LEO satellite power distribution and association method and system based on cellular-free architecture

Abstract

The invention discloses an LEO satellite power distribution and association method and system based on a cellular-free architecture in the technical field of wireless communication, and the method comprises the steps: carrying out the maximum ratio transmission precoding of all satellite access points, and generating a coding modulation signal; after the target users receive the coded modulation signals, receiving signals are formed at a receiving end, and the signal-to-interference ratio and the downlink transmission rate of each target user are calculated through the receiving signals; accumulating downlink transmission rates of all target users through a central satellite, and calculating system energy efficiency in combination with total power consumption of all satellite access points; and constructing an objective function with maximization of the system energy efficiency, solving the objective function through an iterative optimization algorithm under the constraint condition of a satellite transmitting power upper limit and a user-satellite coverage relationship, and determining an optimal power allocation and user-satellite association scheme. The method can adapt to the high-speed motion characteristic of the LEO satellite, and the system energy efficiency and the resource utilization rate are remarkably improved.

Description

Technical Field

[0001] The present invention relates to a method and system for power allocation and association of LEO satellites based on a cell-free architecture, belonging to the field of wireless communication technology. Background Art

[0002] With the rapid development of the metaverse, digital twins, and intelligent Internet of Things, the fifth-generation mobile communication (5G) network has gradually exposed technical bottlenecks in aspects such as ultra-large-scale connection, full-domain coverage, and extreme energy efficiency ratio. To meet the disruptive requirements for communication rate, latency, and connection density in the era of all-things intelligent connection after 2030, the sixth-generation mobile communication technology (6G) has become the global R & D focus. 6G not only deeply integrates the three core scenario characteristics of 5G but also constructs a space-air-ground-sea integrated network through revolutionary technological breakthroughs. Its core features include cross-dimensional innovations such as sub-millisecond latency, terahertz band communication, and intelligent metasurfaces. During this evolution process, the disadvantages of the traditional cellular architecture have become increasingly prominent. For example, the traditional cellular architecture will lead to a sharp drop in edge rate and the problem of frequent handover latency is also inevitable, prompting the academic community to break through the physical boundaries of distributed large-scale MIMO (multi-user) and propose the paradigm innovation of cell-free large-scale MIMO. This technology realizes user-centric seamless service by constructing a distributed collaborative ultra-large-scale antenna array, enabling all users in the whole network to obtain array gain and diversity gain simultaneously, laying a physical layer foundation for 6G to achieve a 10-fold increase in spectral efficiency compared to 5G.

[0003] As a core technology of 6G, cell-free large-scale MIMO technology is a new networking method that breaks the thinking mode of the traditional cellular architecture and cell splitting. It forms large-scale macro diversity through the deployment of a large number of distributed access points and constructs a new architecture centered on users. These APs are connected to the central processor through wireless backhaul links for upstream and downstream user transmission and signal processing. The access points can cooperate flexibly on a large scale, thus significantly improving the performance of each user. Its basic principle is to use multi-antenna technology to achieve spatial multiplexing, diversity transmission, and beamforming to overcome propagation loss and increase system capacity. However, the current research on cell-free large-scale MIMO technology often based on a preset condition that the positions of access points and users are fixed, which fails to fully demonstrate the potential of flexible deployment of this system in practical applications.

[0004] Given the large number and wide distribution of LEO satellites, the traditional cellular network architecture is difficult to meet the requirements of large-scale user access and high-speed data transmission. In contrast, the cell-free massive MIMO network can easily achieve multi-user simultaneous access and high-speed data transmission by virtue of technologies such as spatial multiplexing and diversity transmission, thus fully meeting the requirements of LEO satellite communication for high capacity and high speed. Specifically, the cell-free massive MIMO network not only improves communication capacity and spectral efficiency, but also significantly enhances signal coverage and anti-interference ability. In addition, this network architecture supports flexible network design and dynamic resource allocation, enabling the system to adaptively adjust according to actual needs. At the same time, through optimized design and algorithm improvement, the cell-free massive MIMO network can also effectively reduce system complexity and power consumption, further improving overall performance. More importantly, this network architecture also supports high-frequency communication and Internet of Things applications. Therefore, the cell-free massive MIMO network has become one of the important development directions in the field of LEO satellite communication due to its many advantages.

[0005] In summary, with the continuous growth of data demand, future wireless networks face severe challenges, and LEO satellite communication has become a key solution for future wireless networks due to its low latency, low path loss, and economy. However, the current LEO satellite communication system is limited by the visibility of a single satellite, resulting in short service time. The traditional cellular network architecture is difficult to meet the requirements of large-scale user access and high-speed data transmission, especially in the dynamically changing satellite network environment, and these problems are further exacerbated. Summary of the Invention

[0006] The object of the present invention is to provide a method and system for LEO satellite power allocation and association based on a cell-free architecture, which can solve the problems of sharp drop in edge rate caused by the traditional cellular architecture, service interruption delay caused by frequent inter-satellite handovers, and difficulty in adapting to the drastic change of network topology caused by the high-speed movement of satellites with static resource allocation.

[0007] To solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0008] On the one hand, the present invention provides a method for LEO satellite power allocation and association based on a cell-free architecture, including: Performing maximum ratio transmission precoding on all satellite access points to generate coded modulation signals, and sending the coded modulation signals to target users through each satellite access point; After receiving the coded modulation signals, the target users form received signals at the receiving end, calculate the signal-to-interference-plus-noise ratio and downlink transmission rate of each target user through the received signals, and transmit the downlink transmission rate to the central satellite; The central satellite aggregates the downlink transmission rates of all target users and calculates the system energy efficiency in combination with the total power consumption of all satellite access points. Aiming at the maximization of the system energy efficiency, a target function is constructed. Under the constraint conditions of the upper limit of satellite transmission power and the user-satellite coverage relationship, the target function is solved by an iterative optimization algorithm to obtain the global optimal power allocation matrix and the best user-satellite association matrix. Based on the global optimal power allocation matrix and the best user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined.

[0009] Combined with the first aspect, further, maximum ratio transmission precoding is performed on all satellite access points to generate coded modulation signals, including: Screen all target users within the coverage range of each satellite access point and assign corresponding precoding weights to each target user ; Obtain the baseband signal of each target user , and through the baseband signal and the precoding weight to obtain a weighted signal; Superimpose the weighted signals on all satellite access points to form a coded modulation signal.

[0010] Combined with the first aspect, further, the expression for forming the received signal at the receiving end includes: ; Among them, represents the received signal affected by propagation delay and Doppler frequency shift received by the k-th user at the t-th time slot; m represents the index value of the satellite access point; represents the set of satellite access points associated with the k-th target user; represents the conjugate transpose of the channel impulse response from the m-th satellite access point to the k-th target user; represents the signal transmitted by the m-th satellite access point after considering the propagation delay; j represents the imaginary unit; t represents the time slot; represents the time error caused by the propagation delay; represents the carrier frequency; represents the frequency error caused by the Doppler frequency shift; represents the phase error caused by the Doppler frequency shift; represents the additive noise caused when the k-th user receives the signal at the t-th time slot.

[0011] Combined with the first aspect, further, the signal-to-interference-plus-noise ratio and the downlink transmission rate of each target user are calculated through the received signal, including: Extract the useful signal from the received signal affected by the propagation delay and Doppler frequency shift, and calculate its power, denoted as the first useful signal power ; Based on the frequency error caused by the Doppler frequency shift and the phase error caused by the Doppler frequency shift, separate the co-frequency interference signal from the received signal, and calculate its power, denoted as the second interference signal power ; Through the large-scale fading coefficient between the target user and the satellite access point , separate the co-frequency interference signal between target users, the interference signal of the remaining satellite access points not associated with the current target user, and the noise signal from the received signal, and calculate their powers respectively, denoted as the third interference signal power , the fourth interference signal power and noise power; According to the ratio of the first useful signal power to the cumulative value of the second interference signal power , the third interference signal power , the fourth interference signal power and the noise power , obtain the signal-to-interference-plus-noise ratio of the target user; Determine the downlink transmission rate of the target user according to the signal-to-interference-plus-noise ratio; The expression of the downlink transmission rate includes: ; Among them, represents the downlink transmission rate of the target user; represents the signal-to-interference-plus-noise ratio.

[0012] Combined with the first aspect, further, the expression of the large-scale fading coefficient between the target user and the satellite access point is, including: ; Among them, represents the large-scale fading coefficient between the m-th satellite access point and the k-th user equipment; represents the large-scale fading in the line-of-sight path; represents the large-scale fading in the non-line-of-sight path; represents the line-of-sight; represents the non-line-of-sight.

[0013] Combined with the first aspect, further, the expression of the system energy efficiency includes: ; Among them, denotes the system energy efficiency in the \(t\)-th time slot; \(B\) denotes the total system bandwidth; denotes the total system rate in the \(t\)-th time slot; denotes the total power consumption of the system.

[0014] Combined with the first aspect, further, the constraint conditions of the upper limit of the satellite transmission power and the user-satellite coverage relationship include: ; where \(EE\) denotes the system energy efficiency; denotes the Euclidean distance between the \(m\)-th satellite access point and the \(k\)-th user; denotes the altitude of the \(m\)-th satellite access point; denotes the coverage radius of the \(m\)-th satellite access point; denotes the power allocated by the \(m\)-th satellite access point to the \(k\)-th user; denotes the maximum transmission power of the \(m\)-th satellite; \(P\) denotes the power allocation matrix; \(S\) denotes the user-satellite association matrix.

[0015] Combined with the first aspect, further, determining the optimal power allocation and user-satellite association scheme according to the global optimal power allocation matrix and the best user-satellite association matrix includes: Set the initial power allocation matrix , the coverage range matrix \(Q\), the initial user-satellite association matrix and the Dinkelbach parameter ; In each iteration, fix the current user-satellite association matrix, and solve the convex optimization problem of the objective function through the Dinkelbach algorithm to obtain a new power allocation matrix; fix the new power allocation matrix, select the satellite with the best quality within the coverage range for each target user, and generate the latest user-satellite association matrix; Calculate the latest system energy efficiency according to the new power allocation matrix and the updated user-satellite association matrix; Compare the difference in the system energy efficiency between two adjacent iterations. If the difference in the system energy efficiency is greater than the preset convergence threshold , then update the Dinkelbach parameter \(\lambda\) and enter the next iteration; if the difference in the system energy efficiency is not greater than the preset convergence threshold , then output the current power allocation matrix and the optimal user-satellite association matrix as the optimal solution .

[0016] In the second aspect, a LEO satellite power allocation and association system based on a cell-free architecture includes: Multiple satellite access points for performing maximum ratio transmission precoding, generating coded modulation signals, and transmitting the coded modulation signals to target users through each satellite access point; At least one target user for forming a received signal at the receiving end after receiving the coded modulation signal, calculating the signal-to-interference-plus-noise ratio (SINR) and the downlink transmission rate of each target user through the received signal, and transmitting the downlink transmission rate to the central satellite; The central satellite for accumulating the downlink transmission rates of all target users, and calculating the system energy efficiency in combination with the total power consumption of all satellite access points; constructing an objective function for maximizing the system energy efficiency, and solving the objective function through an iterative optimization algorithm under the constraint conditions of the satellite transmission power upper limit and the user-satellite coverage relationship to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix, and determining an optimal power allocation and user-satellite association scheme based on the global optimal power allocation matrix and the optimal user-satellite association matrix.

[0017] Combined with the second aspect, further, the central satellite is deployed in a central processing unit, the central satellite is connected to the satellite access points through a wireless backhaul link, and the multiple satellite access points communicate with each other through an ultra-high-speed inter-satellite link.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention: In the downlink transmission stage, the present invention uses maximum ratio transmission (MRT) precoding to generate coded modulation signals and transmits them to target users through each satellite access point, effectively improving the signal gain and suppressing interference; after the user end receives the signal, it calculates its signal-to-interference-plus-noise ratio (SINR) and determines the downlink transmission rate according to the Shannon formula. By aggregating all user rates and the total satellite power consumption (including dynamic transmission power and fixed energy consumption), an objective function of system energy efficiency is constructed. Under the constraints of the satellite power upper limit and user coverage, the Dinkelbach algorithm is used to iteratively optimize the power allocation matrix P and the user-satellite association matrix S. The non-convex problem is transformed into a linear optimization through Taylor expansion, and the satellite with the largest Rice factor within the coverage range is dynamically selected to associate with users. Finally, a global optimal solution is output, thus effectively solving the problems of rate edge attenuation, inter-satellite handover delay, and static resource allocation rigidity in the traditional cellular architecture, realizing the co-optimization of spectral efficiency and energy efficiency while reducing the system complexity, and providing technical support for the efficient networking of low-earth orbit satellite communication. Description of the Drawings

[0019] Figure 1 The figure shows a flowchart of a method for LEO satellite power allocation and association based on a cell-free architecture provided by an embodiment of the present invention; Figure 2The figure below shows a comparison chart of the system energy efficiency provided by the embodiments of the present invention; Figure 3 The topology diagram of the satellite communication system provided by the embodiments of the present invention. Detailed implementation manners

[0020] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0021] The term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after. Embodiment 1

[0022] Refer to Figure 1 , this embodiment introduces a LEO satellite power allocation and association method based on a cell-free architecture, including the following steps; Step S1: Perform maximum ratio transmission precoding (MRT precoding) on all satellite access points to generate coded modulation signals, and send the coded modulation signals to the target users through each satellite access point; Step S2: After the target user receives the coded modulation signal, a received signal is formed at the receiving end. The signal-to-interference-plus-noise ratio and the downlink transmission rate of each target user are calculated through the received signal, and the downlink transmission rate is transmitted to the central satellite; Step S3: Aggregate the downlink transmission rates of all target users through the central satellite, and calculate the system energy efficiency in combination with the total power consumption of all satellite access points; Step S4: Construct an objective function with the maximization of the system energy efficiency. Under the constraint conditions of the satellite transmission power upper limit and the user-satellite coverage relationship, solve the objective function through an iterative optimization algorithm to obtain the global optimal power allocation matrix and the best user-satellite association matrix. Based on the global optimal power allocation matrix and the best user-satellite association matrix, determine the optimal power allocation and user-satellite association scheme.

[0023] In summary, through the maximum ratio transmission precoding technology, the present invention effectively improves the signal quality, enhances the anti-interference ability and reliability of the system. At the same time, by calculating the signal-to-interference-plus-noise ratio based on the received signal and determining the downlink transmission rate, the accurate control of the communication quality of each user is realized, and the spectrum utilization rate is improved.

[0024] In addition, taking the obtained system energy efficiency as the optimization goal, optimization is carried out in terms of power allocation and user association, which not only reduces the system energy consumption but also significantly improves the resource utilization rate. This optimization strategy can flexibly adjust the power allocation and user association relationship according to the user distribution and channel conditions, further enhancing the flexibility and adaptability of the system and providing users with more stable and efficient communication services. Embodiment 2

[0025] To maximize the signal power received by the target user while reducing the interference to other target users, maximum ratio transmission precoding processing is performed on all satellite access points to generate coded modulation signals, which specifically includes the following steps: Step S31: Screen out all target users within the coverage range of each satellite access point and assign corresponding precoding weights to each target user ; The expression of the precoding weight is: (1) Where, represents the precoding weight designed by the m-th access point for the k-th user; represents the power assigned by the m-th satellite access point to the k-th user; represents the channel vector between the m-th satellite access point and the k-th target user at the t-th time slot; represents taking the expected value of the squared modulus of the channel vector .

[0026] Obtain the baseband signal of each target user , and through the product of the baseband signal and the precoding weight , obtain a weighted signal; Step S33: Superimpose the weighted signals on all satellite access points to form a coded modulation signal.

[0027] The expression of the coded modulation signal is: (2) Where, represents the modulation signal of the m-th access point at the t-th time slot; K represents the number of single-antenna target users; k represents the user index; represents the baseband signal sent to the k-th user at the t-th time slot.

[0028] Further, the encoded modulation signal is sent to the target user through each satellite access point, and the target user receives the signal sent by the satellite access point. However, due to the effects of propagation delay and Doppler frequency shift, the received signal by the user will have time error, frequency error, and phase error. Therefore, the expression of the received signal after being affected by propagation delay and Doppler frequency shift is: (3) where represents the received signal affected by propagation delay and Doppler frequency shift received by the k-th user at the t-th time slot; m represents the index value of the satellite access point; represents the set of satellite access points associated with the k-th target user; represents the conjugate transpose of the channel impulse response from the m-th satellite access point to the k-th target user; represents the signal sent by the m-th satellite access point considering the propagation delay; j represents the imaginary unit; t represents the time slot; represents the time error caused by the propagation delay; represents the carrier frequency; represents the frequency error caused by the Doppler frequency shift, that is, the frequency error follows a normal distribution with a mean of 0 and a variance of; represents the phase error caused by the Doppler frequency shift, that is, the phase error follows a normal distribution with a mean of 0 and a variance of; represents the additive noise caused when the k-th user receives the signal at the t-th time slot.

[0029] Further, calculate the signal-to-interference-plus-noise ratio (SINR) of each target user through the received signal, and then determine the downlink transmission rate of the target user according to the SINR, including the following steps: Step S51: Extract the useful signal from the received signal affected by propagation delay and Doppler frequency shift, and calculate the power of the useful signal, denoted as the first useful signal power ; The expression of the first useful signal power is: (4) where represents the first useful signal power; represents the impact of the time error caused by the propagation delay on the system performance, and its value range is between 0 and 1; represents the power allocated by the m-th satellite access point to the k-th user; represents the standard deviation of the frequency error; represents the standard deviation of the phase error.

[0030] Step S52: Based on the frequency error and phase error caused by Doppler frequency shift, separate the co-frequency interference signal from the received signal, and calculate the power of the co-frequency interference signal, denoted as the second interference signal power. ; The expression of the second interference signal power is: (5) where, represents the second interference signal power.

[0031] Step S53: Combine the large-scale fading coefficient between the target user and the satellite access point, separate the co-frequency interference signal between target users, the interference signal of the remaining satellite access points, and the noise signal from the received signal, and calculate the power of the co-frequency interference signal between target users, the interference signal of the remaining satellite access points, and the noise signal respectively, denoted as the third interference signal power , the fourth interference signal power and the noise power ; The expressions of the third interference signal power, the fourth interference signal power, and the noise power are respectively: (6) where, represents the third interference signal power; represents the Rice factor between the m-th satellite access point and the k-th user; represents the large-scale fading coefficient between the m-th satellite access point and the k-th user in the line-of-sight Los path; represents the large-scale fading coefficient between the m-th satellite access point and the k-th user in the non-line-of-sight NLos path; N represents the normalization factor.

[0032] (7) where, represents the fourth interference signal power; represents the power allocated by the m-th satellite access point to the i-th user; represents the Rice factor between the m-th satellite access point and the i-th user; represents the large-scale fading coefficient between the m-th satellite access point and the i-th user in the line-of-sight Los path; represents the large-scale fading coefficient between the m-th satellite access point and the i-th user in the non-line-of-sight NLos path; i represents the number of users.

[0033] (8) where, represents the noise power; represents the background noise power spectral density; represents the total system bandwidth; represents the noise figure.

[0034] Furthermore, the Rice factor between the satellite access point and the target user in the formula (6) can be expressed as: (9) where, represents the probability occupied by the line-of-sight Los path, and its expression can be represented as: (10) where, represents the Euclidean distance between the m-th satellite access point and the k-th user.

[0035] In the embodiments of the present invention, in order to accurately simulate and evaluate the signal transmission and noise characteristics in the satellite communication system, the relevant parameters are set in detail. Among them, , , , , the total system bandwidth B = 200 (MHz), NF = 7 (dB). These parameters together determine the noise level and signal transmission quality in the system, providing a basis for subsequent system performance analysis and optimization.

[0036] It should be noted that due to factors such as distance and obstacles, the signal will attenuate during transmission between the target user and the satellite access point. Here, the large-scale fading coefficient is used to represent the fading situation between the target user and the satellite access point. The expression of the large-scale fading coefficient is: (11) where, represents the large-scale fading coefficient between the m-th satellite access point and the k-th user equipment; represents the large-scale fading under the line-of-sight path; represents the large-scale fading under the non-line-of-sight path; represents the line-of-sight, mainly considering the path loss; represents the non-line-of-sight, adding additional losses on the basis of the path loss, such as shadow fading caused by obstacles, etc.

[0037] Specifically, the large-scale fading under the line-of-sight path and the large-scale fading under the non-line-of-sight path in the formula (11) can be expressed as: (12) (13) where, Indicates the path loss when the reference distance is 1 meter in the line-of-sight path; Indicates the path loss caused by the Euclidean distance between the k-th target user and the m-th satellite access point; Indicates the path loss exponent in the line-of-sight path; Indicates the additional loss caused by distance in the non-line-of-sight path, mainly considering signal attenuation caused by factors such as obstacles, and can be expressed as: (14) Among them, Indicates the path loss when the reference distance is 1 meter in the non-line-of-sight path; Indicates the path loss exponent in the non-line-of-sight path; Indicates shadow fading, and , that is, shadow fading follows a normal distribution with a mean of 0 and a variance of ; Indicates the loss caused by the angle of the line of sight; Furthermore, the loss caused by the angle of the line of sight in formula (3) can be expressed as: (15) (16) Among them, Indicates the angle of the line of sight, that is, the angle between the line connecting the target user and the satellite and the satellite height direction; Indicates the antenna factor; Indicates the height of the m-th satellite; Indicates the Euclidean distance between the k-th target user and the m-th satellite access point.

[0038] Step S54, according to the ratio of the first useful signal power to the sum of the second interference signal power, the third interference signal power, the fourth interference signal power, and the noise power, obtain the signal-to-interference-plus-noise ratio of the target user; The expression of the signal-to-interference-plus-noise ratio is: (17) Among them, Indicates the signal-to-interference-plus-noise ratio of the k-th user in the t-th time slot.

[0039] Step S55, based on the signal-to-interference-plus-noise ratio, use the Shannon formula to calculate the downlink transmission rate of the target user.

[0040] The expression of the downlink transmission rate is: (18) Among them, Indicates the downlink transmission rate of the k-th user in the t-th time slot.

[0041] Further, combining the total power consumption of all satellite access points and accumulating the downlink transmission rates of all target users, the system energy efficiency is calculated, including the following steps: Step S61: Aggregate all target users to obtain the system total rate; The expression of the system total rate is: (19) Where, represents the system total rate in the t-th time slot; K represents the number of single-antenna target users; k represents the index value of the target user.

[0042] Step S62: Calculate the total power consumption of the LEO (Low Earth Orbit) satellite communication system; The expression of the total power consumption of the LEO satellite communication system is: (20) Where, represents the system total power consumption; M represents the number of satellite access points; represents the fixed power consumption of each satellite access point; represents the power allocated by the m-th satellite access point to the k-th user.

[0043] In the embodiment of the present invention, the fixed power consumption is set to 10 dBW.

[0044] Step S63: Obtain the system energy efficiency according to the ratio of the system total rate to the system total power consumption.

[0045] The expression of the system energy efficiency is: (21) Where, represents the system energy efficiency.

[0046] Further, a target function is constructed to maximize the system energy efficiency. Under the constraint conditions of the satellite transmission power upper limit and the user-satellite coverage relationship, the target function is solved by an iterative optimization algorithm to obtain the global optimal power allocation matrix and the best user-satellite association matrix. Based on the global optimal power allocation matrix and the best user-satellite association matrix, the optimal power allocation and user-satellite association scheme are determined.

[0047] Specifically, in the process of solving the global optimal power allocation matrix P and the best user-satellite association matrix S, it is first necessary to establish a mathematical model of the optimization problem, and the optimization goal is to maximize the system energy efficiency EE. At the same time, the following constraint conditions are satisfied: a. Coverage constraint: The Euclidean distance between the m-th satellite access point and the k-th user needs to satisfy . Among them, , which is jointly determined by the satellite altitude and the antenna field of view (set to 60 degrees).

[0048] b. Power constraint, the total transmission power of each satellite shall not exceed the upper limit, which is set to 15 dBW in the embodiments of the present invention.

[0049] Further, the expression of the constraint condition is: (22) where EE represents the system energy efficiency; represents the Euclidean distance between the m-th satellite access point and the k-th user; represents the altitude of the m-th satellite access point; represents the coverage radius of the m-th satellite access point; represents the maximum transmission power of the m-th satellite.

[0050] Further, to simplify the optimization problem, the Dinkelbach algorithm is used to transform the non-linear objective function in formula (22) into a linear form.

[0051] Specifically, combining formula (21), the objective function is rewritten as and the total system rate R(t) is further decomposed into two parts and and

[0052] where the rewritten expression of the total system rate is (for brief analysis, the time slot parameter t will be omitted later): (23) where R represents the total rate of the system; represents the total logarithmic capacity of the received signals of all target users; represents the logarithmic capacity containing only interference and noise.

[0053] The decomposed expressions are respectively: (24) (25) The linear upper bound obtained by performing a first-order Taylor expansion at the initial power allocation matrix; The expression of the linear upper bound is: (26) where represents at The linear upper bound at; P represents the independent variable of the Taylor expansion and is the power allocation matrix; represents the initial power allocation matrix; represents the interference-noise logarithmic capacity under the initial power allocation.

[0054] According to the linear upper bound, a linear lower bound of the total system rate is obtained.

[0055] The expression of the linear lower bound of the total system rate is: (27) where, represents the lower bound of the total system rate; Based on the lower bound of the total system rate, the system energy efficiency can be transformed into a linear form.

[0056] The expression after the system energy efficiency is transformed into a linear form is: (28) where, represents the normalized value of the rate lower bound per unit bandwidth, that is .

[0057] By iteratively updating the power allocation matrix P and the association matrix S, the global optimal solution is gradually approximated. Finally, the Dinkelbach algorithm terminates when the convergence condition is satisfied, and the optimal power allocation and user-satellite association scheme are output.

[0058] Furthermore, according to the global optimal power allocation matrix and the best user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined, including the following steps: Step S91, set the initial power allocation matrix, coverage range matrix, initial user-satellite association matrix, and Dinkelbach parameter ; Specifically, assign the set coverage range matrix to the initial user-satellite association matrix , that is . Among them, the element indicates that the k-th user is within the coverage range of the m-th satellite; indicates that the k-th user is not within the coverage range of the m-th satellite.

[0059] Step S92, at each iteration, fix the current user-satellite association matrix , that is, the user-satellite association matrix obtained in the previous iteration, and solve for a new power allocation matrix through the Dinkelbach algorithm , which is the power allocation matrix obtained in the current iteration period; The expression of the new power allocation matrix is: (29) Wherein, represents the new power allocation matrix at the t-th iteration; represents the Dinkelbach parameter of the previous iteration; represents the new total system power consumption at the t-th iteration; represents the new user-satellite association matrix at the t-th iteration; represents the user-satellite association matrix of the previous iteration; represents the power allocated by the m-th satellite access point to the k-th user at the t-th iteration.

[0060] Furthermore, the new total system power consumption is expressed as: (30) Wherein, represents the set of satellites serving the target user k at the t-th iteration.

[0061] Dinkelbach parameter The expression of is: (31) Wherein, represents the latest Dinkelbach parameter; represents the upper bound of the total system rate calculated based on the power allocation matrix at the t-th iteration; represents the upper bound of the first-order Taylor expansion based on the power allocation matrix of the previous iteration; represents the power allocation matrix at the previous iteration.

[0062] Combined with formula (23), by adding the iteration parameters here, the total system rate at the t-th iteration is obtained, that is ; According to the ratio of the total system rate at the t-th iteration to the total system power consumption at the t-th iteration, the system energy efficiency at the t-th iteration is obtained.

[0063] The expression of the system energy efficiency at the t-th iteration is: (32) Wherein, represents the system energy efficiency at the t-th iteration; represents the total system rate at the t-th iteration; represents the system power consumption at the t-th iteration.

[0064] Step S93: Fix the new power allocation matrix , for the k-th target user, select the satellite with the best channel quality, i.e., the largest Rice factor, from the remaining satellites in the coverage satellites, so as to obtain the best user-satellite association matrix ; The expression of the best user-satellite association matrix is: (33) where represents the Rice factor between the -th satellite access point and the target user; represents the set of coverage satellites of the target user k, i.e., all satellites that can cover user k at the current moment; represents traversing the index variable of the satellites in the coverage satellite set ; represents the best user-satellite association matrix;.

[0065] Step S94: Calculate the latest system energy efficiency according to the new power allocation matrix and the best user-satellite association matrix ; Combined with formula (23), the total system rate at the (t + 1)-th iteration is obtained, i.e., ; According to the ratio of the total system rate at the (t + 1)-th iteration to the total system power consumption at the (t + 1)-th iteration, the system energy efficiency at the (t + 1)-th iteration is obtained.

[0066] The expression of the system energy efficiency at the (t + 1)-th iteration is: (34) where represents the system energy efficiency at the (t + 1)-th iteration; represents the total system rate at the (t + 1)-th iteration; represents the system power consumption at the (t + 1)-th iteration.

[0067] Step S95: Compare the difference in system energy efficiency between two adjacent iterations. If the difference in system energy efficiency is greater than the preset convergence threshold , update the Dinkelbach parameter λ and enter the next iteration; if the difference in system energy efficiency is not greater than the preset convergence threshold , i.e., , then output the current power allocation matrix and the optimal user-satellite association matrix as the optimal solution .

[0068] In summary, combined with Figure 2 it can be seen that in the initial stage, the traditional method adopts a fixed power allocation and user-satellite association strategy, with low and smoothly changing energy efficiency; while the present invention gradually optimizes resource allocation during the iteration process by dynamically adjusting the power allocation matrix (as shown in Table 1) and the optimal user-satellite association matrix based on the Rice factor (as shown in Table 2), and the energy efficiency is significantly improved with the number of iterations and finally converges to the global optimal value.

[0069] Table 1 Optimal power allocation matrix obtained by the present invention

[0070]

[0071] Table 2 Optimal user-satellite association matrix obtained by the present invention

[0072]

[0073] It can be observed from the figure that the curve of the present invention is significantly higher than that of the traditional method and tends to be stable in the later stage of iteration, indicating that the algorithm meets the preset convergence threshold through a limited number of optimizations, verifying its high efficiency and robustness. The optimized scheme sparsely associates by screening the satellite with the best channel quality within the coverage range, which not only reduces the occupancy of redundant satellite resources but also reduces the total system power consumption, thereby achieving a multiple-fold improvement in energy efficiency in scenarios adapting to the high-speed movement of satellites and the dynamic change of network topology, providing a feasible solution for the energy efficiency optimization of the space-air-ground integrated communication network.

[0074] Embodiment 3 The present invention abandons the traditional cellular network architecture and instead adopts a more flexible and dynamic cell-free network architecture. Refer to Figure 3 , a LEO satellite power allocation and association system based on a cell-free network architecture, including: M satellite access points, used for maximum ratio transmission precoding, generating coded modulation signals, and sending the coded modulation signals to target users through each satellite access point; K single-antenna target users, used for forming a received signal at the receiving end after receiving the coded modulation signal, calculating the signal-to-interference-plus-noise ratio and the downlink transmission rate of each target user through the received signal, and transmitting the downlink transmission rate to the central satellite; One central satellite is used to aggregate the downlink transmission rates of all target users and calculate the system energy efficiency by combining the total power consumption of all satellite access points. A target function is constructed based on the maximization of the system energy efficiency. Under the constraint conditions of the upper limit of satellite transmission power and the user-satellite coverage relationship, the target function is solved through an iterative optimization algorithm to obtain the global optimal power allocation matrix and the best user-satellite association matrix. Based on the global optimal power allocation matrix and the best user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined.

[0075] Among them, the central satellite is deployed in the central processing unit (CPU). The central satellite is connected to the satellite access points through a wireless backhaul link, and the multiple satellite access points communicate with each other through an ultra-high-speed inter-satellite link, thereby improving the overall performance and coverage of the system.

[0076] Specifically, each target user is randomly and uniformly distributed within a square area with side length D; each satellite access point is randomly and uniformly distributed within a spatial area with length and width both being A and height being C, and the satellite access points are independent of each other.

[0077] Combining Embodiment 1 and Embodiment 2, by setting the number of satellite access points M = 10, the number of target users with single antennas K = 5, and combining the distribution parameters of the satellite access points and target users (the length and width A = 1500 km of the spatial area where the satellite access points are distributed, the side length D = 1000 km of the square area where the users are distributed, the height C = 2000 km of the area where the satellite access points are distributed), a dynamic simulation environment conforming to the typical scenario of low-earth-orbit satellite communication is constructed. Through the line-of-sight and non-line-of-sight path loss parameters (the path loss exponent under the line-of-sight path, the path loss exponent under the non-line-of-sight path, the path loss when the reference distance is 1 m under the line-of-sight path, the path loss when the reference distance is 1 m under the non-line-of-sight path), the antenna factor ; the standard deviation of shadow fading; accurately simulating the channel dynamic characteristics caused by the high-speed movement of the satellite, under the constraint of the satellite height range km, the simulation results verify the effectiveness of the proposed power allocation and association scheme: the system energy efficiency is significantly improved, and at the same time, the algorithm shows strong robustness under complex conditions such as Doppler frequency shift, time delay error, and drastic changes in network topology.

[0078] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A power allocation and association method for LEO satellites based on a cell-free architecture, characterized in that It includes: Performing maximum ratio transmission precoding on all satellite access points to generate coded modulation signals, and sending the coded modulation signals to the target users through each satellite access point; After receiving the coded modulation signals, the target users form received signals at the receiving end, calculate the signal-to-interference-plus-noise ratio (SINR) and the downlink transmission rate of each target user through the received signals, and transmit the downlink transmission rate to the central satellite; Aggregating the downlink transmission rates of all target users through the central satellite, and calculating the system energy efficiency in combination with the total power consumption of all satellite access points; Constructing an objective function for maximizing the system energy efficiency, and solving the objective function through an iterative optimization algorithm under the constraint conditions of the upper limit of satellite transmission power and the user-satellite coverage relationship to obtain the global optimal power allocation matrix and the best user-satellite association matrix, and determining the optimal power allocation and user-satellite association scheme based on the global optimal power allocation matrix and the best user-satellite association matrix.

2. The method for LEO satellite power allocation and association based on a cell-free architecture according to claim 1, wherein, Performing maximum ratio transmission precoding on all satellite access points to generate coded modulation signals, including: Screen all target users within the coverage of each satellite access point and assign corresponding precoding weights to each target user ; Obtain the baseband signal of each target user , through the baseband signal and the precoding weight to obtain a weighted signal by multiplying them; Superposing the weighted signals on all satellite access points to form coded modulation signals.

3. The method for LEO satellite power allocation and association based on a cell-free architecture according to claim 1, wherein The expression for forming the received signal at the receiving end includes: ； Among them, represents the received signal affected by propagation delay and Doppler shift received by the k-th user in the t-th time slot; m represents the index value of the satellite access point; represents the set of satellite access points associated with the k-th target user; represents the conjugate transpose of the channel impulse response from the m-th satellite access point to the k-th target user; represents the signal transmitted by the m-th satellite access point after considering the propagation delay; j represents the imaginary unit; t represents the time slot; represents the time error caused by the propagation delay; represents the carrier frequency; represents the frequency error caused by the Doppler shift; represents the phase error caused by the Doppler shift; represents the additive noise caused when the k-th user receives the signal in the t-th time slot.

4. The method for LEO satellite power allocation and association based on a cell-free architecture according to claim 3, wherein, Calculating the SINR and the downlink transmission rate of each target user through the received signal, including: Extract the useful signal from the received signal affected by the propagation delay and Doppler frequency shift, and calculate its power, denoted as the first useful signal power ; Based on the frequency error caused by the Doppler frequency shift and the phase error caused by the Doppler frequency shift, separate the co-frequency interference signal from the received signal and calculate its power, denoted as the second interference signal power ; Based on the large-scale fading coefficient between the target user and the satellite access point , separate the co-channel interference signals between target users, the interference signals of the remaining satellite access points not associated with the current target user, and the noise signal from the received signal, and calculate their powers respectively, denoted as the power of the third interference signal , the power of the fourth interference signal and the noise power; According to the power of the first useful signal and the power of the second interference signal , the power of the third interference signal , the power of the fourth interference signal and the sum of the noise power to obtain the signal-to-interference-plus-noise ratio of the target user; Determining the downlink transmission rate of the target user according to the SINR; The expression for the downlink transmission rate includes: ； Among them, represents the downlink transmission rate of the target user; represents the signal-to-dry ratio.

5. The method for power allocation and association of LEO satellites based on a cell-free architecture according to claim 4, wherein The expression for the large-scale fading coefficient between the target user and the satellite access point is, including: ； Among them, represents the large-scale fading coefficient between the m-th satellite access point and the k-th user equipment; represents the large-scale fading in the line-of-sight path; represents the large-scale fading in the non-line-of-sight path; represents line-of-sight; represents non-line-of-sight.

6. The method for LEO satellite power allocation and association based on a cell-free architecture according to claim 1, wherein The expression for the system energy efficiency includes: ； Among them, represents the system energy efficiency of the t-th time slot; B represents the total system bandwidth; represents the total system rate of the t-th time slot; represents the total system power consumption.

7. The method for power allocation and association of LEO satellites based on a cell-free architecture according to claim 1, wherein The constraint conditions of the upper limit of satellite transmission power and the user-satellite coverage relationship include: ； Among them, EE represents the system energy efficiency; represents the Euclidean distance between the m-th satellite access point and the k-th user; represents the altitude of the m-th satellite access point; represents the coverage radius of the m-th satellite access point; represents the power allocated by the m-th satellite access point to the k-th user; represents the maximum transmit power of the m-th satellite; P represents the power allocation matrix; S represents the user-satellite association matrix.

8. The method for power allocation and association of LEO satellites based on a cell-free architecture according to claim 1, wherein Determining the optimal power allocation and user-satellite association scheme according to the global optimal power allocation matrix and the best user-satellite association matrix, including: Set the initial power allocation matrix , coverage matrix Q, initial user-satellite association matrix and the Dinkelbach parameter ; At each iteration, Fixing the current user-satellite association matrix, and solving the convex optimization problem of the objective function through the Dinkelbach algorithm to obtain a new power allocation matrix; Fixing the new power allocation matrix, selecting the satellite with the best quality within the coverage range for each target user to generate the latest user-satellite association matrix; Calculating the latest system energy efficiency according to the new power allocation matrix and the updated user-satellite association matrix; Compare the difference in the system energy efficiency between two adjacent iterations. If the difference in the system energy efficiency is greater than a preset convergence threshold , update the Dinkelbach parameter λ and enter the next iteration; if the difference in the system energy efficiency is not greater than the preset convergence threshold , output the current power allocation matrix and the optimal user-satellite association matrix as the optimal solution .

9. A power allocation and association system for LEO satellites based on a cell-free architecture, characterized in that, It includes: Multiple satellite access points for performing maximum ratio transmission precoding, generating coded modulation signals, and sending the coded modulation signals to the target users; At least one target user for forming a received signal at the receiving end after receiving the coded modulation signals, calculating the SINR and the downlink transmission rate of each target user through the received signal, and transmitting the downlink transmission rate to the central satellite; A central satellite for accumulating the downlink transmission rates of all target users and calculating the system energy efficiency in combination with the total power consumption of all satellite access points; Construct an objective function to maximize the energy efficiency of the system. Under the constraint conditions of the upper limit of satellite transmission power and the user-satellite coverage relationship, solve the objective function through an iterative optimization algorithm to obtain the global optimal power allocation matrix and the best user-satellite association matrix. Based on the global optimal power allocation matrix and the best user-satellite association matrix, determine the optimal power allocation and user-satellite association scheme.

10. The cell-free architecture-based LEO satellite power allocation and association system according to claim 9, wherein The central satellite is deployed in the central processing unit. The central satellite is connected to the satellite access points through a wireless backhaul link, and the multiple satellite access points communicate with each other through an ultra-high-speed inter-satellite link.

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