Sensitivity integrated channel modeling method with space consistency

Abstract

The invention discloses a communication and inductance integrated channel modeling method with space consistency, which comprises the following steps: 1) setting an initialization scene, and configuring related parameters of an antenna; 2) generating powers, azimuth angles and pitch angles of the proprietary clusters and the common clusters and distances between the first and the last passing clusters and a transmitting and receiving end, and then generating azimuth angles, pitch angles and propagation distances of sub-paths in the clusters; 3) calculating the probability of existence of the proprietary cluster and the common cluster on the antenna array; 4) calculating and generating channel impulse response (CIR) of the sensing channel and the communication channel; and 5) simulating and calculating the existence condition of the clusters on the array surface, a time autocorrelation function (TACF) and a space autocorrelation function (SCCF), and judging whether the channel modeling method has accuracy and space consistency or not. The channel modeling method disclosed by the invention is based on a common cluster and a proprietary cluster of a sensing channel and a communication channel, the relationship between the two channels is considered, the channel space consistency is comprehensively analyzed, and the channel modeling method has important significance for guiding the design of an ISAC system.

Description

Technical Field

[0001] The present invention relates to the technical field of channel modeling, and in particular to a synaesthesia integrated channel modeling method with spatial consistency. Background Art

[0002] The development of 6G wireless communication technology marks a major transformation in the telecommunications industry, while also presenting significant challenges and opportunities. ISAC is a key element in this transformation. ISAC integrates wireless communication and radar sensing within a unified framework. As a foundational technology for 6G, it enables more intelligent and efficient utilization of the electromagnetic spectrum. Channel modeling is crucial in communication systems because it predicts the behavior of signal propagation in diverse environments. Accurate channel models enable engineers to design more reliable systems, optimize performance, and mitigate issues such as interference, noise, and signal fading, thereby ensuring efficient data transmission. Furthermore, due to the introduction of sensory channels, ISAC channel modeling differs from traditional communication channel modeling and exhibits more complex channel characteristics. For example, factors such as the relationship between the communication and sensory channels, shared clustering, radar cross section (RCS), and other factors must be considered. Therefore, studying ISAC channel models is of great significance. Spatial consistency refers to the ability of a model to maintain coherence and continuity across both spatial and temporal domains. For ISAC channel models, the study of spatial channel consistency is particularly important due to the introduction of sensory channels and the consideration of the correlation between the communication and sensory channels. In addition, during the 6G standardization process, spatial consistency was also identified as a key aspect that needs to be focused on during ISAC channel modeling. Currently, there is very little research and discussion specifically on the spatial consistency of the ISAC channel. Summary of the Invention

[0003] Purpose of the invention: In view of this, the purpose of the present invention is to provide a synaesthesia integrated channel modeling method with spatial consistency, which can characterize the special channel characteristics of the ISAC channel, comprehensively consider the common clusters and proprietary clusters of the perception channel and the communication channel, and establish an accurate and moderately complex geometric random channel model for the ISAC scenario.

[0004] Technical solution, in order to achieve the above purpose, the present invention proposes a synaesthesia integrated channel modeling method with spatial consistency, comprising the following steps:

[0005] Step S1: Set the initialization scene and configure antenna related parameters;

[0006] Step S2: Generate the power, azimuth, elevation angle of the dedicated cluster and the shared cluster, as well as the distance between the first and last clusters passed and the transceiver, and then generate the azimuth, elevation angle, and propagation distance of the subpath within the cluster;

[0007] Step S3: Calculate the probability of the existence of the dedicated cluster and the shared cluster on the antenna array;

[0008] Step S4: Calculate and generate channel impulse responses (CIRs) of the sensing channel and the communication channel;

[0009] Step S5: Based on the existence probability and channel impulse response CIR calculated in the above steps, simulate and calculate the existence of clusters on the array surface, the temporal autocorrelation function TACF and the spatial autocorrelation function SCCF to determine whether the channel modeling method has accuracy and spatial consistency.

[0010] Furthermore, the method of step S1 is as follows:

[0011] Step S101: The scenario is set to an ISAC single-station sensing scenario, and the heights of the base station BS and the receiver Rx, the distance between the BS and the receiver Rx, and the speed, azimuth, and elevation of the BS and the receiver Rx are set.

[0012] Step S102: Initialize the antenna spacing and angle of the antenna array.

[0013] Furthermore, the method of step S2 is as follows:

[0014] Step S201: Generate the power of the dedicated cluster and the shared cluster, the arrival azimuth angle AAOA, the arrival elevation angle AEOA, the departure azimuth angle AAOD, the departure elevation angle AEOD, and the distances from the first and last scattering clusters passed to the transmitter and receiver;

[0015] Step S202: Generate the azimuth angle, elevation angle and propagation distance of the sub-path within the cluster.

[0016] Furthermore, step S3 includes:

[0017] Step S301: Initialize the cluster generation rate r R , disappearance rate r G ;

[0018] For the Tx antenna array, a specific cluster is located within a time interval Δt and an antenna spacing of δ p The probability of persistence in the case of is expressed as:

[0019]

[0020] Among them, the position difference of the transmitting antenna element and It is described by the following formula: v T is the moving speed of the transmitting antenna, and are the scene-dependent correlation coefficients in the array domain and time domain, which are 10m and 30m respectively. and They represent the azimuth angle of the transmitting antenna array movement and the azimuth angle of the array itself respectively;

[0021] For the antenna array of the communication receiving end Rx, a specific cluster is within the time interval Δt and the antenna spacing is δ q The probability of persistence under the condition can be expressed as:

[0022]

[0023] in, and is the position difference value of the communication receiving antenna element, and Perceive differences in the positions of receiving antenna elements, and They represent the azimuth of the communication receiving antenna array or the sensing receiving antenna array and the azimuth of the array itself respectively; for the antenna array of the sensing receiving end Sx, a specific cluster is within the time interval Δt and the antenna spacing is δ q The probability of persistence in the case of is expressed as:

[0024]

[0025] in, and is the position difference value of the sensing receiving antenna element, and They represent the azimuth of the movement of the sensing receiving antenna array and the azimuth of the array itself respectively;

[0026] Step S302: Calculate and generate the probability of existence of dedicated clusters and shared clusters for sensing channels and communication channels respectively;

[0027] The probability that a communication-specific cluster always exists in the link from the transmitter Tx to the communication receiver Rx is calculated as:

[0028] P C,e (Δt,δ p ,δ q )=P Tx (Δt,δ p )·P Rx (Δt,δ q )

[0029] The probability that a sensing-specific cluster always exists in the link from the transmitter Tx to the sensing receiver Sx is calculated as:

[0030] P S,e (Δt,δp ,δ q )=P Tx (Δt,δ p )·P Sx (Δt,δ q )

[0031] The common cluster participates in both the communication channel and the sensing channel, and its probability of always existing is expressed as:

[0032] P s (Δt,δ p ,δ q )=P Tx (Δt,δ p )·P Rx (Δt,δ q )·P Sx (Δt,δ q ).

[0033] Furthermore, the step S4 includes:

[0034] Step S401: Calculate the perceived channel CIR. The perceived channel CIR is calculated as:

[0035]

[0036] Among them, f c Indicates the carrier frequency, N S,e (t) represents the number of sensory-specific clusters, N s (t) represents the number of shared clusters, and Represent the number of rays in the sensing exclusive cluster and the shared cluster respectively. represents the channel gain between the transmitter Tx and the sensor receiver Sx, where λ is the wavelength, σ RCS represents the radar cross section RCS of Sx, D0 is the initial distance between the transmitter Tx and the sensing receiver Sx, G S,e is the channel gain between Tx and sensing-specific clusters, G s is the channel gain between Tx and the shared cluster;

[0037] After LoS transmission, the path delay of the signal transmitted by the dedicated cluster and the shared cluster scatterer is expressed as: τ0 = 2D0 / c, τ k,a =2d k,a / c and τ n,c =2d n,c / c, where c is the speed of light, d k,a is the propagation distance of the signal through the sensing-specific cluster, d n,cis the propagation delay of the signal transmitted through the shared cluster, and the Doppler shift of the signal transmitted by the ISAC through LoS, the sensing dedicated cluster transmission and the shared scatterer transmission are: f D0 (t) = 2ν0(t) / λ and f Dse (t)=2ν S,e (t) / λ,f Ds (t)=2ν s (t) / λ, where ν0(t) is the sensing target, i.e., the moving speed of the sensing receiver Sx, and ν S,e (t) is the moving speed of the sensor-specific cluster, ν s (t) is the moving speed of the shared cluster, A rad (θ A,L ,θ E,L ) is the directional vector product of the LoS transmission signal, is the directional vector product of the signal transmitted by the sensor-specific cluster, is the directional vector product of the signal transmitted through the common cluster, where θ A,L ,θ E,L are the azimuth and elevation angles of the ray transmitted via LoS, are the azimuth and elevation angles of the ray transmitted via the sensor-specific cluster, are the azimuth and elevation angles of the ray transmitted through the shared cluster;

[0038] Step S402: The communication channel CIR is expressed as:

[0039]

[0040] Among them, K R is the Rician factor, p LoS (t) is the LoS transmission probability, the probability of communication-specific cluster transmission and the probability of common cluster transmission Calculated as:

[0041]

[0042] Among them, N C,e (t) and N s (t) represents the number of communication-specific clusters and shared clusters, and denote the number of subpaths in the communication-specific cluster and the shared cluster, respectively;

[0043] The CIR for LoS transmission is calculated as:

[0044]

[0045] Among them, f crepresents the carrier frequency, and the delay of the LoS path at time t is expressed as in, Indicates the distance between Tx and Rx;

[0046] The CIR of the NLoS part transmitted through the communication dedicated cluster is expressed as:

[0047]

[0048] Among them, P qp,lb (t) represents the power of the communication-specific cluster, and the delay of the mth ray in the nth path at time t is calculated as in, represents the sum of the propagation distances from the p transmitting antenna units to the first communication-specific cluster and the propagation distance from the last passed communication-specific cluster to the qth receiving antenna unit, Represents the signal propagation delay between communication-specific clusters;

[0049] The NLoS CIR propagated through the common cluster is expressed as:

[0050]

[0051] Among them, P qp,mc (t) represents the power of the shared cluster. At time t, the delay of the kth ray in the lth path is calculated as in, represents the sum of the propagation distances from the p transmitting antenna units to the first common cluster and the propagation distance from the last common cluster passed to the qth receiving antenna unit, Represents the propagation delay of the signal between shared clusters.

[0052] Furthermore, the step S5 includes:

[0053] Step S501: Based on the communication channel CIR obtained in step S4, the system transfer function of the communication channel can be expressed as:

[0054]

[0055] in, and is the Fourier transform of the CIR in step S4, calculated as:

[0056]

[0057] The local space-time-frequency correlation function STF CF of the communication channel is calculated as:

[0058]

[0059] in, is the system transfer function of the communication channel, {} * represents the conjugate operation, Δr, Δt, and Δf represent the intervals of distance, time, and frequency, respectively;

[0060] The STF CF of signals transmitted via LoS, via a dedicated communication cluster, and via a shared cluster in a communication channel is calculated as:

[0061]

[0062] Set Δr and Δf to 0, and combine the system transfer function expression of the communication channel to simplify the STF CF of the communication channel to TACF, which is calculated as:

[0063]

[0064] in, It can be obtained by simplifying the STF CF of the signal transmitted via LoS, transmitted via the communication dedicated cluster and transmitted via the shared cluster by setting Δr and Δf to 0;

[0065] Set Δt and Δf to 0, and combine the system transfer function expression of the communication channel to simplify the STF CF of the communication channel to SCCF, which is calculated as:

[0066]

[0067] in, It can be obtained by simplifying the STF CF of the signal transmitted via LoS, transmitted via the communication dedicated cluster and transmitted via the shared cluster by setting Δt and Δf to 0;

[0068] Step S502: simulate and calculate the TACF and SCCF of the channel according to the formula, compare the simulation results with the theoretical results, and if the relative error between the two does not exceed 1%, then the channel modeling method is accurate;

[0069] Step S503: Based on the probability of cluster existence calculated in the above steps, simulate the existence of clusters on the antenna array surface. If a cluster exists or does not exist on a certain antenna unit, that is, the existence of clusters on its two adjacent antenna units is different from that of the antenna unit, then it is determined that the unit has no spatial consistency. If the probability that all units on the antenna array surface have spatial consistency exceeds 95%, then the channel modeling method has spatial consistency on the antenna array surface.

[0070] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0071] This paper proposes a spatially consistent synaesthesia integrated channel modeling method. This method considers the channel characteristics of the ISAC channel and models the ISAC channel using the GBSM framework based on shared and proprietary clusters of the perception and communication channels. This method considers and comprehensively analyzes the spatial consistency of the ISAC channel model parameters. Furthermore, the model theoretically derives and simulates statistical properties of cluster-related parameters, such as channel TACF and SCCF, which reflect channel accuracy and consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a synaesthesia integrated channel modeling method with spatial consistency in embodiment 1 of the present invention;

[0073] Figure 2 Schematic diagram of the ISAC communication system in Example 1 of the present invention;

[0074] Figure 3 The TACF theoretical results and simulation results of the ISAC system at t = 0, 1, and 10s in Example 1 of the present invention are shown;

[0075] Figure 4 The SCCF theoretical results and simulation results for the dedicated cluster, shared cluster, and NLoS component in the ISAC system in Example 1 of the present invention are shown;

[0076] Figure 5 Schematic diagram of the existence of clusters along the array surface in Example 1 of the present invention. DETAILED DESCRIPTION

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0078] Example 1:

[0079] See also Figure 1 This embodiment provides a synaesthesia integrated channel modeling method with spatial consistency, which specifically includes:

[0080] Step S1: Build the ISAC system framework and initialize the scene, antenna, cluster and other related parameters.

[0081] Specifically, step S1 includes:

[0082] Step S101: Set the scenario to an ISAC single-station sensing scenario. The altitude of the base station (BS) and the receiver (Rx), the distance between the BS and Rx, and the speed, azimuth, and elevation of the BS and Rx are set. In this embodiment, the altitude of the ISAC BS and Rx is 1.5m, the distance is 60m, the BS's elevation angle is π / 10, the azimuth angle is -π / 4, and the Rx's pitch angle is π / 4, the azimuth angle is π / 3. The initial speed is set to 50m / s.

[0083] Step S102: All transmitting and receiving ends are configured with multi-transmit and multi-receive antenna arrays, so the antenna spacing and angle of the antenna array need to be initialized. In this embodiment, the antenna spacing is λ / 2, and the Tx antenna array elevation angle is π / 6.

[0084] Step S2: Generate the power, azimuth, elevation angle of the dedicated cluster and the shared cluster, as well as the distance between the first and last clusters passed and the transceiver, and then generate the azimuth, elevation angle, and propagation distance of the sub-path within the cluster.

[0085] Specifically, step S2 includes:

[0086] Step S201: Generate the power of the dedicated cluster and the shared cluster, the arrival azimuth angle AAOA, the arrival elevation angle AEOA, the departure azimuth angle AAOD, the departure elevation angle AEOD, and the distances from the first and last scattering clusters passed to the transmitter and receiver;

[0087] Step S202: Generate the azimuth angle, elevation angle and propagation distance of the sub-path within the cluster.

[0088] Step S3: Calculate the probability that the dedicated cluster and the shared cluster exist on the antenna array.

[0089] Specifically, step S3 includes:

[0090] Step S301: Initialize the cluster generation rate r R , disappearance rate r G ; For the Tx antenna array, a specific cluster is within the time interval Δt and the antenna spacing is δ p The probability of persistence in the case of is expressed as:

[0091]

[0092] Among them, the position difference of the transmitting antenna element and It is described by the following formula: v T is the moving speed of the transmitting antenna, and are the scene-dependent correlation coefficients in the array and time domains, respectively, and They represent the azimuth angle of the transmitting antenna array movement and the azimuth angle of the array itself respectively;

[0093] For the antenna array of the communication receiving end Rx, a specific cluster is within the time interval Δt and the antenna spacing is δ q The probability of persistence under the condition can be expressed as:

[0094]

[0095] in, and is the position difference value of the communication receiving antenna element, and ξ1 S Perceive differences in the positions of receiving antenna elements, and They represent the azimuth of the communication receiving antenna array or the sensing receiving antenna array and the azimuth of the array itself respectively; for the antenna array of the sensing receiving end Sx, a specific cluster is within the time interval Δt and the antenna spacing is δ q The probability of persistence in the case of is expressed as:

[0096]

[0097] in, and is the position difference value of the sensing receiving antenna element, and They represent the azimuth of the movement of the sensing receiving antenna array and the azimuth of the array itself respectively;

[0098] Step S302: Calculate and generate the probability of existence of dedicated clusters and shared clusters for sensing channels and communication channels respectively;

[0099] The probability that a communication-specific cluster always exists in the link from the transmitter Tx to the communication receiver Rx is calculated as:

[0100]

[0101] The probability that a sensing-specific cluster always exists in the link from the transmitter Tx to the sensing receiver Sx is calculated as:

[0102]

[0103] The common cluster participates in both the communication channel and the sensing channel, and its probability of always existing is expressed as:

[0104]

[0105] Step S4: Calculate and generate channel impulse responses (CIRs) of the perception channel and the communication channel.

[0106] Specifically, step S4 includes:

[0107] Step S401: Calculate the perceived channel CIR. The perceived channel CIR is calculated as:

[0108]

[0109] Among them, f c Indicates the carrier frequency, N S,e (t) represents the number of sensory-specific clusters, N s (t) represents the number of shared clusters, and Represent the number of rays in the sensing exclusive cluster and the shared cluster respectively. represents the channel gain between the transmitter Tx and the sensor receiver Sx, where λ is the wavelength, σ RCS represents the radar cross section RCS of Sx, D0 is the initial distance between the transmitter Tx and the sensing receiver Sx, G S,e is the channel gain between Tx and sensing-specific clusters, G s is the channel gain between Tx and the shared cluster;

[0110] After LoS transmission, the path delay of the signal transmitted by the dedicated cluster and the shared cluster scatterer is expressed as: τ0 = 2D0 / c, τ k,a =2d k,a / c and τ n,c =2d n,c / c, where c is the speed of light, d k,a is the propagation distance of the signal through the sensing-specific cluster, d n,c is the propagation delay of the signal transmitted through the shared cluster, and the Doppler shift of the signal transmitted by the ISAC through LoS, the sensing dedicated cluster transmission and the shared scatterer transmission are: f D0 (t) = 2ν0(t) / λ and f Dse (t)=2ν S,e (t) / λ,f Ds (t)=2ν s (t) / λ, where ν0(t) is the sensing target, i.e., the moving speed of the sensing receiver Sx, and ν S,e (t) is the moving speed of the sensor-specific cluster, ν s (t) is the moving speed of the shared cluster, A rad (θ A,L ,θ E,L ) is the directional vector product of the LoS transmission signal, is the directional vector product of the signal transmitted by the sensor-specific cluster, is the directional vector product of the signal transmitted through the common cluster, where θ A,L ,θ E,L are the azimuth and elevation angles of the ray transmitted via LoS, are the azimuth and elevation angles of the ray transmitted via the sensor-specific cluster, are the azimuth and elevation angles of the ray transmitted through the shared cluster;

[0111] Step S402: The communication channel CIR is expressed as:

[0112]

[0113] Among them, K R is the Rician factor, p LoS (t) is the LoS transmission probability, the probability of communication-specific cluster transmission and the probability of common cluster transmission Calculated as:

[0114]

[0115] Among them, N C,e (t) and N s (t) represents the number of communication-specific clusters and shared clusters, and denote the number of subpaths in the communication-specific cluster and the shared cluster, respectively;

[0116] The CIR for LoS transmission is calculated as:

[0117]

[0118] Among them, f c represents the carrier frequency, and the delay of the LoS path at time t is expressed as in, Indicates the distance between Tx and Rx;

[0119] The CIR of the NLoS part transmitted through the communication dedicated cluster is expressed as:

[0120]

[0121] in, represents the power of the communication-specific cluster, and the delay of the mth ray in the nth path at time t is calculated as in, represents the sum of the propagation distances from the p transmitting antenna units to the first communication-specific cluster and the propagation distance from the last passed communication-specific cluster to the qth receiving antenna unit, Represents the signal propagation delay between communication-specific clusters;

[0122] The NLoS CIR propagated through the common cluster is expressed as:

[0123]

[0124] in, represents the power of the shared cluster. At time t, the delay of the kth ray in the lth path is calculated as in, represents the sum of the propagation distances from the p transmitting antenna units to the first common cluster and the propagation distance from the last common cluster passed to the qth receiving antenna unit, Represents the signal propagation delay between shared clusters.

[0125] Step S5: Based on the existence probability and channel impulse response CIR calculated in the above steps, simulate and calculate the existence of clusters on the array surface, the temporal autocorrelation function TACF and the spatial autocorrelation function SCCF to determine whether the channel modeling method has accuracy and spatial consistency.

[0126] Specifically, step S5 includes:

[0127] Step S501: Based on the communication channel CIR obtained in step S4, the system transfer function of the communication channel can be expressed as:

[0128]

[0129] in, and is the Fourier transform of the CIR in step S4, calculated as:

[0130]

[0131] The local space-time-frequency correlation function STF CF of the communication channel is calculated as:

[0132]

[0133] in, is the system transfer function of the communication channel, {} * represents the conjugate operation, Δr, Δt, and Δf represent the intervals of distance, time, and frequency, respectively;

[0134] The STF CF of signals transmitted via LoS, via a dedicated communication cluster, and via a shared cluster in a communication channel is calculated as:

[0135]

[0136] Set Δr and Δf to 0, and combine the system transfer function expression of the communication channel to simplify the STF CF of the communication channel to TACF, which is calculated as:

[0137]

[0138] in, It can be obtained by simplifying the STF CF of the signal transmitted via LoS, transmitted via the communication dedicated cluster and transmitted via the shared cluster by setting Δr and Δf to 0;

[0139] Set Δt and Δf to 0, and combine the system transfer function expression of the communication channel to simplify the STF CF of the communication channel to SCCF, which is calculated as:

[0140]

[0141] in, It can be obtained by simplifying the STF CF of the signal transmitted via LoS, transmitted via the communication dedicated cluster and transmitted via the shared cluster by setting Δt and Δf to 0;

[0142] Step S502: simulate and calculate the TACF and SCCF of the channel according to the formula, compare the simulation results with the theoretical results, and if the relative error between the two does not exceed 1%, then the channel modeling method is accurate;

[0143] Figure 3 The TACF of the channel at initial times t=0s, t=1s, and t=10s is shown. As the time interval increases, the temporal correlation of the channel decreases smoothly. This is because the channel model takes into account the movement of the BS, Rx, and cluster. This smooth change further verifies the spatial consistency of the channel model. It can also be observed that the TACF at t=0s and t=1s are almost the same because the initial time is close and the channel conditions have not changed significantly. However, as the initial time increases, by t=10s, due to severe channel fading, the curve is significantly different from the previous two. The theoretical results and simulation results in the curve are completely consistent, with a relative error of no more than 1%, verifying the accuracy of the channel modeling method.

[0144] Figure 4The SCCFs for dedicated clusters, shared clusters, and the entire NLoS segment are shown. The spatial correlation of the channel shows a smoothly decreasing trend as the antenna index increases. If the proposed channel model is not spatially consistent, the clusters will evolve randomly along the antenna array, resulting in a sudden change in the SCCF as the antenna index increases. Furthermore, by comparing the SCCFs for dedicated and shared clusters, it is clear that the SCCF for the shared cluster is closer to that for the entire NLoS segment. This is because the shared cluster combines parameters from both channels, some of which can be directly acquired through channel sensing, resulting in a more accurate representation of the physical channel. The theoretical and simulation results shown in the curves are in perfect agreement, with a relative error of less than 1%, validating the accuracy of the channel modeling approach.

[0145] Step S503: Based on the probability of cluster existence calculated in the above steps, simulate the existence of clusters on the antenna array surface. If a cluster exists or does not exist on a certain antenna unit, that is, the existence of clusters on its two adjacent antenna units is different from that of the antenna unit, then it is determined that the unit has no spatial consistency. If the probability that all units on the antenna array surface have spatial consistency exceeds 95%, then the channel modeling method has spatial consistency on the antenna array surface.

[0146] Figure 5 The evolution and birth and death of clusters along the antenna array are demonstrated. Clusters exhibit significant birth and death along the antenna array. However, clusters do not appear and disappear randomly across the antenna array, but rather exhibit a clear regularity. This phenomenon verifies the spatial consistency of the proposed channel model. Furthermore, all elements on the antenna array plane exhibit spatial consistency, thus demonstrating that the channel modeling method in this embodiment is spatially consistent across the antenna array plane.

[0147] In summary, the present invention proposes a method for modeling geometric random channels in synaesthesia-integrated scenarios with spatial consistency. It has the following advantages: it takes into account the channel characteristics of the ISAC channel, and models the ISAC channel based on the common clusters and proprietary clusters of the perception channel and the communication channel in the GBSM framework; it considers and comprehensively analyzes the spatial consistency of the ISAC channel model parameters; the model theoretically derives and simulates the relevant parameters of the cluster, the statistical characteristics of the channel TACF and SCCF that can reflect the accuracy and consistency of the channel, and verifies and analyzes the accuracy and spatial consistency of the channel model. The present invention has important guiding significance for the design of the ISAC system.

[0148] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A synaesthesia integrated channel modeling method with spatial consistency, characterized by: The method comprises the following steps: Step S1: Set the initialization scene and configure antenna related parameters; Step S2: Generate the power, azimuth, elevation angle of the dedicated cluster and the shared cluster, as well as the distance between the first and last clusters passed and the transceiver, and then generate the azimuth, elevation angle, and propagation distance of the subpath within the cluster; Step S3: Calculate the probability of the existence of the dedicated cluster and the shared cluster on the antenna array; Step S4: Calculate and generate channel impulse responses (CIRs) of the sensing channel and the communication channel; Step S5: Based on the existence probability and channel impulse response CIR calculated in the above steps, simulate and calculate the existence of clusters on the array surface, the temporal autocorrelation function TACF, and the spatial autocorrelation function SCCF to determine whether the channel modeling method has accuracy and spatial consistency.

2. The method for modeling a synaesthesia integrated channel with spatial consistency according to claim 1, characterized in that: The method of step S1 is as follows: Step S101: The scenario is set to an ISAC single-station sensing scenario, and the heights of the base station BS and the receiver Rx, the distance between the BS and the receiver Rx, and the speed, azimuth, and elevation of the BS and the receiver Rx are set. Step S102: Initialize the antenna spacing and angle of the antenna array.

3. The method for modeling a synaesthesia integrated channel with spatial consistency according to claim 1, characterized in that: The method of step S2 is as follows: Step S201: Generate the power of the dedicated cluster and the shared cluster, the arrival azimuth angle AAOA, the arrival elevation angle AEOA, the departure azimuth angle AAOD, the departure elevation angle AEOD, and the distances from the first and last scattering clusters passed to the transmitter and receiver; Step S202: Generate the azimuth angle, elevation angle and propagation distance of the sub-path within the cluster.

4. The method for modeling a synaesthesia integrated channel with spatial consistency according to claim 1, characterized in that: The step S3 comprises: Step S301: Initialize the cluster generation rate r R , disappearance rate r G ; For the Tx antenna array, a specific cluster is within the time interval Δt and the antenna spacing is δ p The probability of persistence in the case of is expressed as: Among them, the position difference of the transmitting antenna element and It is described by the following formula: v T is the moving speed of the transmitting antenna, and are the scene-dependent correlation coefficients in the array and time domains, respectively, and They represent the azimuth angle of the transmitting antenna array movement and the azimuth angle of the array itself, is the elevation angle of the transmitting antenna array itself; For the antenna array of the communication receiving end Rx, a specific cluster is within the time interval Δt and the antenna spacing is δ q The probability of persistence under the condition can be expressed as: in, and is the position difference value of the communication receiving antenna element, and They represent the azimuth angle of movement of the communication receiving antenna array or the sensing receiving antenna array and the azimuth angle of the array itself respectively; For the antenna array of the sensing receiver Sx, a specific cluster is detected within the time interval Δt and the antenna spacing is δ q The probability of persistence in the case of is expressed as: in, and is the position difference value of the sensing receiving antenna element, and They represent the azimuth of the movement of the sensing receiving antenna array and the azimuth of the array itself respectively; Step S302: Calculate and generate the probability of existence of dedicated clusters and shared clusters for sensing channels and communication channels respectively; The probability that a communication-specific cluster always exists in the link from the transmitter Tx to the communication receiver Rx is calculated as: P C,e (Δt,δ p ,d q )=P Tx (Δt,δ p )·P Rx (Δt,δ q ) The probability that a sensing-specific cluster always exists in the link from the transmitter Tx to the sensing receiver Sx is calculated as: P S,e (Δt,δ p ,d q )=P Tx (Δt,δ p )·P Sx (Δt,δ q ) The common cluster participates in both the communication channel and the sensing channel, and its probability of always existing is expressed as: P s (Δt,δ p ,d q )=P Tx (Δt,δ p )·P Rx (Δt,δ q )·P Sx (Δt,δ q )。 5. The method for modeling synaesthesia integrated channels with spatial consistency according to claim 4, characterized in that: The step S4 comprises: Step S401: Calculate the perceived channel CIR. The perceived channel CIR is calculated as: Among them, f c Indicates the carrier frequency, N S,e (t) represents the number of sensory-specific clusters, N s (t) represents the number of shared clusters, and Represent the number of rays in the sensing exclusive cluster and the shared cluster respectively. represents the channel gain between the transmitter Tx and the sensor receiver Sx, where λ is the wavelength, σ RCS represents the radar cross section RCS of Sx, D0 is the initial distance between the transmitter Tx and the sensing receiver Sx, G S,e is the channel gain between Tx and sensing-specific clusters, G s is the channel gain between Tx and the shared cluster; δ is the Dirac function, τ is the propagation delay; After LoS transmission, the path delay of the signal transmitted by the dedicated cluster and the shared cluster scatterer is expressed as: τ0 = 2D0 / c, τ k,a =2d k,a / c and τ n,c =2d n,c / c, where c is the speed of light, d k,a is the propagation distance of the signal through the sensing-specific cluster, d n,c is the propagation delay of the signal transmitted through the shared cluster, and the Doppler shift of the signal transmitted by the ISAC through LoS, the sensing dedicated cluster transmission and the shared scatterer transmission are: f D0 (t) = 2ν0(t) / λ and f Dse (t)=2ν S,e (t) / λ,f Ds (t)=2ν s (t) / λ, where ν0(t) is the sensing target, i.e., the moving speed of the sensing receiver Sx, and ν S,e (t) is the moving speed of the sensor-specific cluster, ν s (t) is the moving speed of the shared cluster, A rad (θ A,L ,θ E,L ) is the directional vector product of the LoS transmission signal, is the directional vector product of the signal transmitted by the sensor-specific cluster, is the directional vector product of the signal transmitted through the common cluster, where θ A,L ,θ E,L are the azimuth and elevation angles of the ray transmitted via LoS, are the azimuth and elevation angles of the ray transmitted via the sensor-specific cluster, are the azimuth and elevation angles of the ray transmitted through the shared cluster; Step S402: The communication channel CIR is expressed as: Among them, K R is the Rician factor, p LoS (t) is the LoS transmission probability, the probability of communication-specific cluster transmission and the probability of common cluster transmission Calculated as: Among them, N C,e (t) and N s (t) represents the number of communication-specific clusters and shared clusters, and denote the number of subpaths in the communication-specific cluster and the shared cluster, respectively; The CIR for LoS transmission is calculated as: Among them, f c represents the carrier frequency, and the delay of the LoS path at time t is expressed as in, Indicates the distance between Tx and Rx; The CIR of the NLoS part transmitted through the communication dedicated cluster is expressed as: in, represents the power of the communication-specific cluster, and the delay of the mth ray in the nth path at time t is calculated as in, represents the sum of the propagation distances from the p transmitting antenna units to the first communication-specific cluster and the propagation distance from the last passed communication-specific cluster to the qth receiving antenna unit, Represents the signal propagation delay between communication-specific clusters; The NLoS CIR propagated through the common cluster is expressed as: in, represents the power of the shared cluster. At time t, the delay of the kth ray in the lth path is calculated as in, represents the sum of the propagation distances from the p transmitting antenna units to the first common cluster and the propagation distance from the last common cluster passed to the qth receiving antenna unit, Represents the signal propagation delay between shared clusters.

6. The method for modeling synaesthesia integrated channels with spatial consistency according to claim 5, characterized in that: The step S5 comprises: Step S501: Based on the communication channel CIR obtained in step S4, the system transfer function of the communication channel is expressed as: in, and is the Fourier transform of the CIR in step S4, calculated as: The local space-time-frequency correlation function STF CF of the communication channel is calculated as: in, is the system transfer function of the communication channel, {} * represents the conjugate operation, Δr, Δt, and Δf represent the intervals of distance, time, and frequency, respectively; The STF CF of signals transmitted via LoS, via a dedicated communication cluster, and via a shared cluster in a communication channel is calculated as: Set Δr and Δf to 0, and combine the system transfer function expression of the communication channel to simplify the STF CF of the communication channel to TACF, which is calculated as: in, It can be obtained by simplifying the STF CF of the signal transmitted via LoS, transmitted via the communication dedicated cluster and transmitted via the shared cluster by setting Δr and Δf to 0; Set Δt and Δf to 0, and combine the system transfer function expression of the communication channel to simplify the STF CF of the communication channel to SCCF, which is calculated as: in, It can be obtained by simplifying the STF CF of the signal transmitted via LoS, transmitted via the communication dedicated cluster and transmitted via the shared cluster by setting Δt and Δf to 0; Step S502: simulate and calculate the TACF and SCCF of the channel according to the formula, compare the simulation results with the theoretical results, and if the relative error between the two does not exceed 1%, then the channel modeling method is accurate; Step S503: Based on the probability of cluster existence calculated in the above steps, simulate the existence of clusters on the antenna array surface. If a cluster exists or does not exist on a certain antenna unit, that is, the existence of clusters on its two adjacent antenna units is different from that of the antenna unit, then it is determined that the unit has no spatial consistency; if the probability that all units on the antenna array surface have spatial consistency exceeds 95%, then the channel modeling method has spatial consistency on the antenna array surface.

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