A communication and sensing integrated transmission method based on osp-ofdm

Abstract

The present application relates to the field of communication and perception integration, and particularly relates to a kind of communication and perception integration transmission method based on OSP-OFDM, including the OSP-OFDM-based communication and perception integration transmission system model is constructed;Communication and perception sending end generates data weighting matrix and pilot weighting matrix according to OFDM symbol number and subcarrier number, respectively linear precoding is carried out to modulation data and known pilot, the linear precoding result is superimposed and sent;The frequency domain receiving signal is obtained by carrying out fast fourier transform to receiving signal;Communication receiving end separates the frequency domain receiving signal and obtains receiving end pilot and receiving end data to carry out channel estimation and channel detection;The frequency domain receiving signal is carried out data interference cancellation by perception receiving end, and receiving end weighted pilot is obtained and channel estimation is carried out;According to the channel estimation result, target perception is carried out;The present application solves the problem that superimposed pilot receiving end pilot and data are difficult to separate, and significantly improves spectrum utilization and perception accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication and sensing integration, and particularly relates to a communication and sensing integration transmission method based on OSP-OFDM. BACKGROUND

[0002] Integrated sensing and communication (ISAC) is one of the potential key technologies of 6G, and its design concept is to make wireless communication and wireless sensing two independent functions in the same system and mutually beneficial. On the one hand, the communication system can use the same spectrum or even reuse hardware or signal processing modules to complete different types of sensing services. On the other hand, the sensing results can be used to assist communication access or management, improve service quality and communication efficiency. ISAC effectively improves the spectrum utilization and solves the spectrum conflict between radar and communication systems.

[0003] Although system components such as antennas can be shared, there are still many technical challenges in the design of communication and sensing integration due to the different purposes of communication and sensing. Communication is to achieve efficient and reliable data transmission, which requires high spectrum efficiency and the ability to resist interference and channel fading. Radar is to achieve high-resolution target sensing, which requires good autocorrelation, large signal bandwidth, large dynamic range and Doppler shift. In view of the problem of poor sensing accuracy of the current communication-oriented communication and sensing integration system, it is of great significance to propose a high-performance communication and sensing integration system that balances communication and sensing.

[0004] Most of the current communication systems use continuous waveforms, represented by orthogonal frequency division multiplexing (OFDM) waveforms. While the sensing system mostly uses pulse wave and periodic continuous wave. In order to adapt to the requirements of high spectrum efficiency, high reliability, low delay and low power consumption of 6G system, using continuous wave has become the mainstream trend of realizing integrated sensing and communication waveform, and OFDM waveform has become the focus of integrated waveform design due to its low computational complexity and high spectrum utilization.

[0005] The pilot part in the OFDM symbol can be used alone to estimate the speed and distance of the target to be measured, but the time-frequency resource grid used for communication competes with the pilot resource grid used for sensing. In order to obtain better transmission performance and increase the ambiguity of radar sensing as much as possible, researchers try to embed communication symbols into radar waveforms through sidelobe control and waveform diversity technology. However, due to the fact that only one bit is allowed to be embedded in each waveform, the communication rate of this method is low, and it is only suitable for ranging scenarios. There is also a stepped index pilot scheme (SMP-OFDM) that places the pilot sequence in the carrier in a stepped period, which further compresses the number of resource grids occupied by the pilot and ensures the maximum unambiguous distance that can be detected. However, false targets will appear in its distance detection, and its spectrum utilization is still limited. SUMMARY

[0006] To solve the above problems, the present invention proposes a communication perception integrated transmission method based on OSP-OFDM (Orthogonal Weighted Superposition Pilot Orthogonal Frequency Division Multiplexing), which includes the following steps:

[0007] S1. Construct a communication-aware integrated transmission system model based on OSP-OFDM, which includes a communication-aware transmitter, a communication receiver, and a perception receiver;

[0008] S2. The communication sensing transmitter generates a data weighting matrix U and a pilot weighting matrix V according to the number of OFDM symbols and the number of subcarriers;

[0009] S3. The communication sensing transmitter uses the data weighting matrix U and the pilot weighting matrix V to linearly precode the modulated data and the known pilot, respectively, and superimposes the linear precoding results before sending;

[0010] S4. Perform a fast Fourier transform on the received signal to obtain a frequency domain received signal;

[0011] S5. The communication receiving end separates the received signal in the frequency domain to obtain the receiving end pilot and receiving end data; and performs channel estimation and channel detection based on the receiving end pilot and receiving end data;

[0012] S6. The sensing receiving end performs data interference elimination on the frequency domain received signal to obtain a receiving end weighted pilot and perform channel estimation;

[0013] S7. The perception receiver performs target perception based on the channel estimation result.

[0014] Beneficial effects of the present invention:

[0015] This paper designs an approximately orthogonal weighting matrix that solves the difficulty of separating pilot and data at the superimposed pilot receiver, significantly improving spectrum utilization and perception accuracy. By designing appropriate transmission schemes and signal processing algorithms, OSP-OFDM can ensure both communication system performance and high perception accuracy and resolution.

[0016] Simulation experiments show that the method proposed in the present invention improves the bit error rate by about 2dB in communication, and can achieve a distance estimation accuracy of 0.1m and a speed estimation accuracy of 3m / s in perception. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the OSP-OFDM communication perception integrated system model of the present invention;

[0018] Figure 2 This is a schematic diagram of the superimposed pilot signal of the present invention;

[0019] Figure 3 The BER performance of different pilot power allocation factors of the OSP-OFDM integrated communication and sensing system of the application;

[0020] Figure 4 The BER performance of different transmission schemes in the time-invariant channel of the application;

[0021] Figure 5 The BER performance of different transmission schemes in the time-varying channel of the application with 4-QAM modulation;

[0022] Figure 6 The simulation of active sensing distance estimation of the OSP-OFDM integrated communication and sensing system of the application;

[0023] Figure 7 The simulation of active sensing speed estimation of the OSP-OFDM integrated communication and sensing system of the application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0025] The application provides a communication and sensing integrated transmission method based on OSP-OFDM, comprising the following steps:

[0026] S1. Constructing a communication and sensing integrated transmission system model based on OSP-OFDM, which comprises a communication and sensing sending end, a communication receiving end and a sensing receiving end.

[0027] Specifically, the communication and sensing integrated transmission system model based on OSP-OFDM is as shown in Figure 1 which comprises a communication and sensing integrated transmitter and a communication and sensing integrated receiver; wherein the communication and sensing integrated transmitter comprises a communication and sensing sending end, and the communication and sensing integrated receiver comprises a communication receiving end and a sensing receiving end. The modulated data X d and the known pilot X p are weighted and superimposed in the communication and sensing sending end to generate an ISAC waveform X TF for communication and sensing. For active sensing, the transmitted ISAC waveform X TF rebounds from the sensing target to form a backscatter signal, and the receiving end pilot Y p in the transmission signal and the known pilot X pTo estimate the target parameters; for passive perception, the transmission signal propagates through the visual path and multiple non-visual paths reflected by the perception target, and transmits the information to the communication perception integrated receiver, which receives the receiving end data Y d and the receiving end pilot Y p Separate and based on the receiving end pilot Y p and the known pilot X p Conduct joint passive perception and data detection.

[0028] Specifically, in the communication perception integrated transmission system model,

[0029] An OFDM symbol can be represented in the time domain as:

[0030]

[0031] Where x[n] represents the representation of the nth OFDM symbol in the time domain, N represents the number of OFDM symbols, and X[k] represents the kth OFDM symbol in the time domain = 0, 1, ..., N c subcarriers, N c Indicates the number of subcarriers on an OFDM symbol;

[0032] After passing through the wireless channel of L paths, the corresponding received signal y[n] is expressed as:

[0033]

[0034] Among them, h i [n] represents the impulse response of the i-th path, τ i represents the delay of the i-th path, f d,i represents the Doppler frequency shift of the i-th path, w[n] represents the additive white Gaussian noise; the time delay of each path is Calculation, τ represents the time delay of a path, d represents the distance of the perceived target, c0 represents the speed of light; the Doppler shift of each path is calculated using Calculate, f d represents the Doppler shift of a path, v represents the relative motion speed of the perceived target, λ represents the wavelength, and f c Indicates the carrier frequency.

[0035] Perform fast Fourier transform on the received signal y[n] to obtain the corresponding frequency domain received signal Y[k]:

[0036]

[0037] Where W(k) represents additive white Gaussian noise, X[m] is the representation of OFDM symbols in the frequency domain, and w[n] is the Fourier transform result; H i[k] represents the impulse response h of the i-th path i The Fourier transform result of [n] can be expressed as:

[0038]

[0039] The formula (3) can be expressed in the form of matrix-vector:

[0040] Y = HX + W (5)

[0041] Where Y represents the received signal vector matrix, H represents the channel matrix, X represents the original signal vector matrix of the sending end, and W represents the noise interference.

[0042] S2. The communication-aware sending end generates a data weighting matrix U and a pilot weighting matrix V according to the number of OFDM symbols and the number of subcarriers.

[0043] Specifically, the step S2 of the communication-aware sending end generating a data weighting matrix U and a pilot weighting matrix V according to the number of OFDM symbols and the number of subcarriers comprises:

[0044] An orthogonal matrix O is generated according to the number of OFDM symbols m is a 2m×2m real matrix; the first to m rows of the orthogonal matrix O are taken, and all column contents constitute a matrix The transpose matrix O of the orthogonal matrix O is taken H The m+1 to 2m rows are taken, and all row contents constitute a matrix is an m×2m real matrix, is a 2m×m real matrix; according to the orthogonal matrix characteristics H ≈VV H ≈E, UV H ≈VU H ≈0.

[0045] S3. The communication-aware sending end adopts the data weighting matrix U and the pilot weighting matrix V to linearly precode the modulated data and the known pilot respectively, and sends the superposition of the linear precoding results.

[0046] Specifically, as shown in Figure 2 , the step S3 specifically comprises:

[0047] S31. Selecting a modulation scheme (such as QAM or PSK) to modulate the information symbol to obtain modulated data X d ; generating known pilot X d matching the modulated data X p according to the number of OFDM symbols and the number of subcarriers;

[0048] S32. Adopting the data weighting matrix U to modulate data X d Linear precoding is performed while adopting the pilot weighting matrix V to known pilot X p Linear precoding is performed.

[0049] S33. For linear precoding modulated data X d and known pilot X p Power is allocated and superposition is obtained to obtain frequency domain sending signal X, which is expressed as:

[0050] X = aX d U + bX p V (6)

[0051] Wherein, a and b are power allocation factors of data and pilot respectively, and a+b=1.

[0052] S34. The frequency domain sending signal is converted into time domain sending signal through inverse fast Fourier transform, and the time domain sending signal is sent.

[0053] S4. The received signal is converted into frequency domain receiving signal through fast Fourier transform.

[0054] Specifically, each OFDM symbol block at the receiving end is converted from time domain to frequency domain through fast Fourier transform (FFT), and the frequency domain receiving signal Y can be expressed as:

[0055] Y = H (aX d U + bX p V) + W (7)

[0056] Wherein, H represents the channel estimation matrix of superimposed pilot signal and information symbol; Since the pilot signal and information symbol are superimposed in the frequency domain in the OSP-OFDM system, the real channel cannot be directly estimated by using the pilot signal, therefore, the separation of the pilot signal and information symbol at the receiving end to estimate the real channel becomes the key of the OSP-OFDM system. The application designs the weighting matrix U, V of the information symbol and the pilot signal. The matrix U, V satisfies UU H ≈VV H ≈E, UV H ≈VU H ≈0, the orthogonal characteristics of U and V can be used at the receiving end to remove the estimated channel matrix and the information symbol when the sensing target.

[0057] S5. The communication receiving end separates the frequency domain receiving signal to obtain the receiving end pilot and the receiving end data, and performs channel estimation and channel detection according to the receiving end pilot and the receiving end data.

[0058] Specifically, the frequency domain receiving signal is separated to obtain the receiving end pilot Y p and the receiving end data Yd , is expressed as:

[0059]

[0060] wherein H p represents a pilot channel estimation matrix, i.e. the channel estimated by the pilot part after the data and pilot are separated at the receiving end; H d represents a data channel estimation matrix, i.e. the channel estimated by the data part after the data and pilot are separated at the receiving end; W represents noise interference, and satisfies H P ≈H d .

[0061] Specifically, the step S5 of performing channel estimation and channel detection according to the receiving end pilot and the receiving end data comprises:

[0062] According to the receiving end pilot Y p and the known pilot X p , the pilot channel estimation matrix is solved:

[0063]

[0064] wherein Y p represents the receiving end pilot, H p represents the pilot channel estimation matrix, H d represents the data channel estimation matrix.

[0065] After the pilot channel estimation matrix is solved, the minimum mean square error (MMSE) signal detection method can be used to recover the communication symbol:

[0066]

[0067] wherein represents the recovered receiving end data, i.e. the estimated communication signal vector matrix; H represents noise variance, I represents a unit matrix, Y d represents the received communication signal vector matrix, i.e. the receiving end data; H p represents the pilot channel estimation matrix.

[0068] S6. The data part of the frequency domain received signal is eliminated at the sensing receiving end to obtain the sensing receiving end weighted pilot and perform channel estimation.

[0069] Specifically, in the sensing aspect, the sensing receiving end of the OSP-OFDM system only needs to know the pilot, and can estimate the speed and distance of the target without knowing the transmitted information symbol. Since the present application considers active sensing in the sensing part, when calculating the delay and frequency offset, it is considered that the waveform needs to be round-trip, and it is assumed that there are M targets, and the round-trip distance of each target is d m, the round trip speed is v m , the reflection coefficient is η m , the radar receiving signal can be expressed as:

[0070]

[0071] wherein, is the target time delay, is the Doppler frequency offset, and w(t) is noise. The FFT transform of the receiving signal can obtain the frequency domain expression:

[0072]

[0073] In the receiving end, the pilot part βX p V in equation (6) is known, then the matrix U, V orthogonal characteristics can be used, combined with equation (7) and equation (13), multiply the matrix V H V at the sensing receiving end to remove the information symbol to restore Y PV :

[0074] Y PV = H (αX d U+βX p V) V H V+WV H V

[0075] = H (αX d UV H V+βX p VV H V)+WV H V

[0076] =βHX p V+WV H V

[0077] Then, the Y PV is divided by each element to obtain the channel estimation matrix H containing the time delay and Doppler information, and the parameter estimation problem of the target is also changed into the frequency estimation problem of the complex exponential signal. H can be expressed as:

[0078]

[0079] wherein, H[k] represents the frequency domain response of the channel at the kth subcarrier, L represents the path number in the multipath channel, and h i represents the gain of the ith path.

[0080] S7. The sensing receiving end performs target sensing according to the channel estimation result.

[0081] S41. Perform an inverse fast Fourier transform on the channel estimation result in the subcarrier domain, and then perform a fast Fourier transform on the inverse fast Fourier transform result in the time domain to obtain a two-dimensional spectrum of distance and speed;

[0082] S42. Perform peak detection on the two-dimensional spectrum of distance and speed to estimate the distance and speed of each target.

[0083] This paper simulates and analyzes the proposed OSP-OFDM integrated communication and sensing system using the MATLAB simulation platform. The system's communication and sensing performance, including bit error rate and sensing accuracy, is evaluated. Specific simulation parameters are shown in Table 1. The system is compatible with 6G WiFi.

[0084] Table 1 Simulation parameter settings

[0085]

[0086]

[0087] Specifically, the present invention evaluates the communication bit error rate performance of the proposed OSP-OFDM through simulation. Under the parameter settings in Table 1, a multipath channel with a visible path and two first-order reflection paths is considered. Figure 3 In this paper, the relationship between OSP-OFDM and bit error rate (BER) under different pilot symbol power allocation factors is simulated. The power ratio of pilot symbols to information symbols is changed by changing the pilot symbol power allocation factor α and the information symbol power allocation factor β = 1-α. α is set to 0.1, 0.3, 0.5, 0.6, and 0.8 respectively. It can be clearly seen from the simulation that the BER performance is best when α = β = 0.5, and the performance decreases when α is lower or higher. When α is low, the signal power allocated to the pilot is very small, resulting in inaccurate pilot-assisted channel estimation and a low signal detection BER. When α is high, the signal power allocated to the data is very small, resulting in a large impact of noise on the separated data, resulting in a low signal detection BER.

[0088] exist Figure 4 In this paper, the bit error rate curve of OSP-OFDM in time-invariant multipath channel is simulated, and the communication bit error rates of two different superimposed pilot schemes PSP-OFDM and SP-OFDM are compared with the communication bit error rate of traditional OFDM. The channel estimation adopts LS algorithm. Figure 4 (a) is 4-QAM modulation, Figure 4(b) is 16-QAM modulation. The conventional OFDM adopts block pilot, and the pilot interval is 4. The PSP-OFDM scheme superimposes the information symbol part on the pilot on the basis of OFDM, keeps the original pilot unchanged, and uses the original pilot for channel estimation at the communication receiving end to recover the information symbol. The SP-OFDM scheme directly superimposes the information symbol and the pilot, does not keep the original pilot, and in the communication receiving end, the information symbol S[k] and the noise W[k] are approximately mean zero independent and identically distributed processes under the observation of a long enough time, and the information symbol and the noise are eliminated to obtain the pilot at the receiving end under the time-invariant channel through the statistical average method, and the pilot is used for channel estimation to recover the information symbol. It can be seen from Figure 4 It can be seen from Figure 5 that the communication BER performance of the PSP-OFDM scheme is about 5dB lower than that of the conventional OFDM due to the interference of the superimposed pilot; the communication BER performance of the SP-OFDM scheme is basically the same as that of OFDM at low-order modulation, and the error rate is greatly reduced at high-order modulation; and the communication BER performance of the OSP-OFDM proposed in the application is basically the same as that of OFDM at low SNR, and is about 2dB higher than that of OFDM at high SNR.

[0089] In Figure 5 , the error rate curve of OSP-OFDM in the time-varying multipath channel is simulated, and the communication error rates of two different superimposed pilot schemes PSP-OFDM and SP-OFDM are compared, and the communication error rate of the conventional OFDM is also compared, and the channel estimation adopts the LS algorithm. It can be seen from Figure 5 that the communication BER performance of the PSP-OFDM scheme is about 5dB lower than that of the conventional OFDM due to the interference of the superimposed pilot; the communication BER performance of the SP-OFDM scheme is greatly reduced compared with the time-invariant channel; and the communication BER performance of the OSP-OFDM proposed in the application is basically the same as that of OFDM at low SNR in the time-varying channel environment, and is about 2dB higher than that of OFDM at high SNR.

[0090] Specifically, the sensing performance of the proposed OSP-OFDM is evaluated by simulation. Under the parameter setting of Table 1, the power allocation factors of the pilot and the signal are set as α=β=0.5, the parameters of the three reference targets are set as A(20m, 20m / s), B(100m, 50m / s), and C(200m, 100m / s) respectively, and the 2D-FFT point number is set as 1024. In Figure 6 , the root mean square error (RMSE) of distance estimation of different numbers of targets P=1, 2, 3 is simulated. In the case of a single target, the RMSE of distance estimation can reach 0.05m at 0dB; for the case of two or three targets, the RMSE of distance estimation is close to 0.1m at 0dB.

[0091] In Figure 7 The root mean square error (RMSE) of velocity estimation is simulated for different number of targets P = 1, 2, 3. In the case of single target, the RMSE of velocity estimation can reach 2 m / s at 5 dB; for 2 or 3 targets, the RMSE of range estimation is close to 3 m / s at 0 dB.

[0092] In this application, unless otherwise clearly specified and limited, the terms "mounting", "setting", "connecting", "fixing", "rotating" and other terms should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integrated; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be the internal communication of two elements or the interaction relationship of two elements, unless otherwise clearly limited, the above terms in this application can be understood according to the specific meaning of the above terms in this application by those skilled in the art.

[0093] Although the embodiments of the present application have been shown and described, those skilled in the art can understand that various changes, modifications, replacements and variations can be made to these embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for communication and sensing integrated transmission based on OSP-OFDM, characterized in that, The method comprises the following steps: S1. Constructing an OSP-OFDM-based communication and perception integrated transmission system model, which comprises a communication and perception sending end, a communication receiving end and a perception receiving end; S2. The communication and perception sending end generates a data weighting matrix U and a pilot weighting matrix V according to the number of OFDM symbols and the number of subcarriers, comprising: An orthogonal matrix is generated according to the number m of OFDM symbols is a real matrix of 2mx2m; let is a real matrix of mx2m, is a real matrix of 2mxm; according to the orthogonal matrix property UU H ≈VV H ≈E, UV H ≈VU H ≈0; S3. The communication and perception sending end performs linear precoding on the modulated data and the known pilot by using the data weighting matrix U and the pilot weighting matrix V respectively, and sends the linear precoding results after superposition; Step S3 specifically comprises: S31. Selecting a modulation scheme to modulate the information symbols to obtain modulation data X d ; generating known pilot X matched to the modulation data X according to the number of OFDM symbols and the number of subcarriers d ; and p ; S32. Adopting the data weighting matrix U to modulate the data X d performing linear precoding while adopting the pilot weighting matrix V to the known pilot X p performing linear precoding; S33. The modulated data X after linear precoding d and known pilot X p allocating power and superimposing to obtain the frequency domain transmission signal X, denoted as: X = aX d U + bX p V, Wherein, α and β are power allocation factors of data and pilot respectively, and α+β=1; S34. Obtain a time domain sending signal by performing inverse fast Fourier transform on the frequency domain sending signal, and send the time domain sending signal; S4. Perform fast Fourier transform on the received signal to obtain a frequency domain received signal; S5. The communication receiving end separates the frequency domain received signal to obtain a receiving end pilot and a receiving end data, and performs channel estimation and channel detection according to the receiving end pilot and the receiving end data; S6. The perception receiving end performs data interference cancellation on the frequency domain received signal to obtain a receiving end weighted pilot and performs channel estimation; S7. The perception receiving end performs target perception according to the channel estimation result. 2.The OSP-OFDM based integrated communication and sensing transmission method of claim 1, wherein, In the communication and perception integrated transmission system model, One OFDM symbol is represented in time domain as: where x[n] represents the representation of the nth OFDM symbol in the time domain, N represents the number of OFDM symbols, X[k] represents the kth = 0, 1, …, N c subcarrier on the OFDM symbol, and N c represents the number of subcarriers on the OFDM symbol. After passing through the wireless channel of L paths, the corresponding received signal y[n] is represented as: where h i [n] represents the impulse response of the i-th path, τ i represents the time delay of the i-th path, f d,i represents the Doppler shift of the i-th path, and w[n] represents additive white Gaussian noise; Perform fast Fourier transform on the received signal y[n] to obtain the corresponding frequency domain received signal Y[k]: where W(k) represents the Fourier transform result of the additive white Gaussian noise w[n], X[m] is the representation of the OFDM symbol in the frequency domain; H i [k] represents the Fourier transform result of the impulse response h i [n] of the i-th path, and is represented as: 3.The OSP-OFDM based integrated communication and sensing transmission method of claim 1, wherein, Step S4: The communication receiving end separates the frequency domain received signal to obtain a receiving end pilot Y p and receiving end data Y d , which is expressed as: Y p = H p (αX d U + βX p V)V H +WV H = H p (αX d UV H +βX p VV H )+WV H = βH p X p +WV H Y d = H d (αX d U+βX p V)U H +WU H = H d (αX d UU H +βX p VU H )+WU H = aH d X d + WU H where H p represents the pilot channel estimation matrix, H d represents the data channel estimation matrix, X d represents the modulated data, X p represents the known pilot, W represents the noise interference, and a, β represent the power allocation factors.

4. The OSP-OFDM based integrated communication and sensing transmission method of claim 1, wherein, Step S5 comprises: Solve the pilot channel estimation matrix according to the receiving end pilot and the known pilot: where Y p represents the received pilot, Y d represents the received data, H p represents the pilot channel estimation matrix, H d represents the data channel estimation matrix, X p represents the known pilot; Perform communication symbol recovery by using the minimum mean square error signal detection method: wherein, represents the recovered receiver data, represents the noise variance, I represents the identity matrix, Y d represents the receiver data, H p represents the pilot channel estimation matrix, H represents the channel estimation matrix.

5. The OSP-OFDM based integrated communication and sensing transmission method of claim 1, wherein, The perception receiving end carries out data interference cancellation on the frequency domain receiving signal to obtain a receiving end weighted pilot Y PV , which is expressed as: Y PV = H (aX d U + βX p V) V H V + W V H V = H (aX d UV H V + βX p VV H V) + W V H V = βHX p V + WV H V where H represents a channel estimation matrix, X d represents modulated data, X p represents known pilots, W represents noise interference, and a and β represent power allocation factors, and U and V are data weighting matrix and pilot weighting matrix, respectively.

6. The OSP-OFDM based integrated communication and sensing transmission method of claim 1, wherein, Step S7 comprises: S41. Perform inverse fast Fourier transform on the channel estimation result in the subcarrier domain, and then perform fast Fourier transform on the inverse fast Fourier transform result in the time domain to obtain a two-dimensional spectrum of distance and velocity; S42. Perform peak value detection on the two-dimensional spectrum of distance and velocity to estimate the distance and velocity of each target.

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