Super-resolution communication perception integration method and system based on sparse MIMO

Abstract

The invention discloses a super-resolution communication perception integration method and system based on sparse MIMO, and belongs to the field of wireless communication and signal processing. Signals emitted by all users are received through a sparse array, and signal processing is carried out to estimate the two-dimensional angle of arrival of the users; pairing the two-dimensional angles by using a permutation matrix; a beam forming matrix is constructed by matching the estimated angle, and channel gain is estimated by using a small amount of pilot frequency, so that super-resolution two-dimensional sensing and efficient channel estimation of users in the system are realized. According to the method, the characteristic of large array aperture of the sparse array is combined, the problem of low positioning precision of dense distribution of multiple users is solved under the condition of not increasing hardware loss, a large amount of pilot frequency resources are saved, and the channel estimation precision is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication and signal processing, and particularly relates to a super-resolution communication and sensing integration method based on sparse MIMO. Background Art

[0002] ISAC (Integrated Sensing and Communication ISAC, communication and sensing integration) has become one of the main usage scenarios of the sixth-generation (6G) wireless network. ISAC refers to the integration of communication and sensing services through the same set of devices and network, aiming to expand the functions of the communication network and popularize the sensing service. Simply understood as the combination of network sensing and sensing communication assistance, it can eliminate the waste of resources caused by duplicate construction of devices, break the information barrier between communication and sensing, and improve the overall network capacity. Further, in the ISAC system, using a super-resolution algorithm to perform super-resolution sensing on some key parameters such as angle, time delay, and Doppler is called super-resolution communication and sensing integration.

[0003] On the other hand, the co-radio technology has been proposed as a promising solution for the future Internet of Things. Among them, the BD (Backscatter device, backscatter device) is mainly a passive device of the Internet of Things, which can use the radio frequency signal transmitted by the active user to transmit its own information. The BD not only utilizes the spectrum of the active user but also the power of the main transmission signal, so it can effectively save frequency and energy. In a large number of co-radio communication application scenarios, such as environmental monitoring, it is often necessary not only to utilize the communication performance of the BD but also to accurately locate it. Therefore, the co-radio communication based on ISSAC (Integrated Super-resolution Sensing and Communication ISSAC, super-resolution communication and sensing integration) can further save spectrum and hardware resources. When multiple BDs are densely distributed, achieving a finer spatial resolution can not only more accurately sense the BDs with dense positions but also effectively improve the communication performance. However, simply increasing the number of array elements will inevitably increase the hardware cost, the complexity of signal processing, and the power consumption.

[0004] A potential solution to the above problems is a sparse array, which eliminates the limitation of half-wavelength antenna spacing and expands the array aperture without increasing the array elements or radio frequency chains. Compared with the traditional compact antenna array with half-wavelength antenna spacing, the sparse array significantly improves the degrees of freedom. Typical sparse array structures include the minimum redundancy array, nested array (NA), modular array, and coprime array. The nested array has received extensive attention because it can be systematically designed and a closed-form expression for the degrees of freedom can be obtained. To achieve two-dimensional positioning, that is, to estimate the azimuth angle and elevation angle, generally three types of array configurations are considered, namely, uniform rectangular array, L-shaped array, and parallel array. Generally speaking, the L-shaped array is easier to construct and can provide a larger array aperture.

[0005] For channel estimation in simultaneous wireless information and power transfer (SWIPT) communication, traditional pilot-based channel estimation methods usually use least squares, linear minimum mean square error, or maximum likelihood estimation methods based on the relationship between the received signal and the known pilot sequence. However, when the number of antennas is large, it will lead to an increase in the required pilot symbols and a higher computational complexity, thus affecting the spectral efficiency and real-time processing requirements. Some scholars have proposed using channel sparsity and various compressive sensing algorithms to reduce the pilot training overhead. However, this method has high requirements for high signal-to-noise ratio, appropriate dictionary matrix selection, and perfect measurement feedback from users to the base station. In addition, some scholars have proposed a low-rank channel model based on a large-scale uniform linear array, which represents the channel model through multipath angles and channel gains and obtains angle information through array signal processing. Techniques such as discrete Fourier transform, MUSIC, and estimation of signal parameters via rotational invariance (ESPRIT) can all be used to estimate the angle parameters in the channel model. Traditional channel estimation methods using angle information rely on transmitting pilot signals, so a large amount of pilot resources are still required. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that for the positioning and communication of a simultaneous wireless information and power transfer (SWIPT) system, aiming at the problem that traditional channel estimation methods using angle information require a large amount of pilot resources, a channel estimation method based on ISSAC is proposed. On the one hand, the array aperture is increased by deploying a nested array to improve the angle resolution and save the resources of antenna elements; on the other hand, very few pilots are used to achieve accurate channel estimation and save pilot resources.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The present invention provides a super-resolution communication and sensing integration method based on sparse MIMO, including the following steps:

[0009] S1. Receive the signals sent by all users through two mutually perpendicular sparse arrays on the L-shaped antenna array, and use array signal processing to estimate the two-dimensional arrival angles of the users respectively;

[0010] S2. Pair the two-dimensional arrival angles through a permutation matrix to obtain a set of paired two-dimensional arrival angles;

[0011] S3. Construct a receiving beamforming matrix composed of beamforming vectors that match the angle directions of all users, use dedicated pilots, and use the least squares estimation method to estimate the channel gains of different users.

[0012] Step S1 is specifically as follows:

[0013] S101. At the base station, receive the signals of all users through two mutually perpendicular sparse arrays on the L-shaped antenna array, and obtain the covariance matrices of the received signals respectively;

[0014] S102. Vectorize the covariance matrix to obtain a virtual array, select and sort the virtual array antennas to obtain a steering matrix with non-repeating antenna positions arranged in ascending order and the corresponding received vector;

[0015] S103. For a single snapshot signal, use spatial smoothing to restore the rank of its covariance matrix;

[0016] S104. Adopt the one-dimensional multiple signal classification algorithm for super-resolution angle estimation, and estimate the two-dimensional arrival angles corresponding to the two sparse arrays in step S101 respectively, that is, the two-dimensional arrival angles of the users.

[0017] Step S2 specifically includes:

[0018] S201. Replace the angles in any direction estimated in step S104 into the column vector equation in step S102, and remove the row affected by noise in the equation to form a new vector equation;

[0019] S202. According to the sorted column vectors and the steering vectors formed by the estimated angles, use the least squares method to find the covariance matrix of the channel gains;

[0020] S203. Construct a cross-covariance matrix equation received by two nested arrays, which is composed of the steering vectors and the covariance matrix of the channel gains that are completely matched in two directions;

[0021] S204. Introduce a permutation matrix, assume that the steering vector matrix in direction 2 is completely matched with the steering vector matrix in direction 1 after permutation, and use the cross-covariance matrix equation to traverse this permutation matrix, thereby obtaining a set of paired two-dimensional arrival angles.

[0022] Step S3 specifically includes:

[0023] S301. The transmitting end sends a dedicated pilot for estimating the user channel gain;

[0024] S302. Construct a receiving beamforming matrix, which is composed of the two-dimensional angles of the users estimated by matching;

[0025] S303. For the signals at the receiving end, use the least squares method to estimate the user channel gain.

[0026] Furthermore, the present invention proposes a super-resolution communication and sensing integrated system based on sparse MIMO, including:

[0027] A two-dimensional angle of arrival calculation module, which receives the signals sent by all users through two mutually perpendicular sparse arrays on an L-shaped antenna array, and uses array signal processing to respectively estimate the two-dimensional angles of arrival of the users;

[0028] A steering vector matrix calculation module, which is used to pair the two-dimensional angles of arrival through a permutation matrix to obtain a set of paired two-dimensional angles of arrival;

[0029] A channel gain estimation module, which is used to construct a receiving beamforming matrix composed of beamforming vectors that match the angle directions of all users, and through dedicated pilots, use the least squares estimation method to estimate the channel gains of different users.

[0030] The present invention also proposes an electronic system, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the present invention.

[0031] Finally, the present invention also proposes a computer-readable storage medium storing computer instructions, and the computer instructions are used to make the computer execute the method of the present invention.

[0032] The present invention adopts the above technical solutions, and compared with the prior art, has the following beneficial effects:

[0033] 1. This method utilizes the characteristics of a large array aperture of a sparse array, can obtain super-resolution angle estimation, and thus can effectively save the number of antenna elements compared with a uniform linear array. And using the L-shaped architecture, two-dimensional angle estimation can be obtained, which is more universal.

[0034] 2. This method does not require the use of dedicated pilots in the angle estimation stage, but uses existing data and pilot symbols, and can greatly reduce pilot consumption compared with existing pilot-based channel estimation schemes.

[0035] 3. When estimating the channel gain, this method can construct a beamforming matrix by matching the estimated angles to obtain the beamforming gain, thus achieving a more accurate channel estimation than traditional channel estimation schemes.

[0036] 4. This method considers the scenario of integrated communication and sensing in coexisting communication. Often, it is necessary not only to communicate with active and passive users but also to locate passive users. When the number of receiving antennas is limited, it provides an effective solution for angle estimation of dense backscatter devices. Description of the Drawings

[0037] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0038] Figure 1 FIG. is a flowchart of a super-resolution communication and sensing integration method based on sparse MIMO according to an embodiment of the present invention.

[0039] Figure 2 FIG. is a scenario diagram of a coexisting system with multiple passive user access based on an L-shaped nested array provided by an embodiment of the present invention.

[0040] Figure 3 FIG. is a schematic diagram of the execution process of a coexisting system positioning and channel estimation method based on sparse array super-resolution communication and sensing integration provided by an embodiment of the present invention.

[0041] Figure 4 FIG. is a comparison diagram of the channel estimation effects based on a nested array and a uniform linear array provided by an embodiment of the present invention. Detailed Embodiments

[0042] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0043] A super-resolution communication and sensing integration method based on sparse MIMO proposed by the present invention, based on the characteristic of a large array aperture of a sparse array, improves the positioning accuracy of multiple dense users without increasing hardware loss. It avoids the disadvantage of consuming a large amount of pilot resources in traditional pilot estimation schemes, estimates the angle of arrival without using dedicated pilots, and further estimates the channel gain using only a small number of pilots. Thus, not only a large amount of pilot resources are saved, but also the channel estimation accuracy is improved due to the beamforming gain during channel gain estimation, achieving high-precision positioning and efficient channel estimation performance.

[0044] Figure 1 As shown in the flowchart of a super-resolution communication and sensing integration method based on sparse MIMO provided according to an embodiment of the present invention, refer to Figure 1 As shown below, the method steps of the present invention are as follows:

[0045] Step 1: Receive signals transmitted by signal sources at different positions through two mutually perpendicular sparse arrays of the L-shaped antenna array, respectively obtain the covariance matrices of the received signals of multiple data sources, and perform vectorization to obtain a virtual array. Further select, sort, use smoothing filtering and use the MUSIC algorithm for super-resolution one-dimensional angle estimation;

[0046] Step 2: Use a permutation matrix to pair the two one-dimensional angles to obtain a steering vector matrix;

[0047] Step 3: Use the obtained angle information to construct a receive beamforming matrix, and compared with the traditional pilot-based channel estimation scheme, use times the pilots, and use the least squares estimation method to estimate the channel

[0048] gains of different users, where K + 1 is the number of users, 2M - 1 is the number of antennas, M represents the number of antennas of each sparse array, and K represents the number of passive user BDs.

[0049] The present invention accurately estimates the angle parameters of multiple dense users by deploying sparse arrays and subspace-based super-resolution algorithms. This process does not require the use of dedicated pilots. Then, use the angle information to obtain the beamforming gain and estimate the channel coefficients of multiple users using a small number of pilots. By combining the advantages of the high array aperture of the sparse array and the advantage of saving pilots in the channel estimation method based on super-resolution communication and sensing integration, the problem of requiring a large amount of antenna resources for positioning dense users is solved, realizing super-resolution angle estimation and accurate channel estimation, and improving the communication performance of the system.

[0050] In an embodiment of the present invention, step 1 specifically includes:

[0051] First, use the existing data signals and pilot signals instead of dedicated pilot signals to respectively obtain the covariance matrices of the received signals in two mutually perpendicular directions of the L-shaped antenna array, and perform vectorization;

[0052] Next, by selecting and sorting the corresponding virtual array antennas, obtain a steering matrix with non-repeating antenna positions arranged in ascending order and the corresponding receive vectors. For this single snapshot signal, further use spatial smoothing to restore the rank of its covariance matrix. Use the one-dimensional MUSIC algorithm to estimate the angles of arrival in two directions corresponding to the two nested arrays respectively.

[0053] In an embodiment of the present invention, step 2 specifically includes:

[0054] First, substitute the estimated angle in any direction into the column vector equation selected and sorted in step 1. Note that only one row of this equation is affected by noise, so this row is removed to form a new vector equation, which is an expression related to the angle steering vector and the covariance matrix related to the channel gain.

[0055] Second, according to the sorted column vectors and the steering vectors formed by the estimated angles, use the least squares method to obtain the covariance matrix of the channel gain.

[0056] Next, construct a cross-covariance matrix equation for the nested arrays received, which is composed of the steering vectors and the covariance matrix of the channel gain after the angles in two directions are completely matched. Introduce a permutation matrix. Assume that the steering vector matrix in direction 2 after permutation is completely matched with the steering vector matrix in direction 1. Use the cross-covariance matrix equation to traverse this permutation matrix, and thus obtain the angle sets in the two directions after matching.

[0057] In an embodiment of the present invention, step 3 specifically includes:

[0058] Construct a receiving beamforming matrix composed of beamforming vectors that match the angle directions of all users, and use times the pilots compared with the traditional scheme, and use the directions estimated by the least squares estimation to estimate the channel gains of different users, where K + 1 is the number of users and 2M - 1 is the number of antennas.

[0059] The method for integrated communication and sensing based on sparse MIMO of the present invention is described below through specific embodiments.

[0060] Figure 2 It is a diagram of an uplink coexisting communication scenario for multiple passive user access based on an L-shaped nested array, including a primary user equipment UE, a base station BS, and K BDs. The UE and each BD are equipped with one antenna, and the BS deploys an L-shaped array on the x-z axis. The L-shaped array is composed of two secondary nested arrays along the x-axis and the z-axis respectively. For convenience, assume that both nested arrays are composed of M = M1 + M2 antennas, where the inner uniform linear array has M1 antennas with an antenna spacing of d0 = λ / 2, and the outer uniform linear array has M2 antennas with an antenna spacing of (M1 + 1)d0, and λ is the signal wavelength. A reference point O is set at the origin of the coordinate system. Since the two nested arrays share the same antenna element at the origin, the L-shaped array has a total of 2M - 1 antennas. Define the distances between the origin O and the mth element on the x-axis and the z-axis as d x,m and d z,m, where \(m = 1, 2, \cdots, M - 1\). Since the distributions of the two nested arrays on the \(x\)-axis and \(z\)-axis are the same, the positions of the antennas can be represented by the same set of integers as follows, where

[0061] The nested array is defined according to the integer parameters \((M_1, M_2)\) and consists of the union of a compact uniform linear array and a sparse uniform linear array, i.e., \(D = D\) in \(\cup D\) ou .

[0062] The set of the compact uniform linear array is: \(D\) in : \(\{0, 1, \cdots, M_1 - 1\}\);

[0063] The set of the sparse uniform linear array is: \(D\) ou : \(\{M_1, 2(M_1 + 1)-1, \cdots, M_2(M_1 + 1)-1\}\).

[0064] By backscattering the radio frequency signals from the UE, \(K\) BDs can modulate their information without consuming additional spectrum and power. Assume represents the direct link channel from the UE to the BS, and \(\alpha\) k represents the channel from the UE to the \(k\)-th BD, represents the channel from the \(k\)-th BD to the BS, then the cascaded channel from the UE through the \(k\)-th BD to the BS is Assume that both the direct link channel from the UE to the BS and the backscattering link channel from the UE through the \(k\)-th BD to the BS are line-of-sight channels. Assume \(\gamma\) ue represents the complex amplitude from the UE to the BS, and \(\theta\) ue and represent the elevation angle and azimuth angle at the BS. The array responses of nested array 1 and nested array 2 related to the UE can be written as:

[0065]

[0066]

[0067] Note that can be simplified as: ue by using the incident direction \(\varphi\)

[0068] where

[0069] Then, by combining the array responses of nested array 1 and nested array 2, removing the repeated antenna elements corresponding to the origin \(O\), the array response of the L-shaped nested array related to the UE can be written as:

[0070]

[0071] h ue can be expressed as h ue =γ ue a(θ ue ,φ ue ).

[0072] Furthermore, the complex amplitude from the k-th BD to the BS is expressed as β k , and the arrival angles corresponding to the BS are expressed as θ k and φ k , then g k can be expressed as g k =β k a(θ k ,φ k ), where a(θ k ,φ k ) is an array response with a similar form to a(θ ue ,φ ue ).

[0073] Figure 3 It is a schematic diagram of the execution process of the co-location and channel estimation method for the co-existing system based on the sparse array ISSAC. The specific implementation steps can be summarized as follows:

[0074] (1) The active user UE and the passive user BD end send wireless signals;

[0075] (2) When the base station end receives the signals of the active and passive users, the two-dimensional arrival angles of the active and passive users are estimated respectively through signal processing of the two secondary nested arrays;

[0076] (3) Pair the two-dimensional angles through the permutation matrix;

[0077] (4) Construct a beamforming matrix by matching the estimated two-dimensional angles, obtain the beamforming gain, and estimate the channel gain using a small number of pilots, thereby obtaining the channel estimation of the active and passive users.

[0078] In step (1), the setting of parasitic co-existing wireless communication is considered, where the symbol rate of the active user UE is equal to the symbol rates of multiple passive users BDs. It is assumed that the information symbols of the UE and BDs are independent and identically distributed, and are expressed as s(n) and c k (n) respectively, both following the symmetric complex Gaussian distribution, that is, s(n)~CN(0,1) and c k (n)~CN(0,1), k = 1,..., K.

[0079] In step (2), the received signal at the receiving end of the base station can be expressed as:

[0080]

[0081] wherein, represents the noise of complex Gaussian distribution, with a mean of zero and a power of σ 2 .

[0082] Next, the two-dimensional angle can be estimated by using existing pilot and data symbols, avoiding the use of dedicated pilots. For the sake of brevity, only the received signal in the data transmission phase is given, and the received signal in the pilot transmission phase is omitted. Taking the angle estimation process of nested array 1 as an example, the received signal of nested array 1 is written as:

[0083] y z (n) = A z (Θ)s com (n) + u z (n), wherein is the array manifold matrix of nested array 1, and there is

[0084] Since the spatial sampling of the nested array is uneven, the classical angle estimation cannot be directly performed, but the virtual received signal model can be used. First, the covariance matrix of the received signal in the above formula is written as

[0085]

[0086] wherein,

[0087]

[0088] is the covariance matrix of S for S com (n).

[0089] Then, vectorize R to obtain

[0090] wherein can be regarded as the equivalent received signal, is the equivalent received noise, wherein e i is a column vector with all positions being zero except the i-th position being 1. The operations of conjugation and the KR product ⊙ are manifested as the differences in the physical positions of the antenna elements, thus forming a virtual array, whose antennas are located at D r = {d i - d j , i, j = 1, 2,..., M}.

[0091] Furthermore, by selecting and sorting the antennas of the virtual array, an equivalent steering matrix can be obtained, whose receiving antennas are located at different D r . It has been proven that when M is even and when, by using only M physical antennas, Dr A compact uniform linear array with up to root antennas can be equivalently formed. Thus, the degrees of freedom and the array aperture are significantly improved, and the sensing performance can be enhanced. Since the above equation represents a single snapshot signal, we use spatial smoothing to recover r the rank of the covariance matrix of. Then, the elevation angles of the UE and the K BDs are estimated by the standard MUSIC algorithm and are respectively estimated as and In addition, we can perform a similar operation to estimate the angle of arrival in another dimension on NA2 At this time, the angle estimates and are usually mismatched and cannot be directly used for two-dimensional direction identification.

[0092] In step (3), the covariance matrix of the received signal at nested array 1 in step (2) is vectorized, and by using the equation after antenna selection and sorting, the estimated is substituted into this equation to obtain

[0093]

[0094] where, represents a column vector with all elements being zero except for the element at the M2(M1 + 1)-th position being 1. Therefore, eliminating the M2(M1 + 1) elements of can eliminate the influence of noise, and the above equation then becomes where, is constructed by deleting the M2(M1 + 1)-th row from . The vector r s can be estimated as where, is the pseudo-inverse operator. Therefore, the covariance matrix of S com (n) can be estimated as

[0095] Furthermore, a permutation matrix is introduced to match the two-dimensional angles, where [T] i,j ∈{0,1}, and By using the permutation matrix T, the angle of incidence can be reordered to match , that is, After substituting the matched into A x (Φ), we have

[0096] The received signal y at nested array 1 z(n) and the received signal y at the nested array 2 x The cross-covariance matrix of (n) can be written as

[0097] The permutation matrix T can be obtained by solving the following minimization problem:

[0098]

[0099] Thus, the angle matching with can be obtained.

[0100] Furthermore, the azimuth angle can be calculated as:

[0101]

[0102] In step (4), assume that the pilot symbols backscattered by K BDs are 1, and the pilot sequence sent by the UE is satisfying which is known to the receiver. Construct the receive beamforming matrix as follows, which is composed of the beamforming vectors of the UE and K BDs:

[0103]

[0104] Assume P t represents the transmit power generated by the UE during the pilot transmission phase. Assume γ = [γ ue , α1β1, …, α K β K T , A(Θ, Φ) = [a(θ ue , φ ue ), a(θ1, φ1), …, a(θ K , φ K )]. The received signal at the BS can be written as:

[0105]

[0106] where, u t (n) represents complex Gaussian noise with zero mean and variance σ 2 . Since the pilot symbols are known, y t (n) can be projected onto . The result is:

[0107]

[0108] where, is the resulting noise vector, satisfying u′ t ~CN(0, τσ 2 I​​M )。Therefore, the least squares estimate of γ can be written as

[0109]

[0110] The estimation error of γ can be written as

[0111]

[0112] If the angle estimation is unbiased, i.e., then the estimation error of γ is expressed as:

[0113]

[0114] Then the mean square error of γ can be expressed as:

[0115]

[0116] After estimating the incident angle and channel gain, the direct link channel and the cascaded channel can be estimated separately as

[0117] Define the combined channel h that includes all UEs and BDs sum = A(Θ,Φ)γ. If the angle estimation is completely accurate, i.e., then the estimation error of h sum is written as h sum The mean square error of h can be written as:

[0118]

[0119] The mean square error of channel estimation for the traditional pilot-based channel estimation scheme is

[0120]

[0121] where 2M - 1 is the number of antennas at the base station. It can be seen that if the same estimation performance as the traditional channel estimation scheme is required, the proposed channel estimation scheme based on integrated super-resolution communication and sensing only needs times the number of pilots, and usually the number of users K + 1 is much smaller than the number of antennas 2M - 1.

[0122] Figure 4 Shows the comparison diagram of the mean square error of channel estimation for all channels under the nested array and the uniform linear array and its variation with the number of antennas. First, it is observed that the mean square error under the nested array is smaller than that under the uniform linear array, and this difference decreases as the number of antennas increases. Therefore, it shows that when the number of antennas is insufficient, the nested array can obtain higher estimation accuracy.

[0123] According to the super-resolution communication and sensing integration method based on sparse MIMO proposed by the embodiments of the present invention, the primary and secondary users send primary and secondary signals, and the base station estimates the two-dimensional angles of arrival of the primary and secondary users respectively through signal processing of two nested arrays; then pairs the two-dimensional angles; constructs a beamforming matrix by matching the estimated angles, and estimates the channel gain using a small number of pilots, realizing super-resolution two-dimensional sensing of passive users and efficient channel estimation of all primary and secondary users. By combining the characteristics of a large array aperture of the sparse array, the present invention solves the problem of low positioning accuracy in the case of dense distribution of multiple users without increasing hardware loss. In addition, the proposed channel estimation method based on super-resolution communication and sensing integration estimates the angle of arrival without using dedicated pilots, and only uses a small number of pilots to estimate the channel gain. Therefore, compared with the traditional pilot channel estimation scheme, a large amount of pilot resources are saved. When estimating the channel gain, a beamforming matrix is constructed by matching the estimated angles, thereby obtaining beamforming gain and further improving the channel estimation accuracy.

[0124] Embodiment 2:

[0125] The present invention proposes a super-resolution communication and sensing integration system based on sparse MIMO, including:

[0126] A two-dimensional angle of arrival calculation module, which receives the signals sent by all users through two mutually perpendicular sparse arrays on an L-shaped antenna array, and uses array signal processing to estimate the two-dimensional angles of arrival of the users respectively;

[0127] A steering vector matrix calculation module, which is used to pair the two-dimensional angles of arrival through a permutation matrix to obtain a set of paired two-dimensional angles of arrival;

[0128] A channel gain estimation module, which is used to construct a receiving beamforming matrix composed of beamforming vectors that match the angle directions of all users, and uses times the pilots compared with the traditional scheme, and uses the least squares estimation method to estimate the channel gains of different users, where K + 1 is the number of users, 2M - 1 is the number of antennas, M represents the number of antennas in each sparse array, and K represents the number of passive users BD.

[0129] Embodiment 3:

[0130] This embodiment proposes an electronic system, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of the present invention.

[0131] Embodiment 4:

[0132] This embodiment provides a computer-readable storage medium storing computer instructions, and the computer instructions are used to cause the computer to execute the method of the present invention.

[0133] It should be noted that the processing flows of Embodiment 2 to Embodiment 4 correspond to the specific steps of the method provided by the embodiments of the present invention, and have the corresponding functional modules and beneficial effects for executing the method. For the technical details not described in detail in this embodiment, reference may be made to the method provided by the embodiments of the present invention.

[0134] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0135] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0136] Any process or method description shown in the flowchart or described in other ways herein may be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

Claims

1. A super-resolution communication and perception integration method based on sparse MIMO, characterized in that: The following steps are involved: S1, receiving the signals sent by all users, and using array signal processing to estimate the two-dimensional arrival angle of each user; S2. Pairing the two-dimensional arrival angles through a permutation matrix to obtain a set of matched two-dimensional arrival angles; S3. Construct a receive beamforming matrix consisting of beamforming vectors that match the angular directions of all users, and estimate the channel gains of different users by using a dedicated pilot through a least squares estimation method.

2. The method according to claim 1, characterized in that: Step S1 is specifically as follows: S101, at a base station, receiving signals of all users through two mutually perpendicular sparse arrays on an L-shaped antenna array, and obtaining covariance matrices of the received signals respectively; S102, vectorizing the covariance matrix to obtain virtual array antennas, selecting and sorting the virtual array antennas to obtain a steering matrix in which antenna positions are not repeated and are arranged in ascending order, and a corresponding receiving vector; S103, for a single snapshot signal, using spatial smoothing to restore the rank of its covariance matrix; S104 , using a one-dimensional multiple signal classification algorithm to perform super-resolution angle estimation, respectively estimating the two-dimensional arrival angles corresponding to the two sparse arrays in step S101 , that is, the two-dimensional arrival angles of the user.

3. The method according to claim 2, characterized in that Step S2 specifically includes: S201, replacing the angle of any direction estimated in step S104 into the column vector equation in step S102, removing a row affected by noise in the equation to form a new vector equation; S202, using the least square method to obtain the covariance matrix of the channel gain according to the sorted column vectors and the estimated steering vector of the angular travel; S203, constructing a cross covariance matrix equation for receiving two nested arrays, which is composed of the covariance matrix of the steering vector and the channel gain after the angles in the two directions are completely matched; S204, introducing a permutation matrix, assuming that after permutation, the steering vector matrix of direction 2 completely matches the steering vector matrix of direction 1, and using the cross-covariance matrix equation to traverse this permutation matrix, thereby obtaining a matched two-dimensional arrival angle set.

4. The method according to claim 1, characterized in that: All users in step S1 include the main transmission user in the symbiotic communication and a plurality of densely distributed passive backscattering devices, and the channels between all users and the receiver are line-of-sight links.

5. The method according to claim 2, characterized in that: The sparse array is selected as a nested array, or a coprime array, or a minimum redundant array.

6. The method according to claim 2, characterized in that Two mutually perpendicular nested arrays are both secondary nested arrays.

7. The method according to claim 1, characterized in that Step S3 specifically includes: S301, the transmitting end sends a dedicated pilot for estimating a user channel gain; S302, constructing a receive beamforming matrix, which is composed of matching the estimated two-dimensional angles of the user; S303: For the signal at the receiving end, estimate the channel gain of the user using the least square method.

8. A super-resolution communication and perception integrated system based on sparse MIMO, characterized in that: include: The two-dimensional arrival angle calculation module receives the signals sent by all users through two mutually perpendicular sparse arrays on the L-shaped antenna array at the base station end, and estimates the two-dimensional arrival angles of each user by using array signal processing; A steering vector matrix calculation module is used to pair the two-dimensional arrival angles through a permutation matrix to obtain a set of matched two-dimensional arrival angles; The channel gain estimation module is used to construct a receiving beamforming matrix composed of beamforming vectors matching the angular directions of all users, and estimates the channel gains of different users using a dedicated pilot and a least squares estimation method.

9. An electronic system comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, characterized in that the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to execute the method according to any one of claims 1 to 7.

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