Intelligent reflecting surface assisted low-orbit satellite transmission method and deployment equipment

Abstract

The embodiment of the invention provides an intelligent reflector-assisted low-orbit satellite transmission method and deployment equipment, and the method comprises the steps: carrying out the quantification of a deployed region environment, and determining an air-ground three-dimensional cascade channel model and environment parameters; constructing a distribution model according to deployment position features of the satellite and the intelligent reflecting surface; constructing a useful and interference signal power distribution model according to channel characteristics; deriving a signal to interference plus noise ratio received at a user according to the signal power model, and giving a coverage rate expression under the model; and determining the optimal deployment parameters of the intelligent reflector-assisted low-orbit satellite network according to the coverage rate. According to the embodiment of the invention, the problem of how to configure the intelligent reflecting surface and the low-orbit satellite deployment parameters to maximize the system coverage rate in the process that the ground user accesses the air-ground satellite network is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-orbit satellite communications, and in particular to a method for configuring deployment parameters of a ground-deployed intelligent reflector-assisted low-orbit satellite communications system under the background of existing ground users supporting low-orbit satellite communications. Background Art

[0002] Low-orbit satellite communications have many advantages, such as wide-area coverage, low latency, and high bandwidth. They can provide stable communication services in remote areas, oceans, and air that are not covered by ground networks. Therefore, low-orbit satellite systems are regarded as an important part of 5G, 6G, and future networks.

[0003] However, due to the satellite deployment location, the path loss is significant, and the satellite communication signal is weak due to the rain attenuation and shadow effect. It is necessary to deploy smart reflectors on the ground to provide additional beamforming gain to enhance the communication signal reception power.

[0004] Therefore, how to configure smart reflective surfaces and low-orbit satellite deployment parameters to maximize the communication performance of ground users is an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a method and deployment equipment for low-orbit satellite transmission assisted by an intelligent reflector, so as to reduce the impact of satellite communication large path loss and shadow effect on system communication performance and improve resource utilization. The specific technical solution is as follows:

[0006] Step 200, quantify the deployment area environment and determine the air-ground three-dimensional cascade channel model and environmental parameters.

[0007] Step 210, constructing a distribution model based on the deployment location characteristics of satellites and smart reflective surfaces.

[0008] Step 220: construct a useful and interference signal power distribution model according to the channel characteristics.

[0009] Step 230: derive the signal-to-interference-and-noise ratio received at the user according to the signal power model, and provide a coverage expression under the model.

[0010] Step 240, determining optimal deployment parameters of the smart reflective surface-assisted low-orbit satellite network according to the coverage rate.

[0011] Optionally, the quantification of the deployed area environment to determine the air-ground three-dimensional cascade channel model and environmental parameters specifically includes:

[0012] According to the channel environment of the deployed area, the channel model parameters are determined. Large-scale path loss and small-scale fading are used to simulate the air-to-ground channel, where small-scale fading is modeled using the shadow Rice model.m ) to typical user UE0, S m To the IRS j The nth reflection unit and IRS j The channel power gain from the nth reflection unit to UE0 can be expressed as

[0013]

[0014] in, and α are the average channel power gain, the average channel power gain at 1m and the path loss index respectively, and the probability density function of ξ can be expressed as:

[0015]

[0016] where 1F1(·,·,·) is the confluent hypergeometric function.

[0017] Optionally, constructing a distribution model of satellites and smart reflective surfaces according to their deployment location characteristics specifically includes:

[0018] According to the properties of the Poisson point distribution of the intelligent reflector and the binomial point process distribution of the satellite, we can know that the distance l from any satellite to UE0 is m The probability density function of is:

[0019]

[0020] Among them, r e , r min are the earth radius and the satellite deployment height respectively. Therefore, the cumulative distribution function of the distance l0 from the service satellite to UE0 can be expressed as:

[0021]

[0022] Among them, N S is the number of satellites. Then its probability density function can be expressed as:

[0023]

[0024] And r min ≤l m ≤2r e +r min .

[0025] Optionally, constructing a useful and interference signal power distribution model according to channel characteristics specifically includes:

[0026] For the convenience of derivation, it is assumed that the small attenuation of the smart reflector-user link is a random variable that obeys the Rayleigh distribution. Therefore, Therefore, the model Obeys Rayleigh distribution, where the scale parameter is Expected to be For satellite-to-terrestrial links |h d,m |, modulus is the expectation respectively A random variable, where ER(k) is the k-th moment, expressed as:

[0027]

[0028] Among them, 2F1(·,·,·,·) is the Gaussian hypergeometric function;

[0029] because and is a random variable that obeys the shadow Rice distribution and Rayleigh distribution, so, The first and second order moments of can be expressed as:

[0030]

[0031] in, G2=ER(2)-(G1) 2 Therefore, its variance can be obtained by first-order moment and second-order moment;

[0032] In the case where the associated smart reflector provides beamforming gain, the associated smart reflector can adjust the phase shift so that the N reflected signals can achieve the same phase effect at the user. Therefore, the S0-IRS0-UE0 channel can be expressed as

[0033]

[0034] Therefore, according to the central limit theorem, can be estimated as a Gaussian distribution, expressed as:

[0035]

[0036] Therefore, its average signal power can be expressed as:

[0037]

[0038] For the second case, the smart reflector only provides random scattering gain, so can be divided into two in-phase and quadrature-phase components, and have a mean of 0 and a variance of Normal distribution of

[0039] Furthermore, according to whether the smart reflector provides beamforming and random scattering gain, the distances D1 and D2 are divided into three categories, namely, beamforming gain exists, only random scattering gain exists, and there is no smart reflector gain;

[0040] For the first case, the first-order moment and second-order moment of the signal can be expressed as

[0041]

[0042] in, E 3,0 =(E 1,0 ) 2 +E 2,0 ;

[0043] For the second case, the first-order moment and second-order moment of signal power can be expressed as:

[0044] E{S}=g d,0 ER(2)(1+NE 1,1 )

[0045] E{S 2}=ER(4)(g d,0 ) 2 +2(g d,0 ) 2 (ER(2)) 2 NE 1,1 +2N 2 (g d,0 ) 2 (ER(2)) 2 E 3,1

[0046] in,

[0047] For the third case, the first-order moment and second-order moment of signal power can be expressed as:

[0048] E{S}=g d,0 ER(2)

[0049] E{S 2}=ER(4)(g d,0 ) 2

[0050] According to the signal power of the three cases, the scale and shape parameters of the useful signal gamma distribution can be obtained. For the interference signal, it can be expressed as:

[0051]

[0052] in, Therefore, in three different cases, it can be expressed as:

[0053]

[0054] Therefore, the Laplace transform of the interference can be expressed as

[0055]

[0056] Optionally, the derivation of the received signal to interference and noise ratio at the user according to the signal power model and providing a coverage expression under the model specifically includes:

[0057] According to the definition of coverage rate, the ground user coverage rate under this model can be expressed as

[0058]

[0059] Optionally, determining optimal deployment parameters of the intelligent reflective surface-assisted low-orbit satellite network according to the coverage rate specifically includes:

[0060] Due to changes in the service environment, the corresponding channel characteristics change accordingly, and the deployment parameters need to be re-selected. According to different deployment environments, the channel environment parameters are determined, and then the optimal smart reflector deployment parameters under maximum coverage are determined.

[0061] Optionally, the intelligent reflective surface assisted low-orbit satellite deployment parameter configuration device specifically includes: a determination module, a network side device, an intelligent reflective surface device and a terminal device.

[0062] Optionally, the determination module is used to determine the number of satellite base stations and channel environment parameters in the deployed area;

[0063] Network side equipment: includes a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, the step of confirming the optimal deployment parameters described in any one of claims 1 to 6 is implemented.

[0064] Beneficial Effects

[0065] From the above description, it can be seen that the intelligent reflector-assisted low-orbit satellite transmission method and deployment parameter configuration device provided by the present invention, after quantifying the distribution characteristics of satellites and intelligent reflector surfaces, model the three-dimensional cascade channel between air and ground, quantify the characteristics of received signals, and adjust the satellite and intelligent reflector surface deployment parameters according to different environmental characteristics to provide high-speed services to ground users. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a system model diagram of the intelligent reflector-assisted low-orbit satellite transmission method and deployment equipment of the present invention;

[0067] Figure 2 It is an implementation flow chart of the present invention;

[0068] Figure 3It is a schematic diagram of the variation of ground user coverage with the number of satellites;

[0069] Figure 4 It is a schematic diagram of the variation of ground user coverage with the deployment density of smart reflective surfaces; DETAILED DESCRIPTION

[0070] In order to clarify the purpose, implementation scheme and technical advantages of the present invention, the present invention is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0071] Figure 1 The practical application scenario of the intelligent reflector-assisted low-orbit satellite transmission method and deployment device of the present invention is as follows: the intelligent reflector serves ground users as a network intermediate node, reconstructs the line-of-sight link through passive reflection signals, and provides additional gain to enhance the signal power transmitted from the satellite base station to the user. The present invention quantifies the distribution characteristics of low-orbit satellites, considers the impact of the signals transmitted by the service satellite and the interference satellite on ground users, and combines the gain of the intelligent reflector surface, aiming to configure the optimal deployment parameters of the intelligent reflector surface in different environments through the device.

[0072] Figure 2 The schematic diagram of the process of the intelligent reflector-assisted low-orbit satellite transmission method and deployment device provided by the present invention includes:

[0073] Step 200, quantify the deployment area environment and determine the air-ground three-dimensional cascade channel model and environmental parameters.

[0074] Step 210, constructing a distribution model based on the deployment location characteristics of satellites and smart reflective surfaces.

[0075] Step 220: construct a useful and interference signal power distribution model according to the channel characteristics.

[0076] Step 230: derive the signal-to-interference-and-noise ratio received at the user according to the signal power model, and provide a coverage expression under the model.

[0077] Step 230, determining optimal deployment parameters of the smart reflective surface-assisted low-orbit satellite network according to the coverage rate.

[0078] In step 200, the environment of the deployed area is quantified to determine the air-to-ground three-dimensional cascade channel model and environmental parameters, which is specifically performed as follows: according to the channel environment of the deployed area, the channel model related parameters are determined. Large-scale path loss and small-scale fading are used to simulate and model the air-to-ground channel, where small-scale fading is modeled using the shadow Rice model. Therefore, the satellite m(S m ) to typical user UE0, S m To the IRS j The nth reflection unit and IRS jThe channel power gain from the nth reflection unit to UE0 can be expressed as

[0079]

[0080]

[0081] in, and α are the average channel power gain, the average channel power gain at 1m and the path loss index respectively, and the probability density function of ξ can be expressed as:

[0082]

[0083] where 1F1(·,·,·) is the confluent hypergeometric function.

[0084] In step 210, a distribution model is constructed based on the deployment location characteristics of satellites and smart reflective surfaces, specifically including:

[0085] According to the properties of the Poisson point distribution of the intelligent reflector and the binomial point process distribution of the satellite, we can know that the distance l from any satellite to UE0 is m The probability density function of is:

[0086]

[0087] Among them, r e , r min are the earth radius and the satellite deployment height respectively. Therefore, the cumulative distribution function of the distance l0 from the service satellite to UE0 can be expressed as:

[0088]

[0089] Among them, N S is the number of satellites. Then its probability density function can be expressed as:

[0090]

[0091] And r min ≤l m ≤2r e +r min .

[0092] In step 220, a useful and interference signal power distribution model is constructed according to the channel characteristics, which specifically includes:

[0093] For the convenience of derivation, it is assumed that the small attenuation of the smart reflector-user link is a random variable that obeys the Rayleigh distribution. Therefore, Therefore, the model Obeys Rayleigh distribution, where the scale parameter is Expected to be For satellite-to-terrestrial links h d,m |, modulus is the expectation respectively A random variable, where ER(k) is the k-th moment, expressed as:

[0094]

[0095] Among them, 2F1(·,·,·,·) is the Gaussian hypergeometric function;

[0096] because and is a random variable that obeys the shadow Rice distribution and Rayleigh distribution, so, The first and second order moments of can be expressed as:

[0097]

[0098] in, G2=ER(2)-(G1) 2 Therefore, its variance can be obtained by first-order moment and second-order moment;

[0099] In the case where the associated smart reflector provides beamforming gain, the associated smart reflector can adjust the phase shift so that the N reflected signals can achieve the same phase effect at the user. Therefore, the S0-IRS0-UE0 channel can be expressed as

[0100]

[0101] Therefore, according to the central limit theorem, can be estimated as a Gaussian distribution, expressed as:

[0102]

[0103] Therefore, its average signal power can be expressed as:

[0104]

[0105] For the second case, the smart reflector only provides random scattering gain, so can be divided into two in-phase and quadrature-phase components, and have a mean of 0 and a variance of Normal distribution of

[0106] Furthermore, according to whether the smart reflector provides beamforming and random scattering gain, the distances D1 and D2 are divided into three categories, namely, beamforming gain exists, only random scattering gain exists, and there is no smart reflector gain;

[0107] For the first case, the first-order moment and second-order moment of the signal can be expressed as

[0108]

[0109] in, E 3,0 =(E 1,0 ) 2 +E 2,0 ;

[0110] For the second case, the first-order moment and second-order moment of signal power can be expressed as:

[0111] E{S}=g d,0 ER(2)(1+NE 1,1 )

[0112] E{S 2}=ER(4)(g d,0 ) 2 +2(g d,0 ) 2 (ER(2)) 2 NE 1,1 +2N 2 (g d,0 ) 2 (ER(2)) 2 E 3,1

[0113] in,

[0114] For the third case, the first-order moment and second-order moment of signal power can be expressed as:

[0115] E{S}=g d,0 ER(2)

[0116] E{S 2}=ER(4)(g d,0 ) 2

[0117] According to the signal power of the three cases, the scale and shape parameters of the useful signal gamma distribution can be obtained. For the interference signal, it can be expressed as:

[0118]

[0119] in, Therefore, in three different cases, it can be expressed as:

[0120]

[0121] Therefore, the Laplace transform of the interference can be expressed as

[0122]

[0123] In step 240, the optimal deployment parameters of the smart reflective surface-assisted low-orbit satellite network are determined according to the coverage rate, specifically including:

[0124] Due to changes in the service environment, the corresponding channel characteristics change accordingly, and the deployment parameters need to be re-selected. According to different deployment environments, the channel environment parameters are determined, and then the optimal smart reflector deployment parameters under maximum coverage are determined.

[0125] Figure 3 It is a schematic diagram of the variation of ground user coverage with the number of deployed satellites. The coverage rate represents the statistical probability of the user service link SINR reaching the standard under a certain SINR threshold requirement. The horizontal axis represents the number of deployed satellites. The simulation results show the impact of adjusting the number of deployed satellites on ground user performance under different numbers of reflection units. It can be seen that with the assistance of intelligent metasurface, there is an optimal deployment range for the number of satellite base stations; compared with the network without intelligent metasurface, its gain can reach about 30.8%.

[0126] Figure 4 It is a schematic diagram of the variation of ground user coverage with the deployment density of smart metasurface. The simulation results show the impact of adjusting the number of deployed smart metasurfaces on ground user performance under different numbers of deployed satellite base stations. It can be seen that under different smart metasurface densities, the optimal number of satellite base stations is different; under different numbers of satellite base stations, the optimal smart metasurface deployment density is also different.

Claims

1. Intelligent reflective surface assisted low-orbit satellite transmission method and deployment equipment, characterized in that: include: The environment of the deployed area is quantified to determine the air-ground three-dimensional cascade channel model and environmental parameters; according to the deployment location characteristics of the satellite and the intelligent reflector, their distribution model is constructed; according to the channel characteristics, the useful and interference signal power distribution model is constructed; according to the signal power model, the received signal-to-interference-noise ratio at the user is derived, and the coverage expression under the model is given; according to the coverage, the optimal deployment parameters of the intelligent reflector-assisted low-orbit satellite network are determined.

2. The method according to claim 1, characterized in that The quantification of the deployed area environment and determination of the air-ground three-dimensional cascade channel model and environmental parameters specifically include: According to the channel environment of the deployed area, the channel model parameters are determined. Large-scale path loss and small-scale fading are used to simulate the air-to-ground channel, where small-scale fading is modeled using the shadow Rice model. m ) to typical user UE0, S m To the IRS j The nth reflection unit and IRS j The channel power gain from the nth reflection unit to UE0 can be expressed as |h d,m | 2 =g d,m x d,m =βl m -α x d,m Among them, g, and α are the average channel power gain, the average channel power gain at 1m and the path loss index respectively, and the probability density function of ξ can be expressed as: where 1F1(·,·,·) is the confluent hypergeometric function.

3. The method according to claim 1, characterized in that The distribution model is constructed according to the deployment location characteristics of the satellite and the intelligent reflector, specifically including: According to the properties of the Poisson point distribution of the intelligent reflector and the binomial point process distribution of the satellite, we can know that the distance l from any satellite to UE0 is m The probability density function of is: Among them, r e , r min are the earth radius and the satellite deployment height respectively. Therefore, the cumulative distribution function of the distance l0 from the service satellite to UE0 can be expressed as: Among them, N S is the number of satellites. Then its probability density function can be expressed as: And r min ≤l m ≤2r e +r min .

4. The method according to claim 1, characterized in that: The constructing of the useful and interference signal power distribution model according to the channel characteristics specifically includes: For the convenience of derivation, it is assumed that the small attenuation of the smart reflector-user link is a random variable that obeys the Rayleigh distribution. Therefore, Therefore, the model It follows the Rayleigh distribution, where the scale parameter is Expected to be For satellite-to-terrestrial links h d,m |, modulus is the expectation respectively A random variable, where ER(k) is the k-th moment, expressed as: Among them, 2F1(·,·,·,·) is the Gaussian hypergeometric function; because and is a random variable that obeys the shadow Rice distribution and Rayleigh distribution, so, The first and second order moments of can be expressed as: in, G2=ER(2)-(G1) 2 Therefore, its variance can be obtained by first-order moment and second-order moment; In the case where the associated smart reflector provides beamforming gain, the associated smart reflector can adjust the phase shift so that the N reflected signals can achieve the same phase effect at the user. Therefore, the S0-IRS0-UE0 channel can be expressed as Therefore, according to the central limit theorem, can be estimated as a Gaussian distribution, expressed as: Therefore, its average signal power can be expressed as: For the second case, the smart reflector only provides random scattering gain, so can be divided into two in-phase and quadrature-phase components, and have a mean of 0 and a variance of Normal distribution of Furthermore, according to whether the smart reflector provides beamforming and random scattering gain, the distances D1 and D2 are divided into three categories, namely, beamforming gain exists, only random scattering gain exists, and there is no smart reflector gain; For the first case, the first-order moment and second-order moment of the signal can be expressed as Among them, E 3,0 = (E 1,0 ) 2 +E 2,0 ; For the second case, the first-order moment and second-order moment of signal power can be expressed as: E{S}=g d,0 ER(2)(1+NE) 1,1 ) E{S 2 }=ER(4)(g d,0 ) 2 +2(g d,0 ) 2 (IS(2)) 2 NE 1,1 +2N 2 (g d,0 ) 2 (IS(2)) 2 E 3,1 in, For the third case, the first-order moment and second-order moment of signal power can be expressed as: E{S}=g d,0 IS(2) E{S 2 }=ER(4)(g d,0 ) 2 According to the signal power of the three cases, the scale and shape parameters of the useful signal gamma distribution can be obtained. For the interference signal, it can be expressed as: in, Therefore, in three different cases, it can be expressed as: Therefore, the Laplace transform of the interference can be expressed as 5. The method according to claim 1, characterized in that: The method of deducing the received signal to interference and noise ratio at the user according to the signal power model and providing a coverage expression under the model specifically includes: According to the definition of coverage rate, the ground user coverage rate under this model can be expressed as 6. The method according to claim 1, characterized in that The method of determining the optimal deployment parameters of the intelligent reflective surface-assisted low-orbit satellite network according to the coverage rate specifically includes: Due to changes in the service environment, the corresponding channel characteristics change accordingly, and the deployment parameters need to be re-selected. According to different deployment environments, the channel environment parameters are determined, and then the optimal smart reflector deployment parameters under maximum coverage are determined.

7. The method according to claim 1, characterized in that The intelligent reflective surface assisted low-orbit satellite deployment device specifically includes: a determination module, a network side device, an intelligent reflective surface device and a terminal device.

8. The method according to claim 7, characterized in that The determination module is used to determine the number of satellite base stations and channel environment parameters in the deployed area; Network side equipment: includes a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, the step of confirming the optimal deployment parameters described in any one of claims 1 to 6 is implemented.

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