Joint communication and environment awareness method

Abstract

A method of processing wireless communication signals for joint communication and environmental awareness in an area of interest represented by voxels arranged in a three-dimensional grid is provided. The environment includes > = 1 access points and > = 1 UEs. Each of the access points has > = 1 antennas. The method comprises: receiving a plurality of transmission symbols comprising pilot signals and data signals sent by all UEs; and iteratively performing soft interference cancellation on the received communication signal, determining soft copies and corresponding MSEs, and updating all soft copies and corresponding MSEs, for each antenna of each of the access points, and all voxels and all transmit symbols in the region of interest, when the termination criterion is not met. Next, for each voxel and each transmitted symbol in the region of interest, a respective final soft estimate is calculated from the corresponding soft replica, which is projected to the symbol constellation and output as a hard estimate.

Description

Technical Field

[0001] The present invention relates to the field of environmental sensing or environmental mapping, and more particularly to using wireless communication signals for such sensing. More specifically, the present invention relates to a method for processing communication signals for environmental perception, a computer program product implementing the method, a computer-readable storage medium storing the computer program product, a receiver configured to execute the method, a system including such a receiver, and a mobile entity (e.g., a vehicle) including such a receiver. Throughout the specification, the term "environmental sensing" will be widely used in various expressions for capturing information about the environment to create a three-dimensional representation thereof.

[0002] Symbols

[0003] A scalar value is represented by italic lowercase letter x herein, while a complex vector and a matrix are represented by bold lowercase letter x and uppercase letter X, respectively. (·) T and (·) * represent the transpose operator and the complex conjugate operator, respectively, and diag(·) and exp[·] represent the diagonalization operator and the exponential operator, respectively. |·| represents the absolute value operator, while |||| l represents the l-norm. and Var x (x) represent the expectation operator and the variance operator of x with respect to the distribution of x given by respectively. and represent the real number field and the complex number field, respectively, and N(μ,ν) and CN(μ,ν) represent the real Gaussian distribution and the complex Gaussian distribution with mean μ and variance ν. Background Art

[0004] Joint communication and sensing (JCAS) is a technology in wireless communication that aims to obtain information about the environment from signal scattering present in the effective channel state information (CSI) (e.g., caused by objects, obstructions, user activities, etc. in the environment), while 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] There are various methods known in JRC, including alternating or sharing the spectrum between radar signals and communication signals, embedding information in 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 attributes and are prone to similar challenges.

[0006] In robot vision and mapping, for example, inFigure 1 In the exemplary environment shown in a), systematic collection of environmental information introduces a 3D voxelized occupancy grid, where the total region of interest (ROI) is defined as a cubic space of dimension L x ×L y ×L z , where each dimension represents the length of the x-axis, y-axis, and z-axis respectively, in meters. The entire ROI is subdivided into a grid consisting of individual voxels, where and N z represent the number of voxels along the x-axis, y-axis, and z-axis respectively, and L V is the side length of the voxel cube, in meters. If represented by a three-dimensional tensor (N x ×N y ×N z ), the voxelized occupancy grid directly represents the discretized model of the ROI (as shown in Figure 1 b) and Figure 1 c)), where the size of the voxel corresponds to the image resolution. In addition to the 3D geometric information provided by the classical voxelized occupancy grid, the electromagnetic scattering behavior of the real environment can be incorporated to customize the modeling method to be used in wireless communication scenarios.

[0007] For this purpose, first consider representing each voxel by a voxel occupancy coefficient v k ∈{0,1} (where k ∈ {1,…,N V}), where v k =0 indicates that the k-th voxel is empty, i.e., the corresponding environment is free space, and v k =1 indicates that the k-th voxel is occupied by a scatterer object (e.g., a table, chair, or object on the wall), as shown in Figure 1 . Note that the binary voxel occupancy coefficient can also be extended to a complex voxel scattering coefficient, i.e., to capture the effect of the occupied voxel on the reflected electromagnetic wave. It is expected that the value of the voxel scattering coefficient will highly depend on the electromagnetic properties of the scatterer object and the incident wave, which can be measured empirically and modeled as a function of properties such as frequency and material. However, this extension and the specific modeling are not within the scope of this specification.

[0008] The voxelized environment first introduced in robot vision and mapping can be used to design joint communication and environment detection methods that can operate without using radar attributes, i.e., mainly relying on pure communication signals.

[0009] For example, in "Joint Multi-User Communication and Sensing Exploiting With Signal and Environment Sparsity" (IEEE Journal of Selected Topics in Signal Processing, Vol. 15, No. 6, pp. 1409 - 1422, November 2021), X. Tong, Z. Zhang, J. Wang, C. Huang, and M. Debbah considered a regular voxelized 3D space where some scatterer objects accommodate a single access point (AP), a single reconfigurable intelligent 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 transmission instances, via line-of-sight (LOS) paths and non-line-of-sight (NLOS) paths from the UE to the scatterers and then to the RIS and finally to the AP.

[0010] Figure 2 The general concept of LOS and NLOS paths in the voxelized space is shown. The LOS path is the direct path between the UE and the AP, while two occupied voxels in the ROI reflect the signal transmitted by the UE towards 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 3 A schematic representation of the 3D space (including the RIS) considered in known systems and methods is shown. Here, the signal reflected from the RIS towards the AP is shown as a dotted line to highlight its specific origin.

[0011] According to the known system model and assumptions, the signal received at the AP will carry not only the user's payload or data, but also the scatter path information that can be used to acquire or sense the environment. To assist in the estimation, it is assumed that the UE transmits a pilot symbol of known length, i.e., a known preamble, at the start of the transmission interval. The purpose of the known algorithm is to return the estimated SCMA code transmitted by the UE (i.e., user detection or identification), as well as the binary voxel occupancy grid corresponding to the environment estimation. For example, 0 is a voxel representing free space, and 1 is a voxel representing occupied space.

[0012] The known system can be mathematically represented by the following model:

[0013] Y=(H + PRQVB)[X p X]+W

[0014] Among them, Y is the received signal matrix of all receiving antennas and the AP over all symbol instances, H is the LOS channel matrix from the UE to the AP, P is the NLOS channel matrix from the RIS to the AP, R is the RIS reflection coefficient matrix, Q is the NLOS channel matrix from the voxel to the RIS, B is the NLOS channel matrix from the UE to the voxel, V is the voxel occupancy matrix, X p is the pilot symbol matrix, X is the data symbol matrix, and W is the AWGN noise matrix. For simplicity, since the matrices P, R, Q, and B are known, the model can be simplified to

[0015] Y = (H + AVB)[X p X] + W

[0016] where A = PRQ is the effective NLOS channel matrix from the voxel to the AP.

[0017] Thus, the main joint communication and sensing problem is formulated, that is, 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.

[0018] To this end, the existing technology methods use three modules:

[0019] · Environment estimation module, which uses the linear generalized approximate message passing (GAMP) algorithm, which uses the known channels and the given data symbols X (pilots or estimated symbols from the previous iteration) to estimate the environment V, and its model is

[0020] Y = (H + AVB)[X]+W

[0021] · Effective channel reconstruction module, which combines the information with the known H, A, B by integrating the known channels and the estimated environment and using V estimated by the GAMP algorithm to calculate the effective total channel,

[0022]

[0023] · Data signal estimation module, which uses the linear SCMA message passing algorithm (MPA) and the estimated effective channel to estimate the unknown data symbol X,

[0024]

[0025] These three modules are iterated in a sliding window manner in the time domain (as Figure 4 shown), such that the environment is estimated first using the known pilot symbols, but for the estimation of the data signal, the window is slid to estimate the unknown data symbols, and so on.

[0026] Existing technical methods rely on the following assumptions: the channel gain of the LOS path from the UE to the AP is known; the channel gains of the NLOS paths from the UE to the voxels, from the voxels to the RIS, and from the RIS to the AP are known; the reflection coefficients of the RIS are known; the voxelized environment model is binary, i.e., the discrete occupancy values can only be 0 or 1; the SCMA communication scheme is used; and there is only a single AP. These assumptions pose severe limitations to applying known methods to the actual 3D real-world environment, thus 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

[0027] This need is met by the method according to claim 1, the computer program product according to claim 8, the receiver according to claim 10, and the communication system according to claim 13. Corresponding computer-readable storage media and vehicles including an improved receiver according to the present invention are presented in claims 9 and 14, respectively.

[0028] Before describing the method according to the present invention, the underlying channel model will be described. Assume that the ROI includes N U single-antenna UEs and N R multi-antenna APs each equipped with N A receiving antennas. As Figure 2 shown, the effective channel between the UE and the AP consists of two components, namely, the line-of-sight (LOS) component and the non-line-of-sight (NLOS) component. The LOS component is the direct path between the UE and the AP, and the NLOS component covers the scattering paths passing through the voxels representing scatterer objects, as further described above. The NLOS component can be further decomposed into two sub-paths, namely, the sub-path from the UE to the voxel and the sub-path from the voxel to the AP, and these two sub-paths together with the voxel scattering coefficient constitute the aggregated NLOS channel.

[0029] In view of the above decomposition, the effective channel between N U UEs and N A N R receiving antennas (i.e., all N A antennas of each of the N R APs) is given by:

[0030]

[0031] where, is the effective channel matrix, and are the constituent channel matrices of the LOS path from the UE to the AP, the NLOS sub-path from the voxel to the AP, and the NLOS sub-path from the UE to the voxel, respectively, and is a vector containing all the scattering coefficients of the voxelized grid. Assume that the elements of the channel matrices H, A, and B follow zero-mean complex normal distributions with variances of and respectively.

[0032] 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, which is similar to the known systems discussed in the background section above. However, in reality, due to various physical phenomena, many paths may become infeasible. For example, if the angle between the incident path and the reflected path is too large and exceeds the critical angle, the corresponding NLOS path will be unavailable, as Figure 5 shown.

[0033] Similarly, if the occupied voxel coincides with the path (also as Figure 5 shown), 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), so a simplified model is proposed to approximately incorporate this phenomenon into the channel matrix of the voxelized grid environment model.

[0034] First, the positions of the UE and AP are discretized into a 3D grid of the voxelized environment model such that their positions can be equivalently described by voxel coordinates. Note that it is also assumed that multiple antennas of the corresponding AP are fully located within a single voxel, so it is assumed that their angles of arrival (AoA) are the same while having different channel path coefficients. Given the 3D coordinates of the UE, AP, and voxel as and then the scattering angle θ of the path at the voxel can be calculated as

[0035]

[0036] where the arccos(·) operator represents the inverse cosine trigonometric function. The scattering angle θ at all voxels can be calculated for all UE-AP pairs, and by introducing an arbitrary critical angle θ crit ∈[0°, 180°], when θ > θ crit it is determined that the scattering path is unavailable and the corresponding path is removed from the NLOS channel matrix.

[0037] Figure 6 The critical angle θ critImpact on the severity of channel matrix punching, where the results are obtained by numerical evaluation using a random number and location of UEs and APs for various voxelized grid resolutions, where the average channel punching rate is normalized by the total number of path vertices (N A +N R ). As expected, the punching rate shows a smooth increase between θ crit = 180° (no punching) and θ crit = 0° (full punching). This essentially includes the case of LOS blocked, as the blockage can be considered as the case of θ = 180°. As mentioned above, the true critical angle depends on the complex electromagnetic properties of the environment, but its determination is outside the scope of this specification. However, a very interesting behavior is observed, where the severity of punching is actually not affected by the resolution of the voxelized environment model (i.e., voxel size), and converges to the same relationship even at sufficiently high resolutions.

[0038] As Figure 6 shown, the convergence curve can be approximated by scaling the height of the Gaussian curve, where the heuristic search yields the optimal parameterization of N(-9.8, 54 ) with a scaling factor of 2 . In summary, an efficient model of the punching behavior is proposed by introducing a feasibility coefficient ξ ∈ [0,1], which follows a Bernoulli distribution with probability p ξ , and this probability is obtained by evaluating the scaled Gaussian distribution crit at θ . Then, the i.i.d. feasibility coefficients are multiplied by each element of the channel matrix, and the behavior of the unavailable and punched infeasible paths is captured.

[0039] Now, in the system shown in Figure 7 and within the model developed above, consider the uplink communication scenario between N U UEs and N A APs. Assume that N A APs are connected to a central processing unit (CPU) via a throughput-unconstrained and error-free backhaul link, such that the received signals at all N A N R receiving antennas are aggregated. The aggregated received signal matrix Y over N T discrete transmission instances (i.e., symbol time slots) is given by

[0040]

[0041] where, is the effective channel matrix as further described above, is the transmit signal matrix that collects transmit symbols from all N U UEs, where each symbol is taken from a symbol constellation X with cardinality N X , and is the received additive white Gaussian noise (AWGN) matrix with independent and identically distributed elements, where these independent and identically distributed elements are taken from CN(0, N0), where N0 is the noise variance.

[0042] The transmit signal X includes a pilot block and a data block, which are described as

[0043]

[0044] where and are the pilot symbol matrix and the data symbol matrix respectively, where N P and N D represent the number of symbol time slots allocated to the pilot sequence and the data sequence respectively. Thus, N T = N P + N D . Assume that the pilot symbol matrix X P is completely known at the CPU. Thus, the communication purpose of the CPU is to estimate the unknown data symbol matrix X D .

[0045] By combining the models of the received signal Y, the transmit signal X, and the channel decomposition G presented above, the overall system model can be written as

[0046]

[0047] where the unknown variables of interest are the environment (i.e., voxel coefficients) vector v and the data symbol matrix X D . These two variables have an atypical relationship described by the asymmetric bilinear system in the above system model equations, where the environment vector v is embedded in the effective channel G. This complex structure means that the joint estimation problem of these two variables v and X D is extremely challenging, and this is the main purpose of performing the JCAS method on the receiver (i.e., the CPU that aggregates the received signals from all APs).

[0048] The present invention uses a bilinear message passing method to estimate these two variables v and X D. However, the unique asymmetric structure of the system model equations presented above precludes the application of existing bilinear estimators such as the bilinear generalized approximate message passing (BiGAMP) proposed by J.T. Parker, P. Schniter, and V. Cevher in "Bilinear generalized approximate message passing - part i: Derivation" (IEEE Transactions on Signal Processing, Vol. 62, No. 22, pp. 5839 - 5853, 2014), or by H. Iimori, T. Takahashi, K. Ishibashi, G.T.F. de Abreu, and W. Yu in "Grant - free access via bilinear inference for cell - free MIMO with low - coherence pilots" (IEEE Transactions on Wireless Communications, Vol. 20, No. 11, pp. 7694 - 7710, 2021), or the parametric BiGAMP presented by J.T. Parker and P. Schnitre in "Parametric bilinear generalized approximate message passing" (IEEE Journal of Selected Topics in Signal Processing, Vol. 10, No. 4, pp. 795 - 808, 2016), or by Z. Yuan, Q. Guo, and M. Luo in "Approximate message passing with unitary transformation for robust bilinear recovery" (IEEE Transactions on Signal Processing, Vol. 69, pp. 617 - 630, 2021), where bilinear generalized approximate message passing operates only on symmetric systems (such as Y = VX+W) to jointly estimate V and X; parametric bilinear generalized approximate message passing is applicable to systems with the structure Y = ∑ k v k A k X+W to utilize the known A k to jointly estimate υ k and X.

[0049] The considered system represented by the system model equations presented above is clearly neither of the above forms nor can it be transformed to fit the general bilinear form. Therefore, according to the present invention, a method for processing wireless communication signals using the Gaussian belief propagation (GaBP) message passing framework for JCAS is provided, which results in a customized bilinear Gaussian belief propagation (BiGaBP) message passing for jointly estimating v and X in the above modeled asymmetric bilinear system. D .

[0050] Belief propagation is used to reason about a graphical model by computing the marginal distribution of each unobserved node or variable, conditioned on any observed node or variable. Gaussian belief propagation is a variant of the belief propagation algorithm when the underlying distribution is approximated by Gaussian distributed variables. Bilinear inference operates on a similar basis as linear inference, but simultaneously attempts to recover an estimate of the function of two sets of variables by independently reasoning about their values from each variable and considering the values inferred from all variable distributions.

[0051] The proposed method utilizes only a single bilinear estimation module, which enables the parallel estimation of both unknown variables by using bilinear message passing techniques that incorporate the uncertainty of the two variable estimates at each iteration, as Figure 8 shown.

[0052] BiGaBP message passing is performed on a factor graph, which is a tripartite graph with two estimated variables, as Figure 9 shown. Each element y of the received signal Y m,t (where, m ∈ {1, …, N A N R} and t ∈ {1, …, N T}) corresponds to a factor node, shown as a square node in the figure. The factor nodes correspond to the known observations, i.e., the received symbols at each antenna of each AP.

[0053] There are two sets of variable nodes corresponding to the unknown environment vector v (where the elements are v k , and k ∈ {1, …, N V ) and the unknown signal matrix X (where the elements are x n,t , and n ∈ {1, …, N U}), shown as circular nodes in the figure.

[0054] Note that the complexity of the factor graph edges is caused by the asymmetric embedded system structure of the system model equations presented above together with the two variables. There is an important difference between the two types of variable nodes, i.e., the data variable nodes only receive from N corresponding to the same time instance tA N R factor nodes receive messages, while the environmental variable nodes receive messages from all N A N R N T factor nodes receive messages.

[0055] The messages transmitted through the graph edges include each variable node element v that is available at each factor node k ,x n,t and at n, represented as and The soft copies can be understood as estimates of the true value variables from the perspective of each given node, i.e., the number of soft copies of a given single variable is equal to the number of observation nodes. The soft copies can be regarded as representing the initialization environment and initialization symbols for inference respectively.

[0056] Since it is assumed that neither of the two variables is known, i.e., they are only called soft copies, the corresponding calculation of the messages will combine the uncertainties of the two variables in the form of the corresponding MSE. Similarly, the corresponding conditional probability distribution function (PDF) of each soft estimate value and is available at each factor node.

[0057] For each variable node element v k , the mean square error (MSE) of the soft copy available at each (m,t)-th factor node on the factor graph is given by:

[0058]

[0059] The soft copy of the transmitted signal matrix element x n,t available at each (m,t)-th factor node on the factor graph has its MSE given by

[0060]

[0061] According to the present invention, the messages exchanged in BiGaBP are constructed based on the soft copies of the variables. Recall that the data variable x P corresponding to t∈{1,…,N n,t} is a pilot symbol that is completely known at the receiver, so all corresponding soft copies are set to the corresponding known pilot values, i.e., and the corresponding MSE values are set to 0. The remaining soft copies and MSEs for t∈{N P +1,…,N T} are determined as defined previously.

[0062] Using the soft copy and its MSE, the factor nodes perform soft interference cancellation (IC) on each variable v k and x n,t as follows:

[0063]

[0064] where, the soft IC of the data variables given in the equation is performed only for t ∈ {N P + 1,…, N T}. Note that SGA is the scalar Gaussian approximation.

[0065] After the soft IC, the corresponding conditional PDFs of the current interference-free signals are obtained via the following equations, which specify the probability that the random variables fall within a particular range of values rather than taking any one value:

[0066]

[0067] where the corresponding conditional variances and are obtained as follows:

[0068]

[0069]

[0070] where the expected values

[0071] are introduced. Further, all variable nodes calculate the interference-cancelled extrinsic confidence PDFs given by the following equation:

[0072]

[0073] where the corresponding extrinsic variances and extrinsic means are given by the following equations:

[0074]

[0075] The updated soft copy and MSE are then obtained as described below.

[0076] According to Bayes' rule, the updated soft copy and MSE of k can be obtained by combining the PDF of the extrinsic confidence with the prior distribution of v, and the updated soft copy is thus obtained via the following equation:

[0077]

[0078] where the corresponding normalization factor is given by integrating the updated posterior over the complex domain, resulting in

[0079]

[0080] and the updated error variance of the soft copy is similarly obtained by evaluation:

[0081]

[0082] The updated soft copy and MSE of are obtained by:

[0083]

[0084] and

[0085]

[0086] The updated soft copy and MSE of each variable node are transmitted back to all factor nodes for the next iteration of the BiGaBP message passing method.

[0087] To prevent premature convergence to a local optimal solution, the well-known damping update technique is applied at the variable nodes to obtain the final updated values:

[0088]

[0089] where l is the iteration number and β ∈ [0,1] is the damping factor.

[0090] After a given number of BiGaBP iterations to refine the soft estimates, confidence consensus is reached on the soft copies at each variable node to obtain a single estimate.

[0091] For obtaining a single estimate the confidence consensus is achieved by:

[0092]

[0093] whose variance and mean are expressed as

[0094]

[0095] and are thus used to obtain the final estimate by:

[0096]

[0097] where

[0098]

[0099] To obtain a single estimated value The confidence consensus is achieved by the following formula:

[0100]

[0101] Its variance and mean are expressed as

[0102]

[0103] Thus, the final soft estimated value is obtained by the following formula:

[0104]

[0105] Wherein,

[0106]

[0107] Using the received signal matrix Y, channel matrix H, A and B, pilot matrix X P , noise variance N0, and environmental symbols and transmitted symbols as inputs, and using the estimated environmental vector and the estimated transmitted signal matrix as outputs, an exemplary full-duplex bilinear JCAS (Bi-JCAS) estimation of the environmental vector v and the signal matrix X can refer to Figure 10 The method 100 steps shown are summarized as follows:

[0108] 102: Receive N A ≥ 1 transmission instances at N R antennas respectively associated with N T access points (APs), and the N T transmission instances carry multiple transmitted communication signals (x U ) including pilot signals and data signals sent by N n,t UEs (UEs).

[0109] Optionally, for data variable nodes corresponding to pilot blocks (i.e., when t ∈ {1,..., N P}) and all m, n:

[0110] 104a: Initialize the soft copy to the pilot by .

[0111] 104b: Initialize to 0.

[0112] Further optionally, for data variable nodes corresponding to data blocks (i.e., when t ∈ {N P +1, …, N T}), and for all m, n:

[0113] 106a: Initialize the soft copy to

[0114] 106b: Initialize via

[0115] Even further optionally, for environmental variable nodes (i.e., when t ∈ {1, …, N T}), and for all m, k:

[0116] 108a: Initialize the environmental soft copy to

[0117] 108b: Initialize via

[0118] Note that steps 104a to 108b (when present) can be executed sequentially or in parallel.

[0119] The core of the method includes repeating the following steps for all m, n, k, t until a termination criterion is met:

[0120] 110: Calculate the soft IC signal and

[0121] 112: Calculate the soft copy and the corresponding

[0122] 114: Update all soft copies and MSE via damping.

[0123] 116: Is the termination criterion met?

[0124] Step 112 may include several sub-steps:

[0125] 112a: Calculate the conditional variance and

[0126] 112b: Calculate the external mean and variance

[0127] 112c: Calculate the external mean and variance

[0128] 112d: Calculate the new soft copy​​ and

[0129] 112e: Calculate the new and

[0130] The method further includes, after meeting the termination criterion, for all n, k, t:

[0131] 118a: Use and to calculate the consensus PDF.

[0132] 118b: Calculate the conditional variance and

[0133] Then, for all n, t:

[0134] 120: Project the final soft estimate value onto the symbol constellation X, and

[0135] 122: Output the projected as the hard estimate value.

[0136] According to a first aspect of the present invention, a method for processing wireless communication signals for joint communication and environmental perception in a region of interest is proposed. The environment or region of interest represented by voxels arranged in a three-dimensional grid includes N A ≥ 1 access points and N U ≥ 1 UEs. Each of the N A access points has N R ≥ 1 antennas. The method includes: receiving N A transmission instances, which are associated with the N R antennas respectively associated with the N T ≥ 1 access points, and the N T transmission instances carry multiple transmission symbols x U including pilot signals and data signals sent by all N n,t UEs. The method further includes: for each of the N A antennas of each of the N R access points, for all voxels in the region of interest and all transmission symbols x n,t , performing soft interference cancellation on the received communication signal y n,t representing the transmission symbol x m,t . The method further includes: for each of the N A antennas of each of the N R access points, determining all voxels in the region of interest and all transmission symbols x n,tsoft copy and corresponding and update (114) all soft copies and corresponding When the termination criterion is not met, repeat the steps of performing soft interference cancellation, determining the soft copies and the corresponding MSE, and updating all soft copies and the corresponding MSE.

[0137] The termination criterion may include, for example, a predetermined numerical iteration limit, or convergence when the estimated value is within a predetermined range or below a predetermined value. Such a convergence criterion may be satisfied, for example, when the average change between the estimated values after consecutive iterations is below a predetermined value.

[0138] The method further includes: after the termination criterion is met, for each voxel and each transmitted symbol x in the region of interest n,t , according to N A corresponding soft copies of each of the N R antennas of each of the N access points, calculate the corresponding final soft estimates and project the final soft estimate of each transmitted symbol onto the symbol constellation X. Finally, output the projected transmitted symbols

[0139] n,t as hard estimates. R In one or more embodiments, the method further includes: for all transmitted symbols x that are not yet known U , and for each of the N m,t antennas and each of the N R UEs, initialize the soft copies and / or the MSE. Alternatively or additionally, the soft copies and / or the MSE may be initialized for all voxels in the region of interest and each of the N

[0140] antennas before performing soft interference cancellation on the received communication signal y n,t n,t In one or more embodiments, the step of initializing the soft copies of all transmitted symbols x that are not yet known and corresponding to the pilot block

[0141] includes: initializing the soft copy of the transmitted signal to the pilot according to the prior knowledge of the pilot signal, and / or initializing the corresponding MSE to 0. n,t Alternatively or additionally, the step of initializing the soft copies of all transmitted symbols x that are not yet known and corresponding to the data block includes: initializing the soft copy of the transmitted signal to a corresponding value according to the expected value based on the known prior probability distribution of the symbol constellation or symbol set. In other words, the most probable value is selected according to the probability distribution within the symbol set. In other words, initialize the soft copy of the transmitted signal to Alternatively or additionally, the corresponding MSE can be initialized to the expected error value of a previously initialized soft copy. The expected error is the average Euclidean distance between the initialized soft copy and all possible symbols in the symbol set or symbol constellation. In other words, via Initialize the MSE to

[0142] In another alternative or as a complementary solution to one or more of the previously mentioned initializations, the step of initializing the soft copy for all voxels in the region of interest includes: initializing the environmental soft copy to the corresponding value according to the expected value of the known prior probability distribution of the voxel coefficients. In other words, assign the soft copy to the most probable value. In other words, initialize the environmental soft copy to

[0143] Alternatively or additionally, the corresponding MSE can be initialized to the expected error value of a previously initialized environmental soft copy. The expected error is the average Euclidean distance between the initialized environmental soft copy value and the feasible voxel occupancy state. In other words, via Initialize the MSE to

[0144] In one or more embodiments, the expected value of the environmental soft copy can be determined based on prior knowledge of a partial environment (e.g., based on geographic information, etc.).

[0145] In one or more embodiments, for each of the N A antennas of each of the N R access points, determining the soft copy of all voxels and all transmitted symbols x n,t in the region of interest and the corresponding includes: calculating the conditional variance and the external mean and the variance the external mean and the variance the new soft copy and and the new and

[0146] In one or more embodiments, for each voxel and each transmitted symbol x n,t in the region of interest, calculating the corresponding final soft estimate value A according to the corresponding soft copy of each of the N R antennas of each of the N access points includes: using and Calculate the consensus PDF and calculate the conditional variance and

[0147] In one or more embodiments, a wireless communication signal carrying transmission symbols uses a transmission frame having data symbols and pilot symbols that are known at the receiver. The data symbols and pilot symbols may be arranged in corresponding transmission blocks, and one or more transmission blocks constitute the transmission frame.

[0148] In one or more embodiments, the frequency of the wireless communication signal is in the radar frequency range, including frequency ranges between 30 GHz and 300 GHz, particularly between 50 GHz and 150 GHz, such as between 57 GHz and 71 GHz. Although the present invention is not limited to these frequency ranges, higher frequencies may exhibit more beneficial NLOS scattering behavior for the methods proposed herein.

[0149] The methods presented above may be represented by computer program instructions of a computer program product. Thus, in a second aspect of the present invention, a computer program product includes computer program instructions that, when executed by a processor of a receiver or a processor functionally coupled to the receiver, cause the processor and / or the receiver to perform the methods of one or more embodiments according to the various embodiments of the first aspect.

[0150] These computer program instructions may be retrievably stored on or retrievably transmitted over 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 disk, flash memory device, etc. However, the medium or data carrier may also include a modulated electromagnetic, electrical, or optical signal that is received by a computer through a corresponding receiver and transmitted to and stored in the memory of the computer.

[0151] According to a third aspect of the present invention, a receiver for a wireless communication signal includes at least one antenna, circuitry for processing radio frequency signals, a microprocessor, volatile memory, and non-volatile memory, and these elements or components are connected via one or more data lines and / or signal lines or buses. The non-volatile memory stores computer program instructions that, when executed by the microprocessor, configure the elements or components of the receiver to implement or perform one or more embodiments of the method according to the first aspect of the present invention.

[0152] In one or more embodiments, the receiver is co-located with a transmitter configured to send communication signals.

[0153] In one or more embodiments, a circuit for processing radio frequency signals includes a low noise amplifier and / or a mixer, which is configured to provide a representation of a received signal at an intermediate frequency. The mixer preferably uses the same oscillator signal as the transmitter co-located with the receiver. The latter can enable environmental perception using signals reflected from an object transmitted by an entity including the receiver.

[0154] A receiver according to a second aspect of the present invention and a corresponding transmitter configured to transmit communication signals can form a system that allows for joint communication and environmental perception according to embodiments of the method presented above.

[0155] A receiver according to a third aspect of the present invention can be arranged in a vehicle, thereby allowing the vehicle to create a representation of its environment, for example, for the purpose of autonomous driving. The vehicle may also include a corresponding transmitter to enable two-way communication.

[0156] The method presented herein provides joint communication and environmental perception in scenarios with multiple independent users and multiple cooperative receivers, such as in fully connected automated factories, warehouses, etc. that utilize centralized processing (such as industrial edge computing). The present invention solves the problem of how to perceive the environment and surrounding objects in the form of a 3D discretized image using only communication signals (i.e., user payloads and pilots), where an MIMO wireless communication system is deployed, which consists of multiple UEs acting as transmitters and multiple antenna access points acting as receivers.

[0157] The present invention advantageously eliminates the limitations of communication and access schemes on specific transmission schemes (such as the SCMA transmission scheme in the known methods discussed in the background art section) in known methods, thereby particularly eliminating the need for multiple frequency subcarriers in deployment and thus allowing for robust application of JCAS in various situations.

[0158] Furthermore, the present invention lifts the limitations imposed on the sliding window length detection in prior art methods, where the sliding window length is determined by the pilot length.

[0159] Even further, the present invention provides a system model that is no longer limited to a single AP and single-antenna UEs, thereby allowing the utilization of greater receive and transmit diversity, as well as multiple-input multiple-output (MIMO) technology, which refers to the practical technology of simultaneously transmitting and receiving more than one data signal on the same radio channel by exploiting multipath propagation.

[0160] In addition, the present invention also eliminates the dependence on a single RIS, which limits some known methods to specific scenarios where such a single RIS is available.

[0161] The JCAS method using BiGaBP presented above advantageously allows for the direct recovery of the environment based on communication signals. As a further advantage, compared to existing technology methods that require up to three iterative modules, BiGaBP only requires a single estimation module. Thus, the present invention provides true joint sensing and communication that now allows for the simultaneous consideration of signal and environmental uncertainties and does not rely on a sparse signal model imposed by forcing the use of sparse coding.

[0162] Even more advantageously, compared to known methods that use pilot signals only in the initial stage of detecting the environment, in the method of the present invention, the information carried in the pilot symbols is used in all steps of the environmental detection procedure, thereby improving stability and convergence.

[0163] The present invention can be advantageously used in several scenarios, especially for UE communicating with a fixed AP and detecting the environment in an indoor scenario, simultaneously achieving vehicle / pedestrian detection while a moving vehicle communicates with a roadside unit (RSU), multiple vehicles collaborating to sense the environment and road conditions in the absence of an RSU, multiple connected UEs (Bluetooth, Wi-Fi, IoT, etc.) passively sensing the environment (i.e., without using a sensing-specific signal), and so on. Description of the Drawings

[0164] The figures in the drawings are used to illustrate the various aspects of the present invention in detail. In the drawings,

[0165] Figure 1 An exemplary environment containing an object in the region of interest is shown,

[0166] Figure 2 A representation showing the general concept of LOS and NLOS paths in a voxelized space is shown,

[0167] Figure 3 An exemplary embodiment considered in the prior art JCAS method is shown,

[0168] Figure 4 Three modules used in the prior art JCAS method and their relationships are shown,

[0169] Figure 5 A schematic representation of infeasible paths in a voxelized space is shown,

[0170] Figure 6 The critical angle θ is shown crit The effect on the severity of channel matrix puncturing at different voxelized grid resolutions is shown,

[0171] Figure 7 An exemplary embodiment considered in the present invention is shown,

[0172] Figure 8Shows a schematic block diagram of BiGaBP according to the present invention,

[0173] Figure 9 Shows a schematic representation of a tripartite factor graph describing the relationship between factor nodes and variable nodes,

[0174] Figure 10 Shows an exemplary schematic flowchart of a method according to the present invention,

[0175] Figure 11 Shows another exemplary schematic flowchart of a method according to the present invention,

[0176] Figure 12 Shows an exemplary block diagram of a receiver according to a third aspect of the present invention,

[0177] Figure 13 Shows an exemplary schematic diagram of a communication system according to the present invention.

[0178] In the drawings, the same or similar elements may be referred to by the same reference numerals. Detailed Description

[0179] Figures 1 to 10 Has been further described above and will not be discussed further.

[0180] Figure 11 Shows another exemplary schematic flowchart of method 100 according to the present invention. This flowchart more abstractly shows the described steps. After initializing the environment and symbols, soft interference cancellation is performed and the conditional PDF is calculated. The previous two steps are performed at the factor nodes. Next, at the variable nodes, an extrinsic confidence calculation is performed based on the results of generating soft copies for all voxels and all transmitted symbols in the region of interest. In addition, the error variance of the soft copies is calculated. The previously generated soft copies and the corresponding error variances are provided to a process updated via damping. Then, the updated soft copies and error variances are iteratively fed back to the soft interference cancellation. After the termination criterion is met, the final update obtained via damping represents the final consensus estimate.

[0181] Figure 12 Shows an exemplary block diagram of a receiver 200 according to a third aspect of the present invention. Receiver 200 includes at least one antenna 202, circuitry 204 for processing radio frequency signals, a microprocessor 206, a volatile memory 508, and a non-volatile memory 510. The above elements are communicatively connected via at least one signal or data connection or bus 212. The non-volatile memory 210 stores computer program instructions that, when executed by the microprocessor 206, cause the receiver 200 to implement or perform the method according to one or more embodiments of the first aspect of the present invention as presented above.

[0182] Figure 13 FIG. 2 shows an exemplary schematic diagram of a communication system 400 according to the present invention. The communication system 400 includes a receiver 200 and a transmitter 300. The transmitter 300 includes a protocol machine 302 that can output a bit sequence according to the protocol used in the communication system 400. The radio frequency (RF)-related components 304 can perform tasks such as pulse shaping on the output of the protocol machine 302. A first mixer 306 can mix the output of the RF-related components 304 with a signal from a high-frequency oscillator 310. The transmitter 300 can transmit a communication signal x via an output stage 308. The output stage 308 can include an antenna, for example, a rod antenna, a dipole antenna, a horn antenna, and / or a set of antennas constituting a MIMO antenna.

[0183] A communication signal x' received directly from the transmitter or reflected from an object in the area of interest before being received can be received by an input stage 220 of the receiver 200. The input stage 220 can include a low-noise amplifier (LNA). A second mixer 222 can provide an intermediate frequency (IF) signal y(t) at the output. In Figure 13 the example of, the second mixer 222 uses the same oscillator 310 signal as the transmitter 300; this variation can be useful, especially in the case where the transmitter 300 and the receiver 200 are co-located (e.g., in the same area of a vehicle, the same housing, and / or the same component (e.g., a board or a chip)). The resulting down-converted signal y(t) can be processed according to the process of the first aspect of the present invention (represented by block 230). Block 230 can include, use, or be implemented by various elements or components of the receiver described with reference to Figure 12 FIG.

[0184] List of Reference Numerals (Part of the Specification)

[0185] 100 Method 118b Calculate conditional variance

[0186] 102 Receive transmit frame 120 Project the final soft estimate onto the symbol constellation

[0187] 104 Initialize pilot block variable nodes 122 Output projected transmit symbols

[0188] 104a Initialize soft copies to pilots 200 Receiver 202 Antenna

[0189] 104b Initialize MSE 204 RF circuit

[0190] 106 Initialize data block variable nodes 206 Microprocessor 208 Volatile memory

[0191] 106a Initialize soft copy 210 Non-volatile memory

[0192] 106b Initialize MSE 212 Data line / signal line / bus

[0193] 108 Initialize environment 220 Input stage

[0194] 108a Initialize soft copy of environment 222 Mixer

[0195] 108b Initialize MSE 230 Process

[0196] 110 Perform soft interference cancellation 300 Transmitter

[0197] 112 Determine soft copy and MSE 302 Protocol machine

[0198] 112a Calculate conditional variance 304 RF component

[0199] 112b Calculate external mean and variance of environment 306 Mixer 308 Output stage

[0200] 112c Calculate external mean and variance of signal 310 Oscillator 400 Communication system

[0201] 112d Calculate new soft copy x(t) output signal

[0202] 112e Calculate new MSE x'(t) input signal

[0203] 114 Update soft copy and MSE y(t) down-converted signal

[0204] 116 Does it meet the termination criterion? AP Access point

[0205] 118 Calculate final soft estimate UE User equipment

[0206] 118a Calculate consensus PDF

Claims

1. A method (100) for processing wireless communication signals for joint communication and environmental perception in a region of interest, the region of interest including N A ≥ 1 access points (APs) and N U ≥ 1 user equipment (UEs), each of the N A access points (APs) having N R ≥ 1 antennas, the region of interest being represented by voxels arranged in a three-dimensional grid, the method comprising: a) At N A antennae respectively associated with N R access points (APs), receive (102) N T ≥1 transmission instances, where the N T transmission instances carry a plurality of transmission symbols (x U ) including pilot signals and data signals sent by all these N n,t user equipment (UEs). b) For each of the N A antennas of each of the N R access points (APs), for all voxels and all transmitted symbols (x n,t ) in the region of interest, perform (110) soft interference cancellation on the received communication signals (y n,t ) representing these transmitted symbols (x m,t ), c) For each of the N A antennas of each of the N R access points (APs), determine (112) all voxels in the region of interest and all soft copies of the transmitted symbols (x n,t ) and the corresponding mean squared error d) Update (114) all soft copies and the corresponding mean squared error e) When the termination criterion is not met, repeat steps b) to d) of (116). f) For each voxel and each transmitted symbol (x n,t ) in the region of interest, compute (118) a respective final soft estimate based on the corresponding soft copies of each of the N A antennas of each of the N R access points (APs). g) Project the final soft estimate value of each transmitted symbol onto the symbol constellation X (120), and h) The projected emission symbols of the output (122). As a hard estimate value.

2. The method (100) according to claim 1, further comprising: Before step b), for all the transmit symbols (x n,t ) that are not yet known, and for each of the N R antennas and each of the N U user equipments (UEs): - initializing (104, 106) these soft copies and / or these mean squared errors, and / or For all voxels in the region of interest and each of the N R antennas: - initializing (108) these soft copies and / or these mean squared errors.

3. The method (100) according to claim 2, wherein Initialize (104) all as yet unknown transmitted symbols (x n,t ) for which soft copies corresponding to pilot blocks are included: - initializing (104a) the soft copy of the transmitted signal as a pilot, and / or - initializing (104b) these corresponding mean squared errors to 0, and / or wherein Initialize all as yet unknown transmitted symbols (x n,t ) corresponding to the data blocks, where the soft copy includes: - initializing (106a) the soft copy of the transmitted signal to corresponding values according to the expected values based on the known prior probability distribution of the symbol constellation or symbol set, and / or - initializing (106b) these corresponding mean squared errors to the expected error values of the previously initialized soft copies, and / or wherein initializing (108) these soft copies for all voxels in the region of interest includes: - initializing (108a) these environmental soft copies to corresponding values according to the expected values based on the known prior probability distribution of these voxel coefficients, and / or - initializing (108b) these corresponding mean squared errors to the expected error values of the previously initialized environmental soft copies.

4. The method (100) according to any one of claims 1 to 3, wherein, For each of the N A antennas of each of the N R access points (APs), determine (112) soft copies of all voxels and all transmitted symbols (x n,t ) in the region of interest and the corresponding mean squared error comprising: - Calculate the conditional variance (112a) and - Calculate the external mean and variance - Calculate the external mean and variance - Calculate (112d) a new soft copy and as well as - Calculate the new mean squared error (112e) and 5. The method (100) according to any one of claims 1 to 4, wherein, Step f) includes: - Use and to calculate the probability distribution function of the (118a) consensus, and - Calculate these conditional variances and 6. The method (100) according to any one of claims 1 to 5, wherein, The wireless communication signal carrying these transmitted symbols uses a transmission frame having data symbols and pilot symbols, and these pilot symbols are known at the receiver.

7. The method according to claim 6, wherein The frequency of the wireless communication signal is within the radar frequency range, including a frequency range between 30 GHz and 300 GHz, particularly between 50 GHz and 150 GHz, for example between 57 GHz and 71 GHz.

8. A computer program product comprising computer program instructions which, when executed by a processor of a receiver (200) or a processor functionally coupled to the receiver, cause the processor and / or the receiver to perform the method according to any one of the preceding claims.

9. A computer-readable medium or data carrier retrievably transmitting or storing the computer program product according to claim 8.

10. A receiver (200) for wireless communication signals, the receiver comprising at least one antenna (202) connected via one or more data lines and / or signal lines or buses (212), circuitry (204) for processing radio frequency signals, a microprocessor (206), a volatile memory (208), and a non-volatile memory (210), wherein, The non-volatile memory (210) stores computer program instructions which, when executed by the microprocessor (206), configure the components of the receiver (200) to implement or perform the method according to any one of claims 1 to 7 preceding.

11. The receiver (200) according to claim 10, wherein, The receiver (200) is co-located with a transmitter configured to send a communication signal.

12. The receiver (200) according to claim 10 or 11, wherein, The circuit (204) for processing radio frequency signals includes a low-noise amplifier and / or a mixer, and the low-noise amplifier and / or the mixer are configured to provide a representation of the received signal at an intermediate frequency, preferably using the same oscillator signal as the co-located transmitter.

13. A communication system (400) comprising a receiver (200) according to any one of claims 10 to 12 and a corresponding transmitter (300) configured to send a communication signal.

14. A vehicle, comprising a receiver (200) as claimed in any one of claims 10 to 12 and / or a communication system (400) as claimed in claim 13.

15. Use of a receiver (200) as claimed in any one of claims 10 to 12 and / or a communication system as claimed in claim 13 or a method for both wireless communication and radar sensing as claimed in any one of claims 1 to 9.

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