A Low-Earth Orbit Satellite Cross-Layer Reliability Constraint System and Method

Abstract

This invention provides a low-Earth orbit (LEO) satellite cross-layer reliability constraint system and method. The system includes a rate adaptive controller, which comprises a fine-grained rate adjustment module and a reliability constraint decision module working collaboratively. The fine-grained rate adjustment module achieves fine-grained continuous adjustment of the transmission rate by allocating non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding format. The reliability constraint decision module is used to statistically model the physical layer decoding reliability and evaluate its statistical confidence level based on the cumulative distribution function of decoding success under different modulation and coding schemes. This invention achieves fine-grained continuous adjustment of the transmission rate through probabilistic shaping technology and introduces a reliability constraint mechanism based on decoding statistical characteristics, thereby improving the throughput and link stability of LEO satellite-to-ground communication under dynamic channels.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and more specifically, to a low-Earth orbit satellite cross-layer reliability constraint system and method. Background Technology

[0002] In low-Earth orbit satellite communication systems, adaptive coding and modulation techniques are typically employed to dynamically match channel conditions in order to cope with the rapid channel changes caused by the high-speed motion of satellites. MODCOD (Modulation and Coding Scheme) is the core concept of this technology; it defines a specific combination of modulation and channel coding schemes used for data transmission. The system switches between multiple preset MODCOD levels to balance transmission rate and reliability under different channel quality conditions. Traditional adaptive coding and modulation methods usually rely on instantaneous SNR (Signal-to-Noise Ratio) measurements of the forward link, comparing them with a pre-set SNR threshold to select the MODCOD with the highest spectral efficiency supported under the current channel conditions. When channel conditions are relatively stable and the instantaneous SNR is far from the switching threshold, this method can achieve a relatively ideal balance between throughput efficiency and decoding reliability.

[0003] However, when the SNR approaches the switching threshold of a certain MODCOD, even if the system determines the ideal SNR and corresponding MODCOD based on theoretical calculations, significant performance fluctuations still occur in actual operation. This phenomenon mainly stems from the superposition of multiple factors: First, the system's SNR measurement and estimation itself have errors, affected by noise interference, multipath fading, and receiver non-ideal characteristics, resulting in the instantaneous SNR estimate used for decision-making not being completely consistent with the true SNR value. Second, the high-speed motion of low-Earth orbit satellites, the Earth's rotation, and changes in the obstruction environment cause rapid fluctuations in the instantaneous SNR of the channel, causing the channel conditions to frequently cross the switching threshold in a short period of time, thus repeatedly triggering MODCOD switching. Furthermore, from SNR measurement to MODCOD decision-making, and then to parameter configuration and signal transmission, there are unavoidable processing and control delays in the system. During this period, the channel continues to change, causing the switching action to lag behind the ideal decision-making timing. In addition, the SNR threshold of MODCOD is usually designed based on long-term channel statistical characteristics, while each system operation faces instantaneous channel conditions. This mismatch between the statistical design and the instantaneous state further increases the possibility of performance jitter. Finally, threshold-based switching is a hard switch or discrete decision, meaning that once the SNR crosses the threshold, it immediately switches to another MODCOD level. This either-or switching mechanism causes the system to repeatedly enter high-order and low-order modes near the threshold, resulting in drastic fluctuations in throughput and bit error rate (BER) within a short period. Therefore, a new method that can achieve fine-grained and highly stable rate adaptation is needed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a low-Earth orbit (LEO) satellite cross-layer reliability constraint system and method. This system achieves fine-grained continuous adjustment of transmission rate through probabilistic shaping technology and introduces a reliability constraint mechanism based on decoding statistical characteristics, thereby improving the throughput and link stability of LEO satellite-to-ground communication under dynamic channels.

[0005] To solve the above problems, the technical solution of the present invention is as follows:

[0006] A low-Earth orbit satellite cross-layer reliability constraint system includes a rate adaptive controller, which comprises a fine-grained rate adjustment module and a reliability constraint decision module working in concert. The fine-grained rate adjustment module achieves fine-grained continuous adjustment of the transmission rate by allocating non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding format. The reliability constraint decision module is used to statistically model the physical layer decoding reliability and evaluate its statistical confidence level based on the cumulative distribution function of decoding success under different modulation and coding schemes.

[0007] Preferably, the fine-grained rate adjustment module employs amplitude-phase keying modulation suitable for satellite communication, and optimizes the probability quality function of the constellation symbols based on the Maxwell-Boltzmann distribution.

[0008] Preferably, when making rate adaptive decisions, the reliability constraint decision module takes a confidence level not lower than a preset threshold as a hard constraint condition, and prioritizes selecting the mode with the highest spectral efficiency from all candidate modes that meet the reliability requirements for transmission.

[0009] Furthermore, the present invention also provides a method for constraining the reliability of low-Earth orbit satellites across layers, comprising the following steps:

[0010] Design and deploy a fine-grained rate control module based on probabilistic shaping. By assigning non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding scheme, fine-grained continuous adjustment of the transmission rate is achieved.

[0011] A reliability constraint decision module based on decoding statistical characteristics is constructed and integrated to statistically model the physical layer decoding reliability and evaluate its statistical confidence based on the cumulative distribution function of decoding success under different modulation and coding schemes.

[0012] A plug-and-play rate adaptive controller is formed and integrated into the low-Earth orbit satellite communication system to improve the system's link stability and overall throughput.

[0013] Preferably, the design and deployment of the fine-grained rate adjustment module based on probability shaping, which achieves fine-grained continuous adjustment of the transmission rate by allocating non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding scheme, specifically includes: the fine-grained rate adjustment function is implemented by a probability shaping controller. At the transmitting end, the probability shaping controller is specifically manifested as a probability shaping mapper integrated after the forward error correction coding module and before the constellation mapping module. The core of the probability shaping mapper is a distribution matcher, which is responsible for mapping the uniformly distributed input bit stream into a constellation symbol sequence that conforms to a non-uniform probability distribution.

[0014] Preferably, the step of constructing and integrating a reliability constraint decision module based on decoding statistical characteristics, statistically modeling the physical layer decoding reliability, and evaluating its statistical confidence level based on the cumulative distribution function of successful decoding under different modulation and coding schemes specifically includes: constructing and integrating a reliability constraint decision module based on decoding statistical characteristics, statistically modeling the physical layer decoding reliability, evaluating its statistical confidence level based on the cumulative distribution function of successful decoding under different modulation and coding schemes, and when making rate decisions, using a decoding confidence level not lower than a preset threshold as a cross-layer constraint condition, prioritizing the selection of the highest spectral efficiency mode that meets reliability requirements, thereby transforming the traditional hard decision switching that relies on instantaneous signal-to-noise ratio into a soft decision based on statistical reliability, effectively suppressing frequent switching and system performance jitter in the critical signal-to-noise ratio region.

[0015] Compared with existing technologies, this invention can effectively improve system throughput and ensure link reliability under dynamic satellite-to-ground channel conditions. It achieves more refined rate adaptation of the original discrete modulation and coding switching through probabilistic shaping. At the same time, by introducing reliability constraints based on the decoding cumulative distribution function, it significantly improves the stability and robustness of adaptive decision-making in the critical signal-to-noise ratio region, thereby optimizing the overall system performance in the rapidly changing low-Earth orbit satellite communication environment. Attached Figure Description

[0016] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0017] Figure 1 This is a diagram illustrating the overall architecture of the low-orbit satellite cross-layer reliability constraint system of the present invention.

[0018] Figure 2 This is a flowchart of the low-orbit satellite cross-layer reliability constraint method of the present invention;

[0019] Figure 3 A schematic diagram of the probability shaping of the 64APSK constellation;

[0020] Figure 4This is a schematic diagram of a cross-layer collaborative design based on decoding statistics.

[0021] Figure 5 A schematic diagram for modeling the cumulative distribution function of decoding. Detailed Implementation

[0022] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0023] Specifically, the present invention provides a low-orbit satellite cross-layer reliability constraint system, such as... Figure 1 As shown, the system includes a rate adaptive controller, which comprises a fine-grained rate adjustment module and a reliability constraint decision module that work together.

[0024] The fine-grained rate adjustment module, based on probabilistic shaping technology, assigns non-uniform transmission probabilities to constellation symbols and dynamically adjusts the shaping entropy under a fixed basic modulation and coding format. This allows the system to achieve continuous or quasi-continuous fine-tuning of the transmission rate without frequently switching the underlying modulation and coding scheme, thus more precisely matching rapidly changing satellite-to-ground channel conditions. This module preferably employs amplitude-phase keying modulation suitable for satellite communication and optimizes the probabilistic quality function of the constellation symbols based on the Maxwell-Boltzmann distribution.

[0025] The core of the reliability constraint decision module lies in probabilistically modeling and explicitly constraining the reliability of physical layer decoding. This module constructs or obtains the cumulative distribution function of decoding success under the current channel conditions for different candidate transmission modes. Based on this statistical model, the module can quantitatively evaluate the statistical confidence level of achieving the target bit error rate when selecting a particular mode. When making rate adaptive decisions, the module uses a confidence level not lower than a preset threshold as a hard constraint and prioritizes the mode with the highest spectral efficiency from all candidate modes that meet this reliability requirement for transmission. This mechanism transforms the traditional hard-decision switching that relies on instantaneous signal-to-noise ratio thresholds into a soft decision based on statistical reliability, thereby effectively suppressing frequent switching and performance jitter caused by small channel fluctuations in the critical signal-to-noise ratio region, significantly improving link stability and decision robustness.

[0026] In one specific implementation, the low-Earth orbit (LEO) satellite cross-layer reliability constraint system achieves coordinated optimization of transmission rate and reliability of the satellite-to-ground link through a rate adaptive controller. This controller is composed of a fine-grained rate adjustment module and a reliability constraint decision module: the former, based on probabilistic shaping technology, continuously fine-tunes the transmission rate by dynamically adjusting the shaping entropy under a fixed basic modulation and coding format; the latter constructs a cumulative distribution function model based on decoding statistical characteristics to ensure link reliability through confidence constraints. The two modules work together to transform the traditional hard-decision switching, which relies on instantaneous signal-to-noise ratio, into a soft-decision system based on statistical reliability, thereby achieving a balance between throughput and stability under dynamic channel conditions. The modular design of this system allows it to be integrated into existing LEO satellite communication architectures at a low modification cost, demonstrating good practical value and deployability.

[0027] Furthermore, the present invention also provides a method for constraining the reliability of low-orbit satellites across layers, such as... Figure 2 As shown, the method includes the following steps:

[0028] S1: Design and deploy a fine-grained rate adjustment module based on probability shaping. By assigning non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding scheme, fine-grained continuous adjustment of the transmission rate is achieved.

[0029] Specifically, a fine-grained rate adjustment module based on probability shaping is designed and deployed. By assigning non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding scheme, fine-grained continuous adjustment of the transmission rate is achieved. This allows for smoother matching of rapidly changing satellite-to-ground channel conditions without frequently switching the underlying modulation and coding scheme.

[0030] The fine-grained rate adjustment function of this invention is implemented by a probability shaping controller. At the transmitting end, this controller is specifically manifested as a probability shaping mapper integrated after the stream adaptation module and before the constellation mapping module. The core of this mapper is a distribution matcher, which is responsible for mapping the uniformly distributed input bit stream into a constellation symbol sequence that conforms to a non-uniform probability distribution.

[0031] In practice, amplitude-phase keying modulation, suitable for satellite communications, is employed. (Constellation symbols) The emission probability distribution is determined by the shaping factor. Control, follow the formula ,in This is a normalized constant. The forming factor is dynamically adjusted. This allows for continuous or quasi-continuous adjustment of the transmission rate by continuously changing the constellation entropy. To visually demonstrate the effect of probability shaping, Figure 3 Taking 64APSK as an example, it demonstrates when When different values ​​such as 0, 3, 10, and 35 are taken, the probability distribution of constellation points changes from uniform to highly concentrated in the inner circle. This process demonstrates the ability of probability shaping to "shape" constellations.

[0032] The probability shaping controller is the core of the execution mechanism that enables continuous rate adaptation. In a specific implementation, a probability shaping mapping module is integrated after the stream adaptation module and before the constellation mapping module in the DVB-S2X transmit chain. The core of this module is a distribution matcher, which is responsible for mapping the uniformly distributed bitstream after stream adaptation to constellation symbols that conform to a non-uniform probability distribution.

[0033] S2: Construct and integrate a reliability constraint decision module based on decoding statistical characteristics, perform statistical modeling of physical layer decoding reliability, and evaluate its statistical confidence based on the cumulative distribution function of decoding success under different modulation and coding schemes;

[0034] Specifically, a reliability constraint decision module based on decoding statistical characteristics is constructed and integrated to statistically model the physical layer decoding reliability and evaluate its statistical confidence based on the cumulative distribution function of decoding success under different modulation and coding schemes. When making rate decisions, the decoding confidence is not lower than a preset threshold as a cross-layer constraint condition, and the highest spectral efficiency mode that meets this reliability requirement is selected first. This transforms the traditional hard decision switching that relies on instantaneous signal-to-noise ratio into a soft decision based on statistical reliability, effectively suppressing frequent switching and system performance jitter in the critical signal-to-noise ratio region.

[0035] The reliability constraint decision-making function of this invention is implemented by a reliability assessment and decision-making module. The core of this module is to statistically model and constrain link reliability using the decoding success cumulative distribution function. By introducing decoding CDF constraints near the MODCOD threshold, the system can further identify potentially high-risk operating ranges under the same SNR conditions, thereby triggering more conservative modulation and coding or shaping parameter adjustment strategies in advance, achieving a more robust trade-off between throughput efficiency and operational stability. A lightweight cross-layer collaborative design approach can be constructed as follows: Figure 4 As shown: The physical layer obtains the decoding statistical characteristics under different signal-to-noise ratio conditions through offline statistics, and provides the reliability information related to a limited number of transmissions to the link layer or higher layers, i.e., adaptive coding and modulation decision.

[0036] First, for each modulation and coding scheme, its traditional signal-to-noise ratio switching threshold is determined through offline simulation, and a limited risk perception range is defined around this threshold, which covers the region where the decoding performance is most sensitive to channel fluctuations.

[0037] Next, construct or obtain the cumulative distribution function model of decoding success for each modulation and coding scheme within its risk perception interval, such as... Figure 5As shown, this model describes the probability of successful decoding within a single or finite number of transmissions under given channel conditions. Based on this model, the statistical confidence level of achieving the target bit error rate when selecting a particular scheme can be quantitatively evaluated. .

[0038] During the rate adaptive decision-making process, this module introduces a preset reliability threshold. (For example, set it to 0.95, which means requiring) The decision-making logic is as follows: When the system detects that the instantaneous signal-to-noise ratio falls within the risk perception range of a certain modulation and coding scheme, it will query the corresponding cumulative distribution function of decoding success and calculate the current confidence level. If the confidence level is lower than... If the system is in a high-risk state, it will adopt a conservative strategy, such as suppressing the switching to higher-order modulation and coding schemes or limiting the shaping factor from increasing to the saturation value, thereby avoiding long-term operation at the edge of decoding performance.

[0039] This mechanism is activated only in the critical signal-to-noise ratio (SNR) region. When channel conditions are significantly better or worse than the threshold, the system still uses the efficient SNR-based lookup table decision-making method to control overall complexity. In this way, decoding statistics are introduced as a cross-layer auxiliary indicator into the decision-making closed loop, aiming to improve the system's decision-making stability in the critical region.

[0040] S3: Forms a plug-and-play rate adaptive controller that can be integrated into low-Earth orbit satellite communication systems to improve the system's link stability and overall throughput.

[0041] Specifically, by integrating the plug-and-play rate adaptive controller formed by the above scheme into the low-Earth orbit satellite communication system, this scheme can achieve a simultaneous improvement in system throughput and link stability while maintaining backward compatibility, without changing the basic coding, modulation and frame structure of the existing standard.

[0042] This invention achieves continuous or quasi-continuous fine-tuning of the transmission rate through the probabilistic shaping module. Compared to traditional schemes that rely on discrete modulation and coding switching, this invention can adapt to the channel by smoothly adjusting the shaping factor when channel conditions change, avoiding throughput loss caused by frequent step switching, thereby significantly improving the average spectral efficiency and overall throughput of the system.

[0043] This invention introduces a cross-layer reliability constraint module based on decoding statistics, transforming the traditional hard-decision switching, which relies on instantaneous signal-to-noise ratio (SNR), into a soft-decision system based on statistical confidence. This mechanism effectively identifies and suppresses frequent switching of modulation and coding schemes caused by minor channel fluctuations in the critical SNR region, significantly improving the stability of link transmission and the robustness of decision-making. Thus, it ensures reliable transmission with low bit error rate while pursuing high throughput.

[0044] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A low-Earth orbit satellite cross-layer reliability constraint system, characterized in that, The system includes a rate adaptive controller, which comprises a fine-grained rate adjustment module and a reliability constraint decision module working in concert. The fine-grained rate adjustment module achieves fine-grained continuous adjustment of the transmission rate by allocating non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding format. The reliability constraint decision module is used to statistically model the reliability of physical layer decoding, evaluating its statistical confidence based on the cumulative distribution function of decoding success under different modulation and coding schemes. For each modulation and coding scheme, its traditional signal-to-noise ratio (SNR) switching threshold is determined through offline simulation, and a finite risk perception interval is defined around this threshold, covering the region where decoding performance is most sensitive to channel fluctuations. During the rate adaptive decision-making process, the reliability constraint decision module introduces a preset reliability threshold. Its decision-making logic is as follows: when the system detects that the instantaneous signal-to-noise ratio falls within the risk perception range of a certain modulation and coding scheme, it will query the corresponding cumulative distribution function of decoding success and calculate the current confidence level; if the confidence level is lower than the reliability threshold... If the system determines that it is currently operating in a high-risk state, it will suppress the switching to a higher-order modulation and coding scheme or limit the shaping factor from increasing to the saturation value, thereby avoiding long-term operation at the edge of decoding performance.

2. The low-orbit satellite cross-layer reliability constraint system according to claim 1, characterized in that, The fine-grained rate adjustment module employs amplitude-phase keying modulation suitable for satellite communication and optimizes the probability quality function of constellation symbols based on the Maxwell-Boltzmann distribution.

3. The low-orbit satellite cross-layer reliability constraint system according to claim 1, characterized in that, When making rate adaptive decisions, the reliability constraint decision module takes a confidence level not lower than a preset threshold as a hard constraint condition, and prioritizes the selection of the mode with the highest spectral efficiency from all candidate modes that meet the reliability requirements for transmission.

4. A method for cross-layer reliability constraints of low-Earth orbit (LEO) satellites, used to implement the LEO satellite cross-layer reliability constraint system as described in claim 1, characterized in that, The method includes the following steps: Design and deploy a fine-grained rate control module based on probabilistic shaping. By assigning non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding scheme, fine-grained continuous adjustment of the transmission rate is achieved. A reliability constraint decision module based on decoding statistical characteristics is constructed and integrated to statistically model the physical layer decoding reliability and evaluate its statistical confidence based on the cumulative distribution function of decoding success under different modulation and coding schemes. A plug-and-play rate adaptive controller is formed and integrated into the low-Earth orbit satellite communication system to improve the system's link stability and overall throughput.

5. The low-orbit satellite cross-layer reliability constraint method according to claim 4, characterized in that, The design and deployment of the probabilistic shaping-based fine-grained rate adjustment module achieves fine-grained continuous adjustment of the transmission rate by allocating non-uniform transmission probabilities to constellation symbols and dynamically adjusting the shaping entropy under a fixed basic modulation and coding scheme. Specifically, the fine-grained rate adjustment function is implemented by a probabilistic shaping controller. At the transmitting end, the probabilistic shaping controller is specifically manifested as a probabilistic shaping mapper integrated after the forward error correction coding module and before the constellation mapping module. The core of the probabilistic shaping mapper is a distribution matcher, which is responsible for mapping the uniformly distributed input bit stream into a constellation symbol sequence that conforms to a non-uniform probability distribution.

6. The low-orbit satellite cross-layer reliability constraint method according to claim 4, characterized in that, The steps of constructing and integrating a reliability constraint decision module based on decoding statistical characteristics, statistically modeling the physical layer decoding reliability, and evaluating its statistical confidence based on the cumulative distribution function of successful decoding under different modulation and coding schemes, specifically include: constructing and integrating a reliability constraint decision module based on decoding statistical characteristics, statistically modeling the physical layer decoding reliability, evaluating its statistical confidence based on the cumulative distribution function of successful decoding under different modulation and coding schemes, and when making rate decisions, using a decoding confidence not lower than a preset threshold as a cross-layer constraint condition, prioritizing the selection of the highest spectral efficiency mode that meets reliability requirements, thereby transforming the traditional hard decision switching that relies on instantaneous signal-to-noise ratio into a soft decision based on statistical reliability, effectively suppressing frequent switching and system performance jitter in the critical signal-to-noise ratio region.

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