A method and system for LEO satellite power allocation and association based on a cell-free architecture

Abstract

The application discloses a kind of LEO satellite power distribution and association method and system based on no cellular architecture in the field of wireless communication technology, the method includes: maximum ratio transmission precoding is carried out to all satellite access points, and encoding modulation signal is generated;After the target user receives the encoding modulation signal, receive signal is formed at receiving end, and the signal-to-interference ratio and downlink transmission rate of each target user are calculated by the receive signal;The downlink transmission rate of all target users is accumulated by central satellite, and the total power consumption of all satellite access points is combined to calculate system energy efficiency;With the maximization of the system energy efficiency to construct objective function, under the constraint condition of satellite transmission power upper limit and user-satellite coverage relationship, the objective function is solved by iterative optimization algorithm to determine the optimal power distribution and user-satellite association scheme.The application can adapt to the high-speed motion characteristics of LEO satellite, and significantly improve system energy efficiency and resource utilization.

Description

Technical Field

[0001] The present invention relates to a LEO satellite power distribution and association method and system based on a non-cellular architecture, belonging to the technical field of wireless communications. Background Art

[0002] With the rapid development of the metaverse, digital twins, and the intelligent Internet of Things, fifth-generation mobile communication (5G) networks are gradually facing technical bottlenecks in terms of ultra-large-scale connectivity, universal coverage, and extreme energy efficiency. To meet the disruptive demands for communication speed, latency, and connection density in the era of intelligent connectivity after 2030, sixth-generation mobile communication technology (6G) has become a global research and development focus. 6G not only deeply integrates the three core scenario characteristics of 5G but also builds an integrated network across air, space, land, and sea through revolutionary technological breakthroughs. Its core features include cross-dimensional innovations such as sub-millisecond latency, terahertz frequency band communication, and intelligent metasurfaces. During this evolution, the drawbacks of traditional cellular architecture have become increasingly prominent. For example, traditional cellular architectures can lead to sharp drops in edge data rates and unavoidable latency issues caused by frequent handovers. This has prompted academics to break through the physical boundaries of distributed massive MIMO (multi-user) and propose a paradigm shift: cell-free massive MIMO. This technology achieves user-centric seamless services by building a distributed collaborative ultra-large-scale antenna array, allowing all users on the entire network to obtain array gain and diversity gain at the same time, laying the physical layer foundation for 6G to achieve a 10-fold increase in spectrum efficiency compared to 5G.

[0003] Cell-free massive MIMO technology, a core 6G technology, is a novel networking approach that breaks away from the traditional cellular architecture and the fragmented cell model. Instead, it deploys a large number of distributed access points to achieve large-scale macro-diversity and builds a new user-centric architecture. These access points connect to a central processor via wireless backhaul links for upstream and downstream user transmission and signal processing. Access points can collaborate flexibly on a large scale, significantly improving performance for each user. Its fundamental principle is to leverage multi-antenna technology to achieve spatial multiplexing, diversity transmission, and beamforming to overcome propagation losses and increase system capacity. However, current research on cell-free massive MIMO technology often assumes that the locations of access points and users are fixed, which fails to fully demonstrate the system's potential for flexible deployment in practical applications.

[0004] Given the large number and widespread distribution of LEO satellites, traditional cellular network architectures struggle to meet the demands of large-scale user access and high-speed data transmission. In contrast, cell-free massive MIMO networks, leveraging technologies such as spatial multiplexing and diversity transmission, easily enable simultaneous multi-user access and high-speed data transmission, fully meeting the high-capacity and high-speed requirements of LEO satellite communications. Specifically, cell-free massive MIMO networks not only improve communication capacity and spectral efficiency, but also significantly enhance signal coverage and interference mitigation. Furthermore, this network architecture supports flexible network design and dynamic resource allocation, enabling the system to adapt to actual needs. Furthermore, through optimized design and algorithm improvements, cell-free massive MIMO networks can effectively reduce system complexity and power consumption, further improving overall performance. Importantly, this network architecture also supports high-frequency communications and IoT applications. Therefore, with its many advantages, cell-free massive MIMO networks have become a key development direction in LEO satellite communications.

[0005] In summary, the continued growth of data demand poses significant challenges to future wireless networks. LEO satellite communications, with their low latency, low path loss, and affordability, are a key solution. However, current LEO satellite communication systems are limited by the visibility of a single satellite, resulting in short service times. Traditional cellular network architectures struggle to cope with the demands of large-scale user access and high-speed data transmission, especially in a dynamically changing satellite network environment, exacerbating these challenges. Summary of the Invention

[0006] The purpose of the present invention is to provide a LEO satellite power allocation and association method and system based on a cellular-free architecture, which can solve the problems of edge rate drop caused by traditional cellular architecture, service interruption delay caused by frequent inter-satellite switching, and static resource allocation being difficult to adapt to the drastic changes in network topology caused by high-speed satellite movement.

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

[0008] In one aspect, the present invention provides a method for power allocation and association of LEO satellites based on a non-cellular architecture, comprising:

[0009] Performing maximum ratio transmission precoding on all satellite access points to generate coded modulated signals, and sending the coded modulated signals to target users through each satellite access point;

[0010] After the target user receives the coded modulated signal, a receiving signal is formed at the receiving end, the signal-to-noise ratio and downlink transmission rate of each target user are calculated based on the received signal, and the downlink transmission rate is transmitted to the central satellite;

[0011] The central satellite aggregates the downlink transmission rates of all target users and calculates the system energy efficiency by combining the total power consumption of all satellite access points;

[0012] An objective function is constructed by maximizing the energy efficiency of the system. Under the constraints of the satellite transmission power upper limit and the user-satellite coverage relationship, the objective function is solved by an iterative optimization algorithm to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix. Based on the global optimal power allocation matrix and the optimal user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined.

[0013] In combination with the first aspect, further, performing maximum ratio transmission precoding on all satellite access points to generate coded modulation signals includes:

[0014] Filter all target users within the coverage of each satellite access point and assign corresponding precoding weights to each target user ;

[0015] Obtain the baseband signal of each target user , through the baseband signal With the precoding weight The product of , gets the weighted signal;

[0016] The weighted signals are superimposed on all satellite access points to form coded modulation signals.

[0017] In combination with the first aspect, further, forming an expression of the received signal at the receiving end includes:

[0018] ;

[0019] in, represents the received signal affected by propagation delay and Doppler frequency shift received by the kth user in the tth 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 mth satellite access point to the kth target user; represents the signal sent by the mth satellite access point after taking into account the propagation delay; j represents the imaginary unit; t represents the time slot; Represents the time error caused by propagation delay; Indicates the carrier frequency; Represents the frequency error caused by Doppler shift; Represents the phase error caused by Doppler shift; represents the additive noise caused by the kth user receiving the signal in the tth time slot.

[0020] In combination with the first aspect, further calculating the signal-to-noise ratio and downlink transmission rate of each target user using the received signal includes:

[0021] Extract the useful signal from the received signal affected by the propagation delay and Doppler frequency shift, and calculate its power, which is recorded as the first useful signal power. ;

[0022] Based on the frequency error caused by the Doppler shift and the phase error caused by the Doppler shift, the same-frequency interference signal is separated from the received signal, and its power is calculated and recorded as the second interference signal power. ;

[0023] By using the large-scale fading coefficient between the target user and the satellite access point , separate the co-channel 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 power respectively, which is recorded as the third interference signal power , the fourth interference signal power as well as Noise power;

[0024] According to the first useful signal power With the second interference signal power , the third interference signal power , the fourth interference signal power and noise power The signal-to-drying ratio of the target user is obtained by calculating the ratio of the accumulated values ​​of

[0025] determining a downlink transmission rate of the target user according to the signal-to-noise ratio;

[0026] The expression of the downlink transmission rate includes:

[0027] ;

[0028] in, Indicates the downlink transmission rate of the target user; Indicates the drying ratio.

[0029] In combination with the first aspect, further, an expression for the large-scale fading coefficient between the target user and the satellite access point is, including:

[0030] ;

[0031] in, represents the large-scale fading coefficient between the mth satellite access point and the kth user equipment; Indicates large-scale fading under line-of-sight path; Indicates large-scale fading under non-line-of-sight paths; Indicates sight distance; Indicates non-line-of-sight.

[0032] In combination with the first aspect, further, the expression of the system energy efficiency includes:

[0033] ;

[0034] in, represents the system energy efficiency of the tth time slot; B represents the total system bandwidth; represents the total system rate in the tth time slot; Indicates the total system power consumption.

[0035] In combination with the first aspect, further, the constraint conditions of the satellite transmission power upper limit and the user-satellite coverage relationship include:

[0036] ;

[0037] Among them, EE represents the system energy efficiency; represents the Euclidean distance between the mth satellite access point and the kth user; represents the height of the mth satellite access point; represents the coverage radius of the mth satellite access point; represents the power allocated by the mth satellite access point to the kth user; represents the maximum transmit power of the mth satellite; P represents the power allocation matrix; S represents the user-satellite association matrix.

[0038] In combination with the first aspect, further, 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 includes:

[0039] Set the initial power allocation matrix , coverage matrix Q, initial user-satellite association matrix and Dinkelbach parameters ;

[0040] At each iteration,

[0041] Fixing the current user-satellite correlation matrix, and solving the convex optimization problem of the objective function by the Dinkelbach algorithm to obtain a new power allocation matrix;

[0042] Fixing the new power allocation matrix, selecting the satellite with the best quality within the coverage area for each target user, and generating the latest user-satellite correlation matrix;

[0043] Calculating the latest system energy efficiency based on the new power allocation matrix and the updated user-satellite correlation matrix;

[0044] 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, , then update the Dinkelbach parameter λ and enter the next iteration; if the system energy efficiency difference 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 .

[0045] In a second aspect, a LEO satellite power distribution and association system based on a non-cellular architecture includes:

[0046] Multiple satellite access points, used to perform maximum ratio transmission precoding, generate coded modulated signals, and send the coded modulated signals to target users through each satellite access point;

[0047] At least one target user, configured to form a received signal at a receiving end after receiving the coded modulated signal, calculate a signal-to-interference ratio and a downlink transmission rate for each target user based on the received signal, and transmit the downlink transmission rate to a central satellite;

[0048] The central satellite is configured to accumulate the downlink transmission rates of all target users and calculate the system energy efficiency in combination with the total power consumption of all satellite access points. An objective function is constructed based on maximizing the system energy efficiency. Under the constraints of the satellite transmit power upper limit and the user-satellite coverage relationship, the objective function is solved through an iterative optimization algorithm to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix. Based on the global optimal power allocation matrix and the optimal user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined.

[0049] In combination with the second aspect, further, the central satellite is deployed in a central processing unit, the central satellite is connected to the satellite access point via a wireless backhaul link, and the multiple satellite access points communicate with each other via ultra-high-speed inter-satellite links.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] During the downlink transmission phase, this invention uses maximum ratio transmission (MRT) precoding to generate a coded modulated signal, which is then transmitted to the target user via each satellite access point, effectively improving signal gain and suppressing interference. After receiving the signal, the user end calculates its signal-to-interference-and-noise ratio (SINR) and determines the downlink transmission rate based on the Shannon equation. By aggregating all user rates and the total satellite power consumption (including dynamic transmit power and fixed energy consumption), a system energy efficiency objective function is constructed. Under the constraints of satellite power caps and user coverage, the Dinkelbach algorithm is used to iteratively optimize the power allocation matrix P and the user-satellite association matrix S. A Taylor expansion is used to transform the non-convex problem into a linear optimization problem. The user associated with the satellite with the largest Ricean factor within the coverage area is dynamically selected, ultimately outputting a globally optimal solution. This effectively addresses the rate edge rolloff, inter-satellite handover latency, and static resource allocation rigidity inherent in traditional cellular architectures. While reducing system complexity, it achieves the coordinated optimization of spectral and energy efficiency, providing technical support for efficient networking of low-orbit satellite communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 FIG2 is a flow chart of a LEO satellite power allocation and association method based on a non-cellular architecture provided by an embodiment of the present invention;

[0053] Figure 2 Shown is a comparison diagram of the energy efficiency of the system provided by the embodiment of the present invention;

[0054] Figure 3 A topology diagram of a satellite communication system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described in detail below through 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. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0056] The term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects. Example 1

[0057] See also Figure 1 ,This embodiment introduces a LEO satellite power allocation and association method based on a cellular-free architecture, comprising the following steps;

[0058] Step S1: Perform maximum ratio transmission precoding (MRT precoding) on ​​all satellite access points to generate a coded modulated signal, and send the coded modulated signal to the target user through each satellite access point;

[0059] Step S2: After the target user receives the coded modulated signal, a received signal is formed at the receiving end, the signal-to-interference ratio and downlink transmission rate of each target user are calculated based on the received signal, and the downlink transmission rate is transmitted to the central satellite;

[0060] Step S3: 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;

[0061] Step S4: construct an objective function based on maximizing the energy efficiency of the system. Under the constraints of the satellite transmission power upper limit and the user-satellite coverage relationship, solve the objective function through an iterative optimization algorithm to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix. Based on the global optimal power allocation matrix and the optimal user-satellite association matrix, determine the optimal power allocation and user-satellite association scheme.

[0062] In summary, this invention effectively improves signal quality and enhances the system's anti-interference capability and reliability through maximum ratio transmission precoding technology. Furthermore, by calculating the signal-to-noise ratio (SNR) based on the received signal and determining the downlink transmission rate, it achieves precise control of each user's communication quality and improves spectrum utilization.

[0063] Furthermore, by using the resulting system energy efficiency as an optimization target, power allocation and user association were optimized, not only reducing system energy consumption but also significantly improving resource utilization. This optimization strategy can flexibly adjust power allocation and user association based on user distribution and channel conditions, further improving the system's flexibility and adaptability, and providing users with more stable and efficient communication services. Example 2

[0064] To maximize the signal power received by the target user while reducing interference to other target users, maximum ratio transmission precoding is performed on all satellite access points to generate coded modulation signals. The specific steps include the following:

[0065] Step S31: Filter out all target users within the coverage of each satellite access point and assign corresponding precoding weights to each target user. ;

[0066] The expression of the precoding weight is:

[0067] (1)

[0068] in, represents the precoding weight designed by the mth access point for the kth user; represents the power allocated by the mth satellite access point to the kth 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 the channel vector The expected value of the square of the modulus is taken.

[0069] Obtain the baseband signal of each target user , through the baseband signal With the precoding weight The product of , gets the weighted signal;

[0070] Step S33: superimpose the weighted signals on all satellite access points to form coded modulation signals.

[0071] The expression of the coded modulation signal is:

[0072] (2)

[0073] in, represents the modulated signal of the mth access point in the tth time slot; K represents the number of target users for a single antenna; k represents the user index; Represents the baseband signal sent to the kth user in the tth time slot.

[0074] Furthermore, the coded modulated signal is transmitted to the target user via each satellite access point, and the target user receives the signal transmitted by the satellite access point. However, due to the influence of propagation delay and Doppler frequency shift, the signal received by the user will have time error, frequency error, and phase error. Therefore, the expression of the received signal after the influence of propagation delay and Doppler frequency shift is:

[0075] (3)

[0076] in, represents the received signal affected by propagation delay and Doppler frequency shift received by the kth user in the tth 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 mth satellite access point to the kth target user; represents the signal sent by the mth satellite access point after taking into account the propagation delay; j represents the imaginary unit; t represents the time slot; Represents the time error caused by propagation delay; Indicates the carrier frequency; It represents the frequency error caused by Doppler shift, that is, the frequency error has a mean of 0 and a variance of is a normal distribution; It represents the phase error caused by Doppler frequency shift, that is, the phase error obeys the normal distribution with mean 0 and variance ; represents the additive noise caused by the kth user receiving the signal in the tth time slot.

[0077] Furthermore, calculating the signal-to-noise ratio of each target user through the received signal, and then determining the downlink transmission rate of the target user according to the signal-to-noise ratio, includes the following steps:

[0078] Step S51: extract the useful signal from the received signal affected by the propagation delay and Doppler frequency shift, and calculate the power of the useful signal, which is recorded as the first useful signal power. ;

[0079] The expression of the first useful signal power is:

[0080] (4)

[0081] in, represents the first useful signal power; Represents the time error caused by propagation delay The impact on system performance, the value range is between 0 and 1; represents the power allocated by the mth satellite access point to the kth user; represents the standard deviation of the frequency error; represents the standard deviation of the phase error.

[0082] Step S52: Separate the co-channel interference signal from the received signal based on the frequency error and phase error caused by the Doppler shift, and calculate the power of the co-channel interference signal, which is recorded as the second interference signal power. ;

[0083] The expression of the second interference signal power is:

[0084] (5)

[0085] in, represents the second interference signal power.

[0086] Step S53: Combine the large-scale fading coefficient between the target user and the satellite access point , separate the co-channel 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 interference signal between target users, the interference signal of the remaining satellite access points, and the noise signal, respectively, and record it as the third interference signal power , the fourth interference signal power and noise power ;

[0087] The expressions of the third interference signal power, the fourth interference signal power and the noise power are respectively:

[0088] (6)

[0089] in, represents the third interference signal power; represents the Rice factor between the mth satellite access point and the kth user; represents the large-scale fading coefficient between the mth satellite access point and the kth user in the line-of-sight Los path; represents the large-scale fading coefficient between the mth satellite access point and the kth user in the non-line-of-sight (NLos) path; N represents the normalization factor.

[0090] (7)

[0091] in, represents the fourth interference signal power; represents the power allocated by the mth satellite access point to the i-th user; represents the Rice factor between the mth satellite access point and the i-th user; represents the large-scale fading coefficient between the mth satellite access point and the i-th user in the line-of-sight Los path; represents the large-scale fading coefficient between the mth satellite access point and the i-th user in the non-line-of-sight (NLos) path; i represents the number of users.

[0092] (8)

[0093] in, represents the noise power; represents the background noise power spectral density; Indicates the total system bandwidth; Represents the noise figure.

[0094] Furthermore, the Rice factor between the satellite access point and the target user in formula (6) can be expressed as:

[0095] (9)

[0096] in, It represents the probability of the line-of-sight Los path, and its expression can be expressed as:

[0097] (10)

[0098] in, represents the Euclidean distance between the mth satellite access point and the kth user.

[0099] In the embodiment 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. 、 、 、 , total system bandwidth B=200 (MHz), NF=7 (dB), these parameters jointly determine the noise level and signal transmission quality in the system, providing a basis for subsequent system performance analysis and optimization.

[0100] It should be noted that the signal will attenuate due to factors such as distance and obstacles when transmitted between the target user and the satellite access point. Here, the large-scale fading coefficient is used to represent the fading between the target user and the satellite access point. The expression of the large-scale fading coefficient is:

[0101] (11)

[0102] in, represents the large-scale fading coefficient between the mth satellite access point and the kth user equipment; Indicates large-scale fading under line-of-sight path; Indicates large-scale fading under non-line-of-sight paths; Indicates line-of-sight distance, mainly considering path loss; Indicates non-line-of-sight, which adds additional losses on the basis of path loss, such as shadow fading caused by obstacles.

[0103] Specifically, the large-scale fading under the line-of-sight path and the large-scale fading under the non-line-of-sight path in formula (11) can be expressed as:

[0104] (12)

[0105] (13)

[0106] in, Indicates the path loss when the reference distance is 1 meter under the line-of-sight path; represents the path loss caused by the Euclidean distance between the kth target user and the mth satellite access point; Represents the path loss exponent under the line-of-sight path; It represents the additional loss caused by distance in non-line-of-sight paths, mainly considering the signal attenuation caused by factors such as obstacles, and can be expressed as:

[0107] (14)

[0108] in, Indicates the path loss when the reference distance is 1 meter under non-line-of-sight path; Represents the path loss exponent under non-line-of-sight path; represents shadow fading, and , that is, shadow fading obeys the mean of 0 and variance of Normal distribution; Indicates the loss caused by the boresight angle;

[0109] Furthermore, the loss caused by the boresight angle in formula (3) can be expressed as:

[0110] (15)

[0111] (16)

[0112] in, Indicates the boresight angle, that is, the angle between the line between the target user and the satellite and the satellite altitude direction; represents the antenna factor; represents the altitude of the mth satellite; represents the Euclidean distance between the kth target user and the mth satellite access point.

[0113] Step S54: Obtain a signal-to-noise ratio (SNR) of the target user based on a 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;

[0114] The expression of the drying ratio is:

[0115] (17)

[0116] in, represents the signal-to-noise ratio of the kth user in the tth time slot.

[0117] Step S55: Calculate the downlink transmission rate of the target user using the Shannon formula based on the signal-to-noise ratio.

[0118] The expression of the downlink transmission rate is:

[0119] (18)

[0120] in, It represents the downlink transmission rate of the kth user in the tth time slot.

[0121] Furthermore, the system energy efficiency is calculated by combining the total power consumption of all satellite access points and the accumulated downlink transmission rates of all target users, including the following steps:

[0122] Step S61: Aggregate all target users to obtain the total system rate;

[0123] The expression of the total system rate is:

[0124] (19)

[0125] in, represents the total system rate of the tth time slot; K represents the number of target users of a single antenna; k represents the index value of the target user.

[0126] Step S62: Calculate the total power consumption of the LEO (low earth orbit) satellite communication system;

[0127] The total power consumption of the LEO satellite communication system is expressed as:

[0128] (20)

[0129] in, Indicates the total power consumption of the system; M represents the number of satellite access points; represents the fixed power consumption of each satellite access point; It represents the power allocated by the mth satellite access point to the kth user.

[0130] In the embodiment of the present invention, the fixed power consumption is set to 10 dBW.

[0131] Step S63: Obtain system energy efficiency according to the ratio of the total system rate to the total system power consumption.

[0132] The expression of the system energy efficiency is:

[0133] (twenty one)

[0134] in, Represents the system energy efficiency.

[0135] Furthermore, an objective function is constructed based on maximizing the energy efficiency of the system. Under the constraints of the satellite transmission power upper limit and the user-satellite coverage relationship, the objective function is solved by an iterative optimization algorithm to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix. Based on the global optimal power allocation matrix and the optimal user-satellite association matrix, the optimal power allocation and user-satellite association scheme is determined.

[0136] Specifically, in the process of solving the global optimal power allocation matrix P and the optimal user-satellite correlation matrix S, it is necessary to first establish a mathematical model for the optimization problem. The optimization goal is to maximize the system energy efficiency EE while meeting the following constraints:

[0137] a. Coverage constraint: The Euclidean distance between the mth satellite access point and the kth user must satisfy .in, , by the satellite altitude and antenna field of view (Set to 60 degrees) Joint decision.

[0138] b. Power constraint: The total transmit power of each satellite must not exceed an upper limit, which is set to 15dBW in this embodiment of the present invention.

[0139] Furthermore, the constraint condition is expressed as:

[0140] (twenty two)

[0141] Among them, EE represents the system energy efficiency; represents the Euclidean distance between the mth satellite access point and the kth user; represents the height of the mth satellite access point; represents the coverage radius of the mth satellite access point; Indicates the maximum transmit power of the mth satellite.

[0142] Furthermore, in order to simplify the optimization problem, the Dinkelbach algorithm is used to transform the nonlinear objective function in formula (22) into a linear form.

[0143] Specifically, combine formula (21) to change the objective function to Written as And further decompose the total system rate R(t) into two parts and

[0144] The rewritten expression for the total system rate is (the time slot parameter t will be omitted for simplicity):

[0145] (twenty three)

[0146] 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 including only interference and noise.

[0147] The decomposed expressions are:

[0148] (twenty four)

[0149] (25)

[0150] The linear upper bound is obtained by performing a first-order Taylor expansion on the initial power allocation matrix;

[0151] The expression of the linear upper bound is:

[0152] (26)

[0153] in, express exist The linear upper bound at ; P represents the independent variable of Taylor expansion, which is the power allocation matrix; represents the initial power allocation matrix; It represents the interference-noise logarithmic capacity under the initial power allocation.

[0154] According to the The linear upper bound of is obtained, and the linear lower bound of the total rate of the system is obtained.

[0155] The linear lower bound expression of the total rate of the system is:

[0156] (27)

[0157] in, It represents the lower bound of the total system rate;

[0158] Based on the lower bound of the total system rate, the system energy efficiency can be converted into a linear form.

[0159] The energy efficiency of the system is converted into a linear expression:

[0160] (28)

[0161] in, Represents the normalized value of the lower bound of the rate under unit bandwidth, that is, .

[0162] By iteratively updating the power allocation matrix P and the correlation matrix S, the global optimal solution is gradually approached. Finally, the Dinkelbach algorithm meets the convergence condition The optimal power allocation and user-satellite association scheme is output.

[0163] Furthermore, determining an optimal power allocation and user-satellite association scheme according to the global optimal power allocation matrix and the best user-satellite association matrix comprises the following steps:

[0164] Step S91: Set the initial power allocation matrix, coverage matrix, initial user-satellite correlation matrix and Dinkelbach parameters ;

[0165] Specifically, the coverage matrix is ​​set Assign to the initial user-satellite association matrix ,Right now Among them, the element Indicates that the kth user is within the coverage of the mth satellite; It means that the kth user is not within the coverage of the mth satellite.

[0166] Step S92: At each iteration, fix the current user-satellite correlation matrix , which is the user-satellite correlation matrix obtained in the previous iteration, and the new power allocation matrix is ​​solved by the Dinkelbach algorithm , that is, the power allocation matrix obtained in the current iteration cycle;

[0167] The expression of the new power allocation matrix is:

[0168] (29)

[0169] in, represents the new power allocation matrix at the tth iteration; represents the Dinkelbach parameter of the previous iteration; represents the new total system power consumption at the tth iteration; represents the new user-satellite association matrix at the tth 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.

[0170] Furthermore, the new system total power consumption is expressed as:

[0171] (30)

[0172] in, represents the set of satellites that provide services to target user k at the tth iteration.

[0173] Dinkelbach parameter The expression is:

[0174] (31)

[0175] in, represents the latest Dinkelbach parameter; represents the upper bound of the total system rate calculated based on the power allocation matrix at the tth iteration; represents the first-order Taylor expansion upper bound of the power allocation matrix based on the previous iteration; Represents the power allocation matrix at the last iteration.

[0176] Combined with formula (23), the iterative parameter is added here to obtain the total system rate at the tth iteration, that is, ;

[0177] The system energy efficiency at the t-th iteration is obtained 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.

[0178] The expression of the system energy efficiency at the t-th iteration is:

[0179] (32)

[0180] in, represents the system energy efficiency at the tth iteration; represents the total system rate at the tth iteration; Represents the system power consumption at the tth iteration.

[0181] Step S93: Fix the new power allocation matrix For the kth target user, the satellite with the best channel quality, i.e. the largest Rice factor, is selected from the remaining satellites in the coverage, thereby obtaining the user-satellite correlation matrix with the best quality: ;

[0182] The expression of the user-satellite correlation matrix with the best quality is:

[0183] (33)

[0184] in, Indicates the The Rice factor between the satellite access point and the target user; represents the set of satellites covering target user k, that is, all satellites that can cover user k at the current moment; Indicates traversing the set of covered satellites Index variable of the satellite in the middle; represents the best quality user-satellite correlation matrix;.

[0185] Step S94: According to the new power allocation matrix and the best quality user-satellite correlation matrix , calculate the latest system energy efficiency;

[0186] Combined with formula (23), the total system rate at the t+1th iteration is obtained, that is, ;

[0187] The system energy efficiency at the t+1th iteration is obtained according to the ratio of the total system rate at the t+1th iteration to the total system power consumption at the t+1th iteration.

[0188] The expression of the system energy efficiency at the t+1th iteration is:

[0189] (34)

[0190] in, represents the system energy efficiency at the t+1th iteration; represents the total system rate at the t+1th iteration; Represents the system power consumption at the t+1th iteration.

[0191] Step S95: compare the system energy efficiency difference between two adjacent iterations. If the system energy efficiency difference is greater than a preset convergence threshold, , then update the Dinkelbach parameter λ and enter the next iteration; if the system energy efficiency difference is not greater than the preset convergence threshold ,Right now , then output the current power allocation matrix and the optimal user-satellite association matrix as the optimal solution .

[0192] 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, which has low energy efficiency and changes slowly. However, 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 Ricean factor (as shown in Table 2). The energy efficiency is significantly improved with the number of iterations and eventually converges to the global optimal value.

[0193] Table 1 Optimal power allocation matrix obtained by the present invention

[0194]

[0195] Table 2 The optimal user-satellite correlation matrix obtained by the present invention

[0196]

[0197] The graph shows that the proposed method's curve is significantly higher than that of the traditional method and stabilizes in the later stages of iteration, demonstrating that the algorithm meets the preset convergence threshold through a limited number of optimizations, validating its efficiency and robustness. The optimized scheme sparsely associates satellites with the best channel quality within their coverage area, reducing both redundant satellite resources and overall system power consumption. This significantly improves energy efficiency in scenarios where high-speed satellite motion and dynamic changes in network topology are a concern, providing a viable solution for optimizing energy efficiency in integrated space-ground communication networks.

[0198] Example 3

[0199] The present invention abandons the traditional cellular network architecture and adopts a more flexible and dynamic non-cellular network architecture. Figure 3 , a LEO satellite power distribution and association system based on a non-cellular network architecture, comprising:

[0200] M satellite access points, configured to perform maximum ratio transmission precoding, generate coded modulated signals, and transmit the coded modulated signals to target users via the satellite access points;

[0201] K target users with single antennas are used to receive the coded modulated signal and form a received signal at the receiving end. The signal-to-interference ratio and downlink transmission rate of each target user are calculated based on the received signal, and the downlink transmission rate is transmitted to the central satellite;

[0202] A central satellite is used to aggregate the downlink transmission rates of all target users and calculate the system energy efficiency in combination with the total power consumption of all satellite access points. An objective function is constructed to maximize the system energy efficiency. Under the constraints of the satellite transmit power upper limit and the user-satellite coverage relationship, the objective function is solved through an iterative optimization algorithm to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix. Based on the global optimal power allocation matrix and the optimal user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined.

[0203] The central satellite is deployed in a central processing unit (CPU), is connected to the satellite access points via a wireless backhaul link, and the multiple satellite access points communicate with each other via ultra-high-speed inter-satellite links, thereby improving the overall performance and coverage of the system.

[0204] Specifically, each target user is randomly and evenly distributed in a square area with a side length of D; each satellite access point is randomly and evenly distributed in a spatial area with a length and width of A and a height of C, and the satellite access points are independent of each other.

[0205] Combining Example 1 and Example 2, by setting the number of satellite access points M = 10, the number of target users per single antenna K = 5, and combining the distribution parameters of satellite access points and target users (the length and width of the spatial area where the satellite access points are distributed A = 1500 kilometers, the side length of the square area where the users are distributed D = 1000 kilometers, and the height of the satellite access point distribution area C = 2000 kilometers), a dynamic simulation environment that meets the typical scenario of low-orbit satellite communication is constructed. By using the line-of-sight and non-line-of-sight path loss parameters (path loss index under line-of-sight path , path loss index under non-distance path , Path loss when the reference distance is 1 meter under line-of-sight path , Path loss when the reference distance is 1 meter under non-line-of-sight path ), antenna factor ; Standard deviation of shadow fading ; Accurately simulate the channel dynamic characteristics caused by the high-speed movement of the satellite, in the satellite altitude range Under the constraint of kilometers, simulation results verify the effectiveness of the proposed power allocation and association scheme: the system energy efficiency is significantly improved, and the algorithm shows strong robustness under complex conditions such as Doppler frequency shift, delay error and drastic changes in network topology.

[0206] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A LEO satellite power allocation and association method based on a non-cellular architecture, characterized in that: include: Performing maximum ratio transmission precoding on all satellite access points to generate coded modulated signals, and sending the coded modulated signals to target users through each satellite access point; After the target user receives the coded modulated signal, a receiving signal is formed at the receiving end, the signal-to-noise ratio and downlink transmission rate of each target user are calculated based on the received signal, and the downlink transmission rate is transmitted to the central satellite; Aggregating the downlink transmission rates of all target users through the central satellite and combining them with the total power consumption of all satellite access points to calculate the system energy efficiency; An objective function is constructed based on maximizing the energy efficiency of the system. Under the constraints of the satellite transmit power upper limit and the user-satellite coverage relationship, the objective function is solved by an iterative optimization algorithm to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix. Based on the global optimal power allocation matrix and the optimal user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined. The determining of the optimal power allocation and user-satellite association scheme based on the global optimal power allocation matrix and the optimal user-satellite association matrix includes: Set the initial power allocation matrix , coverage matrix Q, initial user-satellite association matrix and Dinkelbach parameters ; At each iteration, Fixing the current user-satellite correlation matrix, and solving the convex optimization problem of the objective function by 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 area for each target user, and generating the latest user-satellite correlation matrix; Calculating the latest system energy efficiency based on the new power allocation matrix and the updated user-satellite correlation matrix; 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, , then update the Dinkelbach parameter λ and enter the next iteration; if the system energy efficiency difference 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 .

2. The LEO satellite power allocation and association method based on a non-cellular architecture according to claim 1, characterized in that: Perform maximum ratio transmission precoding on all satellite access points to generate coded modulation signals, including: Filter 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 With the precoding weight The product of , gets the weighted signal; The weighted signals are superimposed on all satellite access points to form coded modulation signals.

3. The LEO satellite power allocation and association method based on a non-cellular architecture according to claim 1, characterized in that: The expression for forming the received signal at the receiving end includes: ； in, represents the received signal affected by propagation delay and Doppler frequency shift received by the kth user in the tth 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 mth satellite access point to the kth target user; represents the signal sent by the mth satellite access point after taking into account the propagation delay; j represents the imaginary unit; t represents the time slot; Represents the time error caused by propagation delay; Indicates the carrier frequency; Represents the frequency error caused by Doppler shift; Represents the phase error caused by Doppler shift; represents the additive noise caused by the kth user receiving the signal in the tth time slot.

4. The LEO satellite power allocation and association method based on a non-cellular architecture according to claim 3, characterized in that: Calculating the signal-to-noise ratio and downlink transmission rate of each target user using 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, which is recorded as the first useful signal power. ; Based on the frequency error caused by the Doppler shift and the phase error caused by the Doppler shift, the same-frequency interference signal is separated from the received signal, and its power is calculated and recorded as the second interference signal power. ; By using the large-scale fading coefficient between the target user and the satellite access point , separate the co-channel 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 power respectively, which is recorded as the third interference signal power , the fourth interference signal power as well as Noise power; According to the first useful signal power With the second interference signal power , the third interference signal power , the fourth interference signal power and noise power The signal-to-noise ratio of the target user is obtained by calculating the ratio of the accumulated values ​​of determining a downlink transmission rate of the target user according to the signal-to-noise ratio; The expression of the downlink transmission rate includes: ； in, Indicates the downlink transmission rate of the target user; Indicates the drying ratio.

5. The LEO satellite power allocation and association method based on a non-cellular architecture according to claim 4, characterized in that: The expression of the large-scale fading coefficient between the target user and the satellite access point is as follows: ； in, represents the large-scale fading coefficient between the mth satellite access point and the kth user equipment; Indicates large-scale fading under line-of-sight path; Indicates large-scale fading under non-line-of-sight paths; Indicates sight distance; Indicates non-line-of-sight.

6. The LEO satellite power allocation and association method based on a non-cellular architecture according to claim 1, characterized in that: The expression of the energy efficiency of the system includes: ； in, represents the system energy efficiency of the tth time slot; B represents the total system bandwidth; represents the total system rate in the tth time slot; Indicates the total system power consumption.

7. The LEO satellite power allocation and association method based on a non-cellular architecture according to claim 1, characterized in that: The constraints on the satellite transmission power upper limit and the user-satellite coverage relationship include: ； Among them, EE represents the system energy efficiency; represents the Euclidean distance between the mth satellite access point and the kth user; represents the height of the mth satellite access point; represents the coverage radius of the mth satellite access point; represents the power allocated by the mth satellite access point to the kth user; represents the maximum transmit power of the mth satellite; P represents the power allocation matrix; S represents the user-satellite association matrix.

8. A LEO satellite power distribution and association system based on a non-cellular architecture, characterized in that: include: Multiple satellite access points, used for performing maximum ratio transmission precoding, generating coded modulated signals, and sending the coded modulated signals to target users; At least one target user, configured to form a received signal at a receiving end after receiving the coded modulated signal, calculate a signal-to-interference ratio and a downlink transmission rate for each target user based on the received signal, and transmit the downlink transmission rate to a central satellite; The central satellite is used to accumulate 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; An objective function is constructed based on maximizing the energy efficiency of the system. Under the constraints of the satellite transmit power upper limit and the user-satellite coverage relationship, the objective function is solved by an iterative optimization algorithm to obtain a global optimal power allocation matrix and an optimal user-satellite association matrix. Based on the global optimal power allocation matrix and the optimal user-satellite association matrix, an optimal power allocation and user-satellite association scheme is determined. The determining of the optimal power allocation and user-satellite association scheme based on the global optimal power allocation matrix and the optimal user-satellite association matrix includes: Set the initial power allocation matrix , coverage matrix Q, initial user-satellite association matrix and Dinkelbach parameters ; At each iteration, Fixing the current user-satellite correlation matrix, and solving the convex optimization problem of the objective function by 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 area for each target user, and generating the latest user-satellite correlation matrix; Calculating the latest system energy efficiency based on the new power allocation matrix and the updated user-satellite correlation matrix; 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, , then update the Dinkelbach parameter λ and enter the next iteration; if the system energy efficiency difference 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 .

9. The LEO satellite power distribution and association system based on a non-cellular architecture according to claim 8, characterized in that: The central satellite is deployed in a central processing unit. The central satellite is connected to the satellite access points via a wireless backhaul link, and the multiple satellite access points communicate with each other via ultra-high-speed inter-satellite links.

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