Low earth orbit satellite network transmission enhancement method under backhaul constraint

Abstract

The embodiment of the invention provides a low earth orbit satellite network transmission enhancement method under backhaul constraint, which comprises the following steps of: finely adjusting service distribution of a low earth orbit satellite network by adopting a dynamic station selection strategy based on backhaul capacity perception and considering backhaul link capacity limitation of low earth orbit satellites, and constructing a backhaul link station selection access mechanism; by analyzing the spatial distribution of low earth orbit satellites serving users in a network, the distribution of the low earth orbit satellites is deduced, and the spatial distribution characteristics of the low earth orbit satellites are disclosed; based on this, further deriving and analyzing a key index of network performance: coverage rate; and maximizing the network performance according to the coverage rate index. According to the embodiment of the invention, the problem of how to select stations and optimally deploy low-orbit satellite equipment by ground users under the backhaul capacity constraint of 6G low-orbit satellites is solved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for configuring parameters of low-Earth orbit satellite deployment density and altitude in the context of existing 6G satellite networks supporting backhaul links. Background Technology

[0002] The global coverage of low-Earth orbit (LEO) satellite networks ensures timely data transmission back to Earth, providing robust support for emergency response and long-distance communication. Satellite communication not only plays a central role in providing major communication links but also serves as an effective solution to address fluctuations in backhaul capacity, significantly improving the performance of LEO satellite networks.

[0003] However, in reality, differences in backhaul capacity have a significant impact on network performance. Geographically based user association strategies do not adequately consider the influence of backhaul capacity on user association decisions and fail to reflect real-world differences in backhaul capacity.

[0004] Therefore, how to implement user association strategies under the backhaul bias model to ensure that the network can provide stable and reliable communication services in a variety of geographical environments, and how to control the transmit power to accurately evaluate system performance by considering the limitations imposed on link capacity by path loss and time delay, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for enhancing low-Earth orbit satellite network transmission under backhaul constraints, so as to fully utilize diversity gains to optimize individual communication experience and improve resource utilization. The specific technical solution is as follows:

[0006] Step 200: A dynamic site selection strategy based on backhaul capacity awareness is adopted. Considering the backhaul link capacity limitation of low-Earth orbit satellites, the service distribution of the low-Earth orbit satellite network is finely adjusted to construct a backhaul link site selection access mechanism.

[0007] Step 210: By analyzing the spatial distribution of low-Earth orbit (LEO) satellites serving users in the network, the LEO satellite distribution is derived, revealing the spatial distribution characteristics of LEO satellites.

[0008] Step 220: Further derive and analyze the key performance indicator of the network: coverage.

[0009] Step 230: Maximize network performance based on coverage metrics.

[0010] Optionally, a dynamic site selection strategy based on backhaul capacity awareness is adopted. Considering the backhaul link capacity limitations of low-Earth orbit (LEO) satellites, the service distribution of the LEO satellite network is finely adjusted to construct a backhaul link site selection access mechanism, specifically including:

[0011] The propagation loss model is modeled using the standard free-space propagation loss formula, expressed as:

[0012]

[0013] Where c is the speed of light, f c It is the carrier frequency. h represents the orbital altitude of the low-Earth orbit satellite deployment, and α is the path loss index.

[0014] For small-scale fading, the shadowed-Rician distribution is used to simulate the fading characteristics of satellite channels. This distribution is widely used to describe multipath and obstruction effects in satellite channels. Channel gain g s The probability density function is as follows:

[0015]

[0016] Where Γ(·) is the gamma function, These are shape parameters. It is a scale parameter.

[0017] Given the high-speed dynamic characteristics of low-Earth orbit satellites, the actual data throughput of users is limited not only by the quality of the received signal but also by the transmission capacity of the backhaul link. A site selection strategy based on the backhaul factor reference signal received power (BRSRP) is proposed. The formula for calculating BRSRP is as follows:

[0018] P i =p s B i L0g s d -α

[0019] Among them, P i This indicates the BRSRP of a low-Earth orbit satellite marked with 'i', p s This represents the satellite's signal transmission power, and d is the distance between the user terminal and the satellite, with a factor B added here. i The backhaul capacity of low-Earth orbit satellites is represented by a probability density function, which quantifies the impact of the backhaul factor on transmission performance.

[0020] Optionally, by analyzing the spatial distribution of low-Earth orbit (LEO) satellites serving users within the network, the LEO satellite distribution is derived, revealing the spatial distribution characteristics of LEO satellites, specifically including:

[0021] Let θ represent the angle from the center of the Earth to the zenith and the edge of the dome. λ represents the angle between the user terminal and the satellite base station. s This represents satellite density. Given a return offset B... iFor satellite base stations with large backhaul capacity, a larger offset value is assigned. Define the BRSRP (Broadband Redirect Resource Ratio) from satellite to user terminal; for each user, select the strongest P (Position). i Satellites serve as service base stations, thus allowing us to deduce the angular distribution of users to the service satellites, specifically:

[0022] Event E1: For any satellite in region R(r1), but

[0023]

[0024] in, It can be represented as

[0025]

[0026] in, Furthermore, since the time periods are independent, it can be further obtained that...

[0027]

[0028] Therefore, there is

[0029]

[0030] Event E2: For any satellite in region S / R(r1) but

[0031]

[0032] and Therefore, we can obtain:

[0033]

[0034] Ultimately, its probability distribution function can be expressed as:

[0035]

[0036] Based on all the above derivations, the probability density function of the contact angle can be obtained as follows:

[0037]

[0038] Optionally, further derive and analyze key network performance metrics: coverage, specifically including:

[0039] The distribution of small-scale fading can be determined based on different channel environments and deployment methods. In this invention, Rayleigh fading is used to represent small-scale fading during the analysis. Based on this, the power of the signal, noise, and interference at the user's location can be obtained, thereby obtaining the received signal-to-interference-plus-noise ratio (SNR) at the user's location.

[0040] Based on the definition of coverage, we can obtain:

[0041]

[0042] The derivation of the above formula utilizes the characteristics of the shaded Rice distribution and the properties of gamma random variables. in, This is based on Alzer's lemma.

[0043] Furthermore, the Laplace transform of the disturbance can be expressed as:

[0044]

[0045] Campbell's theorem was used in the derivation of the above formula. This leads to a theoretical expression for coverage. Using this formula, the relationship between user coverage and satellite deployment parameters and backhaul offset can be determined, thereby enabling the selection of satellite base station deployment methods under different conditions.

[0046] Optionally, network performance can be maximized based on coverage metrics, including:

[0047] Because different deployment environments have different network performance requirements, the threshold T may need to be changed, or the requirements for deployment orbit height and density may also differ. Therefore, the specific deployment parameters of the airborne satellite base station need to be reset according to different needs or deployment requirements in order to select the optimal deployment parameters and achieve economical and high-performance results.

[0048] Beneficial effects

[0049] As described above, this invention provides a method for enhancing low-Earth orbit (LEO) satellite network transmission under backhaul constraints. It proposes a backhaul bias value model, which aims to provide a more reasonable link access mechanism to optimize the quality of service (QoS) in LEO satellite communication networks. To quantify system performance, a stochastic geometric method is used to derive an analytical expression for coverage, reflecting how the user's maximum data rate is affected by backhaul capacity limitations. The backhaul bias value model provides a perspective on link access strategies in LEO satellite networks. By modeling the impact of backhaul capacity on LEO satellite link access, it has significant theoretical and practical value for guiding the design and optimization of future LEO satellite networks. This model is more consistent with actual deployment conditions, facilitating the setting of deployment parameters. Attached Figure Description

[0050] Figure 1 This is a system model diagram of a low-Earth orbit satellite network transmission enhancement method under backhaul constraints according to the present invention;

[0051] Figure 2 This is a flowchart illustrating the implementation of the present invention;

[0052] Figure 3 This is a schematic diagram illustrating how the coverage of satellite base stations and ground users changes with the orbital altitude of the satellite deployment.

[0053] Figure 4 This is a schematic diagram illustrating how the coverage of satellite base stations and terrestrial users changes with the number of satellite base stations deployed; Detailed Implementation

[0054] To clarify the objectives, implementation schemes, and technical advantages of this invention, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0055] Figure 1 This invention provides a practical application scenario for a low-Earth orbit (LEO) satellite network transmission enhancement method under backhaul constraints: In a LEO satellite network scenario, bidirectional data exchange between ground users and satellites relies on the establishment of a backhaul link. This includes uploading user requests received by the ground station to the satellite via the satellite backhaul link. Furthermore, data reception at the user end is accomplished through an access link established with the LEO satellite. After selecting a satellite, the user equipment receives data transmission from the satellite through this link. Notably, the access link and backhaul link utilize different transmission resources. This design avoids interference between resources, ensures the independence of link operation, and thus significantly improves the efficiency and reliability of the entire communication system.

[0056] Figure 2 A flowchart illustrating a method for enhancing low-Earth orbit satellite network transmission under backhaul constraints provided by the present invention includes:

[0057] Step 200: A dynamic site selection strategy based on backhaul capacity awareness is adopted. Considering the backhaul link capacity limitation of low-Earth orbit satellites, the service distribution of the low-Earth orbit satellite network is finely adjusted to construct a backhaul link site selection access mechanism.

[0058] The propagation loss model is modeled using the standard free-space propagation loss formula, expressed as:

[0059]

[0060] Where c is the speed of light, f c It is the carrier frequency. h represents the orbital altitude of the low-Earth orbit satellite deployment, and α is the path loss index.

[0061] For small-scale fading, the shadowed Rice distribution is used to simulate the fading characteristics of satellite channels. This distribution is widely used to describe multipath and obstruction effects in satellite channels. Channel gain g s The probability density function is as follows:

[0062]

[0063] Where Γ(·) is the gamma function, These are shape parameters. It is a scale parameter.

[0064] Given the high-speed dynamic characteristics of low-Earth orbit satellites, the actual data throughput of users is limited not only by the quality of the received signal but also by the transmission capacity of the backhaul link. A site selection strategy based on the backhaul factor reference signal received power (BRSRP) is proposed. The formula for calculating BRSRP is as follows:

[0065] P i =p s B i L0g s d -α

[0066] Among them, P i This indicates the BRSRP of a low-Earth orbit satellite marked with 'i', p s This represents the satellite's signal transmission power, and d is the distance between the user terminal and the satellite, with a factor B added here. i The backhaul capacity of low-Earth orbit satellites is represented by a probability density function, which quantifies the impact of the backhaul factor on transmission performance.

[0067] Step 210: By analyzing the spatial distribution of low-Earth orbit (LEO) satellites serving users in the network, the LEO satellite distribution is derived, revealing the spatial distribution characteristics of LEO satellites.

[0068] Let θ represent the angle from the center of the Earth to the zenith and the edge of the dome. λ represents the angle between the user terminal and the satellite base station. s This represents satellite density. Given a return offset B... i For satellite base stations with large backhaul capacity, a larger offset value is assigned. Define the BRSRP (Broadband Redirect Resource Ratio) from satellite to user terminal; for each user, select the strongest P (Position). i Satellites serve as service base stations, thus allowing us to deduce the angular distribution of users to the service satellites, specifically:

[0069] Event E1: For any satellite in region R(r1), but

[0070]

[0071] in, It can be represented as

[0072]

[0073] in, Let be the Earth's radius and h be the satellite's deployment altitude. Furthermore, since the time intervals are independent, we can obtain...

[0074]

[0075] Therefore, there is

[0076]

[0077] Event E2: For any satellite in region S / R(r1) but

[0078]

[0079] and Therefore, we can obtain:

[0080]

[0081] Ultimately, its probability distribution function can be expressed as:

[0082]

[0083] Based on all the above derivations, the probability density function of the contact angle can be obtained as follows:

[0084]

[0085] Step 220: Further derive and analyze the key performance indicator of the network: coverage.

[0086] Based on the definition of coverage, we can obtain:

[0087]

[0088] The derivation of the above formula utilizes the characteristics of the shaded Rice distribution and the properties of gamma random variables. in, This is based on Alzer's lemma.

[0089] Furthermore, the Laplace transform of the disturbance can be expressed as:

[0090]

[0091] Campbell's theorem was used in the derivation of the above formula. This leads to a theoretical expression for coverage. Using this formula, the relationship between user coverage and satellite deployment parameters and backhaul offset can be determined, thereby enabling the selection of satellite base station deployment methods under different conditions.

[0092] Figure 3 This diagram illustrates the variation of satellite base station and terrestrial user coverage with the orbital altitude of low-Earth orbit (LEO) satellites. Coverage represents the statistical probability of a user service link achieving the required SINR (Self-Incoming Radio Frequency) threshold. The horizontal axis represents the orbital altitude of the LEO satellites. The diagram shows the increase in network coverage as the satellite altitude (h) increases. This reflects that increasing the number of satellites can effectively improve network connectivity and enhance the overall coverage probability. However, the observed slowdown in the rate of coverage growth with further increases in satellite altitude points to the potential impact of a large-scale attenuation effect. Under this effect, the contribution of new satellites to improving the coverage probability diminishes, indicating that after a certain altitude, further increasing the number of satellites will have a reduced marginal benefit in improving coverage. Therefore, when designing satellite networks, a balance between satellite altitude and number should be considered to avoid the diminishing returns effect caused by excessive satellites and to optimize coverage efficiency.

[0093] Figure 4 This figure illustrates how the coverage of satellite base stations and ground users varies with the number of LEO satellites deployed. The graph shows the impact of different numbers of satellites on coverage probability. It reveals that when the number of satellites is small, increasing the number of satellites significantly improves coverage probability; however, when the number of satellites is large, the improvement in coverage probability from further increasing the number of satellites diminishes. This result has practical guiding significance for designing and implementing efficient LEO satellite networks, emphasizing the importance of finding a reasonable balance between the number of satellites and coverage efficiency. The data in the figure reveals that increasing the number of satellites has a positive impact on the probability of users establishing connections with candidate satellites, which is crucial for improving the overall coverage performance of the network. However, an increased number of satellites may also lead to stronger signal interference, especially when densely deployed at lower LEO altitudes. Nevertheless, applying a backhaul link strategy can effectively mitigate interference caused by neighboring satellites. This strategy maintains increased access opportunities even at different orbital altitudes by optimizing the allocation of link resources.

Claims

1. A method for enhancing low-orbit satellite network transmission under backhaul constraints, characterized in that: include: The space-to-ground channel environment parameters are determined according to the channel environment of the deployment area, and the backhaul capacity is taken as an important influencing factor and incorporated into the site selection process, so that the service coverage range of each low-orbit satellite can be dynamically adjusted based on its actual backhaul capacity. A dynamic site selection strategy based on backhaul capacity perception is adopted. Taking into account the backhaul link capacity limitation of low-orbit satellites, the service distribution of the low-orbit satellite network is finely adjusted, and a backhaul link site selection access mechanism is constructed. By analyzing the spatial distribution of low-orbit satellites serving users in the network, the distribution of low-orbit satellites is derived, revealing the distribution characteristics of low-orbit satellites in space. Based on this, the key indicator of network performance is further derived and analyzed: coverage rate. Network performance is maximized according to the coverage rate indicator.

2. The method according to claim 1, characterized in that The dynamic site selection strategy based on backhaul capacity perception is adopted, taking into account the backhaul link capacity limitation of the low-orbit satellite, finely adjusting the service distribution of the low-orbit satellite network, and constructing a backhaul link site selection access mechanism, which specifically includes: The propagation loss model uses the standard free space propagation loss formula, which is expressed as: Where c is the speed of light, f c is the carrier frequency, h represents the deployment orbital altitude of the LEO satellite, and α is the path loss exponent; For small-scale fading, the shadowed-Rician distribution is used to simulate the fading characteristics of the satellite channel. This distribution is widely used to describe the multipath effect and shielding effect in the satellite channel. The channel gain g s The probability density function of is as follows: where Γ(·) is the gamma function, is the shape parameter, is the scale parameter; In view of the high-speed dynamic characteristics of low-orbit satellites, the actual data throughput of users is not only limited by the quality of the received signal, but also by the transmission capacity of the backhaul link; a site selection strategy based on the backhaul factor reference signal received power (BRSRP) is proposed. The calculation formula of BRSRP is as follows: P i =p s B i L0g s d -α Among them, P i represents the BRSRP of the low-orbit satellite marked with i, p s represents the signal transmission power of the satellite, d is the distance between the user terminal and the satellite, and the factor B is added here i To represent the backhaul capacity value of the low-orbit satellite, and quantify the impact of the backhaul factor on the transmission performance through the probability density function.

3. The method according to claim 1, characterized in that By analyzing the spatial distribution of low-orbit satellites serving users in the network, the distribution of low-orbit satellites is derived, revealing the distribution characteristics of low-orbit satellites in space, including: Let θ be the angle from the center of the Earth to the zenith and the edge of the dome, represents the angle between the user terminal and the satellite base station, λ s Indicates the satellite density; given the return bias B i , for satellite base stations with larger backhaul capacity, a larger bias value is assigned; define the BRSRP from satellite to user terminal, and for each user, select the strongest P i The satellite acts as a service base station, thereby deriving the angular distribution from the user to the service satellite, specifically: Event E1: For any satellite in region R(r1), but in, ] can be expressed as in, is the radius of the earth, h is the satellite deployment height; and since the time is independent, we can further get Therefore, there is Event E2: For any satellite in region S / R(r1) but and Therefore, we can get: Finally, its probability distribution function can be expressed as: Combining all the above derivations, the probability density function of the contact angle can be obtained as follows:

4. The method according to claim 1, characterized in that Further derive and analyze the key indicators of network performance: coverage, including: Based on the coverage definition, we can get: Among them, the above formula uses the characteristics of shadow Rice distribution and the properties of gamma random variables. in, This is based on Alzer's lemma; Furthermore, the Laplace transform of interference can be expressed as: Campbell's theorem is used in the derivation of the above formula. Finally, the theoretical expression of coverage can be obtained. Through the above formula, the relationship between user coverage and satellite deployment parameters and backhaul bias can be obtained, so as to determine the deployment method of satellite base stations under different conditions.

5. The method according to claim 1, characterized in that Maximize network performance based on coverage metrics, including: Due to different deployment environments, the threshold T can be changed for different network performance requirements, or the requirements for deployment track height and density are also different. Therefore, in different environments, the specific space The deployment parameters of satellite base stations need to be reset according to different needs or deployment requirements. Select the optimal deployment parameters to achieve economical and high-performance results.

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