Communication sensing channel construction method and device, equipment, storage medium and program

Abstract

The invention discloses a communication sensing channel construction method and device, equipment, a storage medium and a program, which are applied to the technical field of wireless communication, and the method comprises the following steps: constructing a quasi-static multipath channel model according to an obtained scattering signal, and determining the prior distribution of a frequency domain channel gain vector of the quasi-static multipath channel model; establishing a sensing channel model and a communication channel model based on the quasi-static multipath channel model; determining a posterior probability density function of a channel gain of the perceptual channel model based on the priori distribution; and carrying out soft fusion on the basis of the posterior probability density function and the communication channel model to obtain the communication sensing channel. According to the embodiment of the invention, fusion of a communication stage and a sensing stage can be realized, signal loss is reduced, communication quality can be improved, sensing accuracy can be enhanced, and the satisfaction degree of a user to a system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a communication perception channel construction method, device, equipment, storage medium and program. Background Art

[0002] The main problem facing mobile communications is the frequency selectivity and time selectivity caused by multipath fading channels. Orthogonal Frequency Division Multiplexing (OFDM) technology has excellent resistance to frequency fading and has become the core technology of mobile communication systems. The information transmission of the OFDM system is divided into two stages: perception (channel estimation) and communication (signal detection). At present, the "hard fusion" method is adopted in the two stages of perception and communication, that is, the channel estimator directly delivers the channel state information estimation to the communication detector, and the detector regards the estimated value as the actual value for symbol detection. Just like the traditional hard decision loss information, hard fusion does not make full use of the statistical characteristics of the channel estimation soft information and the communication channel model, and there are problems such as reduced communication quality and low perception accuracy. Summary of the invention

[0003] The present invention provides a communication perception channel construction method, device, equipment, storage medium and program to achieve the integration of the communication stage and the perception stage, reduce signal loss, improve communication quality and enhance perception accuracy.

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

[0005] Constructing a quasi-static multipath channel model according to the acquired scattered signals, and determining a priori distribution of a frequency domain channel gain vector of the quasi-static multipath channel model;

[0006] Establishing a perception channel model and a communication channel model based on the quasi-static multipath channel model;

[0007] Determining a posterior probability density function of a channel gain of the perceptual channel model;

[0008] The communication perception channel is obtained based on 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 device is provided, wherein the device comprises:

[0010] A signal acquisition module, used to construct a quasi-static multipath channel model according to the acquired scattered signals, and determine the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model;

[0011] A model determination module, used to establish a perception channel model and a communication channel model based on the quasi-static multipath channel model;

[0012] A perception probability module, used to determine a 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 perception 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 executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the communication-aware channel construction method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the communication-aware channel construction method described in any embodiment of the present invention when executed.

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

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

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

[0027] Figure 6 It is a structural schematic diagram of a communication-aware channel construction device provided according to an embodiment of the present invention;

[0028] Figure 7 It is a structural diagram of an electronic device that implements the communication-aware channel construction method of an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Figure 1 is a flow chart of a communication perception channel construction method provided according to an embodiment of the present invention. This embodiment is applicable to the case where the communication channel and the perception channel are integrated. The method can be executed by a communication perception channel construction device, which can be implemented in the form of hardware and / or software, such as Figure 1 As shown, the method includes:

[0032] Step 110: construct a quasi-static multipath channel model according to the acquired scattered signals, and determine a priori distribution of a frequency domain channel gain vector of the quasi-static multipath channel model.

[0033] Among them, the scattered signal can be a signal formed by the communication signal encountering an inhomogeneous medium, obstacles or particles during the propagation process, resulting in the change of the propagation direction and scattering in all directions. The quasi-static multipath channel model is a model that describes the characteristics of the wireless communication channel. In this model, there are multiple propagation paths for the signal from the transmitter to the receiver, and each propagation path can correspond to a scattered signal. The quasi-static multipath multi-channel model can be expressed as follows:

[0034]

[0035] In the above formula, t is time, τ l is the delay of the lth path, c l is the scattered signal of the lth path, L is the multipath number, δ() represents the Dirac function, and the model can represent the superposition of multiple path signals.

[0036] Specifically, the frequency domain channel gain vector may be a vector describing the signal gain characteristic in the channel. The frequency domain channel gain vector may be determined based on a quasi-static multipath channel model. The kth component of the frequency domain channel gain vector h may be as follows:

[0037]

[0038] Converting the above form into a vector can be:

[0039] h=Ψ(τ)c

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

[0041] τ represents the delay vector; τ l represents the delay of the lth path; k represents the kth component of h; T represents the duration of the OFDM symbol excluding the prefix;

[0042] On the basis of the above embodiment, a priori distribution is constructed based on the frequency domain channel gain vector. The function of the priori 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 rank sufficient, the above formula is modified to:

[0046]

[0047] In the formula represents the pseudo-inverse of π.

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

[0049] Among them, the perception channel model can be a model for information perception through signals under a quasi-static multipath channel model, and the perception channel model is determined by at least information such as a pilot signal and a noise signal, while the communication channel model can be a communication model constructed based on the signals under the quasi-static multipath channel model.

[0050] In the embodiment of the present invention, a perception channel model and a communication channel model may be constructed based on the signal characteristics of a quasi-static multipath channel model. The perception channel model and the communication channel model may be expressed as follows:

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

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

[0053] where ys and c Represent the received signal vectors in the sensing and communication stages respectively. s represents the pilot signal vector, diag(x s ) is x s The diagonal matrix of x. c is the transmission signal vector in the communication phase, diag(x c ) is x c Assuming that the coherence time of the channel is much longer than the duration of a transmission block, the channel gain h in the sensing and communication stages is the same. s and w c Denote the noise vectors in the perception and communication stages respectively. Assume that the noise is a complex additive white Gaussian noise (CAWGN) channel, and the total noise power spectral density is N0.

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

[0055] In an embodiment of the present invention, the channel gain may be a change in the amplitude of a signal corresponding to a perceptual channel model after passing through a perceptual signal. The channel gain may be determined based on the perceptual channel model. The channel gain may be affected by the path loss, shadow fading, and multipath effect of the perceptual channel model. The posterior probability density function of the channel gain may be based on the collected scattered signal and described by the Bayesian theorem for the probability distribution of the channel gain. The posterior probability density function may be calculated by the Bayesian theorem based on the probability density function of the prior distribution (the probability estimate of the channel gain in the absence of observed data), the likelihood function (the probability of receiving a signal given the channel gain), and the evidence factor (the marginal probability of receiving a signal). The posterior probability density function may be expressed as follows:

[0056]

[0057] Among them, ∑ is the covariance matrix of the posterior probability density function, h LS =diag -1 (x s )y s is the frequency domain least squares estimate of the channel gain, γ s Step 140: soft fusion is performed based on the posterior probability density function and the communication channel model to obtain a communication perception channel.

[0058] Among them, soft fusion can refer to the process of probabilistically fusing the perception channel model with the communication information model through the posterior probability density function. The soft fusion can be achieved by substituting the posterior probability density function into the variables in the communication channel model.

[0059] Specifically, the posterior probability density function p(h|y s ) eliminates the communication channel model p(y c |x c h) in which the variable h is:

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

[0061] Among them, p(y c |x c y s ) is the signal vector x sent by the given communication channel c and the perceived channel received signal vector y s The communication perception channel is obtained by fusing the perception and communication probabilities under the condition of .

[0062] The embodiment of the present invention constructs a quasi-static multipath channel model through the received scattered signal, determines the frequency domain channel gain vector according to the quasi-static multipath channel model, and constructs the prior distribution of the frequency domain channel gain vector, establishes a perception channel model and a communication channel model for the quasi-static multipath channel model, establishes a posterior probability density function of the channel gain of the perception channel model, and obtains a communication perception channel based on soft fusion of the posterior probability density function and the communication channel model, which can realize the fusion of the communication stage and the perception stage, reduce signal loss, improve communication quality, enhance perception accuracy, and improve user satisfaction with the system.

[0063] Based on the above-mentioned embodiment of the invention, the invention further includes: determining the Gaussian capacity of the communication perception channel, and converting the perception transmission signal vector X of the communication perception channel into s Satisfy E[‖X s ‖ 2 ]≤NE s Constraints, and in all In the case of c ; Y c |Y s ) is taken as the Gaussian capacity of the communication sensing channel, wherein X c represents the communication transmission signal vector of the communication sensing channel, Y s represents the perceived received signal vector of the communication perceived channel, Y c represents the communication receiving signal vector of the communication sensing channel, π(x c ) represents the prior distribution function of the communication transmission signal of the communication perception channel, is a set of signals on each subcarrier in the communication sensing channel that satisfy the energy zero mean vector Gaussian distribution, N represents the number of subcarriers in the communication sensing channel, E s represents the energy of the signal on each subcarrier of the communication perception channel, and E[] represents the expectation.

[0064] In the embodiment of the present invention, Gaussian capacity may be a parameter for measuring the channel capacity of a communication perception channel. Gaussian capacity may be the mutual information when the input distribution is a Gaussian distribution. Specifically, the communication perception channel is a vector Gaussian distribution, and the mean vector and the covariance matrix are both x c The Shannon channel capacity of this channel is defined as:

[0065]

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

[0067] E[] means to find the expectation; E c It represents the maximum energy of the signal on each subcarrier of the communication channel, E s Indicates the maximum capability of the signal on each subcarrier of the sensing channel; I(X c ; Y c |Y s ) means that at a given Y s X c and Y c The mutual information of X c and Y c The degree of information relevance; X c Indicates the signal vector sent during the communication phase, Y s represents the received signal vector in the perception phase, Y c They represent the received signal vector in the communication stage, π() represents the prior distribution function, and N represents the number of subcarriers in the communication perception channel.

[0068] make represents the set of all zero-mean vector Gaussian distributions that satisfy the energy, then the Gaussian capacity of the communication perception channel generated by soft fusion is defined as:

[0069]

[0070] The derivation process of the Gaussian capacity of the above communication perception channel includes:

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

[0072] Knowing the mutual information I(X c ; Y c |y s ) calculation process:

[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 delay vector; τ l represents the delay of the lth path; k represents the kth 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 s The conditional differential entropy of :

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

[0078] where E[·] is the c The probability distribution of is expected, I represents the unit matrix, N0 represents the noise power spectrum density, and diag() represents the diagonal matrix. Perform eigenvalue decomposition on the covariance matrix ∑

[0079] ∑=QΛQ H

[0080] where Λ=diag(…,λ k ,…) are the eigenvalues ​​of ∑, diag() represents the diagonalized matrix, and Q is a unitary matrix. c With x c The probability distribution of is exactly the same, so:

[0081]

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

[0083] Let the average noise ratio (SNR) of each OFDM subchannel be the same, that is, γc =E[|x c (k)| 2 ] / N0,ρ=|x c (k)| 2 / N0, where ρ represents the instantaneous signal-to-noise ratio. c (k) follows a Gaussian distribution with a mean of zero, so ρ follows an exponential distribution:

[0084]

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

[0086]

[0087] Where Γ(a,z) is the complementary gamma function, defined as

[0088] Γ(a,z)=∫ z ∞ t a-1 e -t dt

[0089] When z=0, Γ(a,0)=Γ(a).

[0090] Considering the calculation problem of quasi-static channel. To find h(Y c |y s ), first find the conditional distribution p(y) for a given h c |hy s ), then hy s Find the expected distribution of .

[0091] When X c When it follows Gaussian distribution, Y c It also obeys the Gaussian distribution, and its mean value is 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)+NOI

[0093] When the power distribution is equal, E[X c X c H ]=E c I, so

[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 ) is the posterior distribution. Thus, we get the given perceived signal y s Gaussian capacity of:

[0100]

[0101] Figure 2 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 specific embodiment based on the above embodiment, and describes the construction process of the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model. Figure 2 The method provided in the embodiment of the present invention specifically includes the following steps:

[0102] Step 210: construct a quasi-static multipath channel model based on the acquired scattered signals according to the orthogonal frequency division multiplexing system model.

[0103] In an embodiment of the present invention, the orthogonal frequency division multiplexing system model can be a model that utilizes the orthogonality between channel subcarriers to modulate the data stream, and can be a model used as a quasi-static multipath channel model based on the orthogonal frequency division multiplexing system model. The collected scattered signals 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 a frequency domain channel gain vector of the quasi-static multipath channel model, and determine a 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, and the quasi-static multipath channel model is:

[0106]

[0107] where τ l is the delay of the lth path, c l is the scattered signal of the lth path, L is the number of multipaths, and in the quasi-static multipath channel model, there are N subcarriers as an example, and the duration of the OFDM signal without considering the prefix is ​​T, then the corresponding frequency domain channel gain vector can be expressed as:

[0108]

[0109] Expressed in vector form as

[0110] h=Ψ(τ)c

[0111] Where 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 that the multipath scattering of the channel is uncorrelated, then P = diag(…, E[|c l | 2 ],…) is a diagonal matrix whose main diagonal elements consist of a vector that corresponds exactly to the delay power spectrum of the channel. In addition,

[0115]

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

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

[0118] Specifically, a complex Gaussian random vector with a mean of zero is determined for the obtained frequency domain channel gain vector, that is,

[0119]

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

[0121] If ∏ is not full rank, the above formula is modified to

[0122]

[0123] In the formula represents the pseudo-inverse of π.

[0124] Step 240: Establish a perception 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 perceptual channel model.

[0126] Step 260: Perform soft fusion based on the posterior probability density function and the communication channel model to obtain a communication perception channel.

[0127] Further, based on the above-mentioned embodiment of the invention, the complex Gaussian random vector at least includes:

[0128]

[0129] Wherein, 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 represents the scattered signal of the lth path; τ represents the time delay vector; τ l represents the delay of the lth path; k represents the kth component of h; T represents the duration of the OFDM symbol excluding the prefix;

[0131] If Π is not full rank, then:

[0132]

[0133] In the formula represents the pseudo-inverse of π.

[0134] On the basis of the above-mentioned embodiments of the invention, a perception channel model and a communication channel model are established based on a priori distribution, including:

[0135] Determining an initial perception channel model and an initial communication channel model;

[0136] The frequency domain channel gain vector of the quasi-static multipath channel model is substituted into the initial perception channel model and the initial communication channel model to obtain the perception channel model and the communication channel model.

[0137] In the embodiment of the present invention, perception and communication are performed through a quasi-static multipath channel model, that is, the OFDM system works in a slow-fading quasi-static channel, the data stream is transmitted in blocks, the channel gain remains unchanged within a transmission block, and is independent of each other between blocks. Each transmission block is divided into two stages: perception and communication. The frequency domain system model of perception 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 c Represent the received signal vectors in the sensing and communication stages respectively. s represents the pilot signal vector, diag(x s ) is x s The diagonal matrix of x. c is the transmission signal vector in the communication phase, diag(x c ) is x c Assuming that the coherence time of the channel is much longer than the duration of a transmission block, the channel gain h in the sensing and communication stages is the same. s and w c Denotes the noise vector in the perception and communication stages. Assume that the noise is a complex additive white Gaussian noise (CAWGN) channel, and the total noise power spectral density is N0.

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

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

[0143] y s and c They represent the signal vector composed of the scattered signals received in the perception phase and the signal vector composed of the scattered signals received in the communication phase, respectively. s represents the pilot signal vector, x c is the transmitted signal vector in the communication phase, diag() represents the diagonal matrix, w s and w c represents the noise vector in the perception stage and the communication stage, and h represents the frequency domain channel gain vector.

[0144] In the embodiment of the present invention, the initial perception channel model can be composed of a signal vector composed of a scattered signal received in the perception stage, a pilot signal vector, and a noise vector in the perception stage, and the initial communication channel model can be determined by the scattered signal, the transmission signal, and the noise received in the perception stage. The initial perception channel model and the initial communication channel model are respectively expressed as: s =diag(x s )h+w s ;y c =diag(x c )h+w c , where the channel gain h in the perception stage and the communication stage is the same.

[0145] Figure 3 is a flowchart of another communication perception channel construction method provided according to an embodiment of the present invention. The embodiment of the present invention is a specific embodiment of the above invention, and describes the soft fusion of the communication stage and the perception stage. Figure 3 The method provided in the embodiment of the present invention specifically includes the following steps:

[0146] Step 310: construct a quasi-static multipath channel model according to the acquired scattered signals, and determine a priori distribution of a frequency domain channel gain vector of the quasi-static multipath channel model;

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

[0148] Step 330: Expand the exponential part of the perceptual 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 the embodiment of the present invention, the exponential part of the perceptual channel model may be expanded to obtain:

[0150]

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

[0152] Since 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 formula and ignoring the constants that are independent of h, we get:

[0153]

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

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

[0156] In the embodiment of the present invention, the Bayesian formula for processing the perceptual 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] The quadratic formula of the above formula is

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

[0164] The above formula is about the N-dimensional vector Gaussian distribution of the channel gain h. According to the properties of the probability density function, the posterior distribution of the channel gain is:

[0165]

[0166] From the above formula, we can see that ∑ is the covariance matrix of the posterior distribution, and the mean vector corresponds to the maximum value of the posterior distribution, so 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, the inverse matrix (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 a communication perception channel, wherein the channel gain is a variable.

[0169] Specifically, use the posterior distribution p(h|y s ) eliminates the communication channel model p(y c |x c h) in which the variable h gets 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 s The communication perception channel is obtained by fusing time perception and communication probability.

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

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

[0173] And E[H]=h MAP ,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(xc )=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, so we have

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

[0179] From this we can know that (x c y s ) when the conditional distribution of the perception and communication probability fusion is

[0180]

[0181] The above formula is the communication perception channel model of soft estimation after probability fusion. In this model, the communication signal Y c The mean of the conditional distribution and the perceived received signal y s But the covariance matrix is ​​related to the perceived signal y s The covariance matrix is ​​related to the communication signal, so the greater the source input power, the greater the interference noise caused.

[0182] Further, based on the above-mentioned embodiments of the invention, the Gaussian capacity of the communication perception channel is determined by Monte-Carlo method simulation.

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

[0184]

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

[0186] E[] means to find the expectation; E c It represents the energy of the signal on each subcarrier of the communication channel, E s Represents the energy of the signal on each subcarrier of the perceived channel; I(X c ; Y c |Y s ) means that at a given Y s X cand Y c The mutual information of c Represents the communication signal vector, Y s represents the perceived received signal vector, Y c They represent the communication received signal vectors respectively.

[0187] The mutual information when the input distribution is Gaussian distribution is used as the evaluation index, which is called Gaussian capacity. represents the set of all zero-mean vector Gaussian distributions that satisfy the energy, then the Gaussian capacity of the perceptual communication channel based on soft fusion is defined as:

[0188]

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

[0190] Mutual information I(X c ; Y c |y s ) calculation process:

[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 delay vector; τ l represents the delay of the lth path; k represents the kth 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 s The conditional differential entropy of :

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

[0197] Where E[·] is the value of X c The probability distribution of is expected, I represents the unit matrix, N0 represents the noise power spectrum density, and diag() represents the diagonal matrix. Perform eigenvalue decomposition on the covariance matrix ∑

[0198] ∑=QΛQ H

[0199] Where Λ=diag(…,λ k ,…) are the eigenvalues ​​of ∑, diag() represents the diagonalized matrix, and Q is a unitary matrix. c With x c The probability distribution of is exactly the same, so:

[0200]

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

[0202] Due to x c (k) obeys a Gaussian distribution with a mean of zero, so ρ obeys an exponential distribution,

[0203]

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

[0205]

[0206] Where Γ(a,z) is the complementary gamma function, defined as

[0207] Γ(a,z)=∫ z ∞ t a-1 e -t dt

[0208] When z=0, Γ(a,0)=Γ(a).

[0209] In the embodiment of the present invention, the communication sensing channel is determined based on the quasi-static channel. c |y s ), first find the conditional distribution p(y) for a given h c |hy s ), then hy s Find the expected distribution of .

[0210] When X c When it follows Gaussian distribution, Y c It also obeys the Gaussian distribution, and its mean value is 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)+NOI

[0212] When the power distribution is equal, E[X c X c H ]=E c I, so

[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 represents the signal-to-noise ratio, N0 represents the noise power spectral density, τ l represents the delay of the lth path; k represents the kth component of h; T represents the duration of the OFDM symbol excluding the prefix.

[0221] Here p(h|y s ) is the posterior distribution. Thus, we get the given perceived signal y s Gaussian capacity

[0222]

[0223] In the embodiment of the present invention, there is currently no differential entropy h(Y c |y s ), since the above formula is a high-dimensional integral problem and the integral interval is the entire complex plane, it is difficult to calculate using the traditional grid method. Considering the differential entropy h(Y c |ys ) is a calculation problem of mathematical expectation, and the generation of Gaussian samples is very convenient, so the Monte-Carlo method can be used for efficient simulation.

[0224] In an exemplary embodiment, see Figure 4 , the communication-aware channel construction process may include the following steps:

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

[0226] Assume that the time domain impulse response of the multipath channel is:

[0227]

[0228] Where τ l is the delay of the lth path, c l is the scattered signal of the lth path, L is the multipath number, t is the time, and δ() represents the Dirac function.

[0229] Assume that the OFDM system has N subcarriers and the duration of the OFDM signal without considering the prefix is ​​T. The kth component of the frequency domain channel gain vector h is:

[0230]

[0231] Expressed in vector form:

[0232] h=Ψ(τ)c

[0233] Where c T =(…,c l ,…), Ψ(τ)=(…,ψ l ,…); τ represents the delay vector; τ l represents the delay of the lth path; k represents the kth component of h; T represents the duration of the OFDM symbol excluding the prefix.

[0234] The covariance matrix Π 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 that the multipath scattering of the channel is uncorrelated, then P = diag(…, E[|c l | 2 ],…) is a diagonal matrix, and the vector composed of its main diagonal elements corresponds exactly to the delay power spectrum of the channel. From the above formula, we can know

[0237]

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

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

[0240]

[0241] Wherein the covariance matrix Π depends on the delay power spectrum of the channel; N represents the dimension of the frequency domain channel gain vector h, and it can be understood that N can be the number of subcarriers of the frequency domain channel;

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

[0243]

[0244] In the formula represents the pseudo-inverse of π.

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

[0246] Assume that the OFDM system operates in a slow-fading quasi-static channel, that is, the data stream is transmitted in blocks, the channel gain remains unchanged within a transmission block, and is independent of each other between blocks. Each transmission block is divided into two stages: perception and communication. The frequency domain system model of perception 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 c Represent the received signal vectors in the sensing and communication stages respectively. s represents the pilot signal vector, diag(x s ) is x s The diagonal matrix of x. c is the transmission signal vector in the communication phase, diag(x c ) is x cAssuming that the coherence time of the channel is much longer than the duration of a transmission block, the channel gain h in the sensing and communication stages is the same. s and w c Denotes the noise vector in the perception and communication stages. Assume that the noise is a complex additive white Gaussian noise (CAWGN) channel, and the total noise power spectral density is N0.

[0250] Step 3: Construct the perception channel model and communication channel model based on the OFDM-ISAC system model.

[0251] Since the noises between OFDM subchannels are 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 can obtain the perceptual channel model:

[0255]

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

[0257]

[0258] In the above formula, ‖·‖ 2 Represents the second-order norm of a vector.

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

[0260] According to the perceptual channel model, the exponential part is expanded to

[0261]

[0262] Where Re(·) represents the real part. Since 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 formula and ignoring the constants that are independent of h, we get

[0263]

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

[0265] By Bayesian 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+γ s Π) -1 Substituting into the above formula, we have

[0270]

[0271] The quadratic formula of the above formula is:

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

[0273] The above formula is about the N-dimensional vector Gaussian distribution of the channel gain h. According to the properties of the probability density function, the posterior distribution of the channel gain is:

[0274]

[0275] From the above formula, we can see that ∑ is the covariance matrix of the posterior distribution, and the mean vector corresponds to the maximum value of the posterior distribution, so 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, the inverse matrix (I+γ s ∏) -1 exist.

[0277] Step 5: Propose a soft fusion method to perform probability fusion on 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 ) eliminates the communication channel model p(y c |x c h) in which the variable h is

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

[0280] Where p(y c |x c y s ) is the given communication sending signal x c and the perceived received signal y s The ISAC channel is obtained by fusion of the perception and communication probabilities. In order to correspond to the traditional hard estimation, the channel p(y c |x c y s ) is called the soft estimation channel.

[0281] The following uses a more concise method to derive the above formula. c Given that, since the channel gain H is a complex Gaussian random vector, the communication receiving signal Y c is also a complex Gaussian vector with a mean

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

[0283] And E[H]=h MAP , so 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 ∑, so we have

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

[0288] From this we can know that (x c y s ) when the conditional distribution of the perception and communication probability fusion is:

[0289]

[0290] The above formula is the soft estimation channel model after probability fusion. From the soft estimation channel model, it can be seen that the communication receiving signal Y c The mean of the conditional distribution and the perceived received signal y s However, the covariance matrix is ​​related to the perceived received signal y s The covariance matrix is ​​related to the communication signal, so the greater the source input power, the greater the interference noise caused.

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

[0292] Given x c When the integrated channel model is a vector Gaussian distribution, the mean vector and covariance matrix are both x c The Shannon channel capacity of this channel is defined as

[0293]

[0294] In the formula, represents the supremum of all input distributions that satisfy the power constraint; E[] represents the expectation; E c It represents the energy of the signal on each subcarrier of the communication channel, E s Represents the energy of the signal on each subcarrier of the perceived channel; I(X c ; Y c |Y s ) means that at a given Y s X c and Y c The mutual information of X cRepresents the communication signal vector, Y s represents the perceived received signal vector, Y c Represents the communication received signal vector.

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

[0296] Next, we derive the Gaussian capacity of the soft-estimated channel. We first calculate the mutual information I(X c ; Y c |y s ), then Y s Seek expectations.

[0297] Mutual information I(X c ; Y c |y s ) calculation process:

[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 delay vector; τ l represents the delay of the lth path; k represents the kth 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 s The conditional differential entropy of :

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

[0303] Where E[·] is the value of X c The probability distribution of is expected, I represents the unit matrix, N0 represents the noise power spectrum density, and diag() represents the diagonal matrix. Perform eigenvalue decomposition on the covariance matrix ∑

[0304] ∑=QΛQ H

[0305] Where Λ=diag(…,λ k ,…) are the eigenvalues ​​of Σ, and Q is a unitary matrix. Since Qx c With x c The probability distribution of is exactly the same, so

[0306]

[0307] Let the average SNR of each OFDM subchannel be the same, that is, γ c =E[|x c (k)| 2 ] / N0,ρ=|x c (k)| 2 / N0. Due to x c (k) obeys a Gaussian distribution with a mean of zero, so ρ obeys an exponential distribution,

[0308]

[0309] Substituting into the differential entropy expression:

[0310]

[0311] Where 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 ), then hy s Find the expected distribution of c When it follows Gaussian distribution, Y c It also obeys the Gaussian distribution, and its mean value is E[Y c ]=0, covariance matrix R(h)=E[Y c Y c H ]for

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

[0316] When the power distribution is equal, E[X c X c H ]=E c I, so: R(h)=N0[I+γ c diag(h)diag(h * )] into the following formula:

[0317]

[0318] Then there is

[0319]

[0320] Here p(h|y s ) is the posterior distribution. Thus, we get the given perceived signal y s Gaussian capacity of:

[0321]

[0322] There is no differential entropy h(Y c |y s ), since the above formula is a high-dimensional integral problem and the integral interval is the entire complex plane, it is difficult to calculate using the traditional grid method. Considering the differential entropy h(Y c |y s ) is a calculation problem of mathematical expectation, and the generation of Gaussian samples is very convenient, so the Monte-Carlo method can be used for efficient simulation.

[0323] In order to verify the effectiveness of the present invention, numerical simulation is performed on the Gaussian capacity of the soft-estimated OFDM channel. Figure 4 It is a flow chart of the present invention. Figure 5 It is the Gaussian capacity of the soft-estimated OFDM channel that changes with the communication SNR under different perceived SNR conditions. OFDM system parameters are: number of subcarriers N = 64, cyclic prefix length is 8, subcarrier spacing is 15kHz, and one OFDM symbol period is 66.7us. The channel model adopts the Extended Vechicular A (EVA) channel recommended by 3GPP. The delay power spectrum of the EVA channel is shown in Table 1. To ensure The delay power spectrum is normalized in the simulation.

[0324] Table 1 Delay power spectrum of Extended Vehicular A channel

[0325]

[0326] For ease of comparison, Figure 5 The Shannon capacity of the Rayleigh channel with ideal CSI is also plotted in Figure 1. The numerical simulation results show that the Gaussian capacity gradually approaches the Shannon channel capacity with 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 drops significantly. The main reason is that the soft estimation channel is a self-interference channel. When γ c >γ s When the equivalent signal to noise ratio increases rapidly, the channel capacity tends to be saturated, but the soft fusion channel does not have the flat bottom phenomenon of the hard fusion method performance. Among them, Pc / No represents the signal to noise ratio, I(X c ; Y c |y s ) represents the Gaussian capacity of the soft-estimated channel, Ps represents the perceived power, and Pc represents the communication power.

[0327] Figure 6 Schematic diagram of the structure of an electronic device that implements the communication-aware channel construction method of an embodiment of the present invention. Figure 6 As shown, the device comprises:

[0328] The signal acquisition module 410 is used to construct a quasi-static multipath channel model according to the acquired scattered signals, 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 perception channel model and a communication channel model based on the quasi-static multipath channel model.

[0330] The channel sensing module 430 is configured to determine a posterior probability density function of a channel gain of a 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 a communication perception channel.

[0332] In the embodiment of the present invention, a quasi-static multipath channel model is constructed through scattered signals received by a signal acquisition module, a frequency domain channel gain vector is determined according to the quasi-static multipath channel model, and a priori distribution of the frequency domain channel gain vector is constructed, a model determination module establishes a perception channel model and a communication channel model for the quasi-static multipath channel model, a channel perception module establishes a posterior probability density function of the channel gain of the perception channel model based on the prior distribution, a soft fusion module softly fuses the posterior probability density function with the communication channel model to obtain a communication perception channel, the fusion of the communication stage and the perception stage can be realized, signal loss can be reduced, communication quality can be improved, perception accuracy can be enhanced, and user satisfaction with the system can be improved.

[0333] Based on the above embodiment of the invention, the invention further includes: a capacity determination module, which is used to determine the Gaussian capacity of the communication perception channel, specifically including: s Satisfy E[‖X s ‖ 2 ]≤NE s Constraints, and in all In the case of c ; Y c |Y s ) is taken as the Gaussian capacity of the communication sensing channel, wherein X c represents the communication transmission signal vector of the communication sensing channel, Y s represents the perceived received signal vector of the communication perceived channel, Y c represents the communication receiving signal vector of the communication sensing channel, π(x c ) represents the prior distribution function of the communication transmission signal of the communication perception channel, is a set of signals on each subcarrier in the communication sensing channel that satisfy the energy zero mean vector Gaussian distribution, N represents the number of subcarriers in the communication sensing channel, E s represents the energy of the signal on each subcarrier of the communication perception channel, and E[] represents the expectation.

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

[0335] The model building unit is used to build a quasi-static multipath channel model according to the orthogonal frequency division multiplexing system model based on the acquired scattered signals.

[0336] The gain vector unit is used to generate a frequency domain channel gain vector of a quasi-static multipath channel model.

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

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

[0339]

[0340] Among them, 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 represents the scattered signal of the lth path; τ represents the time delay vector; τ l represents the delay of the lth path; k represents the kth component of h; T represents the duration of the OFDM symbol excluding the prefix;

[0342] If Π is not full rank, then:

[0343]

[0344] In the formula represents the pseudo-inverse of π.

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

[0346] Based on the above embodiments of the invention, the initial perception 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 c They represent the signal vector composed of the scattered signals received in the perception phase and the signal vector composed of the scattered signals received in the communication phase, respectively. srepresents the pilot signal vector, x c is the transmitted signal vector in the communication phase, diag() represents the diagonal matrix, w s and w c represent the noise vectors in the perception stage and the communication stage respectively, and h represents the frequency domain channel gain vector.

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

[0350] A simplification element is used to expand the exponential part of the perceptual 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 result of the perception channel model to obtain a posterior probability density function.

[0352] On the basis of the above-mentioned 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 a communication perception channel, wherein the channel gain is a variable.

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

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

[0355] Figure 7 It is a schematic diagram of the structure of an electronic device that implements 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, workbenches, 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0356] like Figure 7As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

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

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

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

[0360] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0361] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

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

[0363] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0364] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0365] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0366] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0367] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A communication sensing channel construction method, characterized in that: The method comprises: Constructing a quasi-static multipath channel model according to the acquired scattered signals, and determining a priori distribution of a frequency domain channel gain vector of the quasi-static multipath channel model; Establishing a perception channel model and a communication channel model based on the quasi-static multipath channel model; Determine a posterior probability density function of the channel gain of the perceptual channel model based on the prior distribution; The communication perception channel is obtained based on soft fusion of the posterior probability density function and the communication channel model.

2. The method according to claim 1, characterized in that: Also includes: Determining the Gaussian capacity of the communication-aware channel includes: The sensing transmission signal vector X of the communication sensing channel s Satisfy E[‖X s ‖ 2 ]≤NE s Constraints, and in all In the case of c ; Y c |Y s ) is taken as the Gaussian capacity of the communication sensing channel, wherein X c represents the communication transmission signal vector of the communication sensing channel, Y s represents the perceived received signal vector of the communication perceived channel, Y c represents the communication receiving signal vector of the communication sensing channel, π(x c ) represents the prior distribution function of the communication transmission signal of the communication perception channel, is a set of signals on each subcarrier in the communication sensing channel that satisfy the energy zero mean vector Gaussian distribution, N represents the number of subcarriers in the communication sensing channel, E s represents the energy of the signal on each subcarrier of the communication perception channel, and E[] represents the expectation.

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

4. The method according to claim 3, characterized in that: The complex Gaussian random vector at least includes: Wherein, h represents the frequency domain channel gain vector, Π represents the covariance matrix, and the covariance matrix is: 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 represents the scattered signal of the lth path; τ represents the time delay vector; τ l represents the delay of the lth path; k represents the kth component of h; T represents the duration of the OFDM symbol excluding the prefix; N represents the dimension of the frequency domain channel gain vector h; If Π is not full rank, then: In the formula represents the pseudo-inverse of π.

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

6. The method according to claim 5, characterized in that: The initial perception channel model and the initial communication channel model include: y s =diag(x s )h+w s ;y c =diag(x c )h+w c ; Among them, y s and c They represent the signal vector composed of the scattered signals received in the perception phase and the signal vector composed of the scattered signals received in the communication phase, respectively, s represents the pilot signal vector, x c is the transmitted signal vector in the communication phase, diag() represents the diagonal matrix, w s and w c represent the noise vectors in the perception stage and the communication stage respectively, and h represents the frequency domain channel gain vector.

7. The method according to claim 1, characterized in that: The determining of the posterior probability density function of the channel gain of the perceptual channel model comprises: Expanding the exponential part of the perceptual channel model, and simplifying the expanded exponential part according to the energy relationship of the pilot sequence of the static multipath channel model; The simplified result of the perceptual channel model is subjected to Bayesian filtering to obtain the posterior probability density function.

8. The method according to claim 1, characterized in that: The soft fusion based on the posterior probability density function and the communication channel model to obtain the communication perception channel includes: The communication perceived channel is obtained by eliminating the channel gain in the communication channel model based on the posterior probability density function, wherein the channel gain is a variable.

9. A communication perception channel construction device, characterized in that: The device comprises: A signal acquisition module, used to construct a quasi-static multipath channel model according to the acquired scattered signals, and determine the prior distribution of the frequency domain channel gain vector of the quasi-static multipath channel model; A model determination module, used to establish a perception channel model and a communication channel model based on the quasi-static multipath channel model; A channel sensing module, configured to determine a posterior probability density function of a channel gain of the sensing channel model based on the prior distribution; 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 perception channel.

10. An electronic device, characterized in that: The electronic device comprises: 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, and the computer program is executed by the at least one processor so that the at least one processor can execute the communication-aware channel construction method described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the communication-aware channel construction method described in any one of claims 1-8 when executed.

12. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the communication-aware channel construction method according to any one of claims 1-8.

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