CN102256260B - Method for configuring independent resources based on resource flow - Google Patents
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Abstract
The invention discloses a method for configuring independent resources based on s resource flow in the field of wireless communication resource management control. Regarding the problems of low convergence speed and various resource treating unsuitability in the prior art, a resource configuring method with higher convergence speed of various resources, suitable for treating a wireless communication system, is provided. In the method, a concept based on the resource flow in the wireless communication system is adopted, requests of multiple users to resources are planned to be changes of spatial intensity of a resource field, and the resource configuration realized based on the resource flow does not require interaction. Meanwhile, a closed-form solution of an optimal resource configuration strategy is deduced, so that high-efficiency independent resource configuration is realized to ensure the existence and the optimality of an optimal solution. The problem of more interaction times and the problems that the existence, the optimality and the like of the optimal solution cannot be ensured in the prior art are solved, and the high-efficiency independent resource configuration is realized to ensure the existence and the optimality of the optimal solution.
Description
Technical Field
The invention belongs to the technical field of communication, and further relates to an autonomous resource allocation method based on resource flow in the field of wireless communication resource management control. The method can realize the high-efficiency dynamic autonomous configuration of various multidimensional resources in the wireless communication system and effectively improve the resource utilization rate in the wireless communication system.
Background
The management and control of resources has become one of the key technologies for determining the performance of current wireless communication systems, and it effectively guarantees the quality of service requirements of multiple users through efficient allocation of resources and effectively improves the resource utilization efficiency. In the current environment of highly converged and developed heterogeneous networks, how to realize efficient utilization of various resources has serious challenges in further reducing operation and maintenance expenses for operators, thereby improving economic benefits of the operators' resources and meeting higher and higher requirements of various transmission rate services.
The existing wireless network is undergoing huge development, various types of networks are layered endlessly, multiple network coverage scenes appear in the same geographic area, and effective allocation of resources has a decisive role in improving user experience and performance of a wireless communication system. Currently, in terms of dynamic resource management and allocation technology, the following technologies are basically included: the method comprises a dynamic resource management technology based on an optimization technology, a resource self-adaptive allocation technology adopting a learning algorithm and a non-cooperative resource allocation method based on a game theory. In the current heterogeneous network environment and under the background of highly diversified resources, the three technical methods respectively realize resource allocation based on optimization, learning and game theory.
A joint optimization method for realizing resources such as power, channels and relay nodes is disclosed in the patent application document "joint optimization method for power allocation, channel allocation and relay node selection" (publication No. CN 101483911a, application No. 200910077817.8, application date 2009.1.22) of the university of qinghua. The method adopts a power allocation and channel allocation iteration method to realize the joint optimization of the power allocation and the channel allocation. The method has the disadvantages of low convergence speed and unsuitability for processing more different resource allocations. Meanwhile, the resource management and allocation method based on the optimization technology cannot meet the requirements of dynamic resource allocation and autonomous resource allocation in the current network environment.
An autonomous united wireless resource management system and method based on reinforcement learning is disclosed in the patent application document 'autonomous united wireless resource management system and method based on reinforcement learning' (publication number CN 101132363a, application number 200710120182.6, application date 2007.8.10) of the beijing post and telecommunications university. The method can re-configure the mobile terminal to initiate a channel request, the wireless re-configuration support function module collects the information of the local wireless resource manager, adopts a reinforcement learning method to carry out trial and error interaction according to various network performance parameter indexes, and determines whether to immediately accept a new session according to a corresponding judgment criterion. Compared with the traditional resource allocation scheme based on optimization, the reinforcement learning is a trial-and-error online learning technology with autonomous learning capability. Learners obtain learning experience by continuously interacting with the environment, and further gradually improve the behavior strategy. The reinforcement learning has certain flexibility and self-adaptability. However, the method has the disadvantage that the reinforcement learning technology generally requires a large number of interactions between the learner and the environment, and thus cannot guarantee the real-time performance requirements under the scenes of time-varying wireless data services, dynamic wireless channel fading and the like.
In a patent application document ' power control method based on a normalized game model in cognitive radio technology ' of Nanjing postal and electronic university ' (publication No. CN 101359941A, application No. 200810195893.4, application date 2008.9.12), a power control method based on a non-cooperative game theory is disclosed, and the method is an implementation scheme particularly used for transmitting terminal power control in cognitive radio. The method has the defects that in the design process based on the game theory, the utility function design is one of the key factors influencing the design and the final performance of the power control method, and has serious influence on the existence, the optimality and the like of the equilibrium solution for providing the game model. In addition, in the specific game power control process, a first-order partial derivative needs to be solved, and the calculation is complex. The requirement for autonomous configuration is also not met.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a resource flow-based method for autonomous resource configuration, which realizes autonomous configuration and self control of resources by describing multidimensional resources under various communication scenes through the resource flow.
The specific idea of the invention for achieving the above purpose is that the initial strength of the resource space is calculated according to the total resource amount of the base station; then, the optimal configuration based on the resource flow is realized. In view of the heterogeneous communication network environment, a base station installed in a heterogeneous communication network is responsible for constructing, managing, and maintaining resource flows to achieve efficient configuration and utilization of resources. Resource dissipation of resource flows during operation management is not considered and resource requests by users cause the amplitude of the resource flows to decay without affecting their direction.
The invention realizes the above purpose with the following steps:
(1) base station updating neighbor list
After a base station is started, determining a resource flow replenishment list and a resource flow request list according to the initialized base station distribution;
(2) determining total amount of resources
2a) The base station respectively determines the total amount of resource replenishment and resource expenditure from the resource flow replenishment and the request list;
2b) the base station calculates the total resource request amount of the current user according to the total number of the service users;
2c) the base station calculates the net total resource amount of the current base station by summing the resource supply, expenditure and total user resource request amount;
(3) and calculating the initial strength of each point in the resource space according to the total amount of the resources, the position of the distance base station and other information.
(4) Judging whether the base station moves, if so, turning to the step (1), otherwise, executing the following steps;
(5) judging whether a new user arrives, if so, turning to the step 2c), otherwise, executing the following steps;
(6) calculating the total intensity of the resource space points according to the average resource field intensity in the micro area sensed by the resource space points;
(7) calculating an optimal weight function according to the resource upper limit of the base station, the total strength of the resource field at the resource space point, a partial derivative function of the total strength relative to a certain direction and the like;
(8) calculating the optimal resource field strength according to the optimal weight function and the total resource amount of the current base station;
(9) according to the optimal resource field intensity, realizing the optimal allocation of the resource flow;
(10) and (6) ending.
Compared with the prior art, the invention has the following advantages:
first, the present invention provides a resource allocation method with fast convergence rate for multiple resources in a wireless communication system, which is suitable for solving the problems of slow convergence rate and unsuitability for multiple resource allocation in the prior art.
Secondly, aiming at the problem of more interaction times in the prior art, the invention adopts the concept based on the resource flow in the wireless communication system, plans the request of multiple users for the resource as the change of the space intensity of the resource field, realizes the resource allocation based on the resource flow and does not need interaction.
Thirdly, based on a global system, aiming at the problem that the existence and the optimality of the optimal solution cannot be guaranteed in the prior art, the closed solution of the optimal resource allocation strategy is deduced, and the efficient and autonomous allocation of resources is realized to guarantee the existence and the optimality of the optimal solution.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram illustrating the effect of completing the construction of the resource field space according to the present invention;
fig. 3 is a diagram illustrating the effect of implementing the autonomous and efficient configuration of resources according to the present invention.
The specific implementation mode is as follows:
the invention considers the heterogeneous communication network environment, and the base station installed in the heterogeneous communication network is responsible for constructing, managing and maintaining the resource flow so as to realize the efficient configuration and utilization of the resource. Resource dissipation of resource flows during operation management is not considered and resource requests by users cause the amplitude of the resource flows to decay without affecting their direction.
The invention is further described below with reference to fig. 1.
Step 1, the base station updates the neighbor list
And after the base station is started, determining a resource flow replenishment list and a resource flow request list according to the initialized base station distribution.
Step 2, the base station determines the total amount of resources
2a) The base station determines the corresponding total resource replenishment and resource expenditure amount according to the resource flow replenishment and request list.
2b) And the base station calculates the total resource request amount of the current user according to the total number of the service users.
2c) The base station calculates the net total resource amount of the current base station according to the resource supply, the resource expenditure and the total user resource request amount.
Step 3, calculating the initial strength of the resource space
The base station calculates the initial strength of the resource space point according to the following formula
Wherein E isiIs a distance d from the base stationiThe strength of the point in the resource space,
denotes the sign of the gradient operation, M is the net resource vector of the current base station, and κ isA constant.
And 4, judging whether the base station moves. If the base station moves, turning to the step (1), otherwise, executing the following steps;
and 5, judging whether a new user arrives or not. If a new user arrives, turning to the step 2c), otherwise, executing the following steps;
step 6, calculating the total intensity of the resource space points
Calculating the total intensity of the resource field of the resource space point according to the following formula
Wherein,
is a resource space point diOf (d) total source field intensity, E'iIs the average strength of the resource field from different base stations as perceived by spatial point i,
is a tiny area.
Step 7, calculating an optimal weight function
The base station calculates the optimal weight function according to the following formula
Wherein,
is the function of the optimal weight for the weight,
is the total intensity of the resource farm
Relative to EiPartial derivative of (a)iIs to satisfy lambdai(M-Mmax) Variable of o, MmaxIs the upper limit of the resource amount of the base station,
is a resource space point diThe total strength of the resource field, κ, is a constant.
Step 8, calculating the optimal resource field intensity
The base station calculates the point d in the resource space according to the following formulaiOptimal resource field strength of
Wherein,
is a resource space point diThe optimal resource field strength of (a) is,
and (4) an optimal weight function, wherein M is the total amount of resources of the base station.
Step 9, resource flow optimal allocation
The base station calculates the optimal allocation strategy of the resource flow according to the following formula
Wherein,
is the optimal configuration strategy of the resource flow,
is a resource space point diAnd (4) updating the space of the resource field according to the optimal resource field strength.
And step 10, ending.
The effect of the present invention will be further described with reference to fig. 2 and 3.
Fig. 2 is a diagram of the effect of completing the construction of the resource field space according to the present invention, where a simple wireless communication scenario of three base stations is given, and it is assumed that the total amount of multi-user resource demands for sending resource requests to the three base stations are respectively: 50. 25, 100 units. The method of the invention is adopted to realize the construction of the resource field space in the first five steps, namely the resource field strength of each point in the resource field space is calculated. On the basis of fig. 2, fig. 3 is a diagram illustrating an effect of implementing autonomous and efficient resource allocation according to the present invention, and it is assumed that when the total amount of the multi-user resource demands of the current three base stations changes, an autonomous control process of the method according to the present invention is schematically illustrated. For example, the total amount of multi-user resource demand to three base stations varies as: 10. 8, 200 units, i.e. the resource request issued to the base station 3 requests more resources on the basis of the initial resource field space, as in fig. 2. At this time, the optimized field strength of the resource field space at this time is calculated by adopting the last five steps of the invention. In theory, the resources of base stations 1 and 2 would flow towards base station 3 to satisfy the current multi-user over-request to base station 3. Comparing fig. 2 and fig. 3, it is found that when the resource request to the base station 3 is greatly increased, the optimal flow direction of the resources at each point in the resource field space in fig. 3 is biased toward the base station 3, and even the direction of the resource flow is reversed, compared to fig. 2, and therefore, the base station 1 and the base station 2 realize that the resource flow continuously flows to the base station 3 with a large amount of resources under the control of the method of the present invention, and thus, the resource autonomous and efficient allocation based on the resource flow is realized.
Claims (1)
1. The method for configuring the autonomous resources based on the resource flow comprises the following steps:
(1) base station updating neighbor list
After a base station is started, determining a resource flow replenishment list and a resource flow request list according to the initialized base station distribution;
(2) determining total amount of resources
2a) The base station respectively determines the total amount of resource replenishment and resource expenditure from the resource flow replenishment and the request list;
2b) the base station calculates the total resource request amount of the current user according to the total number of the service users;
2c) the base station calculates the net resource total amount of the current base station by summing the resource supply, the expenditure and the user resource request total amount;
(3) according to the total amount of resources and the position information of the distance base station, calculating the initial strength of each point in the resource space according to the following formula:
wherein E isiIs d from the base stationiThe strength of the point in the resource space,
representing a gradient operation symbol, M is a net resource vector of the current base station, and k is a constant;
(4) judging whether the base station moves, and if so, turning to the step (1); otherwise, executing the following steps;
(5) judging whether a new user arrives, and if so, turning to the step 2 c); otherwise, executing the following steps;
(6) according to the average resource field intensity in the small area sensed by the resource space point, calculating the total intensity of the resource space point according to the following formula:
wherein,
is a resource space point diOf (d) total source field intensity, E'iIs the average strength of the resource field from different base stations as perceived by spatial point i,
is a tiny area;
(7) according to the resource upper limit of the base station, the total intensity of the resource field at the resource space point and the partial derivative function of the total intensity relative to a certain direction, the optimal weight function is calculated according to the following formula:
wherein,
is the function of the optimal weight for the weight,is the total intensity of the resource farm
Relative to EiPartial derivatives of, EiIs d from the base stationiResource space point intensity, λiIs to satisfy lambdai(M-Mmax) Variable of o, MmaxIs the upper limit of the resource amount of the base station,
is a resource space point diThe total strength of the resource field, κ is a constant;
(8) according to the optimal weight function and the total resource quantity of the current base station, calculating a resource space point d according to the following formulaiOptimal resource field strength of (c):
wherein,
is a resource space point diThe optimal resource field strength of (a) is,
an optimal weight function, ln is a natural logarithm symbol, and M is the total amount of resources of the base station;
(9) according to the optimal resource field strength, calculating the optimal configuration strategy of the resource flow according to the following formula:
wherein,
is the optimal configuration strategy of the resource flow,
is a resource space point diThe optimal resource field strength of (1);
(10) and (6) ending.
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| US8725186B2 (en) * | 2008-09-22 | 2014-05-13 | Ntt Docomo, Inc. | Base station apparatus, user equipment and precoding method |
| US8565153B2 (en) * | 2009-05-19 | 2013-10-22 | Qualcomm Incorporated | Dynamic switching between MIMO and DC HSDPA |
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| CN1819701A (en) * | 2000-01-07 | 2006-08-16 | 高通股份有限公司 | System for allocating resources in a communication system |
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