted by l, represents the delay of the l-th path; k represents the k-th component of the frequency domain channel gain vector h; T represents the duration of the OFDM symbol excluding the prefix.

[0076] Given X c and y s Conditional differential entropy:

[0077] h(Y c |X c ;y s )=Nlog(πe)+E[log|diag(X c )∑diag H (X c )+N0I|]

[0078] Where E[·] is a relation to X c The expected value of the probability distribution is calculated, where I represents the identity matrix, N0 represents the noise power spectral density, and diag() denotes the diagonalization matrix. Eigenvalue decomposition is performed on the covariance matrix ∑.

[0079] ∑=QΛQ H

[0080] Where Λ=diag(…,λ) k The diagonal elements of ∑ are the eigenvalues ​​of ∑, diag() denotes the diagonalization of the matrix, and Q is a unitary matrix. Since Qx c With x c Their probability distributions are exactly the same, therefore:

[0081]

[0082] Where N0 represents the noise power spectral density.

[0083] To ensure that the average signal noise ratio (SNR) is the same for each subchannel of OFDM, i.e., γ c =E[|x c (k)| 2 ] / N0, ρ=|x c (k)| 2 / N0, where ρ represents the instantaneous signal-to-noise ratio. Since x c (k) follows a Gaussian distribution with a mean of zero, therefore ρ follows an exponential distribution:

[0084]

[0085] Substituting into the differential entropy expression, we have

[0086]

[0087] In the formula, Γ(a,z) is the complementary gamma function, defined as follows:

[0088] Γ(a,z)=∫ z ∞ t a-1 e -t dt

[0089] When z=0, Γ(a,0)=Γ(a).

[0090] Considering the computational problem of quasi-static channels, to find h(Y)... c |y s First, find the conditional distribution p(y) for a given h. c |hy s ), and then to hy s Find the expected value of the distribution.

[0091] When X c When Y follows a Gaussian distribution c It also follows a Gaussian distribution with a mean E[Y]. c ] = 0, covariance matrix R(h) = E[Y c Y c H ]for

[0092] R(h)=diag(h)E[X c X c H]diag H (h)+N0I

[0093] When power is distributed equally, E[X] c X c H ] = E c I, therefore, have

[0094] R(h)=N0[I+γ c diag(h)diag(h * )]

[0095] Substitute into the following formula

[0096]

[0097] Then there is

[0098]

[0099] Here p(h|y) s The distribution is posterior. Therefore, the given time-sensing signal y is obtained. s Gaussian capacity:

[0100]

[0101] Figure 2 is a flowchart of another communication-aware channel construction method according to an embodiment of the present invention. The embodiment of the present invention is a concretization based on the above embodiment, describing the construction process of the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model. Referring to Figure 2, the method provided by the embodiment of the present invention specifically includes the following steps:

[0102] Step 210: Construct a quasi-static multipath channel model based on the obtained scattering signal according to the orthogonal frequency division multiplexing system model.

[0103] In this embodiment of the invention, the orthogonal frequency division multiplexing system model can be a model that modulates the data stream using the orthogonality between channel subcarriers. It can be used as a quasi-static multipath channel model based on the orthogonal frequency division multiplexing system model. The collected scattering signal can be substituted into the orthogonal frequency division multiplexing system model, and the orthogonal frequency division multiplexing system model can be used as a quasi-static multipath channel model.

[0104] Step 220: Generate the frequency domain channel gain vector of the quasi-static multipath channel model and determine the covariance matrix of the frequency domain channel gain vector.

[0105] Specifically, the frequency domain channel gain vector is determined based on the created quasi-static multipath channel model, which is as follows:

[0106]

[0107] Where τ l It is the time delay of the l-th path, c l Let L be the scattered signal of the l-th path, and L be the multipath number. Taking a quasi-static multipath channel model with N subcarriers as an example, and ignoring the duration T of the OFDM signal without considering the prefix, the corresponding frequency domain channel gain vector can be expressed as:

[0108]

[0109] Represented in vector form as

[0110] h=Ψ(τ)c

[0111] In the formula c T =(…,c l ,…), Ψ(τ)=(…,ψ l ,…).

[0112] The covariance matrix Π of the channel gain vector h is:

[0113] E[hh H ]=Ψ(τ)E[cc H ]Ψ H (τ)

[0114] Let P = E[cc] H Assuming the multipath scattering of the channel is uncorrelated, then P = diag(…, E[|c l | 2 The matrix ],…) is a diagonal matrix whose main diagonal elements form a vector that corresponds precisely to the channel's delay power spectrum. Furthermore,

[0115]

[0116] For ease of discussion, let E[|h(k)| 2 The channel gain is 1, meaning it does not change the average energy of the transmitted and received signals. Let the pilot signal x... s The average energy E of each component s If they are equal, then the perceived signal-to-noise ratio is defined as follows:

[0117] Step 230: Construct a complex Gaussian random vector with zero mean as a prior distribution based on the frequency domain channel gain vector.

[0118] Specifically, for the obtained frequency domain channel gain vector, a corresponding complex Gaussian random vector with zero mean is determined, i.e.

[0119]

[0120] The covariance matrix is ​​determined by the time delay power spectrum of the channel in the quasi-static multipath channel model.

[0121] If ∏ is not of rank, the above equation is modified to:

[0122]

[0123] In the formula This represents the pseudo-reversal of Π.

[0124] Step 240: Establish a sensing channel model and a communication channel model based on the quasi-static multipath channel model.

[0125] Step 250: Determine the posterior probability density function of the channel gain of the sensing channel model.

[0126] Step 260: Softly fuse the posterior probability density function and the communication channel model to obtain the communication sensing channel.

[0127] Furthermore, based on the above embodiments of the invention, the complex Gaussian random vector includes at least:

[0128]

[0129] Where h represents the frequency domain channel gain vector, Π represents the covariance matrix, and the covariance matrix is:

[0130] Π=E[hh H ]=Ψ(τ)E[cc H ]Ψ H (τ), h=Ψ(τ)c, c T =(…,c l ,…), Ψ(τ)=(…,ψ l ,…), E[hh H ] represents the covariance matrix of the frequency domain channel gain vector h, c l τ represents the scattered signal of the l-th path; τ represents the time delay vector; τ l The time delay of the l-th path is represented by ; k represents the k-th component of h; T represents the duration of the OFDM symbol excluding the prefix.

[0131] If Π is not of rank, then:

[0132]

[0133] In the formula This represents the pseudo-reversal of Π.

[0134] Based on the above embodiments of the invention, a sensing channel model and a communication channel model are established based on prior distribution, including:

[0135] Determine the initial sensing channel model and the initial communication channel model;

[0136] Substituting the frequency domain channel gain vector of the quasi-static multipath channel model into the initial sensing channel model and the initial communication channel model yields the sensing channel model and the communication channel model.

[0137] In this embodiment of the invention, sensing and communication are performed using a quasi-static multipath channel model. That is, the OFDM system operates in a slow-fading quasi-static channel, the data stream is transmitted in blocks, the channel gain remains constant within a transmission block and is independent between blocks, and each transmission block consists of two stages: sensing and communication. The frequency domain system model for sensing and communication is as follows:

[0138] y s =diag(xs)h+w s

[0139] y c =diag(x c h+w c

[0140] Among them, y s and y c These represent the received signal vectors during the sensing and communication phases, respectively. s Represents the pilot signal vector, diag(x) s ) is x s The diagonalized matrix. x c It is the transmitted signal vector during the communication phase, diag(x) c ) is x c The diagonalized matrix. Assuming the channel coherence time is much longer than the duration of a transmission block, the channel gain h is the same for the sensing and communication phases. s and w c Let N represent the noise vector during the sensing and communication phases. Assume the noise is a complex additive white Gaussian noise (CAWGN) channel with a total noise power spectral density of N0.

[0141] Based on the above embodiments of the invention, the initial sensing channel model and the initial communication channel model include:

[0142] y s =diag(x s h+w s ;y c =diag(x c h+wc ;

[0143] y s and y c Let x represent the signal vectors formed by the scattered signals received during the sensing phase and the signal vectors formed by the scattered signals received during the communication phase, respectively. s x represents the pilot signal vector. c It is the transmitted signal vector during the communication phase, diag() denotes the diagonalization matrix, w s and w c The noise vector represents the sensing and communication phases, and h represents the frequency domain channel gain vector.

[0144] In this embodiment of the invention, the initial sensing channel model can be composed of a signal vector formed by the scattered signals received during the sensing phase, a pilot signal vector, and a noise vector from the sensing phase. The initial communication channel model can be determined by the scattered signals, transmitted signals, and noise received during the sensing phase. The initial sensing channel model and the initial communication channel model are respectively expressed as: y s =diag(x s h+w s ;y c =diag(x c h+w c In this case, the channel gain h is the same for the sensing phase and the communication phase.

[0145] Figure 3 is a flowchart of another communication-aware channel construction method provided by an embodiment of the present invention. The embodiment of the present invention is a concretization based on the above-described embodiment of the invention, describing the soft fusion of the communication stage and the sensing stage. Referring to Figure 3, the method provided by the embodiment of the present invention specifically includes the following steps:

[0146] Step 310: Construct a quasi-static multipath channel model based on the acquired scattering signal, and determine the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model;

[0147] Step 320: Establish a sensing channel model and a communication channel model based on the quasi-static multipath channel model;

[0148] Step 330: Expand the exponential part of the sensing channel model and simplify the expanded exponential part according to the energy relationship of the pilot sequence of the static multipath channel model.

[0149] In this embodiment of the invention, the exponential part of the sensing channel model can be expanded to obtain:

[0150]

[0151] Where Re(·) denotes taking the real part.

[0152] Due to the energy of the pilot sequence ||x s || 2 =NE s , then diag H (x s )diag(x s ) = E s I. We will ||diag(x) s )H‖ 2 =E s h H h and Substituting into the above equation and ignoring constants that are independent of h, we get:

[0153]

[0154] Among them, h LS =diag -1 (x s )y s It is the frequency domain least squares estimate of the channel gain.

[0155] Step 340: Perform Bayesian filtering on the simplified result of the sensing channel model to obtain the posterior probability density function.

[0156] In this embodiment of the invention, the Bayesian formula for processing the sensing channel model is:

[0157]

[0158] Ignoring constants that are independent of the channel gain h, we have

[0159]

[0160] Let ∑ -1 =Π -1 +γ s I, then ∑=Π(I+γ s Π) -1 Substituting into the above formula, we have

[0161]

[0162] Performing a quadratic formulation on the above equation yields...

[0163] p(h|y s )∝exp(-(h-γ s ∑h LS ) H ∑ -1(h-γ s ∑h LS ))

[0164] The above equation represents the N-dimensional vector Gaussian distribution of the channel gain h. Based on the properties of the probability density function, the posterior distribution of the channel gain is:

[0165]

[0166] From the above equation, it can be seen that ∑ is the covariance matrix of the posterior distribution, and the mean vector corresponds to the maximum value of the posterior distribution. Therefore, h MAP =γ s Σh LS It is the maximum a posteriori estimate of the channel gain.

[0167] In the posterior distribution, although Π is not necessarily full rank, I+γ s Π is full rank, and its inverse matrix is ​​(I+γ). s Π) -1 exist.

[0168] Step 350: Eliminate the channel gain in the communication channel model based on the posterior probability density function to obtain the communication-aware channel, where the channel gain is a variable.

[0169] Specifically, using the posterior distribution p(h|y) s Eliminate the communication channel model p(y) c |x c The variable h in h) gives p(y) c |s c y s )=∮p(y c |x c h)p(h|y s )dh

[0170] Where p(y) c |x c y s () is given x c and y s The communication-aware channel is obtained by fusing time-awareness and communication probability.

[0171] Specifically, when the communication channel sends signal x c Given that the channel gain H is a complex Gaussian random vector, the communication channel output Y... c It is also a complex Gaussian vector, and its mean is...

[0172] E[Y c ] = diag(x c E[H]

[0173] And E[H] = hMAP ,so

[0174] E[Y c ] = diag(x c )h MAP

[0175] Y c The covariance matrix Ξ(x c )=E[(Y c -diag(x c )h MAP (Y) c -diag(x c )h MAP ) H ]for

[0176] Ξ(x c ) = diag(x c )E[(hh MAP (hh) MAP ) H ]diag H (x c )+N0I

[0177] Note that the covariance matrix of the posterior distribution is Σ, therefore we have

[0178] Ξ(x c ) = diag(x c )∑diag H (x c )+N0I

[0179] From this, we can obtain the known (x) c y s The conditional distribution of probabilistic fusion in time-sensing communication is as follows:

[0180]

[0181] The above equation is the communication-aware channel model for soft estimation after probability fusion. In this model, the communication signal Y... c The mean of the conditional distribution and the sensed received signal y s The covariance matrix is ​​related to the sensed signal y, but the covariance matrix is ​​not related to the sensed signal y s No. The covariance matrix is ​​related to the transmitted signal in communication; therefore, the greater the input power of the signal source, the greater the interference noise it causes.

[0182] Furthermore, based on the above embodiments of the invention, the Gaussian capacity of the communication sensing channel is determined by simulation using the Monte Carlo method.

[0183] In this embodiment of the invention, the communication sensing channel is a vector Gaussian distribution, with both the mean vector and the covariance matrix being x. c The function. The Shannon channel capacity of this channel is defined as...

[0184]

[0185] In the formula, It represents the supremum of all input distributions that satisfy the power constraint.

[0186] E[] represents the expectation; E c E represents the energy of the signal on each subcarrier of the communication channel. s I(X) represents the energy of the signal on each subcarrier of the sensing channel; c ;Y c |Y s ) indicates that given Y s X below c and Y c Mutual information; X c Y represents the communication signal vector. s Y represents the vector of the sensed and received signal. c These represent the communication received signal vectors, respectively.

[0187] The mutual information when the input distribution is Gaussian is used as an evaluation metric, which is called Gaussian capacity. Let... Let represent the set of all zero-mean Gaussian vector distributions that satisfy the energy distribution. Then, the Gaussian capacity of a sensing communication channel based on soft fusion is defined as:

[0188]

[0189] The Gaussian capacity of the soft-fusion communication sensing channel can be derived through the following process. We first calculate I(X) c ;Y c |y s ), and then Y s Seeking expectations.

[0190] Mutual information I(X) c ;Y c |y s The calculation process of ) is as follows:

[0191] I(X c ;Y c |y s )=h(Y c |y s )-h(Y c |X c y s )

[0192] in,

[0193]

[0194] τ represents the time delay vector; τ l denoted by l, represents the delay of the l-th path; k represents the k-th component of the frequency domain channel gain vector h; T represents the duration of the OFDM symbol excluding the prefix.

[0195] Given X c and y s Conditional differential entropy:

[0196] h(Y c |X c ;y s )=Nlog(πe)+E[log|diag(X c )∑diag H (X c )+N0I|]

[0197] In the formula, E[·] represents X. c The expected value of the probability distribution is calculated, where I represents the identity matrix, N0 represents the noise power spectral density, and diag() denotes the diagonalization matrix. Eigenvalue decomposition is performed on the covariance matrix ∑.

[0198] ∑=QΛQ H

[0199] In the formula, Λ=diag(…,λ k The diagonal elements of ∑ are the eigenvalues ​​of ∑, diag() denotes the diagonalization of the matrix, and Q is a unitary matrix. Since Qx c With x c Their probability distributions are exactly the same, therefore:

[0200]

[0201] Let the average SNR of each subchannel of OFDM be the same, i.e., γ c =E[|x c (k)| 2 ] / N0, ρ=|x c (k)| 2 / N0, where ρ represents the instantaneous signal-to-noise ratio.

[0202] Because x c (k) follows a Gaussian distribution with a mean of zero, therefore ρ follows an exponential distribution.

[0203]

[0204] Substituting into the differential entropy expression, we have

[0205]

[0206] In the formula, Γ(a,z) is the complementary gamma function, defined as follows:

[0207] Γ(a,z)=∫ z ∞ t a-1 e -t dt

[0208] When z=0, Γ(a,0)=Γ(a).

[0209] In this embodiment of the invention, the communication sensing channel is determined based on a quasi-static channel, in order to calculate h(Y) c |y s First, find the conditional distribution p(y) for a given h. c |hy s ), and then to hy s Find the expected value of the distribution.

[0210] When X c When Y follows a Gaussian distribution c It also follows a Gaussian distribution with a mean E[Y]. c ] = 0, covariance matrix R(h) = E[Y c Y c H ]for

[0211] R(h)=diag(h)E[X c X c H ]diag H (h)+N0I

[0212] When power is distributed equally, E[X] c X c H ] = E c I, therefore, have

[0213] R(h)=N0[I+γ c diag(h)diag(h * )]

[0214] Substitute into the following formula

[0215]

[0216] Then there is

[0217]

[0218] in,

[0219]

[0220] γ c The signal-to-noise ratio is represented by N, and the noise power spectral density is represented by τ. l represents the time delay of the l-th path; k represents the k-th component of h; T represents the duration of the OFDM symbol excluding the prefix.

[0221] Here p(h|y) s The distribution is posterior. Therefore, the given time-sensing signal y is obtained. s Gaussian capacity

[0222]

[0223] In this embodiment of the invention, there is currently no differential entropy h(Y). c |y s The closed expression of ) is difficult to compute using traditional grid methods because it is a high-dimensional integral problem covering the entire complex plane. Considering the differential entropy h(Y) c |y s The problem is to calculate the mathematical expectation, and Gaussian samples are easy to generate. Therefore, the Monte Carlo method can be used for efficient simulation.

[0224] In one exemplary implementation, referring to Figure 4, the communication-aware channel construction process may include the following steps:

[0225] Step 1: Establish a quasi-static multipath channel model and the prior distribution of the frequency domain channel gain vector.

[0226] Let the time-domain impulse response of the multipath channel be:

[0227]

[0228] In the formula τ l It is the time delay of the l-th path, c l Let be the scattered signal of the l-th path, L be the multipath number, t be time, and δ() represent the Dirac function.

[0229] Suppose the OFDM system has N subcarriers, and the duration of the OFDM signal (ignoring the prefix) is T. The k-th component of the frequency domain channel gain vector h is:

[0230]

[0231] Represented in vector form as:

[0232] h=Ψ(τ)c

[0233] In the formula c T =(…,c l ,…), Ψ(τ)=(…,ψ l ,…); τ represents the time delay vector; τ l represents the time delay of the l-th path; k represents the k-th component of h; T represents the duration of the OFDM symbol excluding the prefix.

[0234] The covariance matrix ∏ of the channel gain vector h is:

[0235] E[hh H ]=Ψ(τ)E[cc H ]Ψ H (τ)

[0236] Let P = E[cc] H Assuming the multipath scattering of the channel is uncorrelated, then P = diag(…, E[|c l | 2 The matrix ],…) is a diagonal matrix, and the vector formed by its main diagonal elements corresponds exactly to the time delay power spectrum of the channel. From the above equation, we can see…

[0237]

[0238] For ease of discussion, let E[|h(k)| 2 The channel gain is 1, meaning it does not change the average energy of the transmitted and received signals. Let the pilot signal x... s The average energy E of each component s If they are equal, then the perceived signal-to-noise ratio is defined as follows:

[0239] The prior distribution v(h) of the channel gain vector h is usually modeled as a complex Gaussian random vector with zero mean, i.e.

[0240]

[0241] The covariance matrix Π depends on the channel's delay power spectrum; N represents the dimension of the frequency domain channel gain vector h, which can be understood as the number of subcarriers in the frequency domain channel.

[0242] If Π is not of rank, the above formula is modified to:

[0243]

[0244] In the formula This represents the pseudo-reversal of Π.

[0245] Step 2: Establish a quasi-static OFDM integrated sensing and communication system model.

[0246] Assume the OFDM system operates in a quasi-static channel with slow fading, meaning the data stream is transmitted in blocks, and the channel gain remains constant within a transmission block and is independent between blocks. Each transmission block consists of two stages: sensing and communication. The frequency domain system model for sensing and communication is as follows:

[0247] y s =diag(x s h+w s

[0248] y c =diag(x c h+w c

[0249] Where y s and y c These represent the received signal vectors during the sensing and communication phases, respectively. s Represents the pilot signal vector, diag(x) s ) is x s The diagonalized matrix. x c It is the transmitted signal vector during the communication phase, diag(x) c ) is x c The diagonalized matrix. Assuming the channel coherence time is much longer than the duration of a transmission block, the channel gain h is the same for the sensing and communication phases. s and w c Let N represent the noise vector during the sensing and communication phases. Assume the noise is a complex additive white Gaussian noise (CAWGN) channel with a total noise power spectral density of N0.

[0250] Step 3: Construct the sensing channel model and communication channel model from the OFDM-ISAC system model.

[0251] Since the noise in the OFDM sub-channels is independent of each other, the probability density function of the noise vector is:

[0252]

[0253]

[0254] Substituting the ISAC system model of quasi-static OFDM into the above two equations, we obtain the sensing channel model:

[0255]

[0256] And given x c Communication channel model for h:

[0257]

[0258] In the above formula, ‖·‖ 2 It represents the second norm of a vector.

[0259] Step 4: Derive the posterior probability density function of the channel gain and propose a maximum a posteriori estimation method for the channel gain.

[0260] Based on the perceptual channel model, the exponential part is expanded as follows:

[0261]

[0262] In the formula, Re(·) represents taking the real part. Since the energy of the pilot sequence is ||x|| s || 2 =NE s , then diag H (x s )diag(x s ) = E s I. We will ||diag(x) s )H‖ 2 =E s h H h and Substituting into the above equation and ignoring constants independent of h, we get...

[0263]

[0264] In the formula h LS =diag -1 (x s )y s It is the frequency domain least squares estimate of the channel gain.

[0265] By Bayes' formula

[0266]

[0267] Ignoring constants that are independent of the channel gain h, we have

[0268]

[0269] Let ∑ -1 =Π -1 +γ s I, then ∑=Π(I+γs Π) -1 Substituting into the above formula, we have

[0270]

[0271] Performing a quadratic formulation on the above equation yields:

[0272] p(h|y s )∝exp(-(h-γ s ∑h LS ) H ∑ -1 (h-γ s ∑h LS ))

[0273] The above equation represents the N-dimensional vector Gaussian distribution of the channel gain h. Based on the properties of the probability density function, the posterior distribution of the channel gain is:

[0274]

[0275] From the above equation, it can be seen that ∑ is the covariance matrix of the posterior distribution, and the mean vector corresponds to the maximum value of the posterior distribution. Therefore, h MAP =γ s ∑h LS It is the maximum a posteriori estimate of the channel gain.

[0276] In the posterior distribution, although Π is not necessarily full rank, I+γ s Π is full rank, and its inverse matrix is ​​(I+γ). s ∏) -1 exist.

[0277] Step 5: Propose a soft fusion method to probabilistically fuse the posterior probability density function and the communication channel model to obtain the OFDM channel model.

[0278] Using the posterior distribution p(h|y) s Eliminate the communication channel model p(y) c |x c The variable h in h) is obtained

[0279] p(y c |x c y s )=∮p(y c |x c h)p(h|y s )dh

[0280] In the formula p(y c |x c y s (x) is the given communication signal to be sent.c and sensing received signal y s The ISAC channel is obtained after fusing time-awareness and communication probabilities. To correspond with traditional hard estimation, we define this probabilistically fused channel p(y) as... c |x c y s This is called a soft estimation channel.

[0281] The above formula is derived using a simpler method below. When communication transmits signal x... c Given that the channel gain H is a complex Gaussian random vector, the received communication signal Y... c It is also a complex Gaussian vector, and its mean is...

[0282] E[Y c ] = diag(x c E[H]

[0283] And E[H] = h MAP Therefore, E[Y c ] = diag(x c )h MAP ;

[0284] Y c The covariance matrix Ξ(x c )=E[(Y c -diag(x c )h MAP (Y) c -diag(x c )h MAP ) H ]for:

[0285] Ξ(x c ) = diag(x c )E[(hh MAP (hh) MAP ) H ]diag H (x c )+N0I

[0286] Note that the covariance matrix of the posterior distribution is ∑, therefore we have

[0287] Ξ(x c ) = diag(x c )∑diag H (x c )+N0I

[0288] From this, we can obtain the known (x) c y s The conditional distribution of probabilistic fusion in time-sensing communication is:

[0289]

[0290] The above equation is the soft estimation channel model after probability fusion. From the soft estimation channel model, it can be seen that the received communication signal Y... c The mean of the conditional distribution and the received signal y s However, the covariance matrix is ​​related to the sensed received signal y. s No. The covariance matrix is ​​related to the transmitted signal in communication; therefore, the greater the input power of the signal source, the greater the interference noise it causes.

[0291] Step 6: Derivation and simulation verification of Gaussian capacity of soft fusion channel.

[0292] Given x c The time-integrated channel model is a vector Gaussian distribution, with both the mean vector and covariance matrix being x. c The function. The Shannon channel capacity of this channel is defined as...

[0293]

[0294] In the formula, E[] represents the supremum of all input distributions that satisfy the power constraint; E[] represents the expectation; E c E represents the energy of the signal on each subcarrier of the communication channel. s I(X) represents the energy of the signal on each subcarrier of the sensing channel; c ;Y c |Y s ) indicates that given Y s X below c and Y c mutual information, X c Y represents the communication signal vector. s Y represents the vector of the sensed and received signal. c This represents the vector of received communication signals.

[0295] The mutual information when the input distribution is Gaussian is used as an evaluation metric, which is called Gaussian capacity. Let... Let represent the set of all zero-mean Gaussian vector distributions that satisfy the energy distribution. Then, the Gaussian capacity of a soft-estimation channel is defined as:

[0296] The Gaussian capacity of the soft-estimation channel is derived below. We first calculate the mutual information I(X). c ;Y c |y s ), and then Y s Seeking expectations.

[0297] Mutual information I(X) c ;Y c |y s The calculation process of ) is as follows:

[0298] I(X c ;Y c |y s )=h(Y c |y s )-h(Y c |X c y s )in,

[0299]

[0300] τ represents the time delay vector; τ l denoted by l, represents the delay of the l-th path; k represents the k-th component of the frequency domain channel gain vector h; T represents the duration of the OFDM symbol excluding the prefix.

[0301] Given X c and y s Conditional differential entropy:

[0302] h(Y c |X c ;y s )=Nlog(πe)+E[log|diag(X c )∑diag H (X c )+N0I|]

[0303] In the formula, E[·] represents X. c The expected value of the probability distribution is calculated, where I represents the identity matrix, N0 represents the noise power spectral density, and diag() denotes the diagonalization matrix. Eigenvalue decomposition is performed on the covariance matrix ∑.

[0304] ∑=QΛQ H

[0305] In the formula, Λ=diag(…,λ k The diagonal elements of Σ are the eigenvalues ​​of Σ, and Q is a unitary matrix. Since Qx c With x c Their probability distributions are exactly the same, therefore

[0306]

[0307] Let the average SNR of each subchannel of OFDM be the same, i.e., γ c =E[|x c (k)| 2 ] / N0, ρ=|xc (k)| 2 / N0. Because x c (k) follows a Gaussian distribution with a mean of zero, therefore ρ follows an exponential distribution.

[0308]

[0309] Substituting into the differential entropy expression, we get:

[0310]

[0311] In the formula, Γ(a,z) is the complementary gamma function, defined as:

[0312] Γ(a,z)=∫ z ∞ t a-1 e -t dt

[0313] When z=0, Γ(a,0)=Γ(a).

[0314] Consider the computational problem of a quasi-static channel. To find h(Y)... c |y s First, find the conditional distribution p(y) for a given h. c |hy s ), and then to hy s Find the expectation of the distribution of X. c When Y follows a Gaussian distribution c It also follows a Gaussian distribution with a mean E[Y]. c ] = 0, covariance matrix R(h) = E[Y c Y c H ]for

[0315] R(h)=diag(h)E[X c X c H ]diag H (h)+N0I

[0316] When power is distributed equally, E[X] c X c H ] = E c Therefore, we have: R(h) = N0[I + γ] c diag(h)diag(h * Substitute into the following formula:

[0317]

[0318] Then there is

[0319]

[0320] Here p(h|y) s The distribution is posterior. Therefore, the given time-sensing signal y is obtained. s Gaussian capacity:

[0321]

[0322] Currently, there is no differential entropy h(Y). c |y s The closed expression of ) is difficult to compute using traditional grid methods because it is a high-dimensional integral problem covering the entire complex plane. Considering the differential entropy h(Y) c |y s The problem is to calculate the mathematical expectation, and Gaussian samples are easy to generate. Therefore, the Monte Carlo method can be used for efficient simulation.

[0323] To verify the effectiveness of this invention, numerical simulations were performed on the Gaussian capacity of the soft-estimated OFDM channel. Figure 4 is a flowchart of this invention. Figure 5 shows the Gaussian capacity of the soft-estimated OFDM channel as a function of communication SNR under different sensing SNR conditions. The OFDM system parameters are: number of subcarriers N = 64, cyclic prefix length of 8, subcarrier spacing of 15 kHz, and one OFDM symbol period of 66.7 μs. The channel model adopted is the 3GPP-recommended Extended Vechicular A (EVA) channel, and the delay power spectrum of the EVA channel is shown in Table 1. To ensure... The time delay power spectrum was normalized in the simulation.

[0324] Table 1. Delay Power Spectrum of Extended Vehicular A Channel

[0325]

[0326] For ease of comparison, Figure 5 also shows the Shannon capacity of the Rayleigh channel under ideal CSI. Numerical simulation results show that the Gaussian capacity gradually approaches the Shannon channel capacity under ideal CSI as the perceived SNR increases. When the communication SNR is less than the perceived SNR, the Gaussian capacity increases almost linearly with the communication SNR. However, when the communication SNR exceeds the perceived SNR, the growth rate of the Gaussian capacity decreases significantly. The main reason is that the soft estimation channel is a self-interference channel, and when γ... c >γ sThe time-equivalent signal-to-interference-plus-noise ratio (SIR / NOR) increases rapidly, causing channel capacity to tend to saturate. However, soft-fusion channels do not exhibit the performance plateau phenomenon of hard-fusion methods. Here, Pc / No represents the SIR / NOR, and I(X) c ;Y c |y s Ps represents the Gaussian capacity of the soft-estimated channel, Pc represents the sensing power, and Ps represents the communication power.

[0327] Figure 6 is a schematic diagram of the structure of an electronic device implementing the communication-aware channel construction method of this invention. As shown in Figure 6, the device includes:

[0328] The signal acquisition module 410 is used to construct a quasi-static multipath channel model based on the acquired scattering signal and determine the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model.

[0329] The model determination module 420 is used to establish a sensing channel model and a communication channel model based on a quasi-static multipath channel model.

[0330] The channel sensing module 430 is used to determine the posterior probability density function of the channel gain of the sensing channel model based on the prior distribution.

[0331] The soft fusion module 440 is used to perform soft fusion based on the posterior probability density function and the communication channel model to obtain the communication sensing channel.

[0332] In this embodiment of the invention, a quasi-static multipath channel model is constructed using the scattered signal received by the signal acquisition module. The frequency domain channel gain vector is determined according to the quasi-static multipath channel model, and a prior distribution of this frequency domain channel gain vector is constructed. The model determination module establishes a sensing channel model and a communication channel model based on the quasi-static multipath channel model. The channel sensing module establishes a posterior probability density function of the channel gain of the sensing channel model based on the prior distribution. The soft fusion module softly fuses the posterior probability density function with the communication channel model to obtain the communication sensing channel. This enables the fusion of the communication and sensing stages, reduces signal loss, improves communication quality, enhances sensing accuracy, and increases user satisfaction with the system.

[0333] Based on the above embodiments of the invention, it further includes: a capacity determination module, used to determine the Gaussian capacity of the communication sensing channel, specifically used to include: determining the sensing transmission signal vector X of the communication sensing channel. s Satisfying E[‖X s || 2 ]≤NE s Constraints, and in all In the case of mutual information I(X) c ;Y c |Y sThe maximum value obtained is taken as the Gaussian capacity of the communication sensing channel, wherein the X c Y represents the communication transmission signal vector of the communication sensing channel. s Y represents the sensing and receiving signal vector of the communication sensing channel. c π(x) represents the communication received signal vector of the communication sensing channel. c ) represents the prior distribution function of the communication transmission signal of the communication sensing channel. Let N be the set of signals on each subcarrier in the communication sensing channel that satisfy a zero-mean Gaussian vector distribution with zero energy, and let E be the number of subcarriers in the communication sensing channel. s E[] represents the energy of the signal on each subcarrier of the communication sensing channel, and E[] represents the expectation.

[0334] Based on the above embodiments of the invention, the signal acquisition module 410 includes:

[0335] The model building unit is used to construct a quasi-static multipath channel model from the acquired scattering signal according to the orthogonal frequency division multiplexing system model.

[0336] Gain vector unit, used to generate frequency domain channel gain vector for quasi-static multipath channel model.

[0337] The prior distribution unit is used to construct a complex Gaussian random vector with zero mean as a prior distribution based on the frequency domain channel gain vector.

[0338] Based on the above embodiments of the invention, the complex Gaussian random vector includes at least:

[0339]

[0340] Where h represents the frequency domain channel gain vector, Π represents the covariance matrix, and the covariance matrix is:

[0341] Π=E[hh H ]=Ψ(τ)E[cc H ]Ψ H (τ), h=Ψ(τ)c, c T =(…,c l ,…), Ψ(τ)=(…,ψ l ,…), E[hh H ] represents the covariance matrix of the frequency domain channel gain vector h, c l τ represents the scattered signal of the l-th path; τ represents the time delay vector; τ l The time delay of the l-th path is represented by ; k represents the k-th component of h; T represents the duration of the OFDM symbol excluding the prefix.

[0342] If Π is not of rank, then:

[0343]

[0344] In the formula This represents the pseudo-reversal of Π.

[0345] Based on the above embodiments of the invention, the model determination module 420 is specifically used to: determine the initial sensing channel model and the initial communication channel model; and substitute the frequency domain channel gain vector of the quasi-static multipath channel model into the initial sensing channel model and the initial communication channel model to obtain the sensing channel model and the communication channel model.

[0346] Based on the above embodiments of the invention, the initial sensing channel model and the initial communication channel model include:

[0347] y s =diag(x s h+w s ;y c =diag(x c h+w c ;

[0348] Among them, y s and y c Let x represent the signal vectors formed by the scattered signals received during the sensing phase and the signal vectors formed by the scattered signals received during the communication phase, respectively. s x represents the pilot signal vector. c It is the transmitted signal vector during the communication phase, diag() denotes the diagonalization matrix, w s and w c represents the noise vectors of the sensing phase and the communication phase, respectively, and h represents the frequency domain channel gain vector.

[0349] Based on the above embodiments of the invention, the perception probability module 430 includes:

[0350] The simplified element is used to expand the exponential part of the sensing channel model and simplify the expanded exponential part according to the energy relationship of the pilot sequence of the static multipath channel model.

[0351] The Bayesian unit is used to perform Bayesian filtering on the simplified results of the sensing channel model to obtain the posterior probability density function.

[0352] Based on the above embodiments of the invention, the soft fusion module 440 is specifically used to: eliminate the channel gain in the communication channel model based on the posterior probability density function to obtain the communication-aware channel, wherein the channel gain is a variable.

[0353] Based on the above embodiments of the invention, the Gaussian capacity of the communication sensing channel is determined by simulation using the Monte Carlo method.

[0354] The communication-aware channel construction apparatus provided in this embodiment of the invention can execute the communication-aware channel construction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0355] Figure 7 is a schematic diagram of the structure of an electronic device implementing the communication-aware channel construction method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0356] As shown in Figure 7, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0357] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0358] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as communication-aware channel construction methods.

[0359] In some embodiments, the communication-aware channel construction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the communication-aware channel construction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the communication-aware channel construction method by any other suitable means (e.g., by means of firmware).

[0360] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0361] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0362] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0363] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0364] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0365] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0366] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0367] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for constructing a communication-aware channel, characterized in that, The method includes: constructing a quasi-static multipath channel model based on the acquired scattered signal, and determining the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model; the frequency domain channel gain vector is the frequency domain expression of the multipath delay and scattered signal in an orthogonal frequency division multiplexing (OFDM) system; establishing a sensing channel model and a communication channel model based on the quasi-static multipath channel model; both the sensing channel model and the communication channel model are constructed based on the frequency domain channel gain vector; determining the posterior probability density function of the channel gain of the sensing channel model based on the prior distribution; performing soft fusion based on the posterior probability density function and the communication channel model, and eliminating the channel gain in the communication channel model through the posterior probability density function to obtain the communication sensing channel.

2. The method according to claim 1, characterized in that, Also includes: Determining the Gaussian capacity of the communication sensing channel includes: processing the sensing transmission signal vector of the communication sensing channel. satisfy Constraints, and in all In situations where mutual information is enabled The maximum value obtained is taken as the Gaussian capacity of the communication sensing channel, wherein, This represents the communication transmission signal vector of the communication sensing channel. This represents the sensing and receiving signal vector of the communication sensing channel. This represents the communication received signal vector of the communication sensing channel. This represents the prior distribution function of the communication transmission signal of the communication sensing channel. Let N be the set of signals on each subcarrier in the communication sensing channel that satisfy a zero-mean Gaussian vector distribution with zero energy, and let N represent the number of subcarriers in the communication sensing channel. This represents the energy of the signal on each subcarrier of the communication sensing channel. It indicates a desire for the expected value.

3. The method according to claim 1, characterized in that, The step of constructing a quasi-static multipath channel model based on the acquired scattered signal and determining the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model includes: constructing the quasi-static multipath channel model according to the orthogonal frequency division multiplexing system model based on the acquired scattered signal; generating the frequency domain channel gain vector of the quasi-static multipath channel model; and constructing a complex Gaussian random vector with zero mean as the prior distribution based on the frequency domain channel gain vector.

4. The method according to claim 3, characterized in that, The complex Gaussian random vector includes at least: in, This represents the frequency domain channel gain vector. Let the covariance matrix be: ， ， ， ， ， Represents the frequency domain channel gain vector covariance matrix This represents the scattered signal of the l-th path; Represents the time delay vector; Indicates the first The time delay of each path; This represents the k-th component of h; The duration of the OFDM symbol excluding the prefix is ​​represented; N represents the frequency domain channel gain vector. The dimension; if If the rank is not full, then: In the formula express The false rebellion.

5. The method according to claim 1, characterized in that, The step of establishing a sensing channel model and a communication channel model based on the quasi-static multipath channel model includes: determining an initial sensing channel model and an initial communication channel model; and substituting the frequency domain channel gain vector of the quasi-static multipath channel model into the initial sensing channel model and the initial communication channel model to obtain the sensing channel model and the communication channel model.

6. The method according to claim 5, characterized in that, The initial sensing channel model and the initial communication channel model include: ； ;in, and These represent the signal vectors formed by the scattered signals received during the sensing phase and the signal vectors formed by the scattered signals received during the communication phase, respectively. Represents the pilot signal vector. It is the transmitted signal vector during the communication phase. Represents a diagonalized matrix. and These represent the noise vectors for the sensing and communication phases, respectively. This represents the frequency domain channel gain vector.

7. The method according to claim 1, characterized in that, The step of determining the posterior probability density function of the channel gain of the sensing channel model includes: expanding the exponential part of the sensing channel model and simplifying the expanded exponential part according to the energy relationship of the pilot sequence of the static multipath channel model; performing Bayesian filtering on the simplified result of the sensing channel model to obtain the posterior probability density function.

8. A communication sensing channel construction device, characterized in that, The apparatus includes: a signal acquisition module, configured to construct a quasi-static multipath channel model based on the acquired scattered signal, and determine the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model; the frequency domain channel gain vector is a frequency domain expression of the multipath delay and scattered signal in an orthogonal frequency division multiplexing (OFDM) system; a model determination module, configured to establish a sensing channel model and a communication channel model based on the quasi-static multipath channel model; both the sensing channel model and the communication channel model are constructed based on the frequency domain channel gain vector; a channel sensing module, configured to determine the posterior probability density function of the channel gain of the sensing channel model based on the prior distribution; and a soft fusion module, configured to perform soft fusion with the communication channel model based on the posterior probability density function, and eliminate the channel gain in the communication channel model through the posterior probability density function to obtain the communication sensing channel.

9. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the communication-aware channel construction method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the communication-aware channel construction method according to any one of claims 1-7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the communication-aware channel construction method according to any one of claims 1-7.

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