A superimposed symbol-based integrated design method for communication and sensing

Abstract

The application discloses a communication and sensing integrated design method based on superimposed symbols, which generates and transmits integrated waveforms of superimposed pilots and data, so that communication signals and sensing signals are linearly superimposed in the symbol domain in the same time-frequency resource block by using a power distribution method, and the ability of environmental sensing is increased while the communication performance is not lost; the method comprises the following steps: transmitting a communication and sensing integrated signal in the form of superimposed symbols, and jointly designing environmental parameter extraction and communication data detection based on a received signal. Compared with an RIS-assisted communication system without sensing function, the application can obtain accurate environmental sensing accuracy without additional hardware devices, and can achieve a spectral efficiency close to that of the original communication system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and particularly relates to a communication and perception integrated design method based on superimposed symbols. BACKGROUND

[0002] In order to support emerging applications that require low-latency perception and high-rate communication simultaneously, the next generation of wireless networks proposes communication and perception integrated technology to utilize the same wireless signal and hardware infrastructure to simultaneously achieve wireless perception and communication. In the communication system, the millimeter wave band with large bandwidth is proved to be able to achieve high-rate communication to meet the real-time mass data transmission. At the same time, in the existing perception system, the millimeter wave radar has been widely used to provide accurate perception information. Because of the excellent performance of the millimeter wave band in the communication and perception system respectively, the millimeter wave signal is considered to have great application prospects in the communication and perception integrated system, and it is also a research hotspot in the academic and industrial fields in recent years.

[0003] However, the millimeter wave signal will cause serious propagation loss, which often blocks the LoS path between the receiving end and the transmitting end, hindering the reliable connection service of the communication and perception integrated system. RIS can create a virtual LoS path between the base station and the user by adjusting the phase shift of the reflecting element through digital control, and is considered as an effective solution to the stable connection of the communication and perception integrated system in the millimeter wave band. Specifically, RIS is a completely passive two-dimensional array with many sub-wavelength controllable elements, which can flexibly change the physical properties of the incident electromagnetic wave by adjusting the phase. In addition, RIS is generally manufactured with inexpensive hardware elements and has no radio frequency link, thereby greatly reducing the hardware cost and energy consumption. At present, a large number of studies have proved that the RIS-assisted communication and perception integrated system can significantly improve the performance of communication and perception.

[0004] In the design of RIS-assisted millimeter wave frequency band communication and perception integrated system, a practical and efficient method is to use the existing communication modulation method, protocol architecture and hardware basic equipment combined with perception signal processing technology to realize the functions of communication and perception at the same time. This scheme realizes the perception of the target in the environment while transmitting data, avoiding the additional deployment of hardware cost of the perception network. In the existing technology, the implementation of the perception function will allocate a part of the additional time or frequency resources, so that the perception pilot can use this part of the independent resource to realize the perception function, so some methods are used to study the performance compromise of communication signals and perception pilots in time and frequency resources. However, there are some limitations: the perception pilot consumes additional time or frequency resources, which will damage the spectral efficiency of the original communication system; and the simultaneous realization of the functions of communication and perception in the RIS-assisted system has high demand for computing resources, and depends on the channel state information, which may be difficult to obtain the channel state information in time and accurately in the high-speed mobile scene such as vehicle networking, thereby damaging the accuracy of communication and perception.

[0005] Based on the above problems, in order to improve the performance of RIS-assisted communication and perception, and improve the spectral efficiency of the communication and perception integrated system, a design method of communication and perception integrated waveform based on superposition symbols is proposed to realize the functions of communication and environment perception at the same time. SUMMARY

[0006] The present application aims at the problem of low accuracy and low spectral efficiency of the existing technology of communication signals and perception signals occupying independent time or frequency resources in the RIS-assisted communication and perception integrated system. By transmitting a communication and perception integrated waveform formed based on superimposed communication symbols and pilot symbols, the communication signal and the perception signal are linearly superimposed in the symbol domain through a power allocation coefficient. A new signal processing method is proposed at the receiving end according to the received symbol, which increases the accurate perception function without losing the communication performance.

[0007] The specific technical scheme adopted by the present application to solve its technical problems is: a design method of communication and perception integrated waveform based on superposition symbols, which can be applied to the RIS-assisted millimeter wave communication and perception integrated scene.

[0008] The method provided by the present application realizes joint communication data detection and environment perception parameter extraction by signal processing of the received symbol, and realizes high-speed communication rate and high-precision perception function at the same time. In specific application, the communication and perception integrated waveform based on superposition symbols generated by the present application can support various modulation methods and parameter settings in the existing wireless communication system to realize efficient uplink communication and perception link.

[0009] The application provides a method for simultaneously realizing communication and sensing functions by using an RIS-assisted user-to-base station uplink communication link, and the method comprises the following steps:

[0010] S1. At a signal transmitting end, a communication signal and a pilot signal in the same time-frequency resource are linearly superimposed in a symbol domain by a power allocation coefficient and transmitted to an RIS;

[0011] S2. At a signal receiving end, a signal reflected by the RIS is received, and a plurality of time slots of the received signal are stacked to obtain a receiving matrix;

[0012] S3. The extraction of the environmental angle parameter is converted into the estimation of a diagonal grid value and a sparse vector ω, the received signal is substituted into a preset millimeter wave channel model, and the receiving matrix is vectorized; an initial estimation of the sparse vector ω is obtained by using a least square estimation with a Tikhonov regularization term, and an initial estimation value of an equivalent channel between the base station and the user is obtained by using the estimation value of the sparse vector;

[0013] S4. According to the initial estimation value of the equivalent channel, an initial estimation value of communication data is obtained by using a linear minimum mean square error method, and demodulation and re-modulation are performed;

[0014] S5. The initial estimation value of the re-modulated communication data is iteratively calculated to obtain final estimation values of the sparse vector, the angle grid and the communication data under an expectation maximization framework;

[0015] S6. The communication data, the angle parameter in the environmental channel and the user coordinates are finally recovered.

[0016] Further, the transmitting end generates and transmits superimposed symbols wherein x d，t and x p，t are a communication symbol and a pilot symbol generated in the tth time slot, and ξ is a power allocation coefficient allocated to the communication symbol, and satisfies 0 < ξ < 1.

[0017] Further, the received signal of the RIS and the stacking of the received signal of the plurality of time slots are specifically as follows:

[0018] In the tth time slot, the base station receiving symbol is wherein H r，s is a channel from the s th RIS to the base station, θ s，t is a phase of the s th RIS in the tth time slot, h b，s is a channel from the user to the s th RIS, and n t is noise in the tth time slot; the base station receiving symbols of T time slots are stacked to construct a receiving matrix as

[0019]

[0020] where Θ s = [θ s，1 ,..., θ s，T ], X = diag(x1,..., x T ), N = [n1,..., n T ] are matrices stacking T time slots of θ s，t , x t and n t , respectively.

[0021] Further, the RIS is composed of controllable reflecting units, constructing a virtual LoS path between the base station and the user, and the number of RISs S ≥ 1;

[0022] The mmWave channel between the base station and the s-th RIS is modeled as

[0023]

[0024] where α l，s is the gain of the l-th path, and the array responses are and the effective angle of arrival and departure are and

[0025] Further, the extraction of the environmental angle parameters is converted to the estimation of the angle grid values and the sparse vector ω is specifically: the and are converted to the mmWave channel by selecting the elements within the grid points and , the mmWave channel is converted to

[0026]

[0027] where and are dictionary matrices composed of the array responses about the angle domain grid points and , Ω s is a sparse matrix; is the angle of arrival of the l-th path to the s-th RIS, and are the elevation and azimuth angles corresponding to the angle of departure from the base station to the s-th RIS, respectively.

[0028] Further, the angle domain grid points and are used to estimate L r，s in H sThe first path corresponds to the LoS path, and the effective arrival / departure angle corresponding to the LoS path is determined by the relative positions of the base station and the RIS, so the effective arrival / departure angle is set as and The first element is the effective arrival / departure angle corresponding to the LoS path, and the remaining elements are uniformly quantized in the angle domain by G1-1, G2-1 and G3-1, respectively.

[0029] Further, the initial estimate of the sparse vector ω obtained by the least square estimation with the Tikhonov regularization term is specifically:

[0030]

[0031] where X p is the corresponding pilot matrix in X, is the uncertainty of the data, and the matrix

[0032] Further, the initial estimate of the communication data is specifically:

[0033]

[0034] where N0 is the noise variance, and P0 is the user transmit power; is the initial estimate of the equivalent channel between the base station and the user obtained by the affine transformation.

[0035] Further, the final estimates of the sparse vector ω, the angle grid and the communication data are obtained by iteratively calculating the initial estimate of the communication data after re-modulation under the expectation maximization framework, and the final estimates are specifically:

[0036] S501, first set the prior distribution of the sparse vector ω (j)

[0037]

[0038] where for is the variance of the gth element of ω (j) When tends to zero, the gth element of ω (j) also tends to zero, so the estimate of ω (j) is converted into the estimate of γ (j) ;

[0039] S502, the parameter data set χ=[X d , γ, ν, β] is calculated by using the iterative update of EM, where ​is a vector of setting angle domain grid points, and β is used to characterize the variance of the actual unknown noise;

[0040] S503, obtain ω by maximum a posteriori estimation (j) , and ω (j) is obtained by the following steps: s The angle value corresponding to the L s element with the maximum value in the vector is used to form the estimated value of the grid where L (j) is the number of channel paths from the base station to the s-th RIS;

[0041] S504, obtain the estimated value ω (j) , and set the number less than the preset threshold to zero, and then use the LSE method to obtain a more accurate ω χ , and restore the equivalent channel estimation value between the base station and the user through affine transformation

[0042] S505, obtain the estimated value ω , and subtract the received part corresponding to the sensing pilot from the received matrix Y, and use the maximum expectation step to obtain the communication data estimation value

[0043] S501-S505 are iterated, and j is set to j+1 after each iteration until the iteration threshold is reached or The difference between and is less than δ (j) .

[0044] Further, in order to promote the sparse structure of ω (j) , γ γ is modeled to satisfy the Gamma distribution:

[0045]

[0046] where a γ and b γ are set parameters, a γ , b s →0 to satisfy the sharp peak at zero.

[0047] Through the above steps, the efficiency and accuracy of joint communication and sensing in the RIS assisted system can be effectively improved, and the communication efficiency and sensing accuracy of the communication and sensing integrated waveform based on the superposition symbol are improved.

[0048] Compared with the prior art, the present application has the following advantages:

[0049] The application provides a new waveform design scheme in a RIS-assisted communication and perception integrated system, which superimposes communication signals and perception pilots in a symbol domain through power allocation, can increase the perception function on the basis of the original communication function, and realizes high-precision perception by using an efficient signal processing method, while achieving a transmission rate close to the original communication system.

[0050] The method comprehensively considers the performance of communication and perception, linearly superimposes the communication symbol and the pilot symbol in the same symbol domain according to the power allocation coefficient, can realize the estimation of the environmental parameters, can guarantee the communication ability, and realizes the function of communication and perception integration;

[0051] The efficient signal processing method is used to almost eliminate the interference between the communication signals and the perception signals, and guarantees the efficiency and accuracy of the communication function and the perception function;

[0052] The recovered communication signals can be used as perception signals to participate in the perception accuracy improvement method, and the improvement of the perception accuracy is also beneficial to the improvement of the communication rate, which realizes the mutual enhancement effect of the communication function and the perception function;

[0053] Based on the design of the transmission communication and perception integrated waveform, efficient communication and perception functions can be realized at the same time, and the existing communication system can be fully utilized to increase the high-precision perception function. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 It is a transceiver system block diagram of the RIS-assisted communication and perception integrated system of the application;

[0055] Figure 2 It is a simulation diagram of the convergence effect of the method under different signal-to-noise ratios and power allocation coefficients ξ;

[0056] Figure 3 It is a simulation diagram of the effect of the method under NMSE relative to different signal-to-noise ratios;

[0057] Figure 4 It is a simulation diagram of the effect of the method under BER relative to different signal-to-noise ratios;

[0058] Figure 5 It is a simulation diagram of the effect of the method in compensating for the reduction of spectral efficiency caused by the perception function;

[0059] Figure 6 It is a NMSE performance diagram with the perception function in the method;

[0060] Figure 7is the schematic diagram of the required transmission symbol length for the method to achieve satisfactory perceptual performance at SNR = 30 dB;

[0061] Figure 8 is the performance gain diagram of the present application in terms of effective throughput. DETAILED DESCRIPTION

[0062] The specific embodiment of the present application is further described in detail below in conjunction with the accompanying drawings.

[0063] (1) Communication and perception integrated modeling: The present application studies the uplink of a single-antenna user to a base station assisted by S RISs (S ≥ 1). Because of the easy blocking characteristics of millimeter wave signals, it is assumed that the direct path from the base station to the user is blocked by an obstacle. The base station is composed of M uniform linear array antennas, and the sth RIS is composed of N s = N s，y × N s，z uniform rectangular array, where N s，y and N s，z are the unit numbers along the y and z directions, respectively. The base station detects the user's communication signal and the environmental parameters between the base station and the RIS by the user's uplink signal reflected by the RIS.

[0064] In the tth time slot, the transmitting end generates and transmits superimposed symbols where x d，t and x p，t are the communication symbols and pilot symbols generated in the tth time slot, respectively, and ξ is the power allocation coefficient allocated to the communication symbols, satisfying 0 < ξ < 1.

[0065] The base station receives the user's uplink signal reflected by the RIS

[0066]

[0067] where H r，s is the channel from the sth RIS to the base station, θ s，t is the phase of the sth RIS in the tth time slot, h b，s is the channel from the user to the sth RIS, and n t is the noise in the tth time slot. The received matrix is constructed by stacking the base station received symbols of T time slots

[0068]

[0069] where Θ s = [θ s，1 ,..., θ s，T ], X = diag(x1,..., x T ), and N = [n1,..., n Tare matrices of size T x s，t , t and n t ,

[0070] (2) Angle-domain sparse modeling of mmWave channels: At mmWave frequencies, the wireless channel follows a geometric channel model. Specifically, the mmWave channel between the base station and the s-th RIS is modeled as

[0071]

[0072] where M is the number of base station antennas, N s is the number of elements of the s-th RIS, L s is the number of channel paths from the base station to the s-th RIS, a l，s is the gain of the l-th path, and the array responses are and corresponding to the effective angle of arrival and angle of departure are and d BS and are the spacing between the base station and the s-th RIS array elements, is the angle of arrival of the l-th path to the s-th RIS, and are the elevation and azimuth angles of departure from the base station to the s-th RIS, respectively, and l is the carrier wavelength.

[0073] By selecting the elements within the grid points and , the mmWave channel can be converted to

[0074]

[0075] where M is the number of base station antennas, N s is the number of elements of the s-th RIS, L s is the number of channel paths from the base station to the s-th RIS, and are dictionary matrices composed of the array responses with respect to the angle-domain grid points and , and s is a sparse matrix.

[0076] Specifically, the angle-domain grid points and are used to estimate L r，s in H s ​​The first path corresponds to the LoS path. The effective arrival angle / departure angle corresponding to the LoS path can be determined by the relative position of the base station and RIS, so set and The first element is the equivalent arrival angle / departure angle corresponding to the LoS path, and the remaining elements uniformly quantize the angle domain into G1-1, G2-1, and G3-1 equal parts.

[0077] The RIS is composed of controllable reflection units to construct a virtual LoS path between the base station and the user, and the number of RIS is S≥1.

[0078] like Figure 1 As shown, the specific embodiment steps are as follows:

[0079] Step 1: In the tth time slot, the transmitter generates and transmits the superimposed symbol where x d，t and x p，t are the communication symbol and pilot symbol generated in the tth time slot, ξ is the power allocation coefficient allocated to the communication symbol to account for the entire symbol, satisfying 0<ξ<1;

[0080] Step 2: In the tth time slot, the base station receives the symbol Among them H r，s is the channel from the sth RIS to the base station, θ s，t is the phase of the sth RIS at the tth time slot, h b，s is the channel from the user to the sth RIS, n t is the noise of the t-th time slot. The receiving matrix is ​​constructed by stacking the base station receiving symbols of T time slots:

[0081]

[0082] where Θ s =[θ s，1 ,...,θ s，T ],X=diag(x1,...,x T ), N=[n1,...,n T ] are θ of stacked T time slots s，t , x t and n t Matrix. Then substitute into H r，s The millimeter wave angle domain sparse channel model is constructed and the receiving matrix is ​​vectorized to obtain

[0083]

[0084] Where M is the number of base station antennas, N s is the number of units of the sth RIS, L sis the number of channel paths from the base station to the s-th RIS, and is the dictionary matrix corresponding to the mmWave channel of H r，s s is the sparse matrix corresponding to the mmWave channel of H r，s , vec() is the vectorization operation, n = vec(N), ω s = vec(Ω s ), Z R2B (X, v) = [Z R2B，1 (X, v1),..., Z R2B，S (X, v S )], and ω s = vec(Ω s ). Where A is omitted to write B R2B，s , C is omitted to write D R2B，s ,

[0085] Step three: according to the second order moment information of pilot symbols and communication symbols, the initial estimation value ω (0) of the sparse vector is obtained by using LSE with TR term, and the initial estimation value of the equivalent channel between the base station and the user is further obtained The estimation value of the communication data is further obtained by using linear minimum mean square error (LMMSE)

[0086] The initial estimation of the sparse vector is obtained by using LSE with TR as

[0087]

[0088] where X p is the corresponding pilot matrix in X, is the uncertainty of the data, and the matrix where the subscripts are 1≤i≤S and 1≤j≤S. The initial estimation value of the equivalent channel between the base station and the user is further obtained by affine transformation Correspondingly, the initial estimation of the communication data can be obtained as

[0089]

[0090] where N0 is the noise variance, and P0 is the user transmission power.

[0091] Step four: using the initial estimation value in step three, the following steps are iteratively calculated under the expectation maximization (EM) framework. For the j-th iteration, when entering the iteration for the first time, let j = 1:​

[0092] (1) the communication symbol estimate value in the last iteration recover the superposition symbol estimate value X (j-1) , the grid estimate value is obtained by the off-grid SBL method and the estimate value ω of the sparse vector (j) ; specifically:

[0093] The SBL of the sparse vector ω under the EM framework is based on the following prior distribution

[0094]

[0095] where for is the variance corresponding to ω g . It can be observed that when tends to zero, ω g also tends to zero, so the estimation of ω can be transformed into the estimation of γ.

[0096] In order to promote the sparse structure of ω, γ is modeled to satisfy the Gamma distribution

[0097]

[0098] where a γ and b γ are setting parameters, and α γ , b γ → 0 can be set to satisfy the sharp peak at zero.

[0099] For the EM process, there is a complete data set {y0, ω} and a parameter data set χ = [X d , γ, v, β], where is a vector setting the grid points of the angle domain, and β is used to describe the variance of the actual unknown noise.

[0100] In order to calculate the parameter data set, the EM framework will be divided into an expectation step and a maximization step in each iteration process.

[0101] In the jth iteration, for the expectation step, the log-likelihood function is

[0102]

[0103] where the posterior distribution can be calculated and

[0104]

[0105] By maximizing the expected step with respect to γ:

[0106]

[0107] in and They are and The (g, g)th and gth elements.

[0108] By maximizing the part of the expected step with respect to β, we can get

[0109]

[0110] By maximizing the part of the expected step with respect to v, we can get

[0111]

[0112] in yes For the derivative value of ν2, The step size is adjustable.

[0113] Through maximum a posteriori estimation, we can get ω (j) L with the largest value in the vector s The angle values ​​corresponding to the elements form the estimated value of the grid Among them L s is the number of paths from the base station to the sth RIS channel.

[0114] (2) Substitute the estimated value ω obtained in step (1) (j) is greater than the preset threshold (such as ω (j) After setting the number less than 0.1 in the equation to zero, the LSE method is used to obtain a more accurate ω. (j) , recover the equivalent channel estimation value between the base station and the user through affine transformation

[0115] (3) Based on the estimated value obtained in the previous step The communication data estimate is solved by subtracting the received part corresponding to the sensing pilot from the received matrix Y After demodulation and re-modulation, the processed Specifically:

[0116] By estimated value And the receiving matrix Y minus the receiving part corresponding to the sensing pilot, using the maximization expectation step about part, we can get

[0117]

[0118] in and are respectively the mean and variance of p is the matrix corresponding to the pilot.

[0119] (4) Let j→j+1 after each iteration until j=j max or and the difference is less than δ χ .

[0120] Finally, the communication data, the angle parameters in the environment channel and the user coordinates are recovered.

[0121] The functions and effects of the present application are further illustrated by the following simulation experiments:

[0122] (1) Simulation conditions

[0123] Let the number of RIS be S=2, the number of RIS configuration units be N1=N2=12x12, the positions of the base station, RIS and user be q B =[0,0,0], q R1 =[-30,28,21], q R2 =[-20,30,20] and p U =[10,20,5] meters (m). Set the number of base station antennas M=16, the number of paths L s between the base station and RIS T=256, and the remaining parameters are set as follows: j max =100, δ χ =10 -3 , G1=G2=G3=8.

[0124] In the simulation, the superposition symbol scheme of the present application and the pure pilot and blind estimation in the prior art are respectively compared in terms of sensing accuracy (NMSE), communication bit error rate (BER), spectral efficiency (SE) and other indicators in the communication and communication perception integration mode. Among them, NMSE is specifically wherein and are the estimated values of H r，s and φ s .

[0125] (1.2) Simulation results

[0126] Figure 2The convergence of the proposed method is plotted under different SNRs and power allocation factors ξ. It can be found that the proposed method ensures the monotonic decrease of NMSE and converges within ten iterations. It can also be seen that the accuracy of sensing gradually improves with the increase of SNR, and ξ also has a significant impact on performance.

[0127] Figure 3 and Figure 4 The superiority of the designed method is demonstrated in terms of BER and NMSE with respect to different SNRs. For comparison, we use three on-grid methods and two off-grid methods, and the above comparison methods do not use iterative processes to update parameters. Figure 3 In the first place, the three on-grid methods are compared with the two off-grid methods, and the results show that the use of on-grid methods will cause serious model mismatch problems, which will damage the performance of ISAC. In addition, it can be seen that even if off-grid methods are used, there is no obvious improvement in NMSE and BER, because the symbols used for sensing contain unknown communication data. Specifically, it can be seen from the figure that the bit error rate of these communication data is almost 10 -1 , which will correspondingly reduce the sensing performance. This highlights the benefits of the designed method in updating data parameters using iterative methods, especially in the high SNR range. In addition, Figure 4 contains the theoretical lower limit of the environment sensing using pure pilots as a superimposed symbol scheme. It can be found that the performance of the superimposed symbol scheme is very close to this lower limit, especially at high SNR, indicating that the proposed method can almost eliminate the mutual interference between the sensing pilots and the communication data.

[0128] Figure 5The results demonstrate that the proposed superposition symbol scheme can compensate for the spectral efficiency reduction due to the sensing function. We consider two cases, with and without sensing function, denoted by "W / Sensing Function" and "W / O Sensing Function", respectively. It can be seen that the addition of the sensing function significantly reduces the spectrum, since more sensing pilots consume a portion of the spectrum resources, while the proposed superposition symbol scheme provides comparable spectral efficiency to the conventional communication without the sensing function under medium or high SNR conditions, such as 96% spectral efficiency at SNR = 20 dB. This is because the proposed superposition symbol scheme can obtain accurate channel angle information, almost eliminating the mutual interference between the pilot symbols and the communication symbols. In addition, we also evaluate the blind estimation method that requires few pilots, and it can be seen that it is the closest to the perfect CSI upper bound scheme. However, once the blind estimation method adds the sensing function, the spectral efficiency will be severely weakened due to the need for a large number of pilots to eliminate the quadrant ambiguity in the channel angle extraction process. In addition, compared with the double-RIS setup, the single-RIS scheme will still cause a decrease in spectral efficiency, even though it eliminates the interference between the double RIS, because there is one less channel gain. Figure 6 The performance of the NMSE with the sensing function in the above method is continued to be demonstrated. The numerical results again show that the proposed superposition symbol method can significantly outperform the performance of other benchmarks. This trend is almost consistent with the spectral performance. This is because the method with high spectral efficiency can produce more data symbols, which in turn improves the channel sensing performance.

[0129] Figure 7 The length of the transmission symbol required to achieve satisfactory sensing performance at SNR = 30 dB is demonstrated. It can be found that the NMSE of all methods is a decreasing function of T1, reaching a stable accuracy when the transmission symbol is sufficient. Regarding the case of the superposition symbol scheme, it can be observed that there will be a sharp decline near T1= 112, 160 and 192, where the sharp decline point T1 increases with ξ0. This is because when more power is allocated to the data, more symbols need to be transmitted to meet the lower NMSE.

[0130] Figure 8The performance gain of the superposition symbol scheme proposed by us in terms of effective throughput is illustrated, wherein the number of transmitted and correctly detected data bits is used as a performance indicator. It shows that the superposition symbol scheme performs best over the entire range of T1 considered, and the reason for this effect is that the pilot is inserted into each transmission block together with the data symbol, resulting in no dedicated time-frequency resource consumption for the sensing function. In particular, at T1 = 256, the effective throughput of the superposition symbol scheme is 200% larger than the pilot-based sensing method and 133% larger than the blind estimation method-based sensing method.

[0131] The above results show that the present application has obvious improvement in sensing accuracy, bit error rate and spectral efficiency compared with the existing communication and sensing integrated method using separate pilot and blind estimation, and is close to the spectral efficiency of the conventional communication without sensing function.

[0132] The above embodiments are used to explain and illustrate the present application, but not to limit the present application, and any modification and change made to the present application within the spirit and protection scope of the claims of the present application falls into the protection scope of the present application.

Claims

1. A communication perception integrated design method based on superimposed symbols, characterized in that: The steps of this method are as follows: S1. At the signal transmitting end, the communication signal and the pilot signal in the same time-frequency resource are linearly superimposed in the symbol domain by the power allocation coefficient and transmitted to the RIS; The linear superposition includes: in the tth time slot, the transmitting end generates and transmits the superposition symbol where x d，t and x p，t are the communication symbol and pilot symbol generated in the tth time slot, ξ is the power allocation coefficient allocated to the communication symbol to account for the entire symbol, satisfying 0<ξ<1; S2. At the signal receiving end, the signal reflected by the RIS is received and the received signals of multiple time slots are stacked to obtain a receiving matrix; In the tth time slot, the base station receives the symbol Among them H r，s is the channel from the sth RIS to the base station, θ s，t is the phase of the sth RIS at the tth time slot, h b,s is the channel from the user to the sth RIS, n t is the noise of the t-th time slot; the receiving matrix is ​​constructed by stacking the base station receiving symbols of T time slots: where Θ s =[θ s,1 ,...,θ s,T ],X=diag(x1,...,x T ), N=[n1,...,n T ] are θ of stacked T time slots s，t , x t and n t Matrix of S3. Convert the extracted environmental angle parameters into angle grid values The received signal is substituted into the preset millimeter wave channel model to vectorize the receiving matrix. The sparse vector ω is estimated by using the least squares estimation with the Tychonoff regularization term to obtain the initial estimate of the sparse vector ω. The estimated value of the sparse vector is used to obtain the initial estimate of the equivalent channel between the base station and the user, which includes: Substitute H r，s The millimeter wave angle domain sparse channel model is constructed and the receiving matrix is ​​vectorized to obtain Where M is the number of base station antennas, N s is the number of units of the sth RIS, L s is the number of channel paths from the base station to the sth RIS, and It is H r，s The dictionary matrix corresponding to the millimeter wave channel, Ω s It is H r，s The sparse matrix corresponding to the millimeter wave channel, vec() is a vectorized operation, with n=vec(N), ω s =vec(Ω s ), Z R2B (X, v) = [Z R2B，1 (X, v1), ..., X R2B，S (X, v S )], and ω s =vec(Ω s ); Omit and write A R2B，s ,Will Abbreviated as B R2B，s , According to the second-order moment information of the pilot symbol and the communication symbol, the initial estimate of the sparse vector ω is obtained by using LSE with TR term (0) , and further obtain the initial estimate of the equivalent channel between the base station and the user Further use the linear minimum mean square error to obtain the estimated value of the communication data Use LSE with TR to get the initial estimate of the sparse vector as where X p is the corresponding pilot matrix in X, It is the uncertainty of the data, the matrix where the subscripts are 1≤i≤S and 1≤j≤S; Obtain the initial estimate of the equivalent channel between the base station and the user through affine transformation Accordingly, the initial estimate of the communication data can be obtained as Where N0 is the noise variance and P0 is the user transmit power; S4. Based on the initial estimated value of the equivalent channel, a linear minimum mean square error method is used to obtain an initial estimated value of the communication data and demodulate and remodulate the data; S5, iteratively calculating the initial estimated value of the remodulated communication data under an expectation maximization framework to obtain a sparse vector, an angle grid, and a final estimated value of the communication data; S6. Finally, the communication data, the angle parameters in the environmental channel, and the user coordinates are restored.

2. A communication perception integrated design method based on superimposed symbols according to claim 1, characterized in that: The final estimated values ​​of the sparse vector, angle grid and communication data obtained by iteratively calculating the initial estimated value of the remodulated communication data under the expectation maximization framework are specifically: S501, first set the sparse vector ω (j) The prior distribution of in for is the corresponding ω (j) The variance of the gth element; when When ω approaches zero, (j) The gth element also tends to zero, so for ω (j) The estimate of γ (j) estimates; S502, use EM iterative update to calculate the parameter data set x=[X d ,γ,v,β], where is the vector that sets the grid points in the angle domain, and β is used to characterize the variance of the actual unknown noise; S503, obtain ω through maximum a posteriori estimation (j) , will ω (j) L with the largest value in the vector s The angle values ​​corresponding to the elements form the estimated value of the grid Among them L s is the number of paths from the base station to the sth RIS channel; S504, obtained estimated value ω (j) The numbers smaller than the preset threshold are set to zero, and then the LSE method is used to obtain a more accurate ω (j) , recover the equivalent channel estimation value between the base station and the user through affine transformation S505, by estimated value And the receiving matrix Y minus the receiving part corresponding to the sensing pilot, the communication data estimate is obtained by maximizing the expectation step Iterate S501-S505, and make j←j+1 after each iteration until the iteration number threshold is reached or the expected step log-likelihood function is reached during the iteration. and The difference is less than δ x .

3. The communication perception integrated design method based on superimposed symbols according to claim 2 is characterized in that: To promote ω (j) The sparsity structure of γ (j) Modeled to satisfy the Gamma distribution: where a γ and b γ Is to set parameters, set a γ , b γ →0 to satisfy the peak at zero.

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