Resource Allocation Method and Device for Space-Air-Ground Communication System Based on Buffer Relay

Abstract

The present invention provides a resource allocation method and apparatus for an air-sky-ground communication system based on buffer relays. The method includes: dividing the transmission time into multiple time slots; establishing a resource allocation optimization problem model for the air-sky-ground communication system with the goal of maximizing the system average throughput and combining target constraint conditions; using the alternating iteration method to solve the resource allocation optimization problem model to determine the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot. The present invention can achieve adaptive resource allocation, enhance the system capacity, improve the system resource utilization rate and the robustness of network transmission.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a method and apparatus for resource allocation in a space-air-ground communication system based on buffer relays. Background Art

[0002] In recent years, a three-layer heterogeneous space-air-ground communication network combining satellites, aerial platforms, and ground networks has been proposed. As relay nodes, aerial platforms further shorten the distance between the air and the ground, and lower communication frequencies can also reduce signal fading. However, considering issues such as tight access resources brought about by the access of a large number of network nodes and weak coverage of satellites, resource allocation in space-air-ground communication networks has always been a hot research topic in the industry.

[0003] Existing resource allocation methods often ignore the problem of optimizing the resource configuration of the link between relay nodes and satellites, and default to using fixed resource allocation schemes, which cannot perform adaptive optimization adjustments according to the dynamically changing traffic demands and channel qualities of relay nodes, resulting in low resource utilization. Summary of the Invention

[0004] The present invention provides a method and apparatus for resource allocation in a space-air-ground communication system based on buffer relays, so as to solve the defect that existing resource allocation methods in the prior art often ignore the problem of optimizing the resource configuration of the link between relay nodes and satellites, default to using fixed resource allocation schemes, and cannot perform adaptive optimization adjustments according to the dynamically changing traffic demands and channel qualities of relay nodes, resulting in low resource utilization.

[0005] The present invention provides a method for resource allocation in a space-air-ground communication system based on buffer relays, including:

[0006] Dividing the transmission time into multiple time slots;

[0007] Taking maximizing the system average throughput as the goal, and combining the target constraint conditions, establishing a resource allocation optimization problem model for the space-air-ground communication system;

[0008] Using the alternating iteration method to solve the resource allocation optimization problem model, and determining the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in each time slot.

[0009] According to the method for resource allocation in a space-air-ground communication system based on buffer relays provided by the present invention, the step of taking maximizing the system average throughput as the goal, and combining the target constraint conditions, establishing a resource allocation optimization problem model for the space-air-ground communication system includes:

[0010] Determining the system average throughput based on the data throughput of each ground terminal in each time slot;

[0011] A first objective function is established with the goal of maximizing the average throughput of the system.

[0012] Based on the power ranges of each of the ground terminals and each of the high-altitude platforms, and the number of access nodes for each access channel of each of the high-altitude platforms, the objective constraint conditions are determined.

[0013] Based on the first objective function and the objective constraint conditions, a resource allocation optimization problem model for the air-ground-space communication system is established.

[0014] According to a resource allocation method for an air-ground-space communication system based on buffer relay provided by the present invention, the method further includes:

[0015] With the goal of minimizing the Lyapunov drift plus penalty term for each time slot, the first objective function is transformed into a second objective function.

[0016] Based on the second objective function and the objective constraint conditions, a resource allocation optimization problem model for the air-ground-space communication system is established.

[0017] According to a resource allocation method for an air-ground-space communication system based on buffer relay provided by the present invention, the resource allocation optimization problem model is solved by using the alternating iteration method to determine the channel access information and the transmission power of each channel between the ground terminal and the high-altitude platform, and the channel access information and the transmission power of each channel between the high-altitude platform and the satellite for each time slot, including:

[0018] The resource allocation optimization problem model is decomposed into multiple sub-optimization problem models; the multiple sub-optimization problem models include an auxiliary variable optimization sub-problem, a first sub-optimization problem model for channel allocation and power control between the ground terminal and the high-altitude platform, and a second sub-optimization problem model for channel allocation and power control between the high-altitude platform and the satellite.

[0019] The auxiliary variable optimization sub-problem, the first sub-optimization problem model, and the second sub-optimization problem model are iteratively solved to obtain the channel access information and the transmission power of each channel between the ground terminal and the high-altitude platform, and the channel access information and the transmission power of each channel between the high-altitude platform and the satellite for each time slot.

[0020] According to a resource allocation method for an air-ground-space communication system based on buffer relay provided by the present invention, the auxiliary variable optimization sub-problem, the first sub-optimization problem model, and the second sub-optimization problem model are iteratively solved to obtain the channel access information and the transmission power of each channel between the ground terminal and the high-altitude platform, and the channel access information and the transmission power of each channel between the high-altitude platform and the satellite for each time slot, including:

[0021] Step S1: Based on the data queues of each of the ground terminals, use a convex optimization solution method to solve the auxiliary variable sub-problem to obtain the optimal solution of the auxiliary variable in the current time slot.

[0022] Step S2: Based on the data queues of each of the ground terminals and each of the high-altitude platforms, solve the first sub-optimization problem model to determine the channel access information between the ground terminals and the high-altitude platforms and the transmission power of each channel in the current time slot, and solve the second sub-optimization problem model to determine the channel access information between the high-altitude platforms and the satellites and the transmission power of each channel in the current time slot.

[0023] Step S3: Based on the optimal solution of the auxiliary variable in the current time slot, the channel access information between the ground terminals and the high-altitude platforms and the transmission power of each channel in the current time slot, and the channel access information between the high-altitude platforms and the satellites and the transmission power of each channel in the current time slot, update the data queues of each of the ground terminals and each of the high-altitude platforms.

[0024] Step S4: If it is determined that the current time slot has not reached the preset maximum time slot, continue to execute Steps S1 to S3 until the current time slot reaches the maximum time slot, and then enter Step S5 to obtain the channel access information between the ground terminals and the high-altitude platforms and the transmission power of each channel, and the channel access information between the high-altitude platforms and the satellites and the transmission power of each channel in each time slot.

[0025] According to a resource allocation method for an air-ground-space communication system based on buffer relay provided by the present invention, the first objective function is expressed by the following formula:

[0026]

[0027] The second objective function is expressed by the following formula:

[0028]

[0029] Where, represents the time average of the admitted data a u (t) of the ground terminal u, represents the set of all ground terminals, represents the set of all high-altitude platforms m; a represents the amount of admitted data of the ground terminal, X represents the channel access information between the ground terminal and the high-altitude platform; p represents the transmission power of the channel between the ground terminal and the high-altitude platform; B represents the channel access information between the high-altitude platform and the satellite; P represents the transmission power of the channel between the high-altitude platform and the satellite.

[0030] Among them, V represents the penalty factor of the Lyapunov function; It represents an auxiliary variable; represents the amount of data unloaded by the ground terminal u in time slot t; represents the amount of data received by the buffer of the high-altitude platform m in time slot t; represents the amount of data sent by the buffer of the high-altitude platform m in time slot t; Q m (t) represents the amount of data stored in the buffer of the high-altitude platform m in time slot t; Z u (t) represents the amount of data stored in the buffer of the ground terminal u in time slot t, and its update method is implemented according to the following formula:

[0031]

[0032] H u (t) represents the virtual data queue introduced by the buffer of the ground terminal u, and its update method is implemented according to the following formula:

[0033] H u (t + 1) = max{H u (t) - a u (t), 0} + C u (t).

[0034] The present invention also provides a resource allocation device for an air-ground-space communication system based on buffer relay, including:

[0035] A division module for dividing the transmission time into multiple time slots;

[0036] A building module for building an optimization problem model of resource allocation for the air-ground-space communication system with the goal of maximizing the system average throughput and combining the target constraint conditions;

[0037] A solving module for solving the resource allocation optimization problem model by using the alternating iteration method to determine the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot.

[0038] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the resource allocation method for the air-ground-space communication system based on buffer relay as described in any one of the above.

[0039] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the resource allocation method for the air-ground-space communication system based on buffer relay as described in any one of the above.

[0040] The present invention also provides a computer program product, including a computer program which, when executed by a processor, implements the resource allocation method for the air-space-ground communication system based on buffer relay as described in any one of the above.

[0041] The resource allocation method and device for the air-space-ground communication system based on buffer relay provided by the present invention divide the transmission time into multiple time slots; aiming at maximizing the system average throughput and combining the target constraint conditions, establish an optimization problem model for resource allocation of the air-space-ground communication system; use the alternating iteration method to solve the resource allocation optimization problem model, determine the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel, and can obtain the satellite-ground channel conditions of each time slot based on the actual satellite topology structure, realize adaptive resource allocation, enhance the system capacity, improve the system resource utilization rate and the robustness of network transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 is a schematic flowchart of the resource allocation method for the air-space-ground communication system based on buffer relay provided by the present invention;

[0044] Figure 2 is a schematic structural diagram of the satellite Internet of Things network provided by the present invention;

[0045] Figure 3 is one of the schematic flowcharts of model solution in the resource allocation method for the air-space-ground communication system based on buffer relay provided by the present invention;

[0046] Figure 4 is another schematic flowchart of model solution in the resource allocation method for the air-space-ground communication system based on buffer relay provided by the present invention;

[0047] Figure 5 is yet another schematic flowchart of model solution in the resource allocation method for the air-space-ground communication system based on buffer relay provided by the present invention;

[0048] Figure 6 is a schematic structural diagram of the resource allocation device for the air-space-ground communication system based on buffer relay provided by the present invention;

[0049] Figure 7It is a schematic diagram of the physical structure of the electronic device provided by the present invention. Specific Embodiments

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0051] The following combines Figures 1-7 to describe the resource allocation method and device for the space-air-ground communication system based on buffer relay of the present invention.

[0052] Figure 1 It is a schematic flowchart of the resource allocation method for the space-air-ground communication system based on buffer relay provided by the present invention. As Figure 1 shown, it includes:

[0053] Step 110, dividing the transmission time into multiple time slots;

[0054] Step 120, aiming at maximizing the system average throughput and combining the target constraint conditions, establishing a resource allocation optimization problem model for the space-air-ground communication system;

[0055] Step 130, using the alternating iteration method to solve the resource allocation optimization problem model, and determining the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in each time slot.

[0056] Specifically, in the embodiments of the present invention, the transmission time is divided into multiple time slots with an index t, and the length of each time slot is δ.

[0057] Figure 2 It is a schematic diagram of the structure of the satellite Internet of Things network provided by the present invention. As Figure 2As shown in the figure, the entire network includes a ground layer, a high-altitude layer, and a space layer. Ground users unload traffic in the way that they reach the High Altitude Platform (HAP) in the high-altitude layer through the ground-layer network, the HAP reaches the satellites in the space layer, and the satellites reach the core network. Due to space limitations, this embodiment focuses on the optimized design of the first two-hop communications. Specifically, in the first hop, ground nodes access the high-altitude platform equipped with a buffer in the way of Non-Orthgonal Multiple Access (NOMA), which can not only save the energy consumption of ground Internet of Things nodes, but also improve the spectrum utilization efficiency and address the problem of weak or even interrupted satellite-ground communication links caused by extreme weather or short-term lack of satellite coverage. In the second hop, equipped with multiple independent antennas, the high-altitude platform can be connected to multiple satellite channels simultaneously to improve the backhaul capacity. To reduce the decoding complexity at the satellite side, the way of orthogonal multiple access is used for satellite-ground communication.

[0058] In the embodiment of the present invention, first, a Ground to Air (G2A) communication transmission model needs to be established.

[0059] The set of ground nodes composed of ground terminals is defined respectively, which can be expressed as and the set of high-altitude platforms HAP, which can be expressed as The working frequency band of the ground-to-HAP communication link is set in the C band. Among them, the HAP shares the same frequency resource pool B, that is, the frequency reuse factor is 1, and it is further divided into K sub-channels, then the bandwidth of each sub-channel is To describe the association relationship between ground nodes and different high-altitude platform channels, a binary indicator variable X(t) is introduced. Specifically, x u,m,k (t)=1 means that node u communicates with HAPm through channel k, otherwise x u,m,k (t)=0. The ground nodes equipped with single antennas are allowed to access at most one sub-channel simultaneously, which is expressed as:

[0060]

[0061] Since NOMA technology is used for ground-to-air, successive interference cancellation technology is used to cancel the successive interference caused by spectrum reuse in the high-altitude platform receiver, and it is considered that the decoding order on the HAP is always from the node with better channel quality to the node with worse channel quality. To ensure that the decoding complexity is within an acceptable range, a limit is made on the number of channel access devices for each channel, that is, at most K max ground nodes are simultaneously accessed for each channel. By appropriately setting the value of K max , the decoding complexity can be reduced to a tolerable level, that is:

[0062]

[0063] According to recent research, it is considered that there are good line-of-sight propagation characteristics between the HAP and ground nodes, and the communication link between the HAP and ground nodes is dominated by a line-of-sight wireless transmission (LoS) link. The channel gain g between node u and HAPm u,m obeys the free space propagation loss model and can be given as follows:

[0064]

[0065] where d u,m (t) represents the distance between node u and HAPm, and c and f m,k are the speed of light and the communication center frequency of the uplink, respectively.

[0066] Therefore, the signal of ground node u received by HAPm at subchannel k can be expressed as:

[0067]

[0068] where the index t represents the t-th time slot, s u (t) represents the signal transmitted by ground node u, p u,m,k (t) represents the transmit power of ground node u on subchannel k of high-altitude platform m, g u,m,k (t) represents the corresponding channel coefficient, and the additive white Gaussian noise follows where σ 2 represents the noise variance. represents the intra-cell interference signal caused by multiple users multiplexing the same frequency resource using NOMA. Let S m,k represent the set of ground nodes operating on the k-th subchannel of HAPm. Then S u,m,k ={i|i∈S m,k ,g u,m,k >g i,m,k} represents the set of nodes in set S m,k with a channel quality worse than that of node u. is the inter-cell interference signal generated by different HAPs multiplexing the same frequency resource pool.

[0069] Therefore, the signal-to-interference-plus-noise ratio (SINR) of ground node u received by HAPm through channel k can be expressed as:

[0070]

[0071] Therefore, the throughput of ground node u in time slot t can be expressed as:

[0072]

[0073] Furthermore, in the embodiments of the present invention, an Air to Space (A2S) communication transmission model is established.

[0074] First, define the satellite constellation set as And considering a sufficient number of time slot divisions, each time slot is divided into sufficiently small time intervals such that the influence of the satellite's position change within the time slot on the channel gain and the link angle can be ignored. Different from the G2A communication link, the channel capacity of the A2S link between the low-earth orbit satellite and the ground link varies with the movement of the satellite. Since the orbit of each satellite is preset, information such as the flight speed, altitude, and coverage range of each satellite can be predicted. To describe the coverage of the satellite constellation in each time slot, a visible binary matrix W(t) of the selected area is introduced, where W n,m (t) = 1 indicates that HAPm is within the coverage range of satellite n in time slot t, otherwise W n,m (t) = 0.

[0075] The satellite operating frequency point is set in the Ka band, and the corresponding available bandwidth is further divided into a set of sub-channel collections. Similarly, to describe the connection relationship between the HAP and the corresponding satellite channels, a binary variable matrix B(t) of size M×N×L is introduced for each time slot, where b m,n,l (t) = 1 indicates that the m-th HAP establishes a link with the l-th sub-channel of satellite n, otherwise b m,n,l (t) = 0. Next, describe the channel characteristics of the air-to-space transmission.

[0076] In this embodiment, a composite channel model is considered to describe large-scale and small-scale fading. At the same time, since the A2S link operates in the Ka high-frequency band, rain fade is also a factor that must be considered, and the following definition formula is given:

[0077]

[0078] Among them, the subscript indices m, n(n′), l, t represent the high-altitude platform m, satellite n(n′), sub-channel l, and time slot t respectively. S m,n′,l (t) represents the corresponding small-scale fading,

[0079] integrates the corresponding free space loss, rain fade, and antenna gain, where, represents the off-axis angle between the line connecting HAPm and the target satellite n and the line connecting HAPm and the interfering satellite n′; It represents the signal gain on the channel l of the interfered satellite n' when the target of HAP m is satellite n. When n = n', it represents the signal gain of HAP m on the sub-channel l of the target satellite n.

[0080] 1) Small-scale fading:

[0081] First, in the embodiments of the present invention, the Shadowed-Rician fading model is adopted to describe the small-scale fading characteristics, and its probability density function can be expressed as:

[0082]

[0083] where 2b0 represents the average power of the scattered component, Ω represents the average power of the LoS component, represents the Nakagami fading parameter, and F1(·,·,·) represents the confluent hypergeometric function.

[0084] 2) Rain attenuation:

[0085] According to the ITU P-618 recommendation, the impact of rain attenuation should be considered in satellite-ground communication in the Ka-Ku band. According to the ITU-R P.838 recommendation, rain attenuation is affected by frequency, elevation angle, altitude, and rainfall intensity, and can be calculated by the following formula:

[0086] A R (t)[dB] = L s (t)γ R (t)[dB]; (9)

[0087] where L s (t) represents the effective path length of the signal in the rain, and γ R (t)(dB / km) is the attenuation of the signal per kilometer in time slot t. According to the ITU-R P.618 document, the equivalent path length up to 55 GHz can be expressed by L s (t) as:

[0088]

[0089] In the formula, h s represents the height of the ground station above the mean sea level, and the value given in the terrain height map of ITU-R P.1511 can be used as an estimated value. R e is the effective radius of the earth (8500 km), and θ is the elevation angle of the line connecting the ground station and the satellite, which can be calculated by the longitude difference α lo between the ground station and the satellite and the latitude difference α la as follows:

[0090]

[0091] Among them, h R represents the rainfall height and can be determined by Recommendation ITU-R P.839.

[0092] Furthermore, the effective path length L s (t) of the channel in rain can be calculated according to the above formula. At the same time, according to ITU-R P.838, the attenuation γ R (t) per kilometer is related to the rainfall density and can be expressed as:

[0093] γ R (t) [dB] = ρ · R(t) η [dB]; (12)

[0094] where R(t) (mm / h) is the rainfall density, and the corresponding coefficients ρ and η are functions related to the frequency and can be obtained from ITU-R P.838.

[0095] Finally, due to the long-distance transmission from the HAP to the satellite, the influence of free-space loss needs to be considered. The free-space loss A F (t) can be given as:

[0096] A F,m,n,l (t) [dB] = 32.44 + 20log(d m,n (t)) + 20log(f n,l ); (13)

[0097] where, d m,n (t) represents the distance between the transmitter and the receiver, with the unit of km.

[0098] 3) Antenna gain:

[0099] According to ITU-R R2020 AP8, the antenna gain G is closely related to the off-axis angle ψ between the transmitter or receiver and the beam direction, and the off-axis angle can be calculated based on the positions of the user and the satellite. The expression for calculating the antenna gain is as follows:

[0100] If

[0101]

[0102] If

[0103]

[0104] Among them, D represents the antenna diameter, λ represents the wavelength corresponding to the uplink communication frequency, and G0 represents the antenna peak gain. In addition, the HAP transmitter antenna continuously tracks the connected satellite, so the off-axis angle is 0.

[0105] In summary, considering the antenna gain and the corresponding rain attenuation and free space loss, the total power loss from the transmitter to the receiver can be expressed as:

[0106]

[0107] Furthermore, Converted to That is:

[0108]

[0109] The signal received by satellite n on the l-th subchannel from HAP m can be expressed as:

[0110]

[0111] Among them, P m′,n′,l (t) represents the transmission power of HAP m' on the l-th subchannel of satellite n', represents the signal sent by HAP m, represents the inter-cell interference caused by different satellites multiplexing the same frequency. In the t-th time slot, the signal-to-interference-plus-noise ratio of the l-th subchannel of satellite n receiving HAP m is:

[0112]

[0113] Based on the above signal-to-interference-plus-noise ratio expression, the throughput of HAP m in time slot t can be obtained:

[0114]

[0115] In the embodiment of the present invention, let a u (t) represent the admitted data of ground node u in time slot t, and it has the following expression:

[0116] A u (t) = a u (t) + d u (t);

[0117] Among them, A u (t) and d u (t) respectively represent the sensed data and discarded data of node u in time slot t. Due to the availability of the buffer, ground node u may only accept a part of the arriving data represented by a u (t).

[0118] Define \(Z(t)=[Z_1(t),\ldots,Z U (t)]\), where \(Z U (t)\) represents the amount of data stored in the buffer of ground node \(u\) at time slot \(t\). Then its update equation can be given as follows:

[0119]

[0120] where, that is, the amount of data unloaded by the ground node cannot exceed the existing data in the queue.

[0121] In an embodiment of the present invention, for the dynamic data queue change of each HAP buffer, define the following virtual queue \(Q(t)=[Q_1(t),\ldots,Q M (t)]\), where \(Q m (t)\) represents the amount of data stored in the buffer of HAP \(m\) at time slot \(t\). Then its update equation can be given as follows:

[0122]

[0123] where, and represent the amount of data leaving and arriving at high altitude platform HAP \(m\) at time slot \(t\) respectively.

[0124] In this embodiment, in order to ensure the stability of the system, the following restrictions are imposed on the queues of each node in the system:

[0125]

[0126]

[0127] Next, in an embodiment of the present invention, on the premise of ensuring the stability of the buffer queue of each high altitude platform, the long-term average throughput of the ground node will be maximized. That is, enter step 120, and with the goal of maximizing the system average throughput, combined with the target constraint conditions, establish a resource allocation optimization problem model for the air-ground-space communication system.

[0128] Based on the content of the above embodiment, with the goal of maximizing the system average throughput, combined with the target constraint conditions, establish a resource allocation optimization problem model for the air-ground-space communication system, including:

[0129] Determine the system average throughput based on the data throughput of each ground terminal at each time slot;

[0130] With the goal of maximizing the system average throughput, establish the first objective function;

[0131] Determine the target constraint conditions based on the power range of each ground terminal and each high-altitude platform, and the number of access nodes of each access channel of each high-altitude platform;

[0132] Based on the first objective function and the target constraint conditions, establish a resource allocation optimization problem model for the air-ground-space communication system.

[0133] Specifically, in the embodiments of the present invention, considering the inequality problem caused by the distance between the ground network nodes and the HAP, the following utility function is adopted to balance the system throughput and the fairness problem between nodes:

[0134]

[0135] Among them, is defined as the time average of any statistical process X(t), is the time average of the admitted data of all ground nodes u, Ξ u (x) = ln(1 + x) is a strictly concave and non-increasing utility function, and it can be seen that the benefit function is positively correlated with the throughput of the ground nodes. In particular, it can be seen that the nodes with smaller rates have larger derivatives. This also means that if there are two users u1 and u2, when the same rate increase occurs, the low-rate user will bring higher system benefits, which can shorten the rate gap between ground nodes.

[0136] Therefore, in this embodiment, by jointly optimizing the channel access and power control between the ground nodes and the HAP, on the premise of ensuring the long-term stability of the system queue, the goal of maximizing the long-term average system benefit function of the system, that is, with the goal of maximizing the system average throughput, formulate the optimization problem P1 and establish the first objective function P1 as follows:

[0137]

[0138] Among them, a represents the amount of admitted data of the ground terminal, X represents the channel access information between the ground terminal and the high-altitude platform; p represents the transmission power of the channel between the ground terminal and the high-altitude platform; B represents the channel access information between the high-altitude platform and the satellite; P represents the transmission power of the channel between the high-altitude platform and the satellite.

[0139] At the same time, based on the power range of each ground terminal and each high-altitude platform, and the number of access nodes of each access channel of each high-altitude platform, determine the target constraint conditions C1 to C11, that is:

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] x u,m,k (t),b m,n,l (t) ∈ {0, 1}; (C6)

[0146] 0 ≤ p u,m,k (t) ≤ p max ; (C7)

[0147] 0 ≤ P m,n,l (t) ≤ P max ; (C8)

[0148]

[0149]

[0150]

[0151] Among them, C1 - C6 represent access variable constraints. C1 means that each node can access at most one channel. C2 gives different coverage range information. C3 limits the maximum number of access nodes for each HAP on each access channel. C4 means that any satellite channel is allocated to at most one HAP. C5 means the maximum number of channels that each HAP can access. C6 is a Boolean value constraint; C7 - C8 are power constraints. C7 and C8 respectively give the power ranges of ground nodes and HAPs; C9 is the limit on the admitted data volume, and the admitted data volume cannot exceed the sensed data volume in the current time slot; C10 - C11 limit the smoothness of the task queues of ground nodes and high-altitude platforms.

[0152] Thus, based on the first objective function and objective constraint conditions, the resource allocation optimization problem model of the above space-air-ground communication system can be established.

[0153] The method of the embodiment of the present invention, by constructing a NOMA-based buffer relay-enabled satellite Internet of Things network, uses a high-altitude platform-enabled buffer strategy to effectively alleviate the shortage of satellite access resources and cope with the weak coverage of satellites. At the same time, by considering the inequality problem brought by the distance between ground network nodes and HAPs, it balances the system throughput and fairness between nodes, constructs a resource allocation optimization problem model of the space-air-ground communication system, and through model solving, realizes the adaptive resource allocation of the system, which is beneficial to improving the robustness of system transmission.

[0154] In the embodiment of the present invention, in order to make the above problem easier to solve, in each time slot t, an auxiliary variable C can be introducedu (t), by means of Jensen's inequality and other methods, problem P1 can be equivalently transformed into the following problem P1-1:

[0155]

[0156] s.t.: C1 to C11, that is, the constraint conditions include C1 to C11.

[0157]

[0158] C u (t) ≤ A max ; (C13)

[0159] Among them, A max represents the maximum value of the data volume arriving at the ground node per time slot, represents the average value of the auxiliary variable C u (t) for all time slots t.

[0160] After observing the above optimization problem, it can be summarized that problem P1-1 has the following characteristics: 1) This problem is a long-term and multi-time-slot statistical optimization problem. The cross-time-slot state coupling brought by the queue update equation and the unknown system state of different time slots make this problem unable to be solved by offline algorithms. 2) The optimization variables of this problem simultaneously include continuous variables and discrete variables, which is a mixed-integer optimization problem. In addition, due to the influence of intra-group and inter-group interference on the transmission rate, there is a high degree of coupling between the optimization variables of the original problem. In summary, problem P1 is a long-term statistical mixed-integer non-linear programming problem.

[0161] Based on the content of the above embodiments, the method further includes:

[0162] Taking the minimization of the Lyapunov drift plus penalty term per time slot as the goal, transforming the first objective function into the second objective function;

[0163] Based on the second objective function and the objective constraint conditions, establishing a resource allocation optimization problem model for the space-air-ground communication system.

[0164] Specifically, in the embodiments of the present invention, an adaptive decision algorithm based on the Lyapunov optimization framework will be given to solve the above P1 problem.

[0165] In the embodiments of the present invention, in order to ensure the stability constraint of the introduced auxiliary variable, a virtual queue H(t) = [H1(t), … H U (t)] can be introduced, and its update equation is given as follows:

[0166] H u (t + 1) = max{H u (t) - au (t),0}+C u (t); (25)

[0167] To show the queue states of different time slots of the system, a virtual queue Θ(t) combining the above queues is introduced and defined as follows:

[0168] Θ(t) = {Q(t), H(t), Z(t)}; (26)

[0169] Without loss of generality, the following Lyapunov function is established:

[0170]

[0171] The smaller L(Θ(t)) is, the less the system queue backlog is, that is, the shorter the data queue lengths Q m (t), Z u (t) and the virtual queue H u (t) are.

[0172] Next, the Lyapunov drift term can be introduced:

[0173]

[0174] Furthermore, the Lyapunov drift-plus-penalty term can be expressed as:

[0175]

[0176] where V is a penalty factor representing the importance of the system benefit. By adjusting the value of V, the stability of the system queue and the system benefit are balanced. The larger the value of V, the more likely the system benefit increases and the system queue backlog becomes longer, and vice versa. Therefore, the goal of stabilizing the system queue and maximizing the system benefit can be achieved by minimizing the drift-plus-penalty term U(Θ(t)). Furthermore, through mathematical transformation, an upper bound of the drift-plus-penalty term U(Θ(t)) can be obtained as follows:

[0177]

[0178]

[0179]

[0180] Then the Lyapunov drift-plus-penalty term can be expressed as:

[0181]

[0182] where C is a constant and is expressed as:

[0183]

[0184] In this embodiment, while ensuring the stability of the system queue, the system benefit is maximized by minimizing the drift penalty term. With the goal of minimizing the Lyapunov drift penalty term for each time slot, the first objective function P1 is transformed into the second objective function P2, that is, problem P1 is transformed into P2:

[0185] P2:

[0186]

[0187] Furthermore, based on the second objective function and the objective constraint conditions C1 - C13, an optimization problem model for resource allocation in the air - ground - space communication system is established.

[0188] The transformed problem P2 only depends on the queue backlog status and network status at the current time slot t. The long - term statistical optimization problem P1 is transformed into solving by minimizing the upper bound of the drift penalty term for each time slot. However, due to the existence of intra - cell interference and inter - cell interference, the rate expression is a non - convex expression with multi - variable coupling, and the logarithmic form of the benefit function also makes the solution more difficult.

[0189] The method of the embodiment of the present invention introduces the Lyapunov drift penalty term, minimizes the drift penalty term to achieve the purpose of stabilizing the system queue and maximizing the system benefit, reduces the difficulty of solving the resource allocation optimization problem model, and improves the efficiency of model solution.

[0190] Next, in the embodiment of the present invention, an algorithm for multi - stage joint alternating iteration solution will be designed to find a sub - optimal solution to the above problem.

[0191] That is, in step 130, the alternating iteration method is used to solve the resource allocation optimization problem model to determine the channel access information and transmission power of each channel between the ground terminal and the high - altitude platform, and the channel access information and transmission power of each channel between the high - altitude platform and the satellite for each time slot.

[0192] Based on the content of the above - mentioned embodiment, the alternating iteration method is used to solve the resource allocation optimization problem model to determine the channel access information and transmission power of each channel between the ground terminal and the high - altitude platform, and the channel access information and transmission power of each channel between the high - altitude platform and the satellite for each time slot, including:

[0193] The resource allocation optimization problem model is decomposed and decoupled into multiple sub - optimization problem models; the multiple sub - optimization problem models include an auxiliary variable optimization sub - problem, a first sub - optimization problem model for channel allocation and power control from the ground terminal to the high - altitude platform, and a second sub - optimization problem model for channel allocation and power control from the high - altitude platform to the satellite.

[0194] Iteratively solve the auxiliary variable optimization sub-problem, the first sub-optimization problem model, and the second sub-optimization problem model to obtain the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel at each time slot.

[0195] Specifically, in the embodiments of the present invention, the idea of alternating iteration is adopted to decompose and decouple the original problem, and the resource allocation optimization problem model is decomposed and decoupled into multiple sub-optimization problem models, including an auxiliary variable optimization sub-problem, a first sub-optimization problem model for channel allocation and power control between the ground terminal and the high-altitude platform, and a second sub-optimization problem model for channel allocation and power control between the high-altitude platform and the satellite. Thus, the three sub-problems of introducing auxiliary variables, the ground segment, and the space segment are solved respectively.

[0196] First, since the auxiliary variable C is independent of other variables, it can be obtained by solving the following auxiliary variable optimization sub-problem P3, that is:

[0197]

[0198] The constraint condition is: C u (t) ≤ A max ;

[0199] Since the objective function of the auxiliary variable optimization sub-problem is a standard convex function with respect to the auxiliary variable C, and the constraint condition is a linear constraint condition, the problem P3 can be solved by existing convex optimization solving methods, such as the interior point method, etc.

[0200] Furthermore, the decoupled admission rate sub-problem can be expressed as follows:

[0201]

[0202] The constraint condition is: a u (t) ≤ A u (t),

[0203] Among them, the optimal admission decision can be given as follows:

[0204]

[0205] Next, first solve the first sub-optimization problem model, jointly optimize the G2A channel allocation and power control. The channel access information between the ground terminal and the high-altitude platform at each time slot, that is, the G2A channel allocation information, can be obtained by solving the following problem P5, and there is:

[0206]

[0207] Subject to: C1, C3, C6.

[0208] That is, the constraints include C1, C3, and C6.

[0209] The optimization variable X of problem P5 is the mathematical mapping of the matching association between the ground nodes and the channels of different high-altitude platforms. Therefore, the above optimization problem can be reconstructed into a multi-variable matching game problem with two parties participating. Specifically, associate the HAPs with the corresponding channels to construct a new set of size M×K where each HAP-channel unit can be represented as (m, k). Then the two sets of parties participating in the matching are the users and the HAP-channel units and the following definitions are given:

[0210] Definition 1: Define μ as a many-to-one matching mapping from the set of users to the HAP-channel units if it satisfies the following conditions:

[0211]

[0212]

[0213] (3) μ(u) = (m, k), if and only if u ∈ μ(m, k);

[0214] where |μ(*)| represents the number of matching results, and |μ(*)| = 0 means the player is not matched. Condition (1) means that each user can be matched with at most one HAP-channel unit, condition (2) means that each HAP-channel unit can be matched with at most D ground nodes, and conditions (1) and (2) correspond to the restrictions C1 and C3 respectively. Condition (3) means that if the ground node u is matched with the HAP-channel unit (m, k), then the ground node u establishes a connection with HAP m through channel k, and vice versa. Then the connection variable x u,m,k can be restored according to the following equation:

[0215]

[0216] First, establish the preference list of each ground node according to the channel coefficient |g u,m (t)| 2 while satisfying the constraint conditions of the original problem P3, and randomly access the channels of the high-altitude platforms according to the preference list to initialize the corresponding association X between the users and the channels.

[0217] It should be noted that due to the serial interference of NOMA and the interference across cells, the preference list of each ground node or (high-altitude platform, sub-channel) is dynamically affected by the matching results of the remaining players. This phenomenon of preference interaction among players is called "externality" in matching theory, which will lead to unstable matching results. Therefore, bilateral exchange matching stability is adopted to solve the channel allocation problem between ground nodes and high-altitude platforms.

[0218] First, the following definitions are given:

[0219] Definition 2: Given a matching μ and two ground node - (high-altitude platform, sub-channel) matching pairs (u1, (m1, k1)) and (u2, (m2, k2)), that is, μ(u1) = (m1, k1), μ(u2) = (m2, k2), and u1 ≠ u2, (m1 ≠ m2 || k1 ≠ k2), the exchange matching can be defined as

[0220] According to the above definition, the definition of a blocking pair can be given as follows:

[0221] Definition 3: A pair of ground nodes (u1, u2) is a blocking pair if and only if for the matching μ, μ(u1) = (m1, k1), μ(u2) = (m2, k2), the following conditions are satisfied:

[0222]

[0223] The above formula shows that only when the exchange matching Compared with the matching μ, achieving a higher system benefit φ(X), (u1, u2) is called a blocking pair.

[0224] Definition 4: For a matching μ, if there is no blocking pair, then the matching μ is said to be bilaterally exchange stable.

[0225] Generally speaking, in the initialization stage, as long as all the constraints of the optimization problem P5 are satisfied, the ground nodes and HAP-channel units can be randomly matched. Then, two ground node participants in the randomly selected matching μ are judged whether they satisfy the definition of a blocking pair, and the corresponding matching scheme is updated through exchange matching until there is no blocking pair to achieve bilateral exchange stability.

[0226] Furthermore, the transmission power of the channel between the ground terminal and the high-altitude platform in each time slot is obtained by solving the following problem P6, that is:

[0227]

[0228] Constraint condition: 0 ≤ p u,m,k (t) ≤ pmax ;

[0229] Given the channel allocation X, problem P2 can be transformed into problem P6. It can be found that due to the existence of inter-cell interference and intra-cell interference, the objective function is a non-convex and non-concave function, and it can be solved in the following two cases:

[0230] Case 1: For When (Q m (t) - Z u (t)) ≥ 0, the corresponding optimal power control

[0231] Case 2: For When (Q m (t) - Z u (t)) < 0, the non-convex term can be approximated by the following equation

[0232]

[0233] Among them,

[0234]

[0235] Furthermore, let q u,m,k (t) = logp u,m,k (t), then problem P6 can be transformed into the following convex problem P6-1:

[0236]

[0237] The constraint conditions are: q u,m,k (t) ≤ logp max ;

[0238] Among them,

[0239]

[0240]

[0241] It can be found that the objective function of problem P6-1 is a convex function. Therefore, P6-1 is already a standard convex optimization problem and can be solved using existing convex optimization toolboxes, such as the CVX toolbox; after obtaining the optimal solution the optimal power variable can be obtained according to the equation

[0242] ​Furthermore, solve the second sub-optimization problem model to jointly optimize the A2S channel allocation and power control. The channel access information between the HAP and the satellite in each time slot, i.e., the A2S channel allocation information, can be obtained by solving the following problem P7:

[0243]

[0244] The constraint conditions are: C2, C4, C5, C6;

[0245] To obtain a solution with a lower complexity, similarly, use the matching game theory to solve the above problem. The above high-altitude platform sub-channel access problem can be regarded as a many-to-one matching process, associating the satellite with the corresponding sub-channel to construct a new set of size N×L where each satellite-channel unit can be represented as (n, l), and the following definitions are given:

[0246] Definition 5: Define Π as a many-to-one matching mapping from the set of satellite-channel units to the set of HAPs If it satisfies the following conditions:

[0247]

[0248]

[0249] (3) Π(m) = (n, l), only when m ∈ (n, l);

[0250] where, |Π(*)| represents the number of matching results, and |Π(*)| = 0 means that the player is not matched. Condition (1) means that each satellite-channel unit can be matched with at most one HAP, and condition (2) means that each HAP can be matched with at most M max satellite-channel units. Conditions (1) and (2) correspond to the constraint conditions C4 and C5 respectively. Condition (3) means that if HAPm is matched with the satellite-channel unit (n, l), then HAPm establishes a connection with the NGSO satellite n through channel l, and vice versa. The connection variable b m,n,l can be restored according to the following equation:

[0251]

[0252] First, similar to problem P5, on the premise of satisfying the restrictions of P7, the high-altitude platform randomly accesses the sub-channels of visible satellites to initialize the association between the high-altitude platform and the satellite-channel units.

[0253] Meanwhile, due to the inter-channel interference between cells, this matching also has external characteristics, that is, the matching of each HAP is affected by the remaining matching results within the same set, which may very likely lead to unstable matching. Therefore, similar to Definition 2 - Definition 4, the following definition is given:

[0254] Definition 5: Given a matching Π, if there do not exist two HAP-(satellite, channel) matching pairs, namely (m1,(n1,l1)) and (m2,(n2,l2)), and satisfy the following swap matching, then the matching Π is said to be bilaterally swap stable.

[0255]

[0256] where is defined as the swap matching of Π.

[0257] In the initialization stage, as long as all the constraints of the optimization problem P7 are satisfied, the ground nodes and the HAP-channel units can be randomly matched. In the swap matching stage, any two (satellite, channel) units are randomly selected to determine whether the system benefit increases after the swap, and the matching scheme is updated accordingly until a bilaterally swap stable state is reached.

[0258] Furthermore, the transmission power of the channel between the HAP and the satellite in each time slot is obtained by solving the following problem P8, that is:

[0259]

[0260] The constraint condition is: 0 ≤ P m,n,l (t) ≤ P max ;

[0261] where

[0262] Due to the existence of cross-cell interference, the objective function is a non-convex and non-concave function with respect to the power variable P. To address the above problem, the technique of successive convex approximation is adopted to obtain a tight lower bound of the objective function:

[0263]

[0264] where

[0265] When the above inequality satisfies the equality state, that is, a tight lower bound is obtained.

[0266] Furthermore, let o m,n,l(t) = log P m,n,l (t), then the original function can be expressed as:

[0267]

[0268] where, is a function of variable o m,n,l (t).

[0269]

[0270] Therefore, problem P8 can be transformed into the following problem P8-1 for solution.

[0271]

[0272] The constraint condition is: 0 ≤ o m,n,l (t) ≤ log(P max );

[0273] Since the objective function is a concave function and the constraint condition is a linear function, the above problem is transformed into a typical convex optimization problem, and existing convex optimization solution methods or toolkits can be used for solution, such as the interior point method, CVX toolbox, etc. After obtaining the optimal solution , the optimal power variable can be obtained according to the equation .

[0274] Thus, by iteratively solving the auxiliary variable optimization sub-problem, the first sub-optimization problem model, and the second sub-optimization problem model, the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel can be obtained at each time slot.

[0275] In the embodiment of the present invention, by adopting the idea of alternating iteration, the original problem is decomposed and decoupled, and the resource allocation optimization problem model is decomposed and decoupled into multiple sub-optimization problem models. By performing multi-stage joint alternating iteration calculation on each sub-optimization problem model, the complexity of solving the original problem can be effectively reduced, and the original problem can be solved quickly and effectively.

[0276] The resource allocation method for the space-air-ground communication system based on buffer relay in the embodiments of the present invention divides the transmission time into multiple time slots; aiming at maximizing the system average throughput and combining the target constraint conditions, a resource allocation optimization problem model for the space-air-ground communication system is established; the alternating iteration method is used to solve the resource allocation optimization problem model, and the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot are determined. The space-ground channel condition of each time slot can be obtained based on the actual satellite topology structure, realizing adaptive resource allocation, enhancing the system capacity, and improving the system resource utilization rate and the robustness of network transmission.

[0277] Based on the content of the above embodiments, the auxiliary variable optimization sub-problem, the first sub-optimization problem model, and the second sub-optimization problem model are iteratively solved to obtain the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot, including:

[0278] Step S1, based on the data queues of each ground terminal, use the convex optimization solution method to solve the auxiliary variable optimization sub-problem to obtain the optimal solution of the auxiliary variable in the current time slot;

[0279] Step S2, based on the data queues of each ground terminal and each high-altitude platform, solve the first sub-optimization problem model to determine the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel in the current time slot, and solve the second sub-optimization problem model to determine the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in the current time slot;

[0280] Step S3, based on the optimal solution of the auxiliary variable in the current time slot, the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel in the current time slot, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in the current time slot, update the data queues of each ground terminal and each high-altitude platform;

[0281] Step S4, when it is determined that the current time slot has not reached the preset maximum time slot, continue to execute Step S1 to Step S3 until the current time slot reaches the maximum time slot, and enter Step S5 to obtain the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot.

[0282] Figure 3 is one of the flow schematic diagrams of the model solution in the resource allocation method for the space-air-ground communication system based on buffer relay provided by the present invention, as Figure 3As shown, before step S1, the time slot t = 1 is initialized, the total optimization duration is T, and t ∈ {1, …, T}.

[0283] Further, in step S1, based on the data queues of each ground terminal, the convex optimization solution method is used to solve the auxiliary variable optimization sub-problems P3 and P4, and the optimal solutions {C(t), a(t)} of the auxiliary variables in the current time slot t can be obtained.

[0284] Further, in step S2, based on the data queues H(t) of each ground terminal and the data queues Q(t) of each HAP, the first sub-optimization problem models P5 and P6 are solved to determine the channel access information between the ground terminal and the HAP and the transmission power of each channel in the current time slot t.

[0285] Figure 4 It is the second schematic diagram of the model solution process in the resource allocation method for the air-ground-space communication system based on buffer relay provided by the present invention. As Figure 4 shown, the steps of solving the channel access information between the ground terminal and the HAP and the transmission power of each channel include:

[0286] Step 410, initialize the channel access information X (0) (t) between the ground segment, that is, the ground terminal and the HAP, and the transmission power p (0) (t) of each channel, and set the loop indicator variable i_G2A = 1.

[0287] Step 420, solve problem P5, and the optimal solution {X (i_G2A) (t)} of the channel variable in the i_G2A-th iteration of the ground segment in time slot t can be obtained.

[0288] Step 430, solve problem P6-1, and the optimal solution {p (i_G2A) (t)} of the power variable in the i_G2A-th iteration of the ground segment in time slot t can be obtained.

[0289] Step 440, determine whether the optimal solutions {X (i_G2A) (t)} and {p (i_G2A) (t)} satisfy the convergence discrimination criterion. If not, execute i_G2A = i_G2A + 1 and continue to iterate steps 420 to 430; if satisfied, enter step 450, and the channel access information between the ground segment and the transmission power of each channel in time slot t can be obtained.

[0290] In step S2, the second sub-optimization problem models P7 and P8 are solved to determine the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in the current time slot t.

[0291] Figure 5It is the third schematic diagram of the process for model solution in the resource allocation method of the air-sky-earth communication system based on buffer relays provided by the present invention. As Figure 5 shown, the steps for solving the channel access information between the HAP and the satellite and the transmission power of each channel include:

[0292] Step 510, initialize the channel access information B (0) between the space segment, i.e., between the HAP and the satellite, and the transmission power P (0) of each channel, and set the loop indicator variable i_A2S = 1.

[0293] Step 520, solve Problem P7, and the optimal solution {B (i_A2S) (t)} of the channel variable of the i_A2S-th iteration in the space segment at time slot t can be obtained.

[0294] Step 530, solve Problem P8-1, and the optimal solution {P (i_A2S) (t)} of the power variable of the i_A2S-th iteration in the space segment at time slot t can be obtained.

[0295] Step 540, determine whether the optimal solutions {B (i_A2S) (t)} and {P (i_A2S) (t)} satisfy the convergence criterion. If not, execute i_A2S = i_A2S + 1 and continue to iterate Steps 520 to 530; if satisfied, enter Step 550, and the channel access information of the space segment and the transmission power of each channel at time slot t can be obtained.

[0296] In this embodiment, through Step S2, the solutions {X(t), B(t), p(t), P(t)} of the ground segment and the space segment at time slot t are obtained, and the communication system is configured using this solution.

[0297] Further, in Step S3, based on the optimal solution of the auxiliary variable at the current time slot t, the channel access information and the transmission power of each channel between the ground terminal and the HAP at the current time slot t, and the channel access information and the transmission power of each channel between the HAP and the satellite at the current time slot t, the data queues H(t) and Q(t) of each ground terminal and each HAP are updated.

[0298] Enter Step S4. When it is determined that the current time slot t has not reached the preset maximum time slot T, continue to execute the above Steps S1 to S3 until the current time slot reaches the maximum time slot and enter Step S5, thereby obtaining the channel access information and the transmission power of each channel between the ground terminal and the HAP, and the channel access information and the transmission power of each channel between the HAP and the satellite at each time slot.

[0299] The method of the embodiment of the present invention adopts a method of multi-stage joint alternating iteration to solve the three sub-problems of introducing auxiliary variables, the ground segment and the space segment respectively through iteration, which can ensure the solution accuracy while improving the solution efficiency, and is beneficial to improving the efficiency of resource allocation and resource utilization rate of the air-space-ground communication system.

[0300] The resource allocation device for the air-space-ground communication system based on buffer relay provided by the present invention will be described below. The resource allocation device for the air-space-ground communication system based on buffer relay described below can be correspondingly referred to the resource allocation method for the air-space-ground communication system based on buffer relay described above.

[0301] Figure 6 is a schematic structural diagram of the resource allocation device for the air-space-ground communication system based on buffer relay provided by the present invention, as Figure 6 shown, including:

[0302] A division module 610, configured to divide the transmission time into multiple time slots;

[0303] A establishment module 620, configured to establish an optimization problem model for resource allocation of the air-space-ground communication system with the goal of maximizing the system average throughput and combining the target constraint conditions;

[0304] A solution module 630, configured to solve the resource allocation optimization problem model by using the alternating iteration method, and determine the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot.

[0305] The resource allocation device for the air-space-ground communication system based on buffer relay in this embodiment can be used to execute the resource allocation method embodiment for the air-space-ground communication system based on buffer relay described above, and its principle and technical effect are similar, which will not be elaborated here.

[0306] The resource allocation device for the air-space-ground communication system based on buffer relay in the embodiment of the present invention divides the transmission time into multiple time slots; establishes an optimization problem model for resource allocation of the air-space-ground communication system with the goal of maximizing the system average throughput and combining the target constraint conditions; uses the alternating iteration method to solve the resource allocation optimization problem model, and determines the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, and the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot, can obtain the satellite-ground channel conditions of each time slot based on the actual satellite topology structure, realizes adaptive resource allocation, enhances the system capacity, improves the system resource utilization rate and the robustness of network transmission.

[0307] Figure 7 is a schematic structural diagram of the entity of the electronic device provided by the present invention, asFigure 7 As shown in Figure 7 , the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740. Among them, the processor 710, the communications interface 720, and the memory 730 complete their mutual communication through the communication bus 740. The processor 710 may call the logical instructions in the memory 730 to execute the resource allocation method for the air-ground-space communication system based on buffer relaying provided by the above-mentioned various methods. The method includes: dividing the transmission time into multiple time slots; taking maximizing the system average throughput as the goal and combining the target constraint conditions to establish a resource allocation optimization problem model for the air-ground-space communication system; using the alternating iteration method to solve the resource allocation optimization problem model, and determining the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot.

[0308] In addition, when the logical instructions in the above-mentioned memory 730 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0309] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the resource allocation method for the air-ground-space communication system based on buffer relaying provided by the above-mentioned various methods. The method includes: dividing the transmission time into multiple time slots; taking maximizing the system average throughput as the goal and combining the target constraint conditions to establish a resource allocation optimization problem model for the air-ground-space communication system; using the alternating iteration method to solve the resource allocation optimization problem model, and determining the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel under each time slot.

[0310] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the resource allocation method for the space-air-ground communication system based on buffer relay provided by the above-mentioned various methods. The method includes: dividing the transmission time into multiple time slots; aiming at maximizing the system average throughput and combining with the target constraint conditions, establishing a resource allocation optimization problem model for the space-air-ground communication system; using the alternating iteration method to solve the resource allocation optimization problem model, and determining the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in each time slot.

[0311] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0312] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware. Based on this understanding, the essence of the above technical solutions, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0313] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A resource allocation method for an air-sky-ground communication system based on buffer relays, characterized in that, Including: Dividing the transmission time into multiple time slots; Taking maximizing the system average throughput as the goal and combining the target constraint conditions to establish a resource allocation optimization problem model for the air-ground-space communication system; Using the alternating iteration method to solve the resource allocation optimization problem model, and determining the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in each time slot; The establishing of the resource allocation optimization problem model for the air-ground-space communication system by taking maximizing the system average throughput as the goal and combining the target constraint conditions includes: Determining the system average throughput based on the data throughput of each ground terminal in each time slot; Taking maximizing the system average throughput as the goal to establish a first objective function; Determining the target constraint conditions based on the power ranges of each ground terminal and each high-altitude platform and the number of access nodes of each access channel of each high-altitude platform; Based on the first objective function and the target constraint conditions, establishing the resource allocation optimization problem model for the air-ground-space communication system.

2. The resource allocation method for the air-sky-earth communication system based on buffer relays according to claim 1, wherein, The method further includes: Taking minimizing the Lyapunov drift plus penalty term of each time slot as the goal to transform the first objective function into a second objective function; Based on the second objective function and the target constraint conditions, establishing the resource allocation optimization problem model for the air-ground-space communication system.

3. The resource allocation method for the space-air-ground communication system based on buffer relays according to claim 2, wherein, The using of the alternating iteration method to solve the resource allocation optimization problem model and determining the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in each time slot includes: Decomposing and decoupling the resource allocation optimization problem model into multiple sub-optimization problem models; the multiple sub-optimization problem models include an auxiliary variable optimization sub-problem, a first sub-optimization problem model for channel allocation and power control between the ground terminal and the high-altitude platform, and a second sub-optimization problem model for channel allocation and power control between the high-altitude platform and the satellite; Iteratively solving the auxiliary variable optimization sub-problem, the first sub-optimization problem model and the second sub-optimization problem model to obtain the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in each time slot.

4. The resource allocation method for the air-sky-earth communication system based on buffer relays according to claim 3, wherein, The iteratively solving the auxiliary variable optimization sub-problem, the first sub-optimization problem model and the second sub-optimization problem model to obtain the channel access information between the ground terminal and the high-altitude platform and the transmission power of each channel, as well as the channel access information between the high-altitude platform and the satellite and the transmission power of each channel in each time slot includes: Step S1, based on the data queues of each ground terminal, using a convex optimization solution method to solve the auxiliary variable optimization sub-problem to obtain the optimal solution of the auxiliary variable in the current time slot; Step S2: Based on the data queues of each of the ground terminals and each of the high-altitude platforms, solve the first sub-optimization problem model to determine the channel access information between the ground terminals and the high-altitude platforms and the transmission power of each channel in the current time slot, and solve the second sub-optimization problem model to determine the channel access information between the high-altitude platforms and the satellites and the transmission power of each channel in the current time slot; Step S3: Based on the optimal solutions of the auxiliary variables in the current time slot, the channel access information between the ground terminals and the high-altitude platforms and the transmission power of each channel in the current time slot, and the channel access information between the high-altitude platforms and the satellites and the transmission power of each channel in the current time slot, update the data queues of each of the ground terminals and each of the high-altitude platforms; Step S4: When it is determined that the current time slot has not reached the preset maximum time slot, continue to execute Steps S1 to S3 until the current time slot reaches the maximum time slot, and then enter Step S5 to obtain the channel access information between the ground terminals and the high-altitude platforms and the transmission power of each channel, and the channel access information between the high-altitude platforms and the satellites and the transmission power of each channel in each time slot.

5. The resource allocation method for the space-air-ground communication system based on buffer relays according to claim 2, wherein The first objective function is expressed by the following formula: ； The second objective function is expressed by the following formula: ； Among them, represents the access data of the ground terminal in time average, represents the set of all ground terminals, represents all high-altitude platforms in the set; represents the amount of access data of the ground terminal, represents the channel access information between the ground terminal and the high-altitude platform; represents the transmission power of the channel between the ground terminal and the high-altitude platform; represents the channel access information between the high-altitude platform and the satellite; represents the transmission power of the channel between the high-altitude platform and the satellite; is a constant; is a virtual queue, , , , ; Among them, represents the penalty factor of the Lyapunov function; , which represents an auxiliary variable; represents the ground terminal in time slot the amount of offloaded data; represents the high altitude platform buffer in time slot the amount of received data; represents the high altitude platform buffer in time slot the amount of transmitted data; represents the high altitude platform buffer in time slot the amount of stored data; represents the ground terminal buffer in time slot the amount of stored data, and its update method is implemented according to the following formula: ； Indicates a ground terminal A virtual data queue introduced by the buffer, and its update method is implemented according to the following formula: 。 6. A resource allocation device for an air-space-ground communication system based on buffer relays, characterized in that including: a partitioning module for partitioning the transmission time into multiple time slots; a building module for building a resource allocation optimization problem model of the air-ground-space communication system with the goal of maximizing the system average throughput and combining the target constraint conditions; a solving module for solving the resource allocation optimization problem model by using the alternating iteration method to determine the channel access information between the ground terminals and the high-altitude platforms and the transmission power of each channel, and the channel access information between the high-altitude platforms and the satellites and the transmission power of each channel in each time slot; wherein, building the resource allocation optimization problem model of the air-ground-space communication system with the goal of maximizing the system average throughput and combining the target constraint conditions includes: determining the system average throughput based on the data throughput of each of the ground terminals in each time slot; building a first objective function with the goal of maximizing the system average throughput; determining the target constraint conditions based on the power ranges of each of the ground terminals and each of the high-altitude platforms and the number of access nodes of each access channel of each of the high-altitude platforms; building the resource allocation optimization problem model of the air-ground-space communication system based on the first objective function and the target constraint conditions.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the resource allocation method for the air-ground-space communication system based on buffer relay according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the resource allocation method for the air-ground-space communication system based on buffer relay according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the resource allocation method for the air-ground-space communication system based on buffer relay according to any one of claims 1 to 5.

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