A terahertz MIMO unmanned aerial vehicle non-stationary channel modeling method

Abstract

The application discloses a terahertz MIMO unmanned aerial vehicle non-stationary channel modeling method, a double sphere with the position of a transmitting end unmanned aerial vehicle and a receiving end unmanned aerial vehicle as the center is used to represent a near distance scatterer, a three-dimensional ellipsoid with the position of the transmitting end unmanned aerial vehicle and the receiving end unmanned aerial vehicle as the center is used to represent a far distance scatterer, according to the A2A channel characteristics, the SB component and the DB component generated by the LoS component, the near scatterer and the far scatterer are combined to describe the multipath component distribution around the transmitting end and the receiving end, a three-dimensional ellipsoid and double sphere composite geometric model of the unmanned aerial vehicle broadband non-stationary A2A channel is established, and based on the geometric random model, the expression of the time-varying time-frequency autocorrelation function and the Doppler power spectral density of the composite geometric model is derived, and the terahertz MIMO unmanned aerial vehicle non-stationary channel model is established. The model has universality and adaptability, and can be used for simulating various non-stationary unmanned aerial vehicle intercommunication scenes.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method for modeling non-stationary channels of terahertz MIMO unmanned aerial vehicles. Background Technology

[0002] Following the commercialization of fifth-generation (5G) mobile communication network services, the global industry has begun research on sixth-generation (6G) networks. Terahertz frequency band (95GHz-3THz), a wireless communication technology geared towards sixth-generation mobile communication network services, has become a hot research topic in academia. As an extension of microwaves and millimeter waves, terahertz waves offer significantly greater bandwidth than millimeter waves. Terahertz communication can achieve wireless transmission speeds of up to 10GB / s, hundreds of times faster than current ultra-wideband technologies, making it an excellent broadband information carrier. Furthermore, compared to visible light and infrared, terahertz waves possess extremely high directionality and strong cloud and fog penetration capabilities, enabling high-bandwidth, highly secure satellite communication. Therefore, research on terahertz channels is a key area of ​​focus in order to fully understand the propagation characteristics of terahertz waves.

[0003] In wireless mobile communication, all information must be transmitted through wireless channels. The propagation characteristics of radio waves and wireless channel modeling, as the sole means of wireless system planning, are crucial foundations and prerequisites for communication system architecture design and new technology verification. Channel models are indispensable in wireless communication system design and performance evaluation.

[0004] In recent years, air-to-air (A2A) communication has attracted widespread attention due to its potential in numerous applications, including high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs). A2A networks can serve as sensor platforms to provide remote location coverage in emergency situations such as network disruptions, or as low-cost infrastructure to provide traffic offloading in congested areas such as stadiums.

[0005] UAVs, with their high mobility and low cost, have seen numerous new applications in both civilian and commercial sectors. Typical applications include weather monitoring, forest fire detection, traffic control, cargo transportation, emergency search and rescue, and communication relay. Compared to HAPs, low-altitude UAVs offer several key advantages: First, UAVs are inexpensive and easy to deploy, making them more suitable for sudden and time-constrained missions; second, UAVs can generally establish line-of-sight (LoS) communication links, thus achieving reliable communication performance; and third, UAV mobility can be enhanced through dynamic state adjustments, such as combining adaptive communication with UAV mobility control.

[0006] Unlike traditional vehicle-to-vehicle (V2V) and mobile-to-mobile (M2M) communication methods, UAVs (Unmanned Aerial Vehicles) operate in three-dimensional space, encompassing both horizontal and vertical domains, and fly at relatively low altitudes, requiring consideration of scattering components from buildings, roadside obstacles, and other factors. The mobility of airborne base stations and ground operators leads to significant temporal and spatial variations in non-stationary channels. Severe non-stationarity can cause numerous coverage and connectivity problems. Therefore, traditional channel models cannot be directly applied to UAV communication scenarios. To design and evaluate UAV communication systems and ensure communication security and high reliability, establishing a channel model that accurately captures the characteristics of UAV communication is essential.

[0007] Current research on terahertz band multiple-input multiple-output (MIMO) channel modeling mainly focuses on indoor and microsystem scenarios, leveraging its suitability for short-range transmission. Outdoor environments are less considered. Even when outdoor models are considered, they are limited to point-to-point Loss of Light (LoS) propagation between base stations and users, with limited analysis of channel characteristics in complex scenarios. However, there is significant untapped potential for terahertz wave applications. Since terahertz waves can propagate virtually without loss in outer space, ultra-long-distance transmission can be achieved with extremely low power. If future terahertz antenna systems can be miniaturized and planarized, terahertz communication systems can be deployed on space-based and airborne platforms such as satellites, drones, and airships as wireless communication and relay devices. This could enable high-speed wireless communication scenarios between satellite constellations, between space and ground, and across distances exceeding 1,000 kilometers, realizing future integrated air-space-ground-sea communication.

[0008] Currently, based on existing research on UAV communication, UAV-based channel models can be categorized into deterministic models, non-geometrical stochastic models (NGSM), and geometry-based stochastic models (GBSM). Deterministic models offer high accuracy but require extensive data to characterize specific propagation environments. These models can be used to study large-scale fading effects in the channel, and accurate propagation conditions can provide coverage analysis and indicate the optimal UAV position. NGSM, on the other hand, is based on measurement data and has a certain degree of complexity. Unlike the aforementioned modeling methods, GBSM offers high accuracy and low complexity, making it suitable for characterizing channels in a three-dimensional plane with fewer environmental parameters. It can be widely used to simulate various wireless channels.

[0009] Existing technologies for data acquisition and processing of terahertz wave propagation channels in indoor and micro-systems mostly use fixed transmitters and receivers, without considering the non-stationarity of the channel in dynamic environments. Therefore, there is no research on the time-varying characteristics of the channel, such as time correlation, spatial correlation, and Doppler spectrum.

[0010] Existing terahertz channel models and analyses derived from GBSM simulations are primarily focused on 5G / B5G frequency bands, with limited research on the terahertz band. Because channel characteristics change with increasing frequency, existing models cannot meet the requirements of the terahertz band. Furthermore, current work is limited to air-to-ground (A2G) channel modeling. Compared to A2G, A2A includes both horizontal and vertical directions, allowing both Tx and Rx to move in 3D space. Therefore, A2G movement models cannot directly represent the motion behavior of UAV terminals in A2A communication scenarios. Summary of the Invention

[0011] To address the current lack of an A2A nonstationary model based on UAVs in terahertz MIMO channel modeling, this invention proposes a terahertz MIMO UAV nonstationary channel modeling method based on the GBSM modeling method to model and analyze the characteristics of terahertz MIMO channels. This method not only considers more complex scenarios and more practical mobility models, but also analyzes the impact of more parameters on channel characteristics.

[0012] To achieve the above objectives, the present invention provides the following technical solution:

[0013] On one hand, this invention provides a method for modeling the non-stationary channel of a terahertz MIMO UAV. It uses a double sphere centered at the UAV's location at the transceiver end to represent near-range scatterers, and a three-dimensional ellipsoid centered at the UAV's location at the transceiver end to represent far-range scatterers. Based on the characteristics of the A2A channel, and combining the LoS component, the SB component, and the DB component generated by the near and far scatterers respectively, it describes the distribution of multipath components around the transceiver end, establishing a composite geometric model of the three-dimensional ellipsoid and double sphere for the broadband non-stationary A2A channel of the UAV. Based on the geometric stochastic model, it derives the expressions for the time-varying time-frequency autocorrelation function and Doppler power spectral density of the composite geometric model, establishing a non-stationary channel model for the terahertz MIMO UAV.

[0014] Furthermore, the transceiver drones each contain A q and A p With one omnidirectional antenna element, the A2A channel is represented as follows:

[0015]

[0016] The p-th T of the MIMO channel x Antenna element and the qth R x The complex channel impulse response between antenna elements is expressed as:

[0017]

[0018] in, For direct path channel impulse response, For single-reflection path channel impulse response, This is the impulse response of the dual-reflection path channel.

[0019] Furthermore, the formula for the channel impulse response of the direct path is:

[0020]

[0021] Where K is the Rice factor, ε pq f is the distance between antenna element p and antenna element q. T,max ,f R,max These are the maximum Doppler frequencies of the transmitter and receiver, respectively, Φ LoS ,Ψ LoS These are the direction vectors of the Loss paths at the transmitting and receiving ends, respectively.

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] Φ LoS =[cosα LoS cosβ LoS sinα LoS cosβ LoS sinβ LoS ]

[0029] Ψ LoS =-Φ LoS

[0030] The impulse response formula for a single-reflection path channel is:

[0031]

[0032]

[0033]

[0034] Among them, near-scattering body:

[0035]

[0036]

[0037]

[0038]

[0039] Far-scattering body:

[0040]

[0041]

[0042]

[0043]

[0044] The impulse response formula for a dual-reflection path channel is:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] Furthermore, the horizontal angle of the scattering angle follows a VM distribution, as follows:

[0051]

[0052] Where I0 is the zeroth-order modified Bessel function of the first kind, and σ controls the distribution of the scatterer near the mean.

[0053] Furthermore, the vertical angle of the scattering angle follows a cosine distribution with upper and lower limits, as follows:

[0054]

[0055] Where, β m This represents the maximum value of the vertical scattering angle.

[0056] Furthermore, the time-varying time-frequency autocorrelation function is expressed as:

[0057]

[0058] The LoS path portion is represented as follows:

[0059]

[0060]

[0061]

[0062]

[0063] The single reflection path is represented as:

[0064]

[0065]

[0066]

[0067]

[0068] The dual reflection path portion is represented as:

[0069]

[0070]

[0071]

[0072]

[0073] Furthermore, the expression for the Doppler power spectral density is:

[0074]

[0075]

[0076] On the other hand, the present invention also provides the application of the above-mentioned terahertz MIMO UAV non-stationary channel modeling method in non-stationary UAV communication scenarios in the terahertz or millimeter-wave bands.

[0077] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0078] This invention proposes a method for modeling non-stationary channels in terahertz MIMO UAVs. First, a three-dimensional ellipsoidal and double-sphere composite geometric model of the UAV's broadband non-stationary A2A channel is established. This model combines LoS components, single-bounced (SB) and double-bounced (DB) components to describe the multipath component distribution around the transmitter and receiver. Then, based on the proposed GBSM, expressions for its time-varying time-frequency autocorrelation function and Doppler power spectral density are derived. Finally, a geometry-based 3D terahertz MIMO random channel model is provided. This method is applicable to 6G dense urban environments, using ellipsoids to represent long-range scatterers and spheres to represent short-range scatterers. Parameters are initialized and evolved in the time, space, and frequency domains to generate a complete channel transfer function, and correlation analysis can be performed. This model is universal and adaptable, and can be used to simulate various non-stationary UAV communication scenarios. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments recorded in this invention, and those skilled in the art can obtain other drawings based on these drawings.

[0080] Figure 1 This is a schematic diagram of a uniform linear array MIMO system. Among them, (a) shows only the Loss of Space (LoS) portion and the geometric relationship of the reflection lines on the two spheres, which is the proposed three-dimensional MIMO GBSM that combines two spheres and an ellipsoid, and (b) shows only the geometric relationship of the reflection lines on the ellipsoid, which is the proposed three-dimensional MIMO GBSM that combines two spheres and an ellipsoid.

[0081] Figure 2 The impact of the communication frequency band provided in the embodiments of the present invention on time correlation.

[0082] Figure 3The influence of the relative velocity of the UAV on the Doppler power spectrum is provided in the embodiments of the present invention. Detailed Implementation

[0083] To better understand this technical solution, the method of the present invention will be described in detail below with reference to the accompanying drawings.

[0084] The RS-GBSM model (Regular-Shaped Geometry-Based Stochastic Model) targeted by this invention is as follows: Figure 1 As shown. To generalize the proposed channel model, all possible channel components are included. Based on existing A2A channel measurement data, the LoS component plays an important role in open environments, while the scattering component plays a greater role in complex environments. Figure 1 A general description of the model is given, including the Loss of Scatter (LoS) component and the Side Scatter (SB) and Side Scatter (DB) components generated by near and far scatterers. The impact of scatterer movement on the A2A channel is negligible. MT (Mobile Transmitter) represents the mobile transmitter, MR (Mobile Receiver) represents the mobile receiver, AAoA (Azimuth Angle of Arrival) represents the horizontal angle of the receiver's angle of arrival, AAoD (Azimuth Angle of Departure) represents the horizontal angle of the transmitter's angle of departure, AoA (Angle of Arrival) represents the receiver angle, AoD (Angle of Departure) represents the transmitter angle, EAoA (Elevation Angle of Arrival) represents the elevation angle of the receiver's angle of arrival, and EAoD (Elevation Angle of Departure) represents the elevation angle of the transmitter's angle of departure.

[0085] The equation of the ellipsoid is:

[0086]

[0087] The definitions of each symbol are shown in the table below:

[0088]

[0089]

[0090]

[0091]

[0092] The terahertz MIMO UAV non-stationary channel modeling method of this invention uses a double sphere centered at the UAV's location at the transceiver end to represent near-range scatterers, and a three-dimensional ellipsoid centered at the UAV's location at the transceiver end to represent far-range scatterers. Based on the A2A channel characteristics, and combining the LoS component, the SB component and DB component generated by the near-range and far-range scatterers respectively, the multipath component distribution around the transceiver end is described, and a three-dimensional ellipsoid and double-sphere composite geometric model of the UAV broadband non-stationary A2A channel is established. Based on the geometric stochastic model, the expressions for the model's time-varying time-frequency autocorrelation function and Doppler power spectral density are derived to establish the terahertz MIMO UAV non-stationary channel model.

[0093] Specifically, such as Figure 1 and Figure 2 As shown, the transceiver drones respectively include A q and A p With one omnidirectional antenna element, the A2A channel is represented as follows:

[0094]

[0095] The coverage area of ​​a near-field scatterer is represented by a sphere, while the coverage area of ​​a far-field scatterer is represented by an ellipsoid centered at the location of the transmitting and receiving UAV.

[0096] According to the concept of the time delay line (TDL) model, the p-th T of the MIMO channel x Antenna element and the qth R x The complex channel impulse response (CIR) between antenna elements is expressed as:

[0097]

[0098] in, For direct path channel impulse response, For single-reflection path channel impulse response, This is the impulse response of the dual-reflection path channel.

[0099] The formula for the channel impulse response of the direct path is:

[0100]

[0101] Where K is the Rice factor, ε pq Let f be the distance between antenna element p and antenna element q, and f T,max ,f R,max These are the maximum Doppler frequencies of the transmitter and receiver, respectively, and Φ. LoS ,Ψ LoS These are the LoS radial direction vectors of the transmitter and receiver, respectively.

[0102]

[0103]

[0104]

[0105]

[0106] The moving speeds of the transmitter and receiver can be expressed as:

[0107]

[0108]

[0109] Φ LoS =[cosα LoS cosβ LoS sinα LoS cosβ LoS sinβ LoS ]

[0110] Ψ LoS =-Φ LoS

[0111] The impulse response formula for a single-reflection path channel is:

[0112]

[0113]

[0114]

[0115] Among them, near-scattering body:

[0116]

[0117]

[0118]

[0119]

[0120] Far-scattering body:

[0121]

[0122]

[0123]

[0124]

[0125] The impulse response formula for a dual-reflection path channel is:

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] The horizontal scattering angle follows a VM distribution because it is better suited to describe the statistical properties of the scattering angle. This is represented as follows:

[0132]

[0133] Where I0 is the zeroth-order modified Bessel function of the first kind, and σ controls the distribution of the scatterer near the mean.

[0134] Because the height of the scatterer distribution range is finite, the vertical angle of the scattering angle follows a cosine distribution with upper and lower limits, as shown below:

[0135]

[0136] Where, β m This represents the maximum value of the vertical scattering angle.

[0137] Based on previous research, the above model of this invention considers the influence of different UAV altitudes and A2A pitch angles on the scattering region, and derives and analyzes the statistical characteristics of its channel—time-varying time-frequency autocorrelation function and Doppler power spectral density—as follows.

[0138] The time-varying time-frequency autocorrelation function (STCF) is expressed as:

[0139]

[0140] The LoS path portion is represented as follows:

[0141]

[0142]

[0143]

[0144]

[0145] The single reflection path is represented as:

[0146]

[0147]

[0148]

[0149]

[0150] The dual reflection path portion is represented as:

[0151]

[0152]

[0153]

[0154]

[0155] The expression for the Doppler power spectrum (PSD) density is:

[0156]

[0157]

[0158] This invention also provides the application of the above-mentioned terahertz MIMO UAV non-stationary channel modeling method in non-stationary UAV communication scenarios in the terahertz or millimeter-wave bands.

[0159] For UAV communication scenarios in densely populated urban areas, the parameters used are: D = 100m. θ T =π / 12, θ R =π / 6, v R =3m / s, v T =10m / s, γ T =π / 4, γ R =π / 4, ζ T =π / 12, ζ R =π / 12, α T =π / 6, α R = -π / 3, β T =π / 3, β R =π / 6, h T =500m, h R =300m, and the scattering radius of each scatterer in the model is 20. Assume Δd T =Δd R =0, the time-varying autocorrelation function of the simulation model at t=0s is calculated. For example... Figure 2 As shown.

[0160] The parameters used are: D = 100m. θT =π / 12, θ R =π / 6, v R =3m / s, v T = 4m / s, 6m / s, 13m / s, 33m / s, γ T =π / 4, γ R =π / 4, ζ T =π / 12, ζ R =π / 12, α T =π / 6, α R = -π / 3, β T =π / 3, β R =π / 6, h T =500m, h R =300m, the scattering path number of each scatterer in the model is 20. A comparison of the Doppler power spectrum (PSD) of each scattering component at different relative velocities of the UAV is shown. For example... Figure 3 As shown.

[0161] In summary, this invention models the non-stationary channel in the terahertz band. Addressing the issue that the channel characteristics of terahertz waves are not yet fully understood, it proposes a novel three-dimensional broadband non-stationary A2AGBSM model based on the TDL model to describe the transmission channel in dense urban areas. This model is universal and adaptable and can be used to simulate various non-stationary UAV communication scenarios.

[0162] Furthermore, the channel modeling method of the present invention is not limited to the terahertz band (100 GHz to 10 THz) used as an example in this paper, but is also applicable to the millimeter wave band.

[0163] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. However, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for modeling non-stationary channels of terahertz MIMO unmanned aerial vehicles, characterized in that, A double sphere centered at the UAV's location is used to represent near-range scatterers, and a three-dimensional ellipsoid centered at the UAV's location is used to represent far-range scatterers. Based on the characteristics of the A2A channel, and combining the LoS component, the SB component, and the DB component generated by the near and far scatterers respectively, the multipath component distribution around the transceiver is described, and a composite geometric model of the three-dimensional ellipsoid and double sphere for the UAV's broadband non-stationary A2A channel is established. Based on the geometric stochastic model, the expressions for the time-varying time-frequency autocorrelation function and Doppler power spectral density of the composite geometric model are derived, and a non-stationary channel model for the terahertz MIMO UAV is established. The transceiver drones respectively include A q and A p With one omnidirectional antenna element, the A2A channel is represented as follows: The p-th T of the MIMO channel x Antenna element and the qth R x The complex channel impulse response between antenna elements is expressed as: in, For the direct path channel impulse response, For single-reflection path channel impulse response, The impulse response of the dual-reflection path channel; The formula for the channel impulse response of the direct path is: Where K is the Rice factor, ε pq f is the distance between antenna element p and antenna element q. T,max ,f R,max These are the maximum Doppler frequencies of the transmitter and receiver, respectively, Φ LoS ,Ψ LoS These are the LoS radial direction vectors of the transmitter and receiver, respectively. F LoS =[cosα LoS cosβ LoS Sinai LoS cosβ LoS sinβ LoS ] P LoS =-Φ LoS The impulse response formula for a single-reflection path channel is: Among them, near-scattering body: Far-scattering body: The impulse response formula for a dual-reflection path channel is:

2. The terahertz MIMO UAV non-stationary channel modeling method according to claim 1, characterized in that, The horizontal scattering angle follows a VM distribution, as shown below: Where I0 is the zeroth-order modified Bessel function of the first kind, and σ controls the distribution of the scatterer near the mean.

3. The terahertz MIMO UAV non-stationary channel modeling method according to claim 1, characterized in that, The vertical angle of the scattering angle follows a cosine distribution with upper and lower limits, as shown below: Where, β m This represents the maximum value of the vertical scattering angle.

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