CN113873658B - Method for allocating beam hopping resources by taking user service weight gain as objective function - Google Patents
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- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/046—Wireless resource allocation based on the type of the allocated resource the resource being in the space domain, e.g. beams
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- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
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Abstract
The invention discloses a beam hopping resource allocation method taking user service weight gain as an objective function, which comprises the following steps: calculating the traffic and time tolerance of the current time slot of each cell; establishing an optimization problem based on a decision problem of the maximization of the dispersion with the aim of maximizing the total dispersion of all the attribute pairs to the decision scheme; solving the optimization problem and determining an optimal attribute pair decision scheme; and performing resource allocation on each cell according to the optimal attribute decision scheme to finish the beam hopping resource allocation taking the user service weight gain as an objective function.
Description
Technical Field
The invention belongs to the field of beam hopping satellite systems, and relates to a beam hopping resource allocation method taking user service weight gain as an objective function.
Background
With the development of broadband satellite communication, the demands for large capacity, high speed and wide coverage range have been significantly increased. The current wide satellite system mostly uses multi-spot beams to increase the system capacity, the system uses a fixed mode to allocate resources, the power and frequency resources allocated by each beam are fixed and only occupy a small part of all satellite resources, and the mode of fixedly allocating the resources cannot meet the requirements of efficient transmission and on-demand coverage due to the diversity of service types, the spatial non-uniformity and the time variability of service distribution, so that the waste of communication resources is caused.
In order to effectively utilize satellite resources, researchers propose a beam hopping technology, which uses a small number of beams to perform time-sharing coverage, and based on a time slicing technology, only part of spot beams on a satellite are in a working state at a certain specific moment, so that the resources are fully utilized, and compared with the traditional multi-beam technology, the beam hopping technology can be more suitable for the scene of unbalanced satellite service requirements.
The traditional jump beam resource allocation algorithm takes the system capacity as an optimization target, and further ignores the service delay and QoS requirements. The service types are generally classified into real-time service, normal service and non-real-time service, and the key of QoS guarantee is that different service types have different QoS requirements, so that different service classes exist, for example, real-time service such as voice service has low requirements on bandwidth, but strict requirements on maximum transmission delay, and non-real-time service such as data transmission service has larger tolerance on transmission delay. Different services have different transmission delay requirements, if the traditional jump beam resource allocation algorithm is continuously used, and each time slot pursues the maximum system capacity of the user, the real-time service has high transmission delay requirements due to less request quantity, and the packet loss rate is increased; and secondly, as the tolerance of the non-real-time service to the transmission delay is high, the service can be stored for a period of time and then transmitted to achieve the maximum utilization of resources, and the traditional beam hopping algorithm pursues that each time slot transmits to the user with the maximum service request amount, when the beam hopping can provide a larger amount of resources, the transmission resources are excessive, further the beam hopping resources are wasted, and the overall throughput of the system is reduced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a beam hopping resource allocation method taking the user service weight gain as an objective function.
In order to achieve the above objective, the method for allocating beam hopping resources using the user service weight gain as an objective function according to the present invention includes the following steps:
calculating the traffic and time tolerance of the current time slot of each cell;
establishing an optimization problem based on a decision problem of the maximization of the dispersion with the aim of maximizing the total dispersion of all the attribute pairs to the decision scheme;
solving the optimization problem and determining an optimal attribute pair decision scheme;
and performing resource allocation on each cell according to the optimal attribute decision scheme to finish the beam hopping resource allocation taking the user service weight gain as an objective function.
Constructing an objective function based on maximizing the total dispersion of all attributes to the decision scheme, i.e
Wherein K is the number of cells,
for the normalized attribute value of the kth attribute in the ith scheme of time slot j, W is the combining weight.
The established optimization problem is as follows:
maxF(θ)＝Z 1 ωθ
wherein θ= { θ j Not less than 0|j =1, 2} is the linear marking coefficient of the combined weight vector, Z 1 Is a 2-dimensional row vector.
2-dimensional row vector Z 1 The method comprises the following steps:
the traffic volume and time tolerance of each cell in the optimal attribute decision scheme are as follows:
where i=1, 2,3,..k, j is the current slot.
And solving the optimization problem by using a dispersion maximization combination weighting method.
The invention has the following beneficial effects:
the beam hopping resource allocation method taking the user service weight gain as the objective function aims at maximizing the total dispersion of all attributes to the decision scheme during specific operation, and establishes the optimization problem based on the decision problem of the dispersion maximization, thereby comprehensively considering the user service weight gain, time delay and throughput, meeting the service QoS requirement, leading the real-time service throughput to be high, leading the real-time service average transmission delay to be reduced, leading the overall throughput of the system to be increased, fully utilizing the resources and improving the resource utilization rate.
Drawings
FIG. 1 is a schematic diagram of a system architecture of the present invention;
FIG. 2 is a graph comparing throughput of the present invention with that of the prior art;
FIG. 3 is a graph showing the average time delay of the transmission of the real-time service data packet compared with the conventional beam hopping resource allocation algorithm;
FIG. 4 is a graph comparing the real-time traffic throughput with the conventional beam hopping resource allocation algorithm of the present invention;
FIG. 5 is a graph comparing the conventional beam hopping resource allocation algorithm of the present invention with the throughput of the normal service;
FIG. 6 is a graph comparing the throughput of non-real-time traffic with a conventional hop beam resource allocation algorithm according to the present invention;
fig. 7 is a graph comparing the overall throughput of the system with the conventional hopping beam resource allocation algorithm and the fixed beam resource allocation algorithm.
Detailed Description
In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments, but not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the accompanying drawings, there is shown a schematic structural diagram in accordance with a disclosed embodiment of the invention. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.
Referring to fig. 1, there are three main devices in the system, namely, a gateway, a satellite and a cell, wherein the gateway obtains a service request of a user, stores data such as a user position, a user service request capacity, a beam coverage area, a satellite loading capacity and the like, and generates a beam hopping schedule containing parameters such as a beam hopping number, a beam hopping residence time and the like by calling a beam hopping resource allocation algorithm, and transmits the beam hopping schedule to the satellite; the satellite receives the beam hopping schedule and forwards the service data to the beam in the beam hopping schedule; and receiving the requested service data by the terminals in the cell according to the beam hopping schedule received by the satellite.
The method for allocating the beam hopping resource by taking the user service weight gain as the objective function comprises the following steps:
1) Let the traffic of cell i in time slot j be
The delay tolerance of the cell i in the time slot j is ST i j Which is provided withIn the process, the liquid crystal display device comprises a liquid crystal display device,
Arrive k time of having been issued for this type of service request, +.>
Time to live for this class of service request, amout k For this type of service request traffic, the user service weight gain is +.>
2) Converting the jump beam resource allocation problem into a multi-attribute decision problem, and aiming at the decision attribute
ST (ST) i j And (3) performing normalization treatment to obtain:
the normalized decision matrix B is:
wherein K is the number of cells,
for normalizing the attribute values of the ith scheme for the kth attribute in time slot j, the ith row of matrix B represents the normalized values of the ith scheme for both attribute values, wherein +.>
The larger the value, the better.
3) Subjective weight omega is obtained by subjective weighting method 1 ＝{ω 1,i I=1, 2}, and calculating by adopting an entropy method to obtain an objective weight omega 2 ＝{ω 2,i I=1, 2}, and subjective weight and objective weight are combined based on a dispersion maximization principle.
Combining weight w= (W) c1 ,w c2 ) T The method comprises the following steps:
W＝θ 1 ω 1 +θ 2 ω 2
wherein θ= { θ j The linear marking coefficient of the combined weight vector is equal to or larger than 0|j =j, 2, and the unitized constraint condition is satisfied:
constructing an objective function based on a principle that maximizes the total dispersion of all attributes to the decision scheme as:
let 2-dimensional row vector Z 1 The method comprises the following steps:
the objective function is expressed as:
J(W)＝Z 1 W
wherein J (W) is a θ function of the vector, and the decision problem is converted into an optimization problem based on dispersion maximization, namely:
maxF(θ)＝Z 1 ωθ
constructing Lagrange functions, and solving to obtain:
substituting the combination weight and solving the weight by using a dispersion maximization combination weighting method
The weighted cell traffic and time tolerance are obtained as follows:
where i=1, 2,3,..k, j is the current slot.
4) Selecting an optimal cell by adopting a good-bad solution distance method, and calculating an optimal value and an optimal value of cell traffic and delay tolerance, wherein the optimal value of the cell traffic and the delay tolerance is z max,1 ＝max(z i,1 ),z max,2 ＝max(z i,2 ) The worst value is z min,1 ＝min(z i,1 ),z min,2 ＝min(z i,2 )，i＝1,2,3,...,K。
Calculating the distance D between each candidate cell and the optimal scheme and the worst scheme max,i 、D min,i ：
Obtaining the relative proximity degree U of the candidate cell and the optimal scheme i The method comprises the following steps:
and sorting according to the relative distance between each candidate cell and the worst value, wherein the maximum value is the cell needing to be subjected to resource allocation, finding the cell which is subjected to resource allocation preferentially by each cluster under the current time slot by a combination weighting method based on the maximum dispersion, and completing the final beam hopping resource allocation by a greedy algorithm and introducing the limit of the same-frequency multiplexing distance.
In summary, the specific process of the invention is as follows:
1) Calculating the traffic and time tolerance of the current time slot of each cell;
2) Normalizing the decision attribute, calculating a weight by adopting a combination weighting method with the largest dispersion, and selecting a scheme by adopting a good-bad solution distance method;
3) And allocating resources to the candidate cells according to the selected scheme.
And each iteration takes the cell with the largest current weight as a starting point, allocates resources for the cell, records the cluster where the cell is located, searches the rest cells needing to be allocated with resources under the time slot, and searches all the rest clusters except the cluster where the cell is located by taking the cell as the center of a circle and the same-frequency multiplexing distance as the radius, and selects the cell with the largest weight outside the circle for resource allocation until the power is exhausted or the number of the cells with the resources allocated reaches a threshold value.
FIG. 2 is a diagram showing improvement and comparison of throughput of a system with the same-frequency multiplexing distance introduced by a beam hopping resource allocation algorithm, wherein the same-frequency multiplexing distance is introduced to reduce same-frequency interference, improve signal-to-interference-and-noise ratio of signals, and further improve throughput of the system;
FIG. 3 is a graph comparing the average time delay of the transmission of the data packet of the real-time service with that of the conventional beam hopping resource allocation algorithm, and it can be seen that the improved beam hopping resource allocation algorithm considers the time delay, so that the real-time service with high time delay requirement can be preferentially allocated during resource allocation, and the transmission time delay is reduced;
fig. 4, fig. 5, and fig. 6 are graphs showing the comparison of the throughput of the real-time service, the normal service, and the non-real-time service according to the present invention and the conventional beam hopping resource allocation algorithm, respectively, it can be seen that the throughput of the normal service and the non-real-time service is hardly changed, and the real-time service is due to the service QoS requirement, and the time delay is considered in the multi-attribute decision, so that the real-time service is allocated with more resources;
fig. 7 is a diagram comparing the overall throughput of the system with the conventional hopping beam resource allocation algorithm and the fixed beam resource allocation algorithm, and can show that the hopping beam resource allocation algorithm is superior to the fixed beam resource allocation algorithm, and the improved hopping beam resource allocation algorithm considers the QoS requirement of the service, and fully utilizes the hopping beam resource, so that the overall throughput of the system is improved.
Claims (1)
1. A method for allocating the jump beam resources by taking the user service weight gain as the objective function is characterized by comprising the following steps:
calculating the traffic and time tolerance of the current time slot of each cell;
establishing an optimization problem based on a decision problem of the maximization of the dispersion with the aim of maximizing the total dispersion of all the attribute pairs to the decision scheme;
solving the optimization problem and determining an optimal attribute pair decision scheme;
performing resource allocation on each cell according to the optimal attribute decision scheme to finish the beam hopping resource allocation taking the user service weight gain as an objective function;
constructing an objective function based on maximizing the total dispersion of all attributes to the decision scheme, i.e
Wherein K is the number of cells,
for the normalized attribute value of the kth attribute in the ith scheme of slot j, W is the combining weight, w= (W c1 ,w c2 ) T ，w ck Elements that are combining weights;
the established optimization problem is as follows:
maxF(θ)＝Z 1 ωθ
wherein θ= { θ j Gtoreq 0|j =1, 2} is the linear scaling factor of the combined weight vector, ω= (ω) 1 ,ω 2 ) T As a weight coefficient vector matrix omega 1 And omega 2 Subjective and objective weights, Z 1 Is a 2-dimensional row vector;
2-dimensional row vector Z 1 The method comprises the following steps:
the traffic volume and time tolerance of each cell in the optimal attribute decision scheme are as follows:
where i=1, 2,3,., K, j is the current slot;
solving the optimization problem by using a dispersion maximization combination weighting method;
constructing Lagrange functions, and solving to obtain:
substituting the combination weight and solving the weight W= (W) by using a dispersion maximization combination weighting method c1 ,w c2 ) T The traffic and time tolerance of the weighted cell is obtained as follows:
where i=1, 2,3,., K, j is the current slot;
selecting an optimal cell by adopting a good-bad solution distance method, and calculating an optimal value and an optimal value of cell traffic and delay tolerance, wherein the optimal value of the cell traffic and the delay tolerance is z max,1 ＝max(z i,1 ),z max,2 ＝max(z i,2 ) The worst value is z min,1 ＝min(z i,1 ),z min,2 ＝min(z i,2 )，i＝1,2,3,...,K；
Calculating the distance D between each candidate cell and the optimal scheme and the worst scheme max,i 、D min,i ：
Obtaining the relative proximity degree U of the candidate cell and the optimal scheme i The method comprises the following steps:
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