Sparse channel model modeling method and device, equipment and storage medium

Abstract

A sparse channel model modeling method, apparatus and device, and a storage medium, the method comprising: determining an intra-cluster power factor according to a large-scale parameter of a channel and a cluster delay of the channel, the intra-cluster power factor being related to power distribution of an intra-cluster multipath component; generating small-scale parameters according to the large-scale parameters; generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor; and constructing a channel model of the channel according to the channel coefficient. According to the sparse channel model modeling method provided by the invention, the calculation of the channel coefficient is adjusted through the intra-cluster power distribution factor, so that the constructed model can accurately measure the sparsity of the channel, and the accuracy of the channel model is improved; the problem that in the prior art, the power of a multipath equally-divided cluster in a channel model based on the geometric statistics principle does not conform to the rule of a real channel, so that the model cannot accurately report the sparsity of the channel on the earth surface is solved.

Description

Technical Field

[0001] The present application relates to the field of mobile communication technology, and in particular to a sparse channel modeling method, device, equipment and storage medium. Background Art

[0002] With the rapid growth of communication systems' demand for radio spectrum, the frequency gradually increases, the wavelength becomes shorter, and the diffraction and penetration losses increase, resulting in significant channel sparsity. Channel sparsity can be used to selectively suppress interference and noise that are not within the beam pointing range to reduce the impact of interference on the received signal, which helps to improve the system's signal-to-noise ratio and capacity.

[0003] Statistical channel models are simple and easy to use, especially the geometry-based stochastic model (GBSM), which is widely adopted by standardization organizations as the mainstream channel model. In the definition of GBSM, the channel is generated by the joint contribution of multipath based on geometric principles and small-scale statistical channel parameters obtained by measurement. However, in the channel model based on geometric statistics, the multipath within the cluster divides the power of the cluster equally, which is inconsistent with the law of the real channel, making the model unable to accurately characterize the channel sparsity, resulting in poor channel sparsity capability. Summary of the invention

[0004] At least one embodiment of the present application provides a sparse channel modeling method, apparatus, device and storage medium, which are used to solve the problem in the prior art that the multipath within the cluster in the channel model based on the principle of geometric statistics divides the power of the cluster equally, which is inconsistent with the law of the real channel, making the model unable to accurately characterize the channel sparsity.

[0005] In order to solve the above technical problems, this application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a sparse channel modeling method, including:

[0007] Determining an intra-cluster power factor according to a large-scale parameter of a channel and a cluster delay of the channel, wherein the intra-cluster power factor is related to a power distribution of a multipath component within the cluster;

[0008] Generate small-scale parameters according to the large-scale parameters;

[0009] generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor;

[0010] A channel model of the channel is constructed according to the channel coefficients.

[0011] Further, determining the intra-cluster power factor according to the generated large-scale parameter of the channel and the cluster delay of the channel includes:

[0012] Normalizing the cluster delay;

[0013] Constructing a mathematical model of the intra-cluster power factor according to the normalized cluster delay and the delay spread;

[0014] Calculating the coefficients of the mathematical model by statistical analysis of the measurement results;

[0015] The intra-cluster power factor is determined according to the mathematical model and the coefficients.

[0016] Furthermore, the intra-cluster power factor is a ratio of a power of a first multipath component to a power of a second multipath component; the first multipath component is the first multipath component in the cluster, and the second multipath component is the sum of all multipath components in the cluster except the first multipath component.

[0017] Furthermore, the mathematical model is:

[0018]

[0019] Where ICP is the intra-cluster power factor, τ n is the cluster delay after normalization; DS is the delay spread; a and b are the coefficients.

[0020] Further, generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor includes:

[0021] Determining the angle of each multipath according to the multipath angle offset and the angle of the cluster in the small-scale parameter;

[0022] The channel coefficient is determined according to the angle of each multipath and the intra-cluster power factor.

[0023] Furthermore, before determining the intra-cluster power factor according to the generated large-scale parameter of the channel and the cluster delay of the channel, the method includes:

[0024] Set application scenarios, network layout, and antenna parameters;

[0025] Configure propagation conditions, where the propagation conditions include line-of-sight propagation conditions and non-line-of-sight propagation conditions;

[0026] According to the propagation conditions, the application scenario, the network layout and the antenna parameters, the path loss of the link is calculated and the large-scale parameter is generated.

[0027] Further, constructing a channel model of the channel according to the channel coefficients includes:

[0028] The channel coefficient is coupled with the path loss and shadow fading in the large-scale parameter to obtain an impulse response function of the channel;

[0029] A channel model of the channel is constructed according to the impulse response function.

[0030] In a second aspect, an embodiment of the present application provides a sparse channel modeling device, including:

[0031] Determining an intra-cluster power factor according to a large-scale parameter of a channel and a cluster delay of the channel, wherein the intra-cluster power factor is related to a power distribution of a multipath component within the cluster;

[0032] Generate small-scale parameters according to the large-scale parameters;

[0033] generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor;

[0034] A channel model of the channel is constructed according to the channel coefficients.

[0035] In a third aspect, an embodiment of the present application provides a sparse channel model modeling device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program implements the steps of the sparse channel model modeling method as described above when executed by the processor.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, the steps of the sparse channel modeling method as described above are implemented.

[0037] Compared with the prior art, the sparse channel modeling method provided in the embodiment of the present application determines the intra-cluster power factor used to characterize the power distribution of the multipath components within the cluster based on the large-scale parameters of the channel and the cluster delay of the channel; and adjusts the calculation of the channel coefficient through the intra-cluster power distribution factor, so that the constructed model can accurately characterize the sparsity of the channel and improve the accuracy of the channel model. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0039] Figure 1 A schematic diagram of an application scenario of an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the steps of a sparse channel modeling method according to an embodiment of the present application;

[0041] Figure 3 A schematic diagram of a flow chart of a sparse channel modeling method according to an embodiment of the present application;

[0042] Figure 4 It is the channel sparsity characterization level of the channel sparsity model based on the intra-cluster power factor model in the urban macro line of sight (UMa-LOS) scenario of the embodiment of the present application;

[0043] Figure 5 The channel sparsity characterization level of the channel sparsity model based on the intra-cluster power factor model of the embodiment of the present application in the urban macro non line of sight (UMa-NLOS) scenario;

[0044] Figure 6 The channel sparsity characterization level of the channel sparsity model based on the intra-cluster power factor model in an embodiment of the present application in an indoor scenario;

[0045] Figure 7 This is a module diagram of a sparse channel modeling device according to an embodiment of the present application. DETAILED DESCRIPTION

[0046] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0047] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable in appropriate circumstances, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. "And / or" in the specification and claims represents at least one of the connected objects.

[0048] The technology described herein is not limited to NR systems and Long Time Evolution (LTE) / LTE-Advanced (LTE-A) systems, and can also be used for various wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency Division Multiple Access (SC-FDMA) and other systems. The terms "system" and "network" are often used interchangeably. A CDMA system can implement radio technologies such as CDMA2000, Universal Terrestrial Radio Access (UTRA). UTRA includes Wideband Code Division Multiple Access (WCDMA) and other CDMA variants. A TDMA system can implement radio technologies such as Global System for Mobile Communication (GSM). OFDMA systems can implement radio technologies such as Ultra Mobile Broadband (UMB), Evolution-UTRA (E-UTRA), IEEE 802.21 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, etc. UTRA and E-UTRA are parts of Universal Mobile Telecommunications System (UMTS). LTE and more advanced LTE (such as LTE-A) are new UMTS versions that use E-UTRA. UTRA, E-UTRA, UMTS, LTE, LTE-A, and GSM are described in documents from an organization named "3rd Generation Partnership Project" (3GPP). CDMA2000 and UMB are described in documents from an organization named "3rd Generation Partnership Project 2" (3GPP2).The techniques described herein may be used for the systems and radio technologies mentioned above as well as for other systems and radio technologies. However, the following description describes an NR system for example purposes and uses NR terminology in much of the following description, although the techniques may also be applicable to applications other than NR system applications.

[0049] The following description provides examples and does not limit the scope, applicability, or configuration set forth in the claims. Changes may be made to the functions and arrangements of the elements discussed without departing from the spirit and scope of the present disclosure. Various examples may appropriately omit, replace, or add various procedures or components. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0050] See also Figure 1 , Figure 1 A block diagram of a wireless communication system applicable to an embodiment of the present application is shown. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 may also be referred to as a user terminal or user equipment (UE, User Equipment), and the terminal 11 may be a terminal side device such as a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a personal digital assistant (Personal Digital Assistant, PDA), a mobile Internet device (Mobile Internet Device, MID), a wearable device (Wearable Device) or a vehicle-mounted device. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network device 12 can be a base station and / or a core network element, wherein the above-mentioned base station can be a base station of 5G and later versions (for example: gNB, 5G NR NB, etc.), or a base station in other communication systems (for example: eNB, WLAN access point, or other access point, etc.), wherein the base station can be called a node B, an evolved node B, an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service set (Basic Service Set, BSS), an extended service set (Extended Service Set, ESS), a B node, an evolved B node (eNB), a home B node, a home evolved B node, a WLAN access point, a WiFi node or some other suitable term in the field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is taken as an example, but the specific type of the base station is not limited.

[0051] The base station can communicate with the terminal 11 under the control of a base station controller, and in various examples, the base station controller can be part of the core network or certain base stations. Some base stations can communicate control information or user data with the core network via a backhaul. In some examples, some of these base stations can communicate with each other directly or indirectly via a backhaul link, which can be a wired or wireless communication link. The wireless communication system can support operations on multiple carriers (waveform signals of different frequencies). A multi-carrier transmitter can transmit modulated signals on these multiple carriers at the same time. For example, each communication link can be a multi-carrier signal modulated according to various radio technologies. Each modulated signal can be sent on a different carrier and can carry control information (e.g., reference signals, control channels, etc.), overhead information, data, etc.

[0052] The base station can communicate wirelessly with the terminal 11 via one or more access point antennas. Each base station can provide communication coverage for its respective coverage area. The coverage area of ​​the access point can be divided into sectors that constitute only a portion of the coverage area. The wireless communication system may include different types of base stations (e.g., macro base stations, micro base stations, or micro-micro base stations). The base station may also utilize different radio technologies, such as cellular or WLAN radio access technologies. The base station may be associated with the same or different access networks or operator deployments. The coverage areas of different base stations (including coverage areas of the same or different types of base stations, coverage areas utilizing the same or different radio technologies, or coverage areas belonging to the same or different access networks) may overlap.

[0053] The communication link in the wireless communication system may include an uplink for carrying an uplink (UL) transmission (e.g., from the terminal 11 to the network device 12), or a downlink for carrying a downlink (DL) transmission (e.g., from the network device 12 to the terminal 11). UL transmission may also be referred to as reverse link transmission, and DL transmission may also be referred to as forward link transmission. Downlink transmission may be performed using a licensed frequency band, an unlicensed frequency band, or both. Similarly, uplink transmission may be performed using a licensed frequency band, an unlicensed frequency band, or both.

[0054] As described in the background technology, in the prior art, the terminal may encounter unexpected cell inaccessibility, slice access failure, or slice QoS fallback after cell reselection or access, which seriously affects the user experience. In order to solve at least one of the above problems, the embodiment of the present application provides a XXXX method, which can reduce or avoid the occurrence of the above situations, improve communication efficiency, and improve user experience.

[0055] Please refer to Figure 2 , a sparse channel modeling method provided in an embodiment of the present application includes the following steps:

[0056] Step 201, determining an intra-cluster power factor according to a large-scale parameter of a channel and a cluster delay of the channel, wherein the intra-cluster power factor is related to a power distribution of a multipath component within the cluster;

[0057] Step 202, generating a small-scale parameter according to the large-scale parameter;

[0058] Step 203, generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor;

[0059] Step 204: construct a channel model of the channel according to the channel coefficients.

[0060] Optionally, the large-scale parameter includes at least one of the following: delay spread, angle spread, shadow fading, and Rice fading channel K factor;

[0061] The small-scale parameters include at least one of the following: cluster delay, cluster power, cross-polarization ratio, multipath information, cluster departure angle, and cluster arrival angle of the channel.

[0062] The sparse channel modeling method provided in the embodiment of the present application determines the intra-cluster power factor used to characterize the power distribution of the multipath components within the cluster based on the large-scale parameters of the channel and the cluster delay of the channel; and adjusts the calculation of the channel coefficient through the intra-cluster power distribution factor, so that the constructed model can accurately characterize the sparsity of the channel and improve the accuracy of the channel model.

[0063] In the embodiments of the present application, it is preferably applicable to new mid-frequency bands above 6 GHz.

[0064] Optionally, determining the intra-cluster power factor according to the generated large-scale parameter of the channel and the cluster delay of the channel includes:

[0065] Normalizing the cluster delay;

[0066] Constructing a mathematical model of the intra-cluster power factor according to the normalized cluster delay and the delay spread;

[0067] Calculating the coefficients of the mathematical model by statistical analysis of the measurement results;

[0068] The intra-cluster power factor is determined according to the mathematical model and the coefficients.

[0069] In a feasible embodiment of the present application, the cluster delay is normalized by the following formula:

[0070] τ n =τ′ n -min(τ′ n)

[0071] Among them, τ′ n is the true value of the cluster delay, τ n is the normalized cluster delay.

[0072] Optionally, the intra-cluster power factor is a ratio of a power of a first multipath component to a power of a second multipath component; the first multipath component is the first multipath component in the cluster, and the second multipath component is the sum of all multipath components in the cluster except the first multipath component.

[0073] It should be noted that the first multipath component is the multipath component with the smallest angle offset.

[0074] The sparse channel modeling method provided in the embodiment of the present application obtains a more accurate cluster delay by normalizing the cluster delay, and further can obtain a more accurate intra-cluster power factor.

[0075] Optionally, the mathematical model is:

[0076]

[0077] Where ICP is the intra-cluster power factor, τ n is the cluster delay after normalization; DS is the delay spread; a and b are the coefficients.

[0078] Optionally, the coefficient is calculated by statistical analysis of the measurement results.

[0079] In a feasible embodiment of the present application, through statistical analysis of the measurement results, the reference values ​​of the coefficients are obtained as shown in Table 1:

[0080] Table 1 Coefficients of ICP model

[0081] Scenario a b LOS -0.04 2.8 NLOS -0.04 1.7

[0082] Optionally, generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor includes:

[0083] Determining the angle of each multipath according to the multipath angle offset and the angle of the cluster in the small-scale parameter;

[0084] The channel coefficient is determined according to the angle of each multipath and the intra-cluster power factor.

[0085] It should be noted that within each cluster there is a dominant path that contains most of the power of the cluster;

[0086] The path with the smallest multipath angle is determined as the dominant path, that is, the path with the smallest angle deviation in the multipath is the first multipath (the path with m=1).

[0087] In a feasible embodiment of the present application, the calculation formula of the channel coefficient is:

[0088]

[0089] Where n = 1, 2, N, represents the number of clusters; m = 2, ... M, represents the number of multipaths;

[0090] (·) T Represents matrix transpose;

[0091] λ0 represents the wavelength of the carrier;

[0092] P n is the power of the nth cluster in the channel;

[0093] ICP n is the intra-cluster power factor of the nth cluster in the channel;

[0094] θ n,1,ZOA ,φ n,1,AOA ,θ n,1,ZOD ,φ n,1,AOD They represent the first multipath of the nth cluster in R x End and T x The vertical angle of arrival, horizontal angle of arrival, vertical angle of departure and horizontal angle of departure of the end;

[0095] θ n,m,ZOA ,φ n,m,AOA ,θ n,m,ZOD ,φ n,m,AOD They represent the mth multipath of the nth cluster in R x End and T x The vertical angle of arrival, horizontal angle of arrival, vertical angle of departure and horizontal angle of departure of the end;

[0096] F rx,u,θ , F tx,s,φ , F tx,s,θ , F tx,s,φ Respectively represent R x Terminal antenna and T x The radiation pattern of the end antenna in the θ and φ directions;

[0097] and represents the random initial phase of the first multipath of the nth cluster under the combination of four planned directions (θθ, θφ, φθ, φφ);

[0098] represents the random initial phase of the mth multipath of the nth cluster under the combination of four polarization directions (θθ, θφ, φθ, φφ);

[0099] K n,mrepresents the cross-polarization ratios (XPRs) of the m-th multipath of the n-th cluster;

[0100] and Indicates that the mth multipath of the nth cluster is in R x End and T x The unit spherical coordinate vector of the end;

[0101] and YesR x End and T x The position vector of the end antenna;

[0102] represents the Doppler term of the mth multipath of the nth cluster in the channel.

[0103] In the embodiment of the present application, the power of each multipath is interfered by the power factor within the cluster;

[0104] Specifically, by determining the dominant path, the powers of different paths within the cluster are adjusted differently.

[0105] The sparse channel modeling method of the embodiment of the present application can improve the channel model's ability to characterize channel sparsity by making different adjustments to the powers of different paths, thereby making the channel model more accurate and reliable.

[0106] Optionally, before determining the intra-cluster power factor according to the generated large-scale parameter of the channel and the cluster delay of the channel, the method includes:

[0107] Set application scenarios, network layout, and antenna parameters;

[0108] Configure propagation conditions, where the propagation conditions include line-of-sight propagation conditions and non-line-of-sight propagation conditions;

[0109] According to the propagation conditions, the application scenario, the network layout and the antenna parameters, the path loss of the link is calculated and the large-scale parameter is generated.

[0110] It should be noted that the link is a link between a base station and a user terminal.

[0111] Optionally, the application scenarios include: urban macro cells (Urban Macro, UMa) and indoor scenarios.

[0112] Optionally, the network layout includes:

[0113] Number of base stations, number of users, operating frequency bands and bandwidth.

[0114] Optionally, the antenna parameters include:

[0115] The antenna array settings at the base station, the base station antenna positions, the number of base station antennas, the antenna array settings at the user end, the user antenna positions, and the number of user end antennas.

[0116] In the embodiment of the present application, the scenario type is set to UMa, the number of base stations and users is 1, the operating frequency band is 13 GHz, the bandwidth is 400 MHz, etc., the base station and the user use MIMO array antennas, where the number of antennas at the base station end is 128, and the number of antennas at the user end is 64;

[0117] Specifically, the MIMO array antenna is: the base station uses a uniform planar antenna array (UPA); the user end uses an omnidirectional antenna array (ODA).

[0118] Optionally, constructing a channel model of the channel according to the channel coefficient includes:

[0119] The channel coefficient is coupled with the path loss and shadow fading in the large-scale parameter to obtain an impulse response function of the channel;

[0120] A channel model of the channel is constructed according to the impulse response function.

[0121] like Figure 3 As shown, in one embodiment of the present application, the process of the sparse channel modeling method is as follows:

[0122] S1. Set the scene, network layout, antenna parameters, specific embodiment 1 sets the scene type to UMa, the number of base stations and users is 1, the operating frequency band is 13GHz, the bandwidth is 400MHz, etc., the base station and the user use MIMO array antennas, where the number of base station antennas is 128 and the number of user antennas is 64;

[0123] S2. Configure the line-of-sight LOS propagation conditions according to the antenna parameters;

[0124] S3. Calculate the path loss of the link between the base station and the terminal;

[0125] S4. Generate large-scale parameters of the channel according to the set scenario and working frequency band, including delay spread, angle spread, shadow fading, and Rice fading channel K factor;

[0126] S5. Generate a cluster delay of a subchannel according to the delay spread and the delay scale determined according to the scenario;

[0127] S6. Generate intra-cluster power factor using delay spread and cluster delay;

[0128] S7. Generate cluster power according to the delay spread, delay scale and the power factor within the cluster;

[0129] S8. Generate the angles (departure angle and arrival angle) of the cluster according to the angle expansion and the angle scale parameter determined according to the scene;

[0130] S9. Randomly generate the angle of each multipath component according to the cluster angle and multipath angle offset;

[0131] S10. Generate a cross-polarization ratio coefficient for each multipath according to a random variable generation method of a log-normal distribution;

[0132] S11. Configure a random initial phase for each multipath, and the initial phase is uniformly configured between [-π, π];

[0133] S12. Couple the channel coefficient with path loss and shadow fading to generate a final channel coefficient.

[0134] like Figure 4 As shown, the channel sparsity characterization level of the channel sparsity model based on the intra-cluster power factor model in the Uma-LOS scenario;

[0135] like Figure 5 As shown, the channel sparsity characterization level of the channel sparsity model based on the intra-cluster power factor model in the Uma-NLOS scenario;

[0136] like Figure 6 As shown, the channel sparsity characterization level of the channel sparsity model based on the intra-cluster power factor model in indoor scenarios.

[0137] The above describes various methods of the embodiments of the present application. The following further provides devices for implementing the above methods.

[0138] like Figure 7 As shown, the embodiment of the present application also provides a sparse channel modeling device 700, including:

[0139] A determination module 701 is used to determine an intra-cluster power factor according to a large-scale parameter of a channel and a cluster delay of the channel, wherein the intra-cluster power factor is related to a power distribution of a multipath component within the cluster;

[0140] A first generating module 702, configured to generate a small scale parameter according to the large scale parameter;

[0141] A second generating module 703 is used to generate a channel coefficient according to the small-scale parameter and the intra-cluster power factor;

[0142] The construction module 704 is used to construct a channel model of the channel according to the channel coefficients.

[0143] The sparse channel modeling device provided in the embodiment of the present application determines the intra-cluster power factor used to characterize the power distribution of the multipath components within the cluster based on the large-scale parameters of the channel and the cluster delay of the channel; and adjusts the calculation of the channel coefficient through the intra-cluster power distribution factor, so that the constructed model can accurately characterize the sparsity of the channel and improve the accuracy of the channel model.

[0144] An embodiment of the present application also provides a sparse channel modeling device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program implements the steps of the method described above when executed by the processor.

[0145] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above-mentioned sparse channel modeling method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0146] An embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the various processes of the above-mentioned sparse channel modeling method embodiment and can achieve the same technical effect. To avoid repetition, they are not repeated here.

[0147] It should be noted that, in this article, the terms "include", "include" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0148] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0149] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A sparse channel modeling method, characterized in that: include: Determining an intra-cluster power factor according to a large-scale parameter of a channel and a cluster delay of the channel, wherein the intra-cluster power factor is related to a power distribution of a multipath component within the cluster; Generate small-scale parameters according to the large-scale parameters; generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor; A channel model of the channel is constructed according to the channel coefficients.

2. The sparse channel modeling method according to claim 1, characterized in that: Determining an intra-cluster power factor according to a generated large-scale parameter of the channel and a cluster delay of the channel includes: Normalizing the cluster delay; Constructing a mathematical model of the intra-cluster power factor according to the normalized cluster delay and the delay spread; Calculating the coefficients of the mathematical model by statistical analysis of the measurement results; The intra-cluster power factor is determined according to the mathematical model and the coefficients.

3. The sparse channel modeling method according to claim 1, characterized in that: The intra-cluster power factor is the ratio of the power of the first multipath component to the power of the second multipath component; the first multipath component is the first multipath component in the cluster, and the second multipath component is the sum of all multipath components in the cluster except the first multipath component.

4. The sparse channel modeling method according to claim 2, characterized in that: The mathematical model is: Where ICR is the intra-cluster power factor, τ n is the cluster delay after normalization; DS is the delay spread; a and b are the coefficients.

5. The sparse channel modeling method according to claim 1, characterized in that: Generating a channel coefficient according to the small-scale parameter and the intra-cluster power factor includes: Determining the angle of each multipath according to the multipath angle offset and the angle of the cluster in the small-scale parameter; The channel coefficient is determined according to the angle of each multipath and the intra-cluster power factor.

6. The sparse channel modeling method according to claim 1, characterized in that: Before determining the intra-cluster power factor according to the generated large-scale parameters of the channel and the cluster delay of the channel, the method includes: Set application scenarios, network layout, and antenna parameters; Configure propagation conditions, where the propagation conditions include line-of-sight propagation conditions and non-line-of-sight propagation conditions; According to the propagation conditions, the application scenario, the network layout and the antenna parameters, the path loss of the link is calculated and the large-scale parameter is generated.

7. The sparse channel modeling method according to claim 6, characterized in that: Constructing a channel model of the channel according to the channel coefficients, comprising: The channel coefficient is coupled with the path loss and shadow fading in the large-scale parameter to obtain an impulse response function of the channel; A channel model of the channel is constructed according to the impulse response function.

8. A sparse channel modeling device, characterized in that: include: A determination module, configured to determine an intra-cluster power factor according to a large-scale parameter of a channel and a cluster delay of the channel, wherein the intra-cluster power factor is related to a power distribution of a multipath component within the cluster; A first generating module, used to generate a small-scale parameter according to the large-scale parameter; A second generating module, configured to generate a channel coefficient according to the small-scale parameter and the intra-cluster power factor; A construction module is used to construct a channel model of the channel according to the channel coefficients.

9. A sparse channel modeling device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the sparse channel modeling method according to any one of claims 1 to 7 are implemented.

Images