Joint communication and environment awareness method and system for implementing same

Abstract

A method of processing wireless communication signals for joint communication and environmental awareness in a region of interest (ROI) is presented. The method alternately uses a first iterative process and a second iterative process for determining an initial representation of a voxelized environment of the ROI and for determining the transmitted signal. The first iterative process and the second iterative process may implement a Gaussian approximation belief propagation process.

Description

Technical Field

[0001] This invention relates to the field of environmental sensing or environmental mapping, and more particularly to such sensing techniques using wireless communication signals. More specifically, this invention relates to a method for processing communication signals for environmental sensing, a computer program product implementing the method, a computer-readable storage medium storing the computer program product, and a system configured to perform the method. Throughout this specification, the term "environmental sensing" will be used extensively in various expressions for capturing information about the environment to create its three-dimensional representation.

[0002] symbol

[0003] Scalar values ​​are represented by the italic lowercase letter x in this paper, while complex vectors and matrices are represented by the bold lowercase and uppercase letters x and X, respectively. (·) T and(·) * Let represent the transpose operator and the complex conjugate operator, respectively, and let diag(·) denote the diagonalization operator. |·| denotes the absolute value operator, while || || l This represents the l-norm. and Var x (x) respectively represent x relative to the given information. The expected value operator and variance operator for the x-distribution are given. and Let N(μ,ν) and XN(μ,ν) represent the real number field and the complex number field, respectively, and let N(μ,ν) and XN(μ,ν) represent the real Gaussian distribution and the complex Gaussian distribution with mean μ and variance ν, respectively. Background Technology

[0004] Joint Communication and Sensing (JCAS) is a technique in wireless communication that aims to obtain information about the environment from signal scattering present in effective channel state information (CSI) (e.g., caused by objects in the environment, congestion, user activity, etc.) while simultaneously enabling data communication. Most known JCAS methods utilize radar technology to infer information about the environment. This is also known as Joint Radar and Communication (JRC).

[0005] Various methods are known in the JRC, including alternating or sharing the spectrum between radar and communication signals, embedding information using standard radar signals, extracting radar parameters from standard communication signals, or even designing new waveforms suitable for both tasks. These techniques are largely based on traditional radar signal processing (e.g., ambiguity function estimation) and rely on radar frequency delay properties, and are prone to similar challenges.

[0006] Spatial representation of the environment is widely used in robot vision, localization and mapping, as well as computer graphics and medical imaging. One known technique involves discretizing the real environment by voxelizing an occupied mesh. Typically, voxels are regular cubes arranged in a 3D space covering the environment, and these cubes are assigned information about the space they occupy. The discretization of the resulting voxelized space is well-suited for 3D modeling, where objects in the region of interest can be represented by coarse estimates or more accurate shapes, depending on the size of the unit voxels.

[0007] Figure 1 a) An exemplary environment is shown, in Figure 1 b) and Figure 1 In c), the environment is represented using voxels of different sizes with voxelization at different resolutions. Figure 1 b) and Figure 1 c) illustrates the 3D voxelized occupied mesh, where the total region of interest (ROI) is defined as having a size of L. x ×L y ×L z The ROI is a cubic space, where each dimension represents the length of the x-axis, y-axis, and z-axis, respectively, in meters. A grid composed of individual elements, in which, and These represent the number of voxels or partitions along the x-axis, y-axis, and z-axis, respectively, and L V This is the side length of the voxel cube, in meters, corresponding to the image resolution. If we use a three-dimensional tensor (N... x ×N y ×N z If the voxelization occupies the grid, it directly represents the discretized model of the ROI (e.g., Figure 1 b) and Figure 1 As shown in c), the size of the voxel corresponds to the image resolution. The elements of the 3D tensor indicate the occupancy of the voxels, thus indicating whether that part of the space is empty or filled with a given material.

[0008] Besides the flexibility in estimation accuracy offered by voxelized meshes, one of the biggest advantages of mesh-based models is the simplicity of data representation (especially compared to 3D vertex-based or point cloud-based methods), which makes voxelized meshes more advantageous from a machine learning perspective.

[0009] Recent developments in communication technology have demonstrated the benefits of modeling the space between the transmitter and receiver using a voxelized environment for JCAS (sometimes also called Integrated Sensing and Communication or ISAC). Throughout this specification, the terms JCAS and ISAC are used interchangeably.

[0010] To this end, the electromagnetic scattering behavior of objects in the real environment can be added to the 3D geometric information provided by the classical voxelized occupancy mesh to customize the communication channel modeling method used in wireless communication scenarios. To account for scattering behavior, a voxel occupancy coefficient v is assigned to each voxel. k ∈{0,1} (where k∈{1,…,N) V}), where v k =0 indicates that the k-th voxel is empty, meaning the corresponding environment is free space, and v k =1 indicates that the k-th voxel is occupied by a scattering object (e.g., an object on a table, chair, or wall), such as Figure 1 As shown. Note that the binary voxel occupancy coefficient can also be extended to the complex voxel scattering coefficient, i.e. To capture the effect of occupied voxels on reflected electromagnetic waves. The constant β k and ω k It depends not only on the material itself, but also on the frequency and angle of incidence of the propagating signal, and the effect of the material occupying a given voxel on the electromagnetic wave it reflects or refracts. In other words, the value of the voxel scattering coefficient is expected to be highly dependent on the electromagnetic properties of the scattering object and the impact wave, which can be empirically measured and modeled as functions of properties such as frequency and material properties.

[0011] X. Tong, Z. Zhang, J. Wang, C. Huang, and M. Debbah, in “Joint Multi-User Communication and Sensing Exploiting Both Signal and Environment Sparsity,” IEEE Journal of Selected Topics in Signal Processing, Vol. 15, No. 6, pp. 1409–1422, November 2021, discuss an exemplary regular voxelized 3D space with some scattering objects accommodating a single access point (AP), a single reconfigurable smart surface (RIS), and multiple single-antenna user equipments (UEs). Multiple UEs communicate with the AP via sparse code multiple access (SCMA) through multiple frequency subcarriers and multiple transmit instances, via line-of-sight (LOS) paths from UE to scattering objects, then to the RIS, and finally to the AP.

[0012] Figure 2 a) and Figure 2b) illustrates the general concepts of LOS and NLOS paths in voxelized space, where all paths are available and the reflection angles are within the range of actual reflected impact electromagnetic waves. Figure 2 In a), the LOS path is the direct path between the User Equipment (UE) and the Access Point (AP), indicated by the solid line, while the two occupied voxels in the ROI reflect signals transmitted by the UE back to the AP. The dashed line represents the NLOS path from the UE to the voxel, and the dotted line represents the NLOS path from the voxel to the AP. Figure 2 In b), one of the previously filled voxels is now empty, which will obviously affect the scattering coefficient of that voxel. The dashed line from the empty voxel to AP represents... Figure 2 The path that exists in a) is not considered an existing path.

[0013] Figure 3 A schematic representation of a 3D space (including the RIS) considered in known exemplary systems and methods is shown. Here, signals reflected from the RIS toward the AP are shown with dotted lines to highlight their specific sources.

[0014] Based on known exemplary system models and assumptions, the signal received at the AP will carry not only the UE's payload or data, but also scattering path information that can be used to acquire or perceive the environment. To aid estimation, it is assumed that the UE transmits pilot symbols of a known length at the start of the transmission interval, i.e., a known preamble. The goal of known methods is to return an estimate of the UE's transmit SCMA code (i.e., user detection or identification), and a binary voxel occupancy grid corresponding to the environment estimate, where 0 represents voxels representing free space and 1 represents voxels representing occupied space.

[0015] The known system can be mathematically represented by the following model.

[0016] Y = (H + PRQVB)[X] p X]+W

[0017] Where Y is the received signal matrix of all receiving antennas and AP on all symbol instances, H is the LOS channel matrix from UE to AP, P is the NLOS channel matrix from RIS to AP, R is the RIS reflection coefficient matrix, Q is the NLOS channel matrix from voxel to RIS, B is the NLOS channel matrix from UE to voxel, V is the voxel occupancy matrix, and X... p Let P be the pilot symbol matrix, X be the data symbol matrix, and W be the AWGN noise matrix. Since matrices P, R, Q, and B are known, the model can be simplified to...

[0018] Y = (H + AVB)[X] p X]+W

[0019] Where A = PRQ is the effective NLOS channel matrix from voxels to AP.

[0020] Thus, the main joint communication and sensing problem is formulated, namely, using the known channel matrices H, A, B and the known pilot matrix X. p To estimate the environment matrix V and the data symbol matrix X.

[0021] Therefore, existing technical methods use three modules:

[0022] • The environment estimation module uses the Linear Generalized Approximate Message Passing (GAMP) algorithm, which estimates the environment V using a known channel and given data symbols X (pilot or estimated symbols from the previous iteration). Its model is as follows:

[0023] Y = (H + AVB)[X] + W

[0024] • The effective channel reconstruction module integrates known channel and estimated environment information and uses V estimated by the GAMP algorithm to combine the information with known H, A, and B to calculate the effective total channel.

[0025]

[0026] • The data signal estimation module uses the linear SCMA message passing algorithm (MPA) and the estimated effective channel to estimate the unknown data symbol X.

[0027]

[0028] These three modules iterate in the time domain using a sliding window approach (e.g.) Figure 4 As shown, this allows for initial environmental estimation using known pilot symbols, but for data signal estimation, the window slides to estimate unknown data symbols, and so on.

[0029] However, like other known methods of JCAS in voxelized environments, the prior art methods discussed further above rely on the following idealized assumptions: all paths between all transmit and receive antennas are fully available, i.e., no path is blocked; the channel gain of the LOS path from UE to AP is known; the channel gain of the NLOS paths from UE to voxel, voxel to RIS, and RIS to AP is known; the RIS reflection coefficient is known; the voxelized environment model is binary, i.e., only discrete occupancy values ​​of 0 or 1 are possible; all scattering paths have real reflection angles; the SCMA communication scheme is used; and only a single AP exists.

[0030] These assumptions severely limit the application of known methods to real-world 3D environments, leading to a need for an improved method for processing wireless communication signals for environmental sensing, a receiver configured to perform the improved method, and a communication system including one or more such receivers. Summary of the Invention

[0031] This need is met by the method as claimed in claim 1, the system as claimed in claim 7, and the computer program product as claimed in claim 9. Claim 10 presents a corresponding computer-readable storage medium and a vehicle including an improved receiver according to the invention. Advantageous embodiments and developments are described in the corresponding dependent claims.

[0032] Before describing the various aspects of the present invention, the concepts of unavailable and impractical paths in a voxelized environment will be clarified. Figure 5 a) and Figure 5 b) illustrates exemplary voxelized spaces where some of these paths may become infeasible or unavailable due to various physical phenomena. Figure 5 a) and Figure 5 (b) In both cases, the solid line from the User Equipment (UE) to the Access Point (AP) represents the available LOS path. Clearly, if the occupied voxel lies in a straight line with the path between the UE and the AP (not shown in the figure), the corresponding path will be unavailable. If the scattering angle between the incident and reflected paths is too large and exceeds a critical angle (e.g., close to 180°), the corresponding NLOS path will be impractical and practically unavailable. Figure 5 As shown in a), the dashed line from the voxel to the AP represents the NLOS path of the reflected voxel to the AP, which has an unrealistic reflection angle, i.e., unusable. The determination of the critical angle depends on the electromagnetic properties of the RF wave and the environment (e.g., operating frequency), therefore this invention proposes a simplified model to approximately incorporate this phenomenon into the channel matrix of the voxelized mesh environment model. Path unusability can also be caused by tilted surfaces, such as... Figure 5 As shown in b), the signal is reflected away from the AP. Figure 5 Same as in a), Figure 2 In b), the theoretically possible NLOS path for reflection from an empty voxel will be unusable because the object in the voxel will reflect the incident wave away.

[0033] Furthermore, before describing the various aspects of the present invention, the underlying channel model will be described. It is assumed that the ROI includes N. U Each single-antenna UE and each equipped with N R N receiving antennas A A multi-antenna AP. For example... Figure 5The effective channel between the UE and AP, as shown, comprises two different types of components: LOS components and NLOS components. The LOS component is the direct path between the UE and AP, while the NLOS component covers the path reflected from occupied voxels corresponding to portions of objects in the environment—that is, the path scattered by voxels representing the scattering objects, as further described above. It is also assumed that the bandwidth is high enough that the power of paths reflecting more than once is negligible. Therefore, the NLOS component can be decomposed into two sub-paths: a sub-path from the UE to the voxel and a sub-path from the voxel to the AP. These two sub-paths, together with the voxel scattering coefficients, constitute the aggregated NLOS channel.

[0034] In view of the above, N U A single-antenna UE and all APs N A N R A set of receiving antennas (i.e., N) A All N of each of the APs R The effective channel between (a number of antennas) is given by the following formula.

[0035]

[0036] in, It is the effective channel matrix. and These are the channel matrices formed by the LOS path from UE to AP, the NLOS sub-path from voxel to AP, and the NLOS sub-path from UE to voxel, respectively. It is a vector containing all scattering coefficients of the voxelized grid. Assume the elements of the channel matrices H, A, and B follow a variance of... and It follows a zero-mean complex normal distribution.

[0037] It should be noted that the channel model presented above can be directly extended to multi-carrier systems, thereby generating...

[0038]

[0039] Where f represents the set of all subcarrier frequencies. Specific frequencies within.

[0040] It is important to note that the channel model in the equations shown above assumes that all paths between the UE, AP, and voxel are fully available, as in the known systems discussed in the background section. However, in reality, many paths may become unavailable due to various physical phenomena, such as obstruction by airborne particles, absorption, path loss, or the limited resolution of the voxelization model itself. Furthermore, as mentioned above, if the angle between the incident and reflected paths is too large and exceeds a critical angle, the corresponding NLOS path will be unavailable, such as... Figure 5 As shown in a).

[0041] Similarly, if an occupied voxel is aligned with the LOS path, the corresponding path will be unavailable. The determination of the critical angle depends on the electromagnetic properties of the RF wave and the environment (e.g., operating frequency), therefore a simplified model is proposed to approximately incorporate this phenomenon into the channel matrix of the voxelized grid environment model.

[0042] Since realistically modeling all paths may be computationally forbidden, embodiments of the present invention may consider the statistical feasibility of paths to improve the voxelized mesh model.

[0043] Therefore, consider the physical phenomena that occur when a propagating wave is reflected, especially for any given frequency, and consider the following facts:

[0044] i) There exists a critical angle θ* such that... Figure 5 a) As shown, if the incident angle θ > θ*, the wave is absorbed instead of reflected, therefore the corresponding voxel-to-AP NLOS subpath is unavailable, and

[0045] ii) The curvature of the surface exposed to the impact wave may cause no signal to be reflected toward the AP, such as Figure 5 b) As shown.

[0046] As mentioned earlier, modeling this phenomenon at each voxel can be too complex to perform, especially with a high resolution of the voxelized ROI. Employing a statistical approach that considers the angle between the impact wave and the reflected wave at each voxel (hereinafter referred to as the scattering angle) and thus the availability of the NLOS subpath from each voxel to the AP will significantly reduce computational complexity.

[0047] In view of the above, embodiments of the present invention may use the following stochastic geometric model to integrate the above phenomena into the channel matrix of the voxelized grid environment model.

[0048] First, the locations of the UE and AP are discretized into a 3D mesh of the voxelized environment model, so that their locations can be described equivalently using voxel coordinates. Note that it is also assumed that the multiple antennas of the corresponding AP are entirely located within a single voxel, thus assuming they have the same angle of arrival (AoA) but different channel path coefficients. Given the 3D coordinates of the UE, AP, and voxel, respectively... and The scattering angle θ at the voxel can then be calculated as follows:

[0049]

[0050] The arccos(·) operator represents the inverse cosine trigonometric function.

[0051] The empirical probability distribution function (PDF) of the scattering angle θ can be obtained by applying c to the scattering angle θ respectively. U c A and c V The above equation is used to evaluate all possible combinations of permissible locations of UE, AP, and voxels within the given ROI, and examples of the latter are given in voxelized environments at various resolutions. Figure 6 It is shown in the middle.

[0052] exist Figure 6 As can be seen from this, for a sufficiently large N V The distribution of the scattering angle θ can be well modeled by a mixture of two β distributions, i.e., f x (x) = γ·β(a1,b1) + (1-γ)·(a2,b2), where x ∈ [0; 180°], where γ is a weighting factor, and quantities a1, a2, b1, b2 are shape parameters optimized to match empirical data obtained by evaluating the aforementioned equation, where c U c A and c V Randomly selected within the voxelized mesh.

[0053] Using this empirical stochastic geometry approach, the voxelized environment model used to date can be improved by introducing stochastic blocking of NLOS subpaths based on the selected critical angle, the complementary cumulative distribution of the approximate β-mixed PDF, and the scattering angle at each voxel.

[0054] As mentioned earlier, the scattering angle θ at all voxels is calculated for all UE and AP pairs. This is achieved by introducing an arbitrary critical angle θ. 临界 ∈[0°,180°], when θ>θ 临界 If the scattering path is found to be unavailable, the corresponding path is removed from the NLOS channel matrix.

[0055] exist Figure 7 The critical angle θ is shown in the figure. 临界 The impact on the severity of channel matrix puncture was assessed through numerical evaluation using random numbers and locations of UEs and APs at various voxelized grid resolutions. The average channel puncture rate was determined by the total number of path vertices (N). A +N R Normalization. As expected, the perforation rate exhibits normalization at θ. 临界 =180° (without punching) and θ 临界 A smooth increase between θ = 0° (full punching). This essentially includes the case of blocked LOS, since blocking can be considered as the case where θ = 180°. As mentioned above, the true critical angle depends on the complex electromagnetic properties of the environment, but its determination is beyond the scope of this invention. However, a very interesting behavior was observed where the severity of punching is actually unaffected by the resolution of the pixelated environment model (i.e., voxel size), converging to the same relationship at sufficiently high resolutions.

[0056] like Figure 7 The convergence curve shown can be approximated by scaling the Gaussian curve height, where the scaling factor obtained by the heuristic search is... N(-9.8,54) 2 The optimal parameterization of ) is then achieved. In summary, an efficient model for drilling behavior is proposed by introducing a feasibility coefficient ξ∈[0,1], which follows a probability p. ξ The Bernoulli distribution, the probability is obtained by evaluating θ. 临界 Scaling Gaussian distribution at time To obtain this, the independent and identically distributed (iid) feasibility coefficients are then multiplied by each element of the channel matrix, capturing the behavior of unavailable and punctured infeasible paths.

[0057] In the following discussion, a set of N under the above model is considered. U UEs and a total of N A Uplink communication scenario between N APs, where N A Each AP is connected to the central processing unit (CPU) via a throughput-unrestricted, error-free fronthaul link, enabling all N APs to access the central processing unit (CPU) via a throughput-unrestricted, error-free fronthaul link. A N R Signals received at each receiving antenna are aggregated without loss of information or delay.

[0058] Then, N T The aggregated received signal matrix Y over a discrete transmit instance (symbol slot) is given by the following formula.

[0059]

[0060] in, It is the effective channel matrix as described above.

[0061] It is a collection of data from all N U The transmitted signal matrix of each UE (each symbol is from a base N). Ξ (The constellation is drawn from the Ξ), and XN(0,N0) is the receiver-side additive white Gaussian noise (AWGN) matrix, with independent and identically distributed (iid) elements drawn from XN(0,N0), where N0 is the noise variance.

[0062] Transmitted signal X includes pilot block and data blocks Make

[0063]

[0064] Where, N P and N D N represents the number of symbol time slots allocated to the pilot sequence and the data sequence, respectively. T＝ N P+ N D And in which, assuming the pilot symbol matrix X P It is fully known at the CPU level.

[0065] Given the equations provided above, the objective of the ISAC method presented below can be briefly stated. The CPU's communication objective is, after estimating the channel matrix G, to achieve communication with only X known. P Estimating the unknown data symbol matrix X in the case of intermediate pilot symbols D Furthermore, the sensing target extracts a voxelized model of the environment as an occupancy coefficient vector v from the channel matrix G.

[0066] By combining the previously proposed channel decomposition model of G, the received signal model of Y, and the transmitted signal model of X, the entire system model becomes

[0067]

[0068] Among them, the unknown variables of interest are the environment (voxel coefficient) vector v and the data symbol matrix X. D .

[0069] In the following description, it is assumed that the LOS channel H and the sub-paths from the UE to the voxel and from the voxel to the AP in the aforementioned equation of Y are known, which is still in the case of the two variables diag(v) and X. DThe relationship between them is atypical. In particular, the latter unknowns are related to the asymmetric bilinear system under the aforementioned equation of Y, requiring decoupling or joint estimation of complex calculation methods.

[0070] In light of the above, the following section presents an ISAC method for the joint estimation problem of asymmetric bilinear systems represented by the aforementioned equations of Y, which utilizes the well-known Gaussian belief propagation (GaBP) message passing (MP) framework.

[0071] The proposed method is called Alternating Linear ISAC (AL-ISAC), which uses unknown variables v and X. D Each variable in the equation is combined with two separate linear estimation modules, which estimate the unknown variables in an alternating manner via a feedback chain between the two modules. Both estimation modules are based on equation (6) and can be described as follows:

[0072] - An iterative linear GaBP MP method for estimating the environment vector v given a transmitted signal matrix X, and conversely,

[0073] - An iterative linear GaBP MP method for estimating the transmitted signal matrix X given an environment vector v.

[0074] The following section derives the two linear GaBP methods and the MP rules, and then describes the construction of the complete AL-ISAC method that covers the two derived methods.

[0075] The first iterative linear GaBP MP method for the environment vector v operates on only one unknown variable. Therefore, to estimate v, in addition to the known channel matrices H, A, and B, it must be assumed that the entire transmitted signal matrix X is known. Assuming X is known, the system described by the aforementioned equation for Y can be reformulated as follows:

[0076]

[0077] Among them, because of the channel matrix Matrix product and It is known that the system described is linear on v, from which we can obtain... Figure 8 The corresponding factor graph is shown.

[0078] Each element y of the received signal of Y m,t (where m∈{1,…,N) A N R} and t∈{1,…,N T}) corresponds to the factor nodes represented by squares in the graph, and each element v of the unknown environment variable v k(where k∈{1,…,N) V}) corresponds to the variable nodes represented by circles in the graph. Conversely, each (m,t)th factor node in the factor graph has each variable node element v k The corresponding soft copy is represented as The corresponding mean square error (MSE) is given by the following formula.

[0079]

[0080] Using soft copies and their MSEs, factor nodes for each variable v k Received signal y m,t Perform soft interference cancellation (IC) to generate IC symbols.

[0081]

[0082] Where, x n,t and w m,t Let X and W be the (n,t)th and (m,t)th elements, respectively, where n∈{1,…,N}. U}, and among them, auxiliary variables This represents the aggregated incident signal from all UEs at the k-th voxel.

[0083] Next, by utilizing the Central Limit Theorem (CLT), the sum of the difference and noise terms is approximated as a complex Gaussian scalar, allowing the sign after interference cancellation to be approximated. PDF modeling for

[0084]

[0085] Wherein, the corresponding variance Given by the following formula

[0086]

[0087] in, It is the noise variance.

[0088] All v k The conditional variance is calculated through all factor nodes, and the message is sent to the corresponding variable node. Therefore, the k-th variable node obtains N from all factor nodes. A N R N T Each conditional variance is used to calculate the external confidence level. In GaBP, self-interference is eliminated at the k-th variable node. Conditional PDFs are used to suppress the generation of PDFs using the following methods.

[0089]

[0090] Among them, the external mean and variance The following formulas are given respectively.

[0091] Finally, according to Bayes' rule, the updated posterior can be obtained by comparing the PDF of the external confidence with v. k The prior distribution is combined to obtain the updated soft copy, which is then obtained through the following formula.

[0092]

[0093] The normalization factor in the denominator is the updated posterior of the integral over the complex field.

[0094] Similarly, the error variance of the soft copy update is obtained by evaluating the following formula.

[0095]

[0096] Given information about the voxel coefficient distribution, i.e., binary coefficients with discrete priors given by the Bernoulli distribution (with occupancy probabilities) ), soft copies and their MSEs can be obtained efficiently in a closed-form manner, as given by the following formulas respectively.

[0097]

[0098] Then, the updated soft copy and MSE of each variable node are transmitted back to all factor nodes for the next iteration of the GaBPMP computation method. After a given number of GaBP iterations to improve the soft estimates, a confidence consensus is reached on the soft copy at each variable node to obtain a single estimate via the following formula.

[0099]

[0100] Among them, consensus mean and variance Represented as

[0101]

[0102] Therefore, the final estimate is obtained using the following formula.

[0103]

[0104] Equations (8) to (20) fully describe the linear GaBP MP method for estimating the voxel environment v (i.e., voxel coefficients), see reference. Figure 9The steps of method 200 shown are summarized below.

[0105] The inputs to this method are the received signal matrix Y, the channel matrix H, A and B, the transmitted signal matrix X, the noise variance N0, and the prior distribution of the environment voxels.

[0106] The iterative process begins with initialization, because the data comes from earlier iterations. soft copy and corresponding MSE Currently unavailable. In step 202, according to The first signal matrix C is calculated, which describes the effective signal modified by the channel between the UE and the voxel. Then, in step 204, soft copies of all variables are initialized. And in step 206, according to equation (8) Initialize the MSE at all variable nodes Perform initialization.

[0107] The following steps are iterated until the termination criterion is met. In step 210, equation (9) is used to evaluate the received signal y. m,t Execute soft IC, generate In step 212, the corresponding conditional variance is determined according to equation (11). In step 214, the external mean is determined according to equation (13). and variance In step 216, the new soft copy is calculated according to equations (14) and (15), respectively. and MSE And in step 218, the soft copy is updated via damping. and MSE For example, as discussed in P. Som, T. Datta, A. Chokalingam, and B.S. Rajan, “Improved large-MIMO detection based on damped belief propagation,” IEEE Inf. Theory Workshop on Inf. Theory, 2010, pp. 1-5. The damping factor can be selected from the interval η∈[0,1] and is used to prevent premature convergence to a local optimum. It should be noted that... Perform each of steps 210 to 218. In step 220, perform a check to determine if the termination criterion is met. If negative, proceed to the "No" branch of step 220, repeating steps 210 to 218 using the latest values ​​of the soft copy and MSE as input. If positive, proceed to the "Yes" branch of step 220, in step 222... Calculate the consensus mean according to equation (19). and variance And in step 224, the final soft estimate is calculated according to equation (20). The termination criteria for the iterative loop may include a predetermined maximum number of iterations, or a predetermined convergence threshold that can be evaluated in terms of the soft-copy MSE. Note that... Perform subsequent steps that meet the termination criteria.

[0108] It is important to note that the signal matrix X is assumed to be given; that is, X is not estimated by this method and therefore remains constant throughout the iteration, as with the effective signal c. k,t As seen in the pre-calculation.

[0109] Next, we will describe a complementary second linear GaBPMP method specifically designed for estimating the transmitted signal matrix X given v.

[0110] To derive the linear GaBP MP method for estimating the signal matrix X given v, the overall system model, further derived above and represented by equation (6), is first simplified to the aggregated received signal matrix Y initially presented in equation (4), where the effective channel... The corresponding factor plot is in Figure 10 The diagram shows that each element x of the unknown signal matrix X is... n,t (where n∈{1,…,N) U}) are variable nodes, represented by circles.

[0111] Note that in this case, since the variable X is two-dimensional, the linear system produces a factor graph that is split into "pages" such that different time indices t∈{1,…,N} T The variable nodes and factor nodes are independent, and messages are exchanged only by nodes with the same time index t. Apart from this separation of the factor graph, the derivation of the MP rule is similar to the derivation of the linear GaBP MP method for the environment vector v presented further above.

[0112] Transmitted signal matrix element x n,t The soft replica to the (m,t)th factor node is generated by This indicates that the corresponding MSE is given by the following formula.

[0113]

[0114] Soft copies and MSE are used for x in accordance with the following formula n,t The received signal in the soft IC

[0115]

[0116] The conditional PDF is described by the following formula.

[0117]

[0118] And conditional variance Given by the following formula

[0119]

[0120] Conditional PDF is combined with self-interference elimination at variable nodes to generate external confidence following the formula below.

[0121]

[0122] Among them, the external mean and variance The following formulas are given respectively.

[0123]

[0124] Furthermore, since the symbols have uniformly discrete priors from the symbol constellation χ, where the symbol probability is... Therefore, its soft copy and MSE are obtained through the following formula.

[0125]

[0126]

[0127] For the specific case of M-QAM (where M=4), the soft copy and MSE calculations simplify to an efficient closed-form expression given by the following equation.

[0128]

[0129] in, Let represent the average sign power of constellation Ξ, and tanh(·) denote the trigonometric hyperbolic tangent function.

[0130] Finally, the consensus PDF obtained after iteration is given by the following formula.

[0131]

[0132] Among them, consensus mean and variance Represented as

[0133]

[0134] This results in a soft estimate.

[0135]

[0136] Equations (21) to (33) fully describe the linear GaBP MP method for estimating the transmitted signal matrix X given the environment vector v, see reference. Figure 11 The steps of method 300 shown are summarized below.

[0137] The inputs to this method are the received signal matrix Y, the channel matrix H, A and B, the environment vector v, the noise variance N0, and the prior distribution of the transmitted symbols.

[0138] Similar to calculation method 1, calculation method 2 begins with initialization, wherein the effective channel matrix is ​​calculated in step 302. Then, in step 304, the soft copies at all variable nodes are initialized. Furthermore, in step 306, the MSE at all variable nodes is initialized according to equation (21). Perform initialization.

[0139] The following steps are iterated until the termination criterion is met. In step 310, the received signal after the soft IC is calculated according to equation (22). Furthermore, in step 312, the corresponding conditional variance is calculated using equation (24). Next, in step 314, the external mean is calculated according to equation (26). and variance This allows the new soft copy to be calculated in step 316 according to equations (27) and (28), respectively. and the corresponding MSE Now, in step 318, the soft copy can be updated via damping. and MSE Similar to the update process in computational method 1 discussed further above. Here, damping is also used to prevent premature convergence to a local optimum. It should be noted that... Perform each of steps 310 to 318. In step 320, perform a check to determine if the termination criterion is met. If negative, proceed to the "No" branch of step 320, repeating steps 310 to 318 using the latest values ​​of the soft copy and MSE as input. If positive, proceed to the "Yes" branch of step 320, in step 322... Calculate the consensus mean according to equation (32). and variance And in step 324, the final soft estimate is calculated according to equation (33). Then, in step 326, the final soft estimate is... Projected onto the symbol constellation Ξ, and in step 328, the projected... The output is the final hard estimate. The termination criteria for the iterative loop may include a predetermined maximum number of iterations or a predetermined convergence threshold that can be evaluated in terms of the soft replica MSE.

[0140] The first and second methods described earlier need to be combined to complete the proposed full AL-ISAC method for estimating two unknown variables.

[0141] As previously mentioned, each of the first and second estimation methods presented above is suitable for estimating only one of the two variables v or X (assuming that complete information about the corresponding other variable is available in each case). However, a very inherent problem with ISAC in the overall system model further derived above is that the variables are not fully known, making linear methods potentially unsuitable for direct application in estimation.

[0142] To address this issue, the proposed AL-ISAC method consecutively invokes two linear GaBP MP methods to estimate two sets of variables. This requires separating the received signal into blocks corresponding to the pilot and data phases, as follows:

[0143]

[0144] in, and For example Defined.

[0145] First, using only the pilot phase of the system as represented by equation (34a), the first linear GaBP MP process for estimating the voxel environment v is invoked to utilize the pilot block X. P The initial environment vector is estimated using the known input signal matrix. This includes separating the pilot signal Y contained in the received signal Y. P and data signal Y DNext, only the data phase of the system represented by equation (34b) is used, that is, only the received data signal Y is used. D The second linear GaBP MP process for estimating the transmitted signal matrix X is invoked to use the initial environment estimates. Using known input environment vectors to estimate unknown data blocks Finally, by calling the first linear GaBP MP process for estimating the voxel environment v again, but using the initial environment estimate... As the initial value for soft copies at all factor nodes and used (i.e., the known pilot signal and the previously estimated data signal) The environment vector is obtained by using the known input signal matrix. The estimated environment vector is then output. and the estimated transmitted signal matrix The transmitted signal matrix includes the known pilot signal X. P and previously estimated data signals exist Figure 12 The diagram shows a schematic block diagram of the proposed AL-ISAC method.

[0146] While offering potential advantages in terms of complexity, particularly for scenarios with a large number of UEs, the alternation pattern of the AL-ISAC method has the following drawbacks, as will be further illustrated below: it heavily relies on the pilot sequence X. P The length of the signal, as will be further shown below, not only involves a clear trade-off in total communication throughput, but also affects the performance of both environmental and data signal estimation.

[0147] In Table I, based on the system size parameter N U N V N A N R N P N T The complexity order of the proposed method and its constituent modules is given, where, λ represents the pilot length ratio, and λ represents the number of iterations. For comparative purposes, the complexity of bilinear ISAC (Bi-ISAC) as described in German Patent Publication DE 10 2022 212 615A1 (the contents of which are hereby incorporated by reference) is also provided in the table.

[0148]

[0149] Table I: Complexity Order of the Proposed Estimation Module and Method

[0150] A comparison of the complete complexity orders shows that Bi-ISAC has a second-order complexity that depends on all system size parameters and a linear complexity that depends on the number of iterations. Interestingly, AL-ISAC is found to require the same complexity order as Bi-ISAC, but the difference lies in the time complexity depending on N. U scaling factor of ρ Therefore, the latter is a measure of the relative complexity of the proposed method and the reference method. Figure 13 China targets N U The various values ​​depict the relative complexity as a function of ρ.

[0151] First consider N U When the ratio is 1, it can be seen that for all values ​​of the pilot length ratio ρ∈[0,1], the relative complexity is greater than 1. This indicates that in the single UE case, regardless of the number of pilot symbols, the AL-ISAC algorithm requires a higher complexity than the Bi-ISAC algorithm. However, it can also be seen that in N... U In multi-UE scenarios with ≥2, if the pilot length is greater than... Then the two methods have the same complexity order; and further, when ρ>ρ eq When the relative complexity drops below 1, it indicates that as N... U With increased density and sufficient ρ, AL-ISAC achieves lower complexity than Bi-ISAC.

[0152] Figure 13 It also shows that the relative complexity follows a truncated quadratic behavior, with the minimum occurring at a specific pilot ratio. At this point, the pilot ratio converges to 1 as N→∞. This means that the AL-ISAC method requires a sufficiently high pilot length ratio to achieve optimal complexity, as N... U This is especially true for the increase in UE count. In summary, the Bi-ISAC method has robust complexity with respect to the number of UEs and pilot length (throughput), while the AL-ISAC method has decreasing complexity with the number of UEs, but at the cost of throughput.

[0153] In the next section, the proposed AL-ISAC method and the BI-ISAC method introduced in the previous section for comparative purposes are compared with the method proposed by X. Tong, Z. Zhang, J. Wang, C. Huang, and M. Debbah in “Joint multi-user communication and sensation exploiting both signal and environment sparsity”, IEEE J. Sel. Topics Signal Process., Vol. 15, No. 6, pp. 1409–1422, 2021, which also utilizes voxelized meshes to perform ISAC via multiple linear MP algorithms. However, it should be noted that this known method utilizes the support of fully known intelligent reflective surfaces (IRS) in the ROI and heavily relies on the sparsity in the received signal provided by the SCMA interface between the UE and AP. In contrast, the proposed method and the BI-ISAC method are not limited to such specific conditions. Conversely, the proposed method, as well as the BI-ISAC method, operates on fully dense received signals and does not require IRS support.

[0154] Due to this difference in system setup, appropriate system parameterization must be considered to ensure a fair comparison between the proposed method and known methods. Specifically, in the SCMA scheme used in known methods, each single-antenna UE utilizes d from R orthogonal frequency bands. f One emits an M-bit code, which is generated by an N-bit... R The system is received by a single AP with one receiving antenna. Since the system considered by the method proposed in this paper is the single-frequency model represented in equation (4), the number of APs is set to N. A =d f To mimic the diversity gain between each UE and the CPU, so that N A N R =d f ·N R That is, the number of nodes and edges in the final factor graph is the same in both systems.

[0155] Figure 14 This paper demonstrates the sensing and communication performance of the proposed ISAC method in terms of least squares error (MSE) and symbol error rate (SER), compared to the BI-ISAC method and known ISAC algorithms. Firstly, in... Figure 14In section a, sensing performance is evaluated, specifically the MSE of the environment estimate in the form of voxel occupancy coefficients. Under equivalent system parameters, particularly at a pilot ratio of ρ = 0.5 (taken from a paper discussing known ISAC methods used for direct comparison), the Bi-ISAC method is found to significantly outperform the known methods at all signal-to-noise ratio (SNR) values, while AL-ISAC is found to slightly outperform the latter at high SNR scenarios.

[0156] exist Figure 14 In section b, the communication performance of the ISAC system is evaluated in terms of the SER of the estimated symbols. It can be seen that the proposed method and the Bi-ISAC method exhibit superior symbol estimation performance compared with known methods at ρ=0.5. Among them, Bi-ISAC exhibits more desirable characteristics, that is, no error plane is observed even at higher SNR.

[0157] In summary, these results confirm the initial statement that the proposed method outperforms known reference methods in both sensing and communication capabilities.

[0158] Given the superior performance of the proposed ISAC method compared to reference methods, the robustness of the proposed method is analyzed in the following sections. Although the MSE of the estimated voxel coefficients and the SER of the estimated communication symbols were previously used to evaluate the performance of the sensing and communication functions and compared with known methods, different performance metrics are used in the following sections.

[0159] Specifically, regarding sensing capabilities, it should be noted that due to the unique nature of the voxelized occupancy grid-based method utilized in this paper, the metrics used for radar-based ISAC cannot be directly applied. Therefore, it is useful to alternatively introduce a new metric called Voxel Occupancy Error Rate (VOER), which measures the ratio of false positive (FP) and false negative (FN) elements. False positive and false negative elements are defined, respectively, as the cases where a voxel is incorrectly estimated as occupied in the presence of empty true data and the cases where it is incorrectly estimated as empty voxel in the presence of occupied true data. Mathematically, VOER is defined as... Wherein, v is the actual data and It is the voxel coefficient estimation vector, and ||·||0 represents the l0 norm of the vector.

[0160] Note that for a trivial, fully empty (or "blind") estimator, its return value is... VOER decreases to This is the average sparsity of the environment. Therefore, this figure can be used as an absolute reference for performance; in a sense, VOER << VOER 空This indicates that the considered ISAC method has good sensing performance.

[0161] Finally, instead of SER, which is frequently used in related literature, the communication performance of the proposed ISAC method is evaluated here in terms of the more descriptive bit error rate (BER), which is defined as... Among them, B e X represents D The number of data bits used for error detection in X, and B is X. D The total number of bits transmitted.

[0162] In the first evaluation, the effect of changing the number of pilot symbols (e.g., captured by parameter ρ) on the performance of the proposed method was investigated. Figure 15 The sensing performance results shown in figure a demonstrate that, for the same ρ value, the Bi-ISAC method outperforms the AL-ISAC method across the entire SNR range. In other words, for a given SNR, the Bi-ISAC method requires significantly fewer pilots to achieve the same VOER performance as the AL-ISAC method.

[0163] exist Figure 15 Table b evaluates the impact of pilot ratio on communication performance in terms of BER. It can be seen that for smaller pilot ratios, i.e., ρ < 0.2, the proposed AL-ISAC method achieves similar performance to the reference Bi-ISAC method. However, for larger pilot ratios, i.e., ρ > 0.3, the AL-ISAC method outperforms the Bi-ISAC method at moderate SNR (i.e., 5 dB). This result may seem counterintuitive, but it is actually expected and can be explained by the fact that the linear MP method of the AL-ISAC method is built on the assumption of perfect symbol knowledge (i.e., zero uncertainty in symbol estimation), and this assumption is increasingly satisfied at larger pilot ratios.

[0164] However, ultimately, when the SNR is significantly higher (e.g., SNR ≥ 15 dB), the Bi-ISAC method again outperforms the AL-ISAC method. This is a direct result of the erroneous planarization behavior exhibited by the AL-ISAC method, as shown in... Figure 14 This has already been observed in b.

[0165] at last, Figure 16 The impact of random channel blocking is elucidated. Specifically, the figure compares the VOER and BER performance of the proposed ISAC method and the reference Bi-ISAC method with respect to the critical angle θ*, following the further derived stochastic geometric empirical model above, which determines the channel blocking rate (see [link to figure]). Figure 6 ).

[0166] Figure 16The environmental sensing performance shown in Figure 'a' exhibits similar behavior relative to the effect of ρ, with the Bi-ISAC method achieving superior performance in all cases. Furthermore, the curves for the Bi-ISAC method show a slower gradient increase compared to the AL-ISAC method, indicating that the Bi-ISAC method is more robust to path hindrance.

[0167] The superior robustness of the Bi-ISAC method to short pilot lengths and random channel blocking can be attributed to the increased number of edges in the full factor graph caused by the bilinear representation of the system. Compared to factor nodes in a linear factor graph, each factor node in a bilinear factor graph connects to a significantly larger number of variable nodes, meaning that even after removing a large number of edges, more edges remain for stable message passing. Message passing via a pruned graph is only feasible when there is still sufficient pilot data connecting to the main graph, thus making the Bi-ISAC scheme more dependent on the pilot ratio for stability.

[0168] Regarding Figure 16 The communication performance comparison in section b revealed that the performance of the two methods differs with the pilot ratio. For low pilot ratios, the AL-ISAC method performs slightly better than the Bi-ISAC method and exhibits similar robustness to path blocking, while for high pilot ratios, the Bi-ISAC method shows significantly better performance than the AL-ISAC method.

[0169] According to a first aspect of the invention, a method is proposed for processing wireless communication signals for joint communication and environmental awareness in a region of interest (ROI). The ROI, represented by a voxelized environment (VE) comprising voxels arranged in a three-dimensional mesh, includes N. A ≥1 access point and N U ≥1 UE. All APs serving the ROI are communicatively connected to the central processing unit (CPU), and N A Each of the access points has N R ≥1 antenna. The method includes receiving a signal matrix Y at the CPU, which represents the LOS and NLOS paths via VE at points N and N respectively. A N associated with each AP R N received at each antenna T ≥1 launch instance. N T Each launch instance carries the data from all N U Multiple transmit symbols x, including pilot signals and data signals, are transmitted by a UE. n,t The method further includes prior channel matrices for receiving the LOS and NLOS paths, and prior distributions of voxels for the VE. Noise variance N0, matrix X representing the pilot signal used in transmission.P and the prior distribution of the emitted symbols The method further includes separating the pilot signal Y included in the received signal Y. P and data signal Y D and only the received pilot signal Y P The process undergoes a first iteration based on the known emission symbol X. P Estimate the environment vector v representing the VE of the ROI to obtain an initial estimated environment vector. Then, using the previously estimated initial vector As a known environmental input, only the received data signal Y D The process undergoes a second iteration, which estimates the transmitted data signal in a known environment. Next, using the previously determined initial environment vector... As an initialization vector, make the pilot signal X P and previously estimated data signals The process involves the first iteration to estimate the vector v representing the environment (VE) of the ROI. Finally, the estimated environment vector is output. and the estimated transmitted signal matrix

[0170] In one or more embodiments of the method, the prior distribution of voxels in VE The acquisition methods include: based on previous execution results of the method, based on an empirical random process based on the positions of the AP and UE in the VE and the geometric LOS and NLOS paths between them, or a random distribution. The empirical random process can include the process described in International Patent Application No. PCT / EP2024 / 056582 (the contents of which are hereby incorporated by reference). Prior distribution It provides some information about the scattering or blocking properties of the corresponding voxels, and in particular, determines the channel matrix of the NLOS path.

[0171] In one or more embodiments of the method, the prior channel matrices for the LOS and NLOS paths are obtained by: based on previous execution results of the method, or by geometric analysis of the VE based on the positions of the AP and UE in the VE. In the latter case, at least the NLOS path is optionally based on the prior distribution of voxels in the VE. Alternatively, holes can be punched according to a random distribution.

[0172] In one or more embodiments of the method, the prior distribution of the emitted symbols The methods of obtaining the data include: statistical analysis of previous execution results of the method, or random or pseudo-random distribution of the transmitted symbols contained in the possible transmitted symbol constellation.

[0173] In one or more embodiments of the method, the first iterative process of estimating the environment vector v of the VE representing the ROI based on known transmitted symbols includes a first linear GaBP process. The first linear GaBP process includes, after initialization: calculating the received signal after soft interference cancellation; calculating the conditional variance of the received signal; calculating the external mean and variance for each voxel based on the conditional variance; calculating a new soft copy and corresponding error for each voxel based on the external mean and variance and the distribution for the corresponding voxel from previous iterations; and updating the soft copy and error via damping. These calculations and updates are repeated until a termination criterion is met. After the termination criterion is met, the consensus mean and variance for each voxel are calculated, and finally, a final soft estimate for each voxel is calculated.

[0174] In one or more embodiments of the method, the transmitted data signal is estimated in a known environment. The second iterative process includes a second linear GaBP process. The second linear GaBP process includes: calculating the received signal after soft interference cancellation following initialization; calculating the conditional variance of the received signal; calculating the external mean and variance for the signal based on the conditional variance; calculating a new soft copy and corresponding error for the signal based on the external mean and variance and the distribution for the corresponding voxels from previous iterations; and updating the soft copy and error via damping. These calculations and updates are repeated until a termination criterion is met. After the termination criterion is met, the consensus mean and variance for the signal, and the final soft estimate for the signal, are calculated. The soft estimate is projected onto a symbol constellation, and the projected symbols are output as hard estimates.

[0175] The termination criteria for the first and / or second iteration process may include, for example, predetermined numerical iteration limits, or convergence when the corresponding estimate is within a predetermined range or below a predetermined value. Such a convergence criterion may be satisfied, for example, when the average change between estimates after successive iterations is below a predetermined value.

[0176] Therefore, according to a second aspect of the invention, a system for wireless communication is provided. The system includes a CPU and one or more access points (APs) communicatively connected to the CPU. The CPU includes a microprocessor, volatile and non-volatile memory, and a communication interface for communicating with the one or more APs. Elements or components of the CPU are communicatively connected via one or more data or signal lines or buses. Each AP has at least one antenna, circuitry for processing radio frequency signals, a microprocessor, volatile and non-volatile memory, and a communication interface for communicating with the CPU. Elements or components of the respective APs are connected via one or more data and / or signal lines or buses. The non-volatile memory of each AP stores computer program instructions that, when executed by the microprocessor, cause the respective AP to transmit received transmission instances to the CPU. The non-volatile memory of the CPU stores computer program instructions that, when executed by the microprocessor, configure the components of the CPU to implement or perform the method according to the first aspect of the invention.

[0177] In one or more embodiments of the system, the circuitry for processing the radio frequency signals of the AP includes a low-noise amplifier and / or a mixer configured to provide a representation of the received signals at an intermediate frequency.

[0178] The methods described above can be represented by computer program instructions. Therefore, according to a third aspect of the invention, a computer program product includes computer program instructions that, when executed by a microprocessor of a CPU of a system according to a second aspect of the invention, or a microprocessor functionally coupled to the CPU, cause the processor and / or the CPU to perform the methods according to a first aspect of the invention, and / or, when executed by a microprocessor of an AP of a system according to a second aspect of the invention, or a microprocessor functionally coupled to the AP, cause the processor and / or the AP to transmit received transmission instances to the CPU.

[0179] These computer program instructions may be retrievably stored on or retrievably transmitted on a computer-readable medium or data carrier. The medium or data carrier may be physically embodied, for example, in the form of a hard disk, solid-state drive, flash memory device, etc. However, the medium or data carrier may also include modulated electromagnetic, electrical, or optical signals, which are received by the computer through a corresponding receiver and transmitted to and stored in the computer's memory.

[0180] The method presented in this paper represents a novel JCAS or ISAC approach, in which a voxelized 3D representation of the ROI is extracted from scattering features present in valid CSIs by utilizing the same physical layer communication air interface connecting multiple UEs acting as transmitters and one or more APs acting as receivers via uplink links. The ISAC method, referred to as AL-ISAC, relies on a modular feedback structure in which transmitted data and the environment are estimated alternately. Computer simulations of this method outperform known methods in accurately reconstructing the transmitted data and obtaining a voxelized 3D image of the environment. Analysis of the computational complexity of the proposed method reveals a significant advantage: AL-ISAC offers lower complexity, especially in scenarios with a large number of UEs.

[0181] The methods presented herein can be used to provide joint communication and environmental awareness in scenarios with multiple independent users and multiple cooperative receivers, such as in fully connected automated factories, warehouses, etc., utilizing centralized processing (e.g., industrial edge computing). In particular, the invention can be used in scenarios with fixed access points (APs) for communication and environmental detection via: mobile vehicles communicating with roadside units (RSUs) to simultaneously achieve vehicle / pedestrian detection; multiple vehicles collaboratively sensing environmental and road conditions in the absence of RSUs; multiple connected user units (UEs) (Bluetooth, Wi-Fi, IoT, etc.) for passive environmental sensing (i.e., without using specific sensing signals), and so on. The use cases and benefits of the communication and environmental sensing methods presented herein relate to the same wireless interface and can be implemented within that wireless interface.

[0182] This invention advantageously eliminates the limitation of communication and access schemes on specific transmission schemes existing in known methods (such as the SCMA transmission scheme in the known methods discussed in the background section), thereby particularly eliminating the need for multiple frequency subcarriers in deployment, and thus allowing robust application of JCAS in various situations.

[0183] Furthermore, the present invention removes the limitation imposed on the detection of the sliding window length found in the prior art method, where the sliding window length is determined by the pilot length.

[0184] Furthermore, the present invention provides a system model that is no longer limited to a single AP and a single antenna UE, thereby allowing the use of greater receive and transmit diversity, as well as multiple-input multiple-output (MIMO) technology, which refers to a practical technique that utilizes multipath propagation to simultaneously transmit and receive more than one data signal on the same radio channel.

[0185] In addition, the present invention eliminates the dependence on a single RIS, which limits some known methods to specific scenarios where such a single RIS is available. Attached Figure Description

[0186] The figures in the accompanying drawings are used to illustrate various aspects of the invention. In the drawings:

[0187] Figure 1 An exemplary environment with objects in the region of interest and its voxelized representation are shown.

[0188] Figure 2 A representation of the general concept of LOS and NLOS paths in voxel space is shown.

[0189] Figure 3 A schematic representation of the 3D space considered in known systems and methods is shown.

[0190] Figure 4 The process modules of the prior art JCAS method, which iterates using a sliding window approach, are shown.

[0191] Figure 5 A schematic representation of an unavailable and impractical communication path in voxelized space is shown.

[0192] Figure 6 An empirical β-mixing model of the scattering angle distribution is shown.

[0193] Figure 7 The critical angle θ is shown. 临界 The impact of channel matrix perforation severity at different voxel grid resolutions

[0194] Figure 8 A factor plot of a linear system, designed for estimating the voxel coefficients v, is shown.

[0195] Figure 9 An exemplary flowchart is shown, illustrating the method steps invoked to estimate the voxel coefficients v.

[0196] Figure 10 The factor plot of a linear system, designed for estimating the signal matrix X, is shown.

[0197] Figure 11 An exemplary flowchart of the method steps invoked to estimate the signal matrix X is shown.

[0198] Figure 12 A schematic flowchart of the proposed AL-ISAC method is shown.

[0199] Figure 13 It shows the results for ρ and N U The relative complexity order of the proposed methods for different values,

[0200] Figure 14The MSE and SER performance of known methods and the Bi-ISAC method relative to the proposed AL-ISAC method are shown.

[0201] Figure 15 This paper presents a comparison of the performance of the proposed AL-ISAC and Bi-ISAC methods in systems with the following parameters as a function of pilot length ratio ρ at different SNR values: N U =4, N R N R =12, N T =100, N V =512, E v =1.5%,

[0202] Figure 16 The performance of the proposed AL-ISAC method and the Bi-ISAC method as a function of the critical angle θ* is shown in a system with the following parameters under random channel path blocking: N U =4, N T =100, ρ=0.5, N V =512, E v =1.5%, SNR=15dB,

[0203] Figure 17 An exemplary block diagram of a system for wireless communication according to a second aspect of the present invention is shown, and

[0204] Figure 18 An exemplary flowchart of the complete AL-ISAC method according to the first aspect of the present invention is shown. Detailed Implementation

[0205] Figures 1 to 16 This has already been described in more detail above and will not be discussed further.

[0206] Figure 17An exemplary block diagram of a system for wireless communication according to a second aspect of the present invention is shown. The exemplary wireless communication system includes a central processing unit (CPU) 400 and two access points (APs) 500, which are communicatively connected to the CPU 400 (indicated by arrows with dotted lines). The CPU 400 includes a microprocessor 402, volatile memory 404 and non-volatile memory 406, and a communication interface 408 for communicating with the two APs 500. Elements or components of the CPU 400 are communicatively connected via one or more data or signal lines or buses 410. Each AP 500 has at least one antenna 502, circuitry 504 for processing radio frequency signals, a microprocessor 506, volatile memory 508 and non-volatile memory 510, and a communication interface 512 for communicating with the CPU 400. Elements or components of the respective AP 500 are connected via one or more data and / or signal lines or buses 514. Each AP 500 has a non-volatile memory 510 storing computer program instructions that, when executed by a microprocessor 506, cause the AP 500 to transmit a transmission instance received via one or more antennas 502 to a CPU 400. The CPU 400 has a non-volatile memory 406 storing computer program instructions that, when executed by a microprocessor 402, configure components of the CPU 400 to implement or perform the method according to the first aspect of the invention.

[0207] Figure 18 An exemplary flowchart of a complete AL-ISAC method 100 according to a first aspect of the present invention is shown. In step 110, the CPU 400 receives information representing the LOS path and NLOS path via VE, respectively connected to N... A N associated with each AP 500 R N received at each antenna T The signal matrix Y of ≥1 transmission instance, and the N T Each launch instance carries the data from all N U Multiple transmit symbols x, including pilot signals and data signals, are transmitted by a UE. n,t It also receives the prior channel matrices for the LOS and NLOS paths, and the prior distribution of voxels for the VE. Noise variance N0, matrix X representing the pilot signal used in transmission. P and the prior distribution of the emitted symbols In step 120, the pilot signal Y included in the received signal Y is separated. P and data signal Y D In step 130, only the received pilot signal Y is... P The process undergoes a first iteration (200), which is based on the known emission symbol X.P Estimate the environment vector v representing the VE of the ROI to obtain an initial estimated environment vector. In step 140, the previously estimated initial vector is used. As a known environmental input, only the received data signal Y D The process undergoes a second iteration 300, which estimates the transmitted data signal based on the known environment. In step 150, the previously determined initial environment vector is used. As an initialization vector, make the pilot signal X P and previously estimated data signals The process undergoes a first iteration 200, estimating the vector v representing the environment (VE) of the ROI. Finally, in step 160, the estimated environment vector is output. and the estimated transmitted signal matrix

[0208] List of reference numerals (part of the instruction manual)

[0209] 100 methods, 400 CPUs

[0210] 110 Receiver signal matrix 402 Microprocessor

[0211] 120 Separate pilot signal and data signal 404 Volatile memory

[0212] 130 subject the pilot signal to the first method 406 non-volatile memory

[0213] 140 subject the pilot signal to second method 408 communication I / F

[0214] 150 enables pilot and data signals; 410 is a signal / data line / bus.

[0215] Experiencing the first method

[0216] 160 Output estimated environment vector and 500 AP

[0217] Signal Matrix 502 Antenna

[0218] 200 First Iteration Method 504 RF Circuit

[0219] Method steps 202 to 224, 506: Microprocessor

[0220] 300 Second Iteration Method 508 Volatile Memory

[0221] Method steps 510 (302 to 328): Non-volatile memory

[0222] 512 Communication I / F

[0223] 514 Signal / Data Line / Bus

Claims

1. A method (100) for processing wireless communication signals for joint communication and environmental awareness in a region of interest (ROI), the region of interest comprising N A ≥1 access point (AP) (500) and N U ≥1 UE (UE), the N A Each of the 500 APs has N R ≥1 antenna, and all APs (500) serving the ROI are communicatively connected to a central processing unit (CPU) (400), the region of interest is represented by a voxelized environment (VE) comprising voxels arranged in a three-dimensional mesh, the method comprising: At CPU (400), a signal matrix (Y) is received (110), which represents the line-of-sight (LOS) path and non-line-of-sight (NLOS) path via the VE at the N. A N associated with each AP(500) R N received at each antenna T ≥1 launch instance, the N T Each launch instance carries the N... U Multiple transmit symbols (x) sent by a UE, including pilot signals and data signals. n,t The CPU (400) also receives the prior channel matrices of these LOS paths and these NLOS paths, and the prior distribution of the voxels of the VE. Noise variance (N0), and the matrix (X) representing the pilot signals used in this transmission. P ), and the prior distribution of these emission symbols. The pilot signal (Y) included in the received signal (Y) by separation (120) P ) and data signal (Y) D ), Only the received pilot signal (Y) P ) undergoes a first iteration process (200) based on a known emission symbol (X) P Estimate the environment vector (v) representing the VE of the ROI to obtain an initial estimated environment vector. Using the previously estimated initial vector As a known environmental input, only the received data signal (Y) D The signal undergoes a second iteration (300) in a known environment, which estimates the transmitted data signal. Using the previously determined initial environment vector As an initialization vector, make the pilot signal (X) P ) and previously estimated data signals The first iterative process (200) is subjected to (150) estimation of the vector (v) representing the VE of the ROI, and Output (160) estimated environment vector and the estimated transmitted signal matrix 2. The method (100) as claimed in claim 1, wherein, The prior distribution of voxels in this VE The acquisition methods include: based on the previous execution results of the method (100), based on an empirical random process based on the location of AP (500) and UE in the VE and the geometric LOS and NLOS paths between them, or a random distribution.

3. The method (100) as claimed in claim 1 or 2, wherein, The prior channel matrix for LOS and NLOS paths can be obtained by: based on the previous execution results of method (100), or by geometric analysis of the VE based on the positions of the AP (500) and UE in the VE. In the latter approach, the prior distribution of voxels in the VE is obtained. Optionally, based on the prior distribution of voxels in the VE Alternatively, holes can be punched according to a random distribution.

4. The method (100) as described in any one or more of claims 1 to 3, wherein, Prior distribution of emitted symbols The acquisition methods include: statistical analysis based on previous execution results of the method (100), or random or pseudo-random distribution of the emitted symbols contained in the possible emitted symbol constellation.

5. The method (100) as claimed in any one or more of claims 1 to 4, wherein, The first iterative process (200) of estimating the environment vector (v) representing the VE of the ROI based on the known emission symbols includes a first linear Gaussian approximation belief propagation (GaBP) process, which includes, after initialization: Calculate the received signal after soft interference cancellation. Calculate the conditional variance of the received signal. Based on this conditional variance, the external mean and variance for each voxel are calculated. Based on the external mean and variance, and the distribution for the corresponding voxel from previous iterations, a new soft copy and corresponding error are calculated for each voxel. The soft copy and the error are updated via damping. Repeat these calculations and updates until the termination criteria are met. Calculate the consensus mean and variance for each voxel, and Calculate the final soft estimate for each voxel.

6. The method (100) as claimed in claim 1, wherein, Estimate the transmitted data signal in a known environment The second iterative process (300) includes a second linear Gaussian approximate belief propagation (GaBP) process, which includes, after initialization: Calculate the received signal after soft interference cancellation. Calculate the conditional variance of the received signal. Based on this conditional variance, calculate the external mean and variance for this signal. Based on the external mean and variance, and the distribution for the corresponding voxel from previous iterations, a new soft copy and corresponding error for the signal are calculated. The soft copy and the error are updated via damping. Repeat these calculations and updates until the termination criteria are met. Calculate the consensus mean and variance for this signal. Calculate the final soft estimate for this signal. Projecting this soft estimate onto the sign constellation, and The projected sign is output as a hard estimate.

7. A system for wireless communication, the system comprising a central processing unit (CPU) (400) and one or more access points (APs) (500), the one or more APs being communicatively connected to the CPU (400), wherein, The CPU (400) includes a microprocessor (402), volatile memory (404) and non-volatile memory (406), and a communication interface (408) for communicating with one or more APs (500). These components are communicatively connected via one or more data or signal lines or buses (410). Each of the APs (500) has at least one antenna (502), circuitry (504) for processing radio frequency signals, a microprocessor (506), volatile memory (508) and non-volatile memory (510), and a communication interface (512) for communicating with the CPU (400). The elements or components of P(500) are connected via one or more data and / or signal lines or buses (514), wherein each AP(500) has a non-volatile memory (510) storing computer program instructions that, when executed by the microprocessor (506), cause the AP(500) to transmit received transmission instances to the CPU (400), wherein the CPU(400) has a non-volatile memory (406) storing computer program instructions that, when executed by the microprocessor (402), configure the components of the CPU(400) to implement or perform the method as described in any one of claims 1 to 6.

8. The system of claim 7, wherein, The circuit (504) for processing the radio frequency signals of these APs (500) includes a low-noise amplifier and / or a mixer configured to provide a representation of the received signals at an intermediate frequency.

9. A computer program product comprising computer program instructions that, when executed by a microprocessor (402) of a central processing unit (CPU) (400) of the system according to claim 7 or 8, or a microprocessor (402) functionally coupled to the CPU, cause the processor and / or the CPU (400) to perform the method as described in any one of claims 1 to 6, and / or, when executed by a microprocessor (506) of an access point (AP) (500) of the system according to claim 7 or 8, cause the processor and / or the AP (500) to transmit a received transmission instance to the CPU (400).

10. A computer-readable medium or data carrier retrievably transmits or stores a computer program product as described in claim 9.

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