A unified channel modeling method and device for 6G

Abstract

This application provides a unified channel modeling method and apparatus for 6G, relating to the field of wireless communication technology. The method includes: constructing a wireless communication scenario, configuring basic parameters and a first parameter of the 6G channel, and obtaining large-scale parameters and path loss of the channel propagation; the first parameter includes at least one of the following: the stationary region size of the E-MIMO antenna array, RIS parameters, target parameters, shared parameters, and a high-frequency band intra-cluster sparse K-factor; generating cluster path parameters of the 6G channel or its sub-channels based on the basic parameters, large-scale parameters, and channel type; generating a second parameter characterizing the new characteristics of the 6G channel based on the first parameter; and generating channel coefficients of the 6G channel based on the second parameter and the cluster path parameters. These channel coefficients are used to couple with path loss and shadowing fading to obtain the channel impulse response of the 6G channel. Thus, the constructed channel model can characterize the new characteristics of 6G.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a unified channel modeling method and apparatus for 6G. Background Technology

[0002] In mobile communications, the wireless channel is the medium for transmitting radio signals between the transmitting end (such as a base station) and the receiving end (such as a terminal), and its physical characteristics determine the upper limit of the system's performance. A channel model is a mathematical description of the propagation characteristics of radio waves in a wireless channel. An accurate channel model is the foundation and prerequisite for the design, technology optimization, and performance evaluation of each generation of mobile communication systems. Statistical channel models are simple and easy to use. In particular, the geometry-based stochastic model (GBSM) has been adopted by standardization organizations (such as the International Telecommunication Union (ITU) and the 3rd Generation Partnership Project (3GPP)) and is widely used as the mainstream channel model in 4G and 5G mobile communication systems. GBSM is a cluster-based model that abstracts scatterers in the environment as randomly distributed clusters. Each cluster consists of multiple paths, which are received at the receiving end in an approximately superimposed manner, exhibiting a multipath effect. In 5G systems, GBSM introduces the pitch angle parameter, expanding from the time-frequency-horizontal dimension of 4G to the time-frequency-horizontal-vertical dimension, thereby meeting the research needs of three-dimensional (3D) multiple-input multiple-output (MIMO) technology. Furthermore, 5G 3D GBSM supports statistical channel modeling in urban macro (UMa), urban micro (UMi), rural macro (RMa), and indoor scenarios at frequencies of 0.5-100GHz, capable of characterizing propagation characteristics at various scales, including path loss, shadowing fading, latency, angle, Doppler, and polarization.

[0003] However, existing channel models based on statistical principles are designed for 5G systems. They are difficult to accurately describe the new characteristics of 6-Generation (6G) channels during the modeling process, and therefore cannot be applied to the simulation and evaluation of 6G systems. Summary of the Invention

[0004] This application provides a unified channel modeling method and apparatus for 6G, which solves the problem that existing GBSMs for 5G systems are difficult to accurately describe the new characteristics of 6G channels during the modeling process, and are therefore not suitable for simulation and evaluation of 6G systems.

[0005] Firstly, to achieve the above objectives, embodiments of this application provide a unified channel modeling method for 6G, comprising:

[0006] Construct a wireless communication scenario, configure the basic parameters and first parameters of the 6G channel, and obtain the large-scale parameters and path loss of channel propagation; wherein, the first parameter includes at least one of the following: the size of the stationary region of the ultra-large-scale multiple-input multiple-output E-MIMO antenna array, the intelligent metasurface RIS parameter, the target parameter and the shared parameter, and the high-frequency band intra-cluster sparse K factor;

[0007] Based on the basic parameters, the large-scale parameters, and the channel type, cluster path parameters for the 6G channel or its sub-channels are generated; wherein, when the channel type is a very large-scale multiple-input multiple-output (E-MIMO) channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters for the 6G channel; when the channel type is a RIS channel or a communication-aware integrated (ISAC) channel, the generated cluster path parameters are the cluster path parameters for each sub-channel of the 6G channel.

[0008] Based on the first parameter, a second parameter is generated to characterize the new characteristics of the 6G channel, wherein the second parameter is used to characterize at least one of the following new characteristics of the 6G channel: spatial non-stationarity and near-field characteristics of E-MIMO channel, intra-cluster sparsity of high-frequency channel, RIS radiation response cascade characteristics of RIS channel, and target RCS and sharing characteristics of ISAC channel.

[0009] Based on the second parameter and the cluster path parameter, the channel coefficients of the 6G channel are generated, wherein the channel coefficients are used to couple with the path loss and shadow fading to obtain the channel impulse response of the 6G channel.

[0010] Optionally, based on the first parameter, a second parameter characterizing the new features of the 6G channel is generated, including at least one of the following:

[0011] Based on the size of the stationary region, parameters S characterizing the spatial nonstationarity of the E-MIMO channel and parameters A characterizing the near-field characteristics of the E-MIMO channel are generated.

[0012] Based on the intra-cluster sparsity K-factor of the high-frequency band, a parameter I characterizing the intra-cluster sparsity of the high-frequency channel is generated;

[0013] Based on the RIS parameters, scattering coefficients characterizing the RIS radiative response are generated, wherein the RIS parameters include at least one of RIS size, RIS location, RIS codebook, and RIS area;

[0014] Based on the shared parameters and the target parameters, an RCS scattering coefficient characterizing the IACS sensing target characteristics and a shared factor characterizing the IACS shared characteristics are generated, wherein the target parameters include the number, location, and velocity of the sensing targets, or the target parameters include the number and location of environmental targets.

[0015] Optionally, the parameter I corresponding to the m-th path within the n-th cluster is represented as:

[0016]

[0017] Among them, K R is the sparse K-factor within the high-frequency cluster, and M is the number of paths within the nth cluster.

[0018] Optionally, the channel coefficients of the 6G channel are generated based on the second parameter and the cluster path parameter, including:

[0019] Based on the cluster path parameters, the second parameter corresponding to the channel type, and the pre-configured general formula, the cluster path complex gain of each path in each cluster is generated;

[0020] Based on the generated cluster path complex gain, the channel coefficients of the 6G channel are generated;

[0021] The general formula is as follows:

[0022]

[0023] Where p represents the p-th transmitting antenna element, the p-th RIS element, or the p-th target equivalent scattering point, and q represents the q-th receiving antenna element, the q-th RIS element, or the q-th target equivalent scattering point; h q,p,n,m (t,τ) represents the cluster path complex gain, S q,n (t) represents the second parameter used to characterize the spatial non-stationary characteristics of the nth cluster in the E-MIMO channel when receiving the qth element, S p,n (t) represents the second parameter used to characterize the spatial non-stationary characteristics of the nth cluster in the E-MIMO channel when transmitting the pth element; A q,n,m (t) represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when receiving the q-th array element, A p,n,m (t) represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when transmitting to the p-th array element; I n,mThe second parameter represents the intra-cluster sparsity used to characterize the high-frequency channel; when the channel type is an E-MIMO channel or a high-frequency channel, This represents the complex gain of the m-th resolvable multipath in the n-th cluster from the transmit antenna s to the receive antenna u, generated according to the 5G channel model.

[0024] Optionally, when the channel type is a RIS channel, the second parameter corresponding to the channel type is the scattering coefficient of the RIS radiation response characteristics, and the scattering coefficient of the RIS radiation response characteristics is the RIS radiation pattern;

[0025] Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type is the RCS scattering coefficient that characterizes the characteristics of the sensing target.

[0026] Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type includes a sharing factor that characterizes the sharing characteristics of ISAC.

[0027] Optionally, based on the cluster path parameter, the second parameter corresponding to the channel type, and pre-configured general parameters, the cluster path complex gain of each path in each cluster is generated, including:

[0028] When the sensing channel is an ISAC channel, the cluster path parameters are divided into shared cluster path parameters and non-shared cluster path parameters according to the sharing factor.

[0029] The complex gain of the shared portion of the cluster path is generated based on the shared cluster path parameters, and the complex gain of the non-shared portion of the cluster path is generated based on the non-shared cluster path parameters.

[0030] The cluster path complex gain is obtained by superimposing the complex gain of the shared portion and the complex gain of the non-shared portion.

[0031] Optionally, the channel coefficients of the 6G channel are generated based on the generated cluster path complex gain, including:

[0032] When the channel type is a RIS channel or an ISAC channel, the cluster path complex gain of the first sub-channel is convolved with the cluster path complex gain of the second sub-channel to generate the cluster path complex gain of the 6G channel.

[0033] The channel coefficients of the 6G channel are generated based on the cluster path complex gain of the 6G channel.

[0034] Wherein, when the channel type is RIS channel, the first sub-channel is the channel from the transmitting end Tx to RIS, and the second sub-channel is the channel from RIS to the receiving end Rx;

[0035] When the sensing channel is of type ISAC, the first sub-channel is the channel from Tx to the sensing target, and the second sub-channel is the channel from the sensing target to Rx.

[0036] Secondly, to achieve the above objectives, embodiments of this application provide a unified channel modeling apparatus for 6G, comprising:

[0037] The processing module is used to construct a wireless communication scenario, configure the basic parameters and first parameters of the 6G channel, and obtain the large-scale parameters and path loss of the channel propagation; wherein, the first parameter includes at least one of the following: the size of the stationary region of the ultra-large-scale multiple-input multiple-output E-MIMO antenna array, the intelligent metasurface RIS parameter, the target parameter and the shared parameter, and the high-frequency band cluster sparse K factor.

[0038] The first generation module is used to generate cluster path parameters of the 6G channel or a sub-channel of the 6G channel based on the basic parameters, the large-scale parameters, and the channel type; wherein, when the channel type is an E-MIMO channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters of the 6G channel, and when the channel type is a RIS channel or a communication-aware integrated ISAC channel, the generated cluster path parameters are the cluster path parameters of each sub-channel of the 6G channel;

[0039] The second generation module is used to generate a second parameter that characterizes the new characteristics of the 6G channel based on the first parameter. The second parameter is used to characterize at least one of the following new characteristics of the 6G channel: spatial non-stationarity and near-field characteristics of the E-MIMO channel, intra-cluster sparsity of the high-frequency channel, RIS radiation response cascade characteristics of the RIS channel, and target RCS and sharing characteristics of the ISAC channel.

[0040] The third generation module is used to generate the channel coefficients of the 6G channel based on the second parameter and the cluster path parameter, wherein the channel coefficients are used to couple with the path loss and shadow fading to obtain the channel impulse response of the 6G channel.

[0041] Thirdly, to achieve the above objectives, embodiments of this application provide an electronic device including a transceiver, a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the unified channel modeling method for 6G as described in the first aspect.

[0042] Fourthly, to achieve the above objectives, embodiments of this application provide a readable storage medium having a program or instructions stored thereon, which, when executed by a processor, implement the unified channel modeling method for 6G as described in the first aspect.

[0043] Fifthly, to achieve the above objectives, embodiments of this application provide a computer program product including computer instructions that, when executed by a processor, implement the unified channel modeling method for 6G as described in the first aspect.

[0044] The beneficial effects of the above technical solution in this application are as follows:

[0045] In the unified channel modeling method for 6G of this application, firstly, a wireless communication scenario is constructed, basic parameters and first parameters of the 6G channel are configured, and large-scale parameters and path loss of channel propagation are obtained; wherein, the first parameter includes at least one of: the stationary region size of the ultra-large-scale multiple-input multiple-output (E-MIMO) antenna array, intelligent metasurface RIS parameters, target parameters and sharing parameters, and high-frequency intra-cluster sparse K-factor; secondly, based on the basic parameters, the large-scale parameters, and the channel type, the cluster path parameters of the 6G channel or the sub-channels of the 6G channel are generated; wherein, when the channel type is an E-MIMO channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters of the 6G channel, and the channel... When the channel type is RIS channel or ISAC channel, the generated cluster path parameter is the cluster path parameter of each sub-channel of the 6G channel. Next, based on the first parameter, a second parameter characterizing the new characteristics of the 6G channel is generated. This second parameter characterizes at least one of the following new 6G channel characteristics: spatial non-stationarity and near-field characteristics of E-MIMO channels, intra-cluster sparsity of high-frequency channels, RIS radiation response cascade characteristics of RIS channels, and target RCS and sharing characteristics of ISAC channels. Finally, based on the second parameter and the cluster path parameter, the channel coefficients of the 6G channel are generated. These channel coefficients are used to couple with path loss and shadowing fading to obtain the channel impulse response of the 6G channel. In this way, the channel coefficients of the 6G channel are generated considering the new characteristics of 6G, enabling the channel impulse response generated based on the channel coefficients to accurately characterize the new 6G characteristics of the channel. This expands the support capabilities of the channel model in 6G scenarios, frequency bands, and technologies, and improves the accuracy of the model. Attached Figure Description

[0046] Figure 1 A schematic diagram of the existing 5G 3D GBSM channel modeling process;

[0047] Figure 2 This is a flowchart illustrating the unified channel modeling method for 6G according to an embodiment of this application.

[0048] Figure 3 This is a schematic diagram of the 6G 3D GBSM channel model according to an embodiment of this application;

[0049] Figure 4This is a schematic diagram of the structure of a unified channel modeling device for 6G according to an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0051] To make the technical problems, technical solutions and advantages of this application clearer, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments.

[0052] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0053] In the various embodiments of this application, it should be understood that the sequence number of each process described below does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0054] In addition, the terms "system" and "network" are often used interchangeably in this article.

[0055] In the embodiments provided in this application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0056] Before describing the embodiments of this application, the relevant technical points will be explained first:

[0057] I. GBSM

[0058] The GBSM model based on statistical principles has been adopted by standardization organizations such as the ITU and 3GPP, and is widely used as the mainstream channel model in 4G and 5G systems. In the definition of GBSM, the channel is generated by the combined contributions of multipath propagation based on geometric principles and small-scale statistical channel parameters obtained from measurements. Geometric modeling makes the channel independent of the antenna and allows for the configuration of different antennas and array patterns. Furthermore, for a single channel snapshot, channel parameters (such as delay, power, angle of arrival, and angle of departure) are randomly determined based on a statistical distribution extracted from channel measurements. Assuming that the transmitter (Tx) and receiver (Rx) are configured with arrays of S and U antenna elements respectively, the channel coefficients from the s-th antenna of Tx to the u-th antenna of Rx are expressed as:

[0059]

[0060] Where N represents the number of channel clusters, and M represents the number of multipaths contained in each cluster. Let the complex gain of the m-th resolvable multipath in the n-th cluster from the transmitting antenna s to the receiving antenna u be represented as follows:

[0061]

[0062] in:

[0063] (·) T λ represents the transpose of the matrix, and λ represents the carrier wavelength.

[0064] P n τ represents the power of the nth cluster. n,m This represents the time delay corresponding to multipath propagation, v. n,m It is the Doppler frequency shift of the multipath.

[0065] Let (n, m) represent the nth and mth digits, respectively. th The Zenith angle of arrival (ZOA), azimuth angle of arrival (AOA), Zenith angle of departure (ZOD), and azimuth angle of departure (AOD) of a multipath.

[0066] These represent the vertical and horizontal polarization components of the radiation pattern of the receiving antenna u, and the vertical and horizontal polarization components of the radiation pattern of the transmitting antenna s, respectively.

[0067] Represents (n, m)th The initial phase of the multipath propagation. The upper right subscript indicates that the signal exits from the transmitting antenna s with polarization p1 and arrives at the receiving antenna u with polarization p2. κ n,m This represents the cross polarization power ratio (XPR) of the multipath.

[0068] r Rx,n,m ,r Tx,n,m These represent the unit direction vectors corresponding to the multipath arrival and departure angles, respectively. Correspondingly, d Rx,u ,d Tx,s This represents the position vectors of the receiving antenna u and the transmitting antenna s.

[0069] II. 5G 3D GBSM Simulation

[0070] like Figure 1As shown, the 5G 3D GBSM simulation implementation includes 12 steps: setting the scenario, network layout and antenna parameters; configuring propagation conditions (Non-Line of Sight (NLOS) / Line of Sight (LOS)); calculating path loss; generating related large-scale parameters (Delay spread (DS), Angle spread (AS), Slow Fading (SF), Rice K-factor); generating delays; generating cluster power; generating arrival and departure angles; performing random coupling of rays; generating XPR; setting random initial phases; generating channel coefficients; and applying path loss and shadowing. In other words, 5G 3D... The establishment of GBSM first involves configuring the scenario, network layout, antenna parameters, propagation conditions, etc., and then calculating the path loss of the channel based on the configuration, and generating large-scale statistical parameters such as delay spread and angle spread. Then, based on the large-scale parameters and the corresponding statistical distribution, small-scale parameters such as channel delay, power, angle of arrival and departure angle, and XPR are generated by random sampling. Finally, the initial phase of the channel is set, and channel coefficients coupled with path loss and shadow fading are generated to obtain the impulse response function of the overall channel.

[0071] III. New Features of 6G Channels

[0072] With the application of new frequency bands, new scenarios, and new technologies, 6G channels exhibit more complex and diverse characteristics: 1) 6G will deploy Extra-large-scale massive multiple-input multiple-output (E-MIMO) technology based on larger antenna arrays. As the distance between users and base stations gradually decreases, the plane wave assumption in 5G 3DGBSM no longer holds in the near-field range. Furthermore, due to the increased physical aperture of the antenna array, antenna elements at different spatial locations observe different channel multipath scenarios, resulting in spatially non-stationary channel characteristics. 2) As the frequency bands of 6G communication increase and the wavelength decreases, the channel's diffraction and transmission capabilities weaken. The vast majority of energy is contained in a small portion of the channel components, meaning the channel exhibits sparse characteristics in high-frequency bands such as terahertz. 3) Reconfigurable intelligence surfaces (RIS), as a new technology, are artificial electromagnetic surface structures with programmable electromagnetic properties. The propagation path of signals reflected through RIS in the channel can be controlled via a codebook. Compared to the traditional 5G transmitter-receiver (Tx-Rx) link, the introduction of RIS adds Tx-RIS and RIS-Rx links. The RIS channel requires modeling the Tx-RIS sub-channels, RIS physical characteristics, and the cascading of RIS-Rx sub-channels. 4) Integrated sensing and communication (ISAC) is defined as one of the typical scenarios of 6G in the ITU standard proposal. ISAC technology enables base stations or terminals to sense the surrounding environment while communicating. Due to the reuse of communication sensing system resources and propagation environment, some objects in the environment act as both communication propagation scatterers and sensing targets. The ISAC channel exhibits scatterer sharing characteristics.

[0073] To accurately characterize the new 6G channel characteristics, such as near-field and far-field non-stationarity of E-MIMO channels, high-frequency channel sparsity, RIS channel concatenation, and ISAC channel sharing, and to be compatible with 5G standard channel models, it is necessary to extend the 3D GBSM principle and propose an extended-GBSM channel modeling method based on statistical principles for the new 6G characteristics, so as to support the design and simulation evaluation of 6G systems.

[0074] Based on the foregoing, embodiments of this application provide a unified channel modeling method and apparatus for 6G. The implementation process of these embodiments will now be described in detail with reference to the accompanying drawings.

[0075] Embodiments of this application provide a unified channel modeling method for 6G, such as... Figure 2 As shown, it includes:

[0076] Step 201: Construct a wireless communication scenario, configure the basic parameters and first parameters of the 6G channel, and obtain the large-scale parameters and path loss of channel propagation; wherein, the first parameter includes: the size of the stationary region of the ultra-large-scale multiple-input multiple-output E-MIMO antenna array (e.g., the number of antennas contained in a stationary region, specifically, configuring 8 antennas as a group of stationary regions), the intelligent metasurface RIS parameter, the target parameter and the shared parameter, and at least one of the following: high-frequency band cluster sparse K factor;

[0077] Here, the first parameter is a parameter related to the new characteristics of the channel. The timing of configuring the first parameter can be set as needed. For example, it can be configured in any step before the step that needs to use the first parameter. Specifically, the high-frequency band intra-cluster sparse K factor in the first parameter can be configured in step 4 of the 5G 3D GBSM standard modeling process, and the parameters in the first parameter other than the high-frequency band intra-cluster sparse K factor can be configured in step 1 of the 5G 3D GBSM standard modeling process.

[0078] The basic parameters include, for example: scene type, antenna parameters, network layout, outdoor-to-indoor (O2I) probability, base station location, user location, operating frequency band, simulation time, etc. Specifically:

[0079] The scene types include, but are not limited to, at least one of the following: UMi, Uma, Indoorhotspot (InH), RMa, and O2I;

[0080] The antenna parameters include, but are not limited to, at least one of the following: antenna type, number of antennas, antenna downtilt angle, etc., wherein the antenna type includes, but is not limited to, one or more of the following: Uniform Planar Array (UPA) and Uniform Linear Array (ULA);

[0081] The network layout includes, but is not limited to: configuring links from transmitter (base station) to receiver (user), configuring sub-links from transmitter to RIS and from RIS to receiver, configuring sub-links from transmitter to sensing target / environment target and from sensing target / environment target to receiver, etc.

[0082] The large-scale parameters of the channel propagation include, but are not limited to: DS, AS, SF, Rice K factor, etc., where AS specifically includes: Azimuth Spread of Departure Angle (ASD), Azimuth Spread of Arrival Angle (ASA), Zenith Spread of Departure Angle (ZSD), and Zenith Spread of Arrival Angle (ZSA).

[0083] Regarding path loss: When the channel is an E-MIMO channel or a high-frequency channel, the path loss is the path loss of the link between the transmitting and receiving ends. When the channel is a cascaded channel such as a RIS channel or an ISAC channel, the path loss includes the path loss of each sub-channel of that channel.

[0084] Step 202: Generate cluster path parameters for the 6G channel or its sub-channels based on the basic parameters, the large-scale parameters, and the channel type; wherein, when the channel type is an E-MIMO channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters for the 6G channel; when the channel type is a RIS channel or a communication-aware integrated ISAC channel, the generated cluster path parameters are the cluster path parameters for each sub-channel of the 6G channel.

[0085] Here, it should be noted that step 202 above can be implemented by referring to steps 2 to 10 in the modeling process of the GBSM standard; where, when the 6G channel is a cascaded channel (such as a RIS channel or an ISAC channel), this step should obtain the corresponding cluster path parameters for each cascaded sub-channel. That is, when the 6G channel is a cascaded channel, it is also necessary to divide the 6G channel into a first cascaded sub-channel and a second cascaded sub-channel according to the parameters related to the channel type of the 6G channel in the first parameter (such as RIS parameters, target parameters, etc.), so that step 202 above can be performed for the first sub-channel and the second sub-channel respectively.

[0086] Here, the cluster path parameters in step 202 above include, but are not limited to, at least one of the following: time delay, angle (ZoA, ZoD, AoA, AoD), cluster power, angle of arrival and angle of departure of the transmitting and receiving ends (specifically based on the statistical distribution of the response (horizontal angle conforms to Laplace distribution, vertical angle conforms to truncated Gaussian distribution)), multipath parameters generated by intra-cluster sampling, cross-polarization ratio coefficient of each multipath (generated according to the random variable generation method of log-normal distribution), random initial phase of each multipath, etc.

[0087] Step 203: Based on the first parameter, generate a second parameter that characterizes the new characteristics of the 6G channel, wherein the second parameter is used to characterize at least one of the following new characteristics of the 6G channel: spatial non-stationarity and near-field characteristics of the E-MIMO channel, intra-cluster sparsity of the high-frequency channel, RIS radiation response cascade characteristics of the RIS channel, and target RCS and sharing characteristics of the ISAC channel.

[0088] Step 204: Generate the channel coefficients of the 6G channel based on the second parameter and the cluster path parameter, wherein the channel coefficients are used to couple with the path loss and shadow fading to obtain the channel impulse response of the 6G channel;

[0089] It should be noted that in step 204 above, when generating channel coefficients, it is necessary to generate them based on the second parameter corresponding to the channel type and the cluster path parameter. For example, when the channel type is a RIS channel, the second parameter used is a second parameter used to characterize the spatial non-stationarity and near-field characteristics of the E-MIMO channel; when the channel type is an ISAC sensing channel, the second parameter used is a second parameter used to characterize the target RCS of the ISAC channel; and when the channel type is an ISAC channel, the second parameter used is a second parameter used to characterize the ISAC sharing characteristics. The second parameters characterizing various 6G new characteristics will be explained in detail later.

[0090] It should also be noted that the path loss coupled with the channel coefficient should be the path loss of the 6G channel. Therefore, when the 6G channel is a channel with cascading characteristics, the path loss configured in step 201 (the path loss of each sub-channel) needs to be cascaded to obtain the path loss of the entire 6G channel.

[0091] In the unified channel modeling method for 6G in this application embodiment, firstly, a wireless communication scenario is constructed, basic parameters and first parameters of the 6G channel are configured, and large-scale parameters and path loss of channel propagation are obtained; wherein, the first parameter includes at least one of: the stationary region size of the ultra-large-scale multiple-input multiple-output (E-MIMO) antenna array, intelligent metasurface (RIS) parameters, target parameters and shared parameters, and high-frequency intra-cluster sparse K-factor; secondly, based on the basic parameters, the large-scale parameters, and the channel type, cluster path parameters of the 6G channel or its sub-channels are generated; wherein, when the channel type is an E-MIMO channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters of the 6G channel, and when the channel type is a RIS channel or a communication-aware integrated (ISAC) channel, the generated cluster path parameters are the cluster path parameters of each sub-channel of the 6G channel. Next, based on the first parameter, a second parameter is generated to characterize the new characteristics of the 6G channel. This second parameter characterizes at least one of the following new 6G channel characteristics: spatial non-stationarity and near-field characteristics of the E-MIMO channel, intra-cluster sparsity of the high-frequency channel, RIS radiation response cascade characteristics of the RIS channel, and target RCS and sharing characteristics of the ISAC channel. Then, based on the second parameter and the cluster path parameter, channel coefficients for the 6G channel are generated. These channel coefficients are coupled with path loss and shadowing fading to obtain the channel impulse response of the 6G channel. This ensures that the new characteristics of the 6G channel are fully considered during the generation of channel coefficients. Consequently, the final generated channel impulse response accurately characterizes the new 6G characteristics of the channel, expanding the channel model's support capabilities in 6G scenarios, frequency bands, and technologies, and improving the model's accuracy.

[0092] As an optional implementation, step 203 includes at least one of the following:

[0093] (1) Based on the size of the stationary region, generate a parameter S characterizing the spatial nonstationarity of the E-MIMO channel and a parameter A characterizing the near-field characteristics of the E-MIMO channel; specifically, the implementation process of this step includes:

[0094] First, the E-MIMO antenna array is divided into multiple stable regions according to the size of the stable region; for example, every 8 antennas are configured as one stable region.

[0095] Secondly, based on the Poisson distribution, parameters S of a reference stationary region are generated, and parameters S of other stationary regions are generated according to the parameters S of the reference stationary region and a first-order Markov process; wherein, the reference stationary region is the first stationary region among the plurality of stationary regions; that is, for the plurality of stationary regions, the parameters S of each stationary region are configured sequentially, wherein, for the first stationary region (reference stationary region), the parameters can be configured based on the Poisson distribution, and for other stationary regions besides the first stationary region, the parameters can be configured based on a first-order Markov process and the S parameters of the adjacent preceding stationary region. Here, S... q,n Taking (t) as an example, let's explain the value of the S-parameter. When the cluster (represented by n) is not occluded (or can be observed) on this element (represented by q), S... q,n (t) is configured as 1, otherwise it is configured as 0;

[0096] Next, based on the time delay, angle of arrival, and departure angle in the cluster path parameters, and the principle of spherical waves, the parameter A of the antenna array is calculated; this is used to characterize the near-field characteristics A of the m-th path in the n-th cluster when transmitting the p-th array element. p,n,m Taking (t) as an example, it can be expressed as:

[0097]

[0098] Where, d p,n,m d is the distance vector of the m-th path in the n-th cluster when transmitting to the p-th array element. ref,n,m It is the distance vector of the reference array element, where the reference element is, for example, the element located at the center point;

[0099] Here, parameter S is used to characterize the spatial nonstationarity of the E-MIMO channel, and parameter A is used to characterize the near-field characteristics of the E-MIMO channel;

[0100] Here, it should be noted that, as a special case, when S q,n (t)=S p,n (t)=1 and A q,n,m (t)=A p,n,m When (t) = 1, the above formula can characterize the channel characteristics under the far-field stationary assumption. Thus, the channel model generated by the embodiments of this application can characterize the new non-stationary characteristics and near-field characteristics of the channel in 6G technology, and is also compatible with the 3GPP 5G standard channel model.

[0101] (2) Generate a parameter I that characterizes the intra-cluster sparsity of the high-frequency channel based on the intra-cluster sparsity K factor of the high-frequency band;

[0102] Here, it's important to clarify two points: First, a high-frequency channel refers to a channel that supports high-frequency bands (e.g., terahertz), where high-frequency bands are those higher than the 0.5-100GHz bands supported by existing 5G channels. Second, parameter I is used to characterize multipath sparsity. That is, when considering channel sparsity, the dominant power path within a cluster and other multipath paths need to be superimposed with different multiplicative factors (parameter I) to model and allocate the power of the paths within the cluster, thus characterizing channel sparsity and enabling modeling of high-frequency bands such as terahertz. Specifically, I... n,m For example, it is used to describe the multipath sparsity within the nth cluster of the channel. Therefore, for the mth path within the nth cluster, the parameter I can be expressed as:

[0103]

[0104] Among them, K R M is the sparse K-factor within the high-frequency cluster, and M is the number of paths within the nth cluster;

[0105] It should be noted that, as a special case, when the signal carrier frequency is in the low frequency band, parameter I can be set to 1. In this way, the channel model generated by the embodiments of this application can not only characterize the channel sparsity characteristics of 6G technology, but also be compatible with the 3GPP 5G standard channel model.

[0106] (3) Based on the RIS parameters, generate scattering coefficients that characterize the RIS radiative response characteristics, wherein the RIS parameters include at least one of RIS size, RIS position, RIS codebook and RIS area;

[0107] Here, for the k-th element of the l-th RIS in the channel, the scattering coefficients (scattering coefficients characterizing the RIS radiative response) incident from the m1-th path in the n1-th cluster and exiting from the m2-th path in the n2-th cluster can be modeled / represented as:

[0108]

[0109] The meanings of the parameters on the left-hand side of the equation in the above formula can be found in the previous text. The meaning of each parameter in the formula, for example, They represent the nth (n) i ,m i ) th Multipath ZOA, AOA, ZOD, and AOD; This represents the radiation pattern of the RIS in the vertical and horizontal directions. The scattering coefficients mentioned above can be modeled using fixed values ​​or statistically random values, and can be expressed as a function related to the angles of incidence and rejection of the RIS, i.e.:

[0110] (4) Based on the shared parameters and the target parameters, generate the RCS scattering coefficient characterizing the IACS sensing target characteristics and the shared factor characterizing the IACS shared characteristics, wherein the target parameters include the number, position and velocity of the sensing targets, or the target parameters include the number and position of the environmental targets.

[0111] Here, the RCS scattering coefficient, which characterizes the target sensing properties of ISAC, can be modeled / represented as follows: The scattering coefficient of the k-th scattering point of the l-th target in the channel, incident from the m3-th path in the n3-th cluster and exiting from the m4-th path in the n4-th cluster, can be represented as:

[0112]

[0113] Based on the above formula, the scattering coefficient is expressed as: The meanings of the parameters on the left side of the equals sign in the above formula can also be found in the previous explanation. The meaning of each parameter in the formula.

[0114] Here, for the sharing factor that characterizes the sharing feature, a very simple example is the sharing parameter configured above.

[0115] As an optional implementation, step 202 includes:

[0116] Based on the cluster path parameters, the second parameter corresponding to the channel type, and the pre-configured general formula, the cluster path complex gain of each path in each cluster is generated; here, as mentioned above, the second parameter corresponding to the E-MIMO channel is parameter S and parameter A, the second parameter corresponding to the high-frequency channel is parameter I, the second parameter corresponding to the RIS channel is the scattering coefficient characterizing the RIS radiation response characteristics, the second parameter corresponding to the sensing channel of the ISAC channel is the RCS scattering coefficient characterizing the ISAC sensing target characteristics, and the second parameter corresponding to the ISAC channel is the sharing factor characterizing the sharing characteristics;

[0117] It should be noted that the channel model proposed in this embodiment is based on the 5G statistical channel model and its coefficients, and is extended to support new 6G channel features. The model principle is as follows: Figure 3 As shown. The model considers the traditional communication channel (Tx-Rx) from transmitter to receiver that traverses ordinary scatterers, as well as sub-channels from transmitter to RIS (Tx-RIS), RIS to receiver (RIS-Rx), transmitter to sensing target (Tx-TAR), and sensing target to receiver (TAR-Rx). Based on this, considering the generality of the model expression, these channels can be modeled uniformly; therefore, the above general formula is:

[0118]

[0119] in:

[0120] h q,p,n,m (t,τ) represents the cluster path complex gain;

[0121] When the 6G channel is a non-cascaded channel (such as an E-MIMO channel or a high-frequency channel), p represents the p-th transmit antenna element and q represents the q-th receive antenna element. When the 6G channel is a cascaded channel (such as a RIS channel or an ISAC channel), in the first sub-channel, p represents the p-th transmit antenna and q represents the q-th RIS element or the q-th target equivalent scattering point. In the second sub-channel, p represents the p-th RIS element or the p-th target equivalent scattering point and q represents the q-th receive antenna element.

[0122] n represents the nth cluster, where n = 1, 2, ..., N, and N represents the total number of clusters in the channel;

[0123] m represents the m-th multipath in a cluster, where m = 1, 2, ..., M; M represents the total number of multipaths in the cluster;

[0124] S q,n (t) and S p,n (t) are S-parameters, where S q,n (t) represents the second parameter used to characterize the spatial non-stationary characteristics of the nth cluster in the E-MIMO channel when receiving the qth element, S p,n (t) represents the second parameter used to characterize the spatial nonstationary characteristics of the nth cluster in the E-MIMO channel when transmitting the pth element;

[0125] A q,n,m (t) and A p,n,m (t) is a parameter of A, where A q,n,m (t) represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when receiving the q-th array element, A p,n,m (t) represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when transmitting to the p-th array element; I n,m This represents the second parameter used to characterize the intra-cluster sparsity of high-frequency channels;

[0126] This represents the complex gain of the m-th resolvable multipath in the n-th cluster from the transmit antenna s to the receive antenna u, generated according to the 5G channel model.

[0127] Based on the generated cluster path complex gain, the channel coefficients of the 6G channel are generated; specifically, this step can be achieved using the formula... The expression is represented as follows: N represents the total number of clusters in the channel, and M represents the total number of paths in each cluster.

[0128] It should be noted that the second step of the above optional implementation method should generate the channel coefficients of the 6G channel based on the cluster path complex gain of the 6G channel. Based on this, when the 6G channel is a cascaded channel, it is necessary to first obtain the cluster path gain of the 6G channel based on the cluster path gains of each sub-channel generated in the first step of the above optional implementation method.

[0129] In this optional implementation, based on the aforementioned general formula and the generated second parameter and cluster path parameter, the complex gain of each cluster path is calculated, and the complex gains of each cluster path are accumulated to obtain the channel coefficient of the 6G channel. This allows the impulse response of the subsequently generated 6G channel to characterize the new features of the 6G channel, solving the problem that existing 5G models are not applicable to 6G technology. Furthermore, by setting the second parameter to a specific value, such as setting S... q,n (t)=S p,n (t)=1 and A q,n,m (t)=A p,n,m (t) = 1, which makes the channel model generated in this application embodiment compatible with the 5G channel model, and makes the channel model in this application embodiment universal.

[0130] As a specific implementation, when the channel type is a RIS channel, the second parameter corresponding to the channel type is the scattering coefficient of the RIS radiative response characteristics (as described above). The scattering coefficient of the RIS radiation response characteristics is the RIS radiation pattern.

[0131] Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type is the RCS scattering coefficient that characterizes the sensing target (as described above).

[0132] Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type includes a sharing factor (such as the aforementioned sharing parameter) that characterizes the sharing characteristics of ISAC.

[0133] As a specific implementation, based on the cluster path parameter, the second parameter corresponding to the channel type, and pre-configured general parameters, the cluster path complex gain of each path in each cluster is generated, including:

[0134] When the sensing channel is of type ISAC, the cluster path parameters are divided into shared cluster path parameters and non-shared cluster path parameters according to the sharing factor. Here, the shared cluster path parameter is the cluster path parameter of the shared cluster, for example, the shared cluster path is the cluster path used for communication and sensing; the non-shared cluster path parameter is the cluster path parameter of the non-shared cluster path, for example, the non-shared cluster path is the cluster path used for communication or the cluster path used for sensing. As an example, when the sharing factor is 0, the corresponding cluster path is a non-shared cluster path, and when the sharing factor is 1, the corresponding cluster path is a shared cluster path.

[0135] The complex gain of the shared portion of the cluster path is generated based on the shared cluster path parameters, and the complex gain of the non-shared portion of the cluster path is generated based on the non-shared cluster path parameters.

[0136] The cluster path complex gain is obtained by superimposing the complex gain of the shared portion and the complex gain of the non-shared portion.

[0137] Here, taking the communication from the s-th transmitting antenna element to the u-th receiving antenna element as an example, its cluster path complex gain can be expressed as: in, This represents the cluster path complex gain corresponding to the shared portion of the communication. This represents the cluster path complex gain corresponding to the non-shared portion of the communication.

[0138] Here, taking the sensing of the s′-th transmitting antenna element to the u′-th receiving antenna element as an example, its cluster path complex gain can be expressed as: in, It is the cluster path complex gain corresponding to the shared portion of the perception. It is the cluster path complex gain corresponding to the non-shared portion of the perception;

[0139] Based on the aforementioned example of an ISAC sensing channel comprising two cascaded sub-channels, h sen,u′,s′ (t,τ) can also be further expressed as:

[0140]

[0141] Where l = 1, 2, ... L tar,0 Let l = 1, 2, ..., L represent the shared target. tar,1 Indicates a non-shared target.

[0142] As another specific implementation, the channel coefficients of the 6G channel are generated based on the generated cluster path complex gain, including:

[0143] When the channel type is a RIS channel or an ISAC channel, the cluster path complex gain of the first sub-channel is convolved with the cluster path complex gain of the second sub-channel to generate the cluster path complex gain of the 6G channel.

[0144] The channel coefficients of the 6G channel are generated based on the cluster path complex gain of the 6G channel.

[0145] Wherein, when the channel type is RIS channel, the first sub-channel is the channel from the transmitting end Tx to RIS, and the second sub-channel is the channel from RIS to the receiving end Rx;

[0146] When the sensing channel is of type ISAC, the first sub-channel is the channel from Tx to the sensing target, and the second sub-channel is the channel from the sensing target to Rx.

[0147] The implementation process of the above specific implementation methods will be explained below, taking the 6G channel as either a RIS channel or an ISAC sensing channel as examples.

[0148] For a RIS cascaded channel, the channel coefficient between two antenna elements (the u-th transmit antenna element and the s-th receive antenna element) is expressed as:

[0149]

[0150] The RIS board count is l = 1, 2, ..., L ris That is, l represents the l-th RIS; the array elements of each RIS board are represented as k = 1, 2, ..., K ris That is, k represents the k-th RIS oscillator in the l-th RIS; L ris K represents the total number of RIS; ris Represents the total number of RIS oscillators in the l-th RIS; where h u,k (t,τ) represents the vector formed by the cluster path complex gains of the first sub-channel, h k,s (t,τ) represents the vector formed by the cluster path complex gain of the second sub-channel.

[0151] For a sensing channel (via a cascaded channel of the sensing target) in an ISAC channel, its channel impulse response function can be expressed as:

[0152]

[0153] Wherein, the number of perceived targets is l = 1, 2, ..., L tar That is, l represents the l-th RIS; the scattering point of each sensing target is represented as k = 1, 2, ..., K tar That is: k represents the k-th RIS oscillator in the l-th RIS; L tar K represents the total number of perceived targets. tar h represents the total number of equivalent scattering points of the target in the l-th sensing target; similar to the RIS cascaded channel, h u,k(t,τ) represents the vector formed by the cluster path complex gains of Tx-TAR (first sub-channel), h k,s (t,τ) represents the vector formed by the cluster path complex gains of TAR-Rx (the second sub-channel), and similarly, Indicates the nth subchannel i The m-th cluster i Complex gain of multiple paths.

[0154] As an optional implementation, after step 202, the method further includes:

[0155] When the channel type is a RIS channel or an ISAC channel, a multipath cluster pruning operation is performed. This reduces the number of calculations and lowers the computational complexity of the model during subsequent calculations. Specifically, taking the RIS channel as an example, this step can be performed in two ways, but is not limited to these: Method 1: Prune paths whose angles deviate too far from the RIS detection based on the RIS operating range; Method 2: Based on power ranking, retain the top c% of multipaths by power percentage (c% can be set according to actual needs). Furthermore, the multipath cluster pruning method for the ISAC channel is similar to that for the RIS channel, and will not be elaborated here.

[0156] It should be noted that, compared with existing 5G channel models, this application discloses a unified channel modeling method for 6G, specifically proposing an extended channel model (E-GBSM) based on statistical principles. This model is based on the 5G statistical channel model and its coefficient generation, and extends to support new 6G channel characteristics. In other words, this application can model new 6G characteristics within a unified framework. This model can characterize new 6G channel characteristics such as near-field and far-field non-stationarity of E-MIMO channels, high-frequency channel sparsity, RIS channel cascading, and ISAC channel sharing, and is compatible with 3GPP 5G standard channel models. It expands the channel model's support capabilities in 6G scenarios, frequency bands, and technologies, and improves the model's accuracy. Specifically:

[0157] (1) Compared with 5G 3D GBSM which considers the plane wave assumption, the embodiments of this application introduce parameter A to characterize the channel near-field characteristics and introduce parameter S to characterize the channel non-stationary characteristics of different antenna array elements, which can support channel modeling for 6G E-MIMO technology deployment.

[0158] (2) Compared with 5G 3D GBSM, which only supports the 0.5-100GHz frequency band, the embodiments of this application introduce parameter I, use the intra-cluster K factor to model and allocate the power of the intra-cluster diameter, characterize the channel sparsity, and can support channel modeling of high frequency bands such as terahertz.

[0159] (3) Regarding the RIS technology introduced in the 6G system, the embodiments of this application model the Tx-RIS / RIS-Rx sub-channels in a cascade manner to characterize the RIS scattering coefficient and channel cascade characteristics;

[0160] (4) In response to the ISAC technology introduced in the 6G system, the embodiments of this application model the Tx-TAR / TAR-Rx sub-channels in a cascade manner, characterize the RCS of the sensing target and the cascade characteristics of the channels, model the shared and non-shared clusters of communication and sensing channels, and characterize the ISAC channel sharing characteristics.

[0161] The differences between the channel modeling process of this application embodiment and the standard process of 5G 3D GBSM channel modeling are shown below using Table 1.

[0162] Table 1. Extended Steps of the 6G 3D GBSM Channel Modeling Method

[0163]

[0164] In other words, the embodiments of this application are based on the 5G 3D GBSM modeling framework defined in the 3GPP standard (such as...). Figure 1 The modeling process is expanded upon the original 12 simulation implementation steps, as shown in Table 1. 1) To characterize the near-field and far-field characteristics and spatial non-stationary properties of the E-MIMO channel, the size of the stationary region of the antenna array is expanded in modeling step 1, i.e., the number of antenna elements within the stationary region; step 11 calculates the near-field and far-field parameters A and the spatial non-stationary parameter S, and applies them to the generation of E-MIMO channel coefficients. 2) To characterize the sparsity of the high-frequency channel, the intra-cluster K-factor is expanded in modeling step 4, and the sparsity parameter I is generated in step 11 and applied to the generation of high-frequency channel coefficients. 3) To accurately characterize the RIS channel cascade characteristics, the position, size, and array element parameters of the RIS are expanded in modeling step 1; steps 2-10 configure and generate the corresponding sub-channel parameters for TX-RIS and RIS-Rx, respectively; step 11 couples the RIS scattering coefficients and sub-channel parameters to generate the RIS cascaded channel coefficients. 4) To characterize the cascading and sharing characteristics of ISAC channels, modeling step 1 extends the configuration of TAR position, size, speed and other parameters; steps 2-10 configure and generate TX-TAR and TAR-Rx sub-channel parameters respectively, set sharing parameters and generate communication-aware shared cluster and non-shared cluster parameters; finally, step 11 couples RCS and target sub-channel parameters to generate target cascaded channel coefficients, couples shared and non-shared cluster parameters to generate communication channel coefficients and sensing channel coefficients.

[0165] The specific implementation process of the channel modeling method in the embodiments of this application will be described below.

[0166] Step 1: Set the scene, network layout, antenna parameters (ULA, UPA type, number of antennas, downtilt angle, etc.), scene type (Umi, Uma, InH, RMa, O2I, etc.), O2I probability, base station, user location, operating frequency band, simulation time, and the size of the stable region of the antenna array (e.g., 8 antennas per stable region). Based on this:

[0167] For E-MIMO channels, the size of the stable region of the antenna array is also configured (e.g., 8 antennas form a stable region).

[0168] For the RIS channel, RIS parameters are also configured, including RIS size, position, codebook, area, etc.

[0169] For the ISAC channel, sensing target / environmental target parameters are also configured. The sensing target is the object to be evaluated for applications such as localization and tracking. The sensing target parameters include information such as the number of targets, their location, and their velocity. The environmental target is not used for evaluation, but it affects the propagation law of the channel. The environmental target parameters include the number and location.

[0170] Step 2, set the link state: calculate the corresponding LOS probability based on the placement and distance of Tx and Rx, as well as the type of communication scenario, and determine the link state of LOS / NLOS / O2I.

[0171] Step 3, calculate link path loss: Calculate the link path loss based on the placement position and distance of Tx and Rx, antenna height, operating frequency band, and the type of communication scenario.

[0172] Step 4, calculate the large-scale parameters of the link: After setting up the scenario, determine the statistical parameter table for different scenarios, calculate the seven large-scale parameters (AS, DS, ASA, ASD, ZSA, ZSD, SF) of the corresponding link through different probability distributions (log-normal distribution, normal distribution), and introduce the correlation between the large-scale parameters through a 7*7 correlation matrix.

[0173] In addition, for high-frequency sparse channels, it is also necessary to configure the intra-cluster K factor.

[0174] Step 5, Calculate cluster latency: After obtaining DS in the previous step, calculate the latency of different clusters in the link.

[0175] Step 6, Calculate cluster power: After obtaining the cluster delay, calculate the normalized power of the corresponding cluster according to the exponential distribution.

[0176] Step 7, generate the arrival and departure angles of the transmitting and receiving ends: Based on the corresponding statistical distributions (horizontal angles conform to Laplace distribution, vertical angles conform to truncated Gaussian distribution), calculate the cluster arrival and departure angles of the corresponding clusters at the transmitting and receiving ends respectively.

[0177] Step 8: Perform intra-cluster sampling, generate multipath parameters, and randomly couple them.

[0178] Step 9, Generate cross-polarization ratio: Generate the cross-polarization ratio coefficient for each multipath using the random variable generation method based on the log-normal distribution.

[0179] Step 10: Configure a random initial phase for each multipath.

[0180] It should be noted that, for the RIS channel, steps 2-10 are applied to the Tx-RIS and RIS-Rx sub-channels respectively (i.e., steps 2-10 are performed on the two cascaded sub-channels in the RIS channel), generating the cluster path parameters for the Tx-RIS and RIS-Rx sub-channels. Then, multipath cluster reduction is performed, with two methods: 1. Reducing paths whose angles deviate too far from the RIS normal based on the RIS operating range; 2. Retaining the top c% of multipaths based on power ranking (c% is determined by the user). Next, based on physical optics, the RIS radiation pattern is calculated according to the RIS codebook configuration and incident multipath information.

[0181] Similarly, for the ISAC channel, steps 2-10 can be applied to the Tx-TAR and TAR-Rx sub-channels respectively to generate the Tx-TAR and TAR-Rx sub-channel cluster path parameters; analogous to the RIS sub-channel, cluster reduction can also be performed; generate the RCS of the sensing target, for example, a fixed value can be generated based on the type and material of the sensing target, or a random value can be generated through distribution; set sharing parameters to determine the sharing status of the sensing target or environmental target, and generate corresponding communication sensing shared cluster and non-shared cluster parameters;

[0182] Step 11: For the E-MIMO channel, generate parameters A and S based on information such as cluster path parameters and the size of the stable region;

[0183] For high-frequency channels, parameter I is generated based on information such as cluster path parameters and intra-cluster K-factor;

[0184] For the RIS channel, the cluster path parameters of the Tx-RIS and RIS-Rx sub-channels after the convolutional clusters are reduced, as well as the RIS radiation pattern, can be specifically represented by the formulas related to the RIS channel in the third specific implementation method mentioned above.

[0185] For the ISAC channel, the cluster path parameters of the convolutional Tx-RIS and RIS-Rx sub-channels and the RCS of the sensing target can be specifically represented by the formulas related to the RIS channel in the third and fourth specific implementation methods mentioned above.

[0186] Furthermore, based on the aforementioned general channel coefficient formula, the final channel coefficients are generated.

[0187] Step 12: Based on the preset method, couple path loss and shadow fading to generate the final channel impulse response. For the RIS channel, path loss needs to be generated by cascading Tx-RIS and RIS-Rx sub-channels; for the ISAC channel, path loss needs to be generated by cascading Tx-TAR and TAR-Rx sub-channels.

[0188] like Figure 4 As shown, embodiments of this application also provide a unified channel modeling apparatus for 6G, comprising:

[0189] The processing module 401 is used to construct a wireless communication scenario, configure the basic parameters and first parameters of the 6G channel, and obtain the large-scale parameters and path loss of the channel propagation; wherein, the first parameter includes at least one of the following: the size of the stationary region of the ultra-large-scale multiple-input multiple-output E-MIMO antenna array, the intelligent metasurface RIS parameter, the target parameter and the shared parameter, and the high-frequency band cluster sparse K factor.

[0190] The first generation module 402 is used to generate cluster path parameters of the 6G channel or a sub-channel of the 6G channel according to the basic parameters, the large-scale parameters and the channel type; wherein, when the channel type is an E-MIMO channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters of the 6G channel, and when the channel type is a RIS channel or a communication-aware integrated ISAC channel, the generated cluster path parameters are the cluster path parameters of each sub-channel of the 6G channel;

[0191] The second generation module 403 is used to generate a second parameter that characterizes the new characteristics of the 6G channel according to the first parameter. The second parameter is used to characterize at least one of the following new characteristics of the 6G channel: spatial non-stationarity and near-field characteristics of E-MIMO channel, intra-cluster sparsity of high-frequency channel, RIS radiation response cascade characteristics of RIS channel, and target RCS and sharing characteristics of ISAC channel.

[0192] The third generation module 404 is used to generate the channel coefficients of the 6G channel according to the second parameter and the cluster path parameter, wherein the channel coefficients are used to couple with the path loss and shadow fading to obtain the channel impulse response of the 6G channel.

[0193] Optionally, the second generation module 403 includes at least one of the following:

[0194] The first generation submodule is used to generate parameters S, which characterize the spatial nonstationarity of the E-MIMO channel, and parameters A, which characterize the near-field characteristics of the E-MIMO channel, based on the size of the stationary region.

[0195] The second generation submodule is used to generate a parameter I that characterizes the intra-cluster sparsity of the high-frequency channel based on the intra-cluster sparsity K factor of the high-frequency band.

[0196] The third generation submodule is used to generate scattering coefficients that characterize the RIS radiative response based on the RIS parameters, wherein the RIS parameters include at least one of RIS size, RIS position, RIS codebook and RIS area;

[0197] The fourth generation submodule is used to generate an RCS scattering coefficient characterizing the IACS sensing target characteristics and a sharing factor characterizing the IACS shared characteristics based on the shared parameters and the target parameters, wherein the target parameters include the number, position and velocity of the sensing targets, or the target parameters include the number and position of environmental targets.

[0198] Optionally, the parameter I corresponding to the m-th path within the n-th cluster is represented as:

[0199]

[0200] Among them, K R is the sparse K-factor within the high-frequency cluster, and M is the number of paths within the nth cluster.

[0201] Optionally, the third generation module 404 includes:

[0202] The fifth generation submodule is used to generate the cluster path complex gain of each path in each cluster according to the cluster path parameter, the second parameter corresponding to the channel type and the pre-configured general formula;

[0203] The sixth generation submodule is used to generate the channel coefficients of the 6G channel based on the generated cluster path complex gain;

[0204] The general formula is as follows:

[0205]

[0206] Where p represents the p-th transmitting antenna element, the p-th RIS element, or the p-th target equivalent scattering point, and q represents the q-th receiving antenna element, the q-th RIS element, or the q-th target equivalent scattering point; h q,p,n,m (t,τ) represents the cluster path complex gain, S q,n (t) represents the second parameter used to characterize the spatial non-stationary characteristics of the nth cluster in the E-MIMO channel when receiving the qth element, S p,n (t) represents the second parameter used to characterize the spatial non-stationary characteristics of the nth cluster in the E-MIMO channel when transmitting the pth element; A q,n,m(t) represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when receiving the q-th array element, A p,n,m (t) represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when transmitting to the p-th array element; I n,m The second parameter represents the intra-cluster sparsity used to characterize the high-frequency channel; when the channel type is an E-MIMO channel or a high-frequency channel, This represents the complex gain of the m-th resolvable multipath in the n-th cluster from the transmit antenna s to the receive antenna u, generated according to the 5G channel model.

[0207] Optionally, when the channel type is a RIS channel, the second parameter corresponding to the channel type is the scattering coefficient of the RIS radiation response characteristics, and the scattering coefficient of the RIS radiation response characteristics is the RIS radiation pattern;

[0208] Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type is the RCS scattering coefficient that characterizes the characteristics of the sensing target.

[0209] Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type includes a sharing factor that characterizes the sharing characteristics of ISAC.

[0210] Optionally, the fifth generation submodule includes:

[0211] The first processing unit is configured to, when the sensing channel is an ISAC channel, divide the cluster path parameters into shared cluster path parameters and non-shared cluster path parameters according to the sharing factor.

[0212] The first generation unit is used to generate the complex gain of the shared part of the cluster path according to the shared cluster path parameters, and to generate the complex gain of the non-shared part of the cluster path according to the non-shared cluster path parameters.

[0213] The second processing unit is used to superimpose the complex gain of the shared portion and the complex gain of the non-shared portion to obtain the cluster path complex gain.

[0214] Optionally, the sixth generation submodule includes:

[0215] The third processing unit is used to generate the cluster path complex gain of the 6G channel by convolving the cluster path complex gain of the first sub-channel with the cluster path complex gain of the second sub-channel when the channel type is a RIS channel or an ISAC channel.

[0216] The second generation unit is used to generate the channel coefficients of the 6G channel based on the cluster path complex gain of the 6G channel.

[0217] Wherein, when the channel type is RIS channel, the first sub-channel is the channel from the transmitting end Tx to RIS, and the second sub-channel is the channel from RIS to the receiving end Rx;

[0218] When the sensing channel is of type ISAC, the first sub-channel is the channel from Tx to the sensing target, and the second sub-channel is the channel from the sensing target to Rx.

[0219] The apparatus embodiments of this application are apparatuses corresponding to the embodiments of the methods described above. All implementation means in the method embodiments described above are applicable to the apparatus embodiments and can achieve the same technical effects. The apparatus provided in this application embodiments can implement all the method steps implemented in the method embodiments described above and can achieve the same technical effects. Therefore, the parts and beneficial effects that are the same as those in the method embodiments in this embodiment will not be described in detail here.

[0220] Embodiments of this application also provide an electronic device, including: a transceiver 510, a processor 500, a memory 520, and a program or instructions stored in the memory 520 and executable on the processor 500, wherein the program or instructions, when executed by the processor 500, implement the unified channel modeling method for 6G as described above.

[0221] The transceiver 510 is used to receive and send data under the control of the processor 500.

[0222] Among them, Figure 5 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 500 and memory represented by memory 520 together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 510 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. For different devices, the user interface 530 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0223] The processor 500 is responsible for managing the bus architecture and general processing, while the memory 520 can store the data used by the processor 500 when performing operations.

[0224] This application provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the steps in the unified channel modeling method for 6G described above and achieve the same technical effect. To avoid repetition, the details will not be repeated here.

[0225] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0226] Embodiments of this application also provide a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the steps in the unified channel modeling method for 6G as described above, and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0227] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A unified channel modeling method for 6G, characterized in that, include: A wireless communication scenario is constructed, configuring the basic parameters and first parameters of the 6G channel, and obtaining the large-scale parameters and path loss of channel propagation. The basic parameters include at least: scenario type, antenna parameters, network layout, outdoor-to-indoor O2I probability, base station location, user location, operating frequency band, and simulation time. The first parameters include at least one of the following: the stationary region size of the ultra-large-scale multi-input multi-output (E-MIMO) antenna array, intelligent metasurface (RIS) parameters, target parameters, shared parameters, and high-frequency band intra-cluster sparse K-factor. The large-scale parameters of channel propagation include: delay spread (DS), angle spread (AS), shadow fading (SF), and Rice K-factor. The target parameters include the number, location, and velocity of sensed targets, or the target parameters include the number and location of environmental targets. Based on the basic parameters, the large-scale parameters, and the channel type, cluster path parameters of the 6G channel or its sub-channels are generated; wherein, when the channel type is an E-MIMO channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters of the 6G channel; and when the channel type is a RIS channel or a communication-aware integrated ISAC channel, the generated cluster path parameters are the cluster path parameters of each sub-channel of the 6G channel. Based on the first parameter, a second parameter is generated to characterize the new characteristics of the 6G channel, wherein the second parameter is used to characterize at least one of the following new characteristics of the 6G channel: spatial non-stationarity and near-field characteristics of E-MIMO channel, intra-cluster sparsity of high-frequency channel, RIS radiation response cascade characteristics of RIS channel, and target RCS and sharing characteristics of ISAC channel. Based on the second parameter and the cluster path parameter, the channel coefficients of the 6G channel are generated, wherein the channel coefficients are used to couple with the path loss and shadow fading to obtain the channel impulse response of the 6G channel; The second parameter, which describes the new characteristics of the 6G channel, is generated based on the first parameter and includes at least one of the following: Based on the size of the stationary region, parameters S characterizing the spatial nonstationarity of the E-MIMO channel and parameters A characterizing the near-field characteristics of the E-MIMO channel are generated. Based on the intra-cluster sparsity K-factor of the high-frequency band, a parameter I characterizing the intra-cluster sparsity of the high-frequency channel is generated; Based on the RIS parameters, scattering coefficients characterizing the RIS radiative response are generated, wherein the RIS parameters include RIS size, RIS location, RIS codebook, and RIS area; Based on the shared parameters and the target parameters, an RCS scattering coefficient characterizing the target sensing characteristics of the ISAC and a shared factor characterizing the shared characteristics of the ISAC are generated.

2. The method according to claim 1, characterized in that, The parameter I corresponding to the m-th path within the n-th cluster is expressed as: in, is the sparse K-factor within the high-frequency cluster, and M is the number of paths within the nth cluster.

3. The method according to claim 1, characterized in that, Based on the second parameter and the cluster path parameter, the channel coefficients of the 6G channel are generated, including: Based on the cluster path parameters, the second parameter corresponding to the channel type, and the pre-configured general formula, the cluster path complex gain of each path in each cluster is generated; Based on the generated cluster path complex gain, the channel coefficients of the 6G channel are generated; The general formula is as follows: Where p represents the p-th transmitting antenna element, the p-th RIS element, or the p-th target equivalent scattering point, and q represents the q-th receiving antenna element, the q-th RIS element, or the q-th target equivalent scattering point; This represents the cluster path complex gain. This represents the second parameter used to characterize the spatial non-stationary characteristics of the nth cluster in an E-MIMO channel when receiving the qth element. This represents the second parameter used to characterize the spatial non-stationary characteristics of the nth cluster in an E-MIMO channel when transmitting the pth element; This represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when receiving the q-th array element. This represents the second parameter used to characterize the near-field characteristics of the m-th path in the n-th cluster when transmitting to the p-th array element; The second parameter represents the intra-cluster sparsity used to characterize the high-frequency channel; when the channel type is an E-MIMO channel or a high-frequency channel, This represents the complex gain of the m-th resolvable multipath in the n-th cluster from the transmit antenna s to the receive antenna u, generated according to the 5G channel model. In the case of a non-cascaded 6G channel, the q-th array element is the q-th receiving antenna element, and the p-th array element is the p-th transmitting antenna element. In the case of a RIS channel or an ISAC channel, in the first sub-channel, the p-th array element represents the p-th transmitting antenna element, and the q-th array element represents the q-th RIS element or the q-th target equivalent scattering point. In the second sub-channel, the p-th array element represents the p-th RIS element or the p-th target equivalent scattering point, and the q-th array element represents the q-th receiving antenna element.

4. The method according to claim 3, characterized in that: When the channel type is a RIS channel, the second parameter corresponding to the channel type is the scattering coefficient of the RIS radiation response characteristics, and the scattering coefficient of the RIS radiation response characteristics is the RIS radiation pattern; Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type is the RCS scattering coefficient that characterizes the characteristics of the sensing target. Alternatively, when the channel type is an ISAC channel, the second parameter corresponding to the channel type includes a sharing factor that characterizes the sharing characteristics of ISAC.

5. The method according to claim 4, characterized in that, Based on the cluster path parameters, the second parameter corresponding to the channel type, and pre-configured general parameters, the cluster path complex gain of each path in each cluster is generated, including: When the sensing channel is an ISAC channel, the cluster path parameters are divided into shared cluster path parameters and non-shared cluster path parameters according to the sharing factor. The complex gain of the shared portion of the cluster path is generated based on the shared cluster path parameters, and the complex gain of the non-shared portion of the cluster path is generated based on the non-shared cluster path parameters. The cluster path complex gain is obtained by superimposing the complex gain of the shared portion and the complex gain of the non-shared portion.

6. The method according to claim 3, characterized in that, Based on the generated cluster path complex gain, the channel coefficients of the 6G channel are generated, including: When the channel type is a RIS channel or an ISAC channel, the cluster path complex gain of the first sub-channel is convolved with the cluster path complex gain of the second sub-channel to generate the cluster path complex gain of the 6G channel. The channel coefficients of the 6G channel are generated based on the cluster path complex gain of the 6G channel. Wherein, when the channel type is RIS channel, the first sub-channel is the channel from the transmitting end Tx to RIS, and the second sub-channel is the channel from RIS to the receiving end Rx; When the sensing channel is of type ISAC, the first sub-channel is the channel from Tx to the sensing target, and the second sub-channel is the channel from the sensing target to Rx.

7. A unified channel modeling device for 6G, characterized in that, include: The processing module is used to construct a wireless communication scenario, configure the basic parameters and first parameters of the 6G channel, and obtain the large-scale parameters and path loss of channel propagation. The basic parameters include at least: scenario type, antenna parameters, network layout, outdoor-to-indoor O2I probability, base station location, user location, operating frequency band, and simulation time. The first parameters include at least one of the following: the stationary region size of the ultra-large-scale multi-input multi-output (E-MIMO) antenna array, intelligent metasurface (RIS) parameters, target parameters, shared parameters, and high-frequency cluster sparse K-factor. The large-scale parameters of channel propagation include: delay spread (DS), angle spread (AS), shadow fading (SF), and Rice K-factor. The target parameters include the number, location, and velocity of sensed targets, or the target parameters include the number and location of environmental targets. The first generation module is used to generate cluster path parameters of the 6G channel or a sub-channel of the 6G channel based on the basic parameters, the large-scale parameters, and the channel type; wherein, when the channel type is an E-MIMO channel or a high-frequency channel, the generated cluster path parameters are the cluster path parameters of the 6G channel, and when the channel type is a RIS channel or a communication-aware integrated ISAC channel, the generated cluster path parameters are the cluster path parameters of each sub-channel of the 6G channel; The second generation module is used to generate a second parameter that characterizes the new characteristics of the 6G channel based on the first parameter. The second parameter is used to characterize at least one of the following new characteristics of the 6G channel: spatial non-stationarity and near-field characteristics of the E-MIMO channel, intra-cluster sparsity of the high-frequency channel, RIS radiation response cascade characteristics of the RIS channel, and target RCS and sharing characteristics of the ISAC channel. The third generation module is used to generate the channel coefficients of the 6G channel according to the second parameter and the cluster path parameter, wherein the channel coefficients are used to couple with the path loss and shadow fading to obtain the channel impulse response of the 6G channel; The second generation module includes at least one of the following: The first generation submodule is used to generate parameters S, which characterize the spatial nonstationarity of the E-MIMO channel, and parameters A, which characterize the near-field characteristics of the E-MIMO channel, based on the size of the stationary region. The second generation submodule is used to generate a parameter I that characterizes the intra-cluster sparsity of the high-frequency channel based on the intra-cluster sparsity K factor of the high-frequency band. The third generation submodule is used to generate scattering coefficients that characterize the RIS radiative response based on the RIS parameters, wherein the RIS parameters include RIS size, RIS position, RIS codebook and RIS area; The fourth generation submodule is used to generate the RCS scattering coefficient characterizing the IACS sensing target characteristics and the sharing factor characterizing the IACS shared characteristics based on the shared parameters and the target parameters.

8. An electronic device comprising a transceiver, a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the unified channel modeling method for 6G as described in any one of claims 1 to 6.

9. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or instructions are executed by the processor, they implement the unified channel modeling method for 6G as described in any one of claims 1 to 6.

Images