CN103313251A - Multi-cell cooperative resource allocation method based on potential game theory - Google Patents
Multi-cell cooperative resource allocation method based on potential game theory Download PDFInfo
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Abstract
The embodiment of the invention relates to a multi-cell cooperative resource allocation method based on a potential game theory. The method is provided by aiming at a wireless communication system, the interference among cooperative cells is reduced and the throughput capacity is maximized. The specific implementation method comprises the steps of taking the maximum transmission power on each same-frequency sub-channel as a constraint condition; taking maximizing the throughput capacities of all the cooperative cells as an objective function; building a utility function which takes the aim of maximizing the throughput capacities of same-frequency resource blocks on all the cooperative cells according to the traditional game theory; mapping the game problem as a complete potential game problem with a pricing factor; building a potential function which takes power distribution as an optimizing strategy; updating user channel distribution information firstly; updating power information by using an improved gradient projection law and a Jacobian iteration timing sequence according to a channel distribution result to reach a Nash equilibrium finally; and optimizing the throughput capability of the whole cooperative cells.
Description
Technical field
The present invention relates to the RRM field, relate in particular to a kind of multi-cell coordination resource allocation methods based on the gesture theory of games.
Background technology
The active user is more and more higher to content and the quality requirement of mobile communication, in order to adapt to the growing QoS requirement of user, also for some emerging mobile communication technology such as WiMAX, the Wi-Fi competition, the end of the year 2004, the 3GPP HSDPA that continues after the technical standards such as HSUPA, has proposed the Long Term Evolution (LTE) of 3G.Two-forty, low time delay, the improved system capacity, expanding the coverage area is the target of LTE.OFDMA has strict requirement as the multiple access technology of Radio Link to the orthogonality between each subchannel, and therefore in next generation mobile communication system, interference can be ignored in the residential quarter.Meanwhile, the common-channel interference of adjacent cell has become the principal element of restriction system throughput.Because each residential quarter all uses identical frequency spectrum, the user is subject to the interference of the descending homogenous frequency signal of adjacent cell inevitably, Cell Edge User especially, and suffered interference is particularly serious.The presence of intercell interference that exists in the system (ICI) can cause communication performance sharply to descend, and how to solve the problem of co-channel interference of minizone, becomes the major issue that next generation mobile communication system faces.Suppress to have become problem demanding prompt solution among the LTE so how to carry out many area interference.
Many local resources are optimized difficult point the following aspects: although each intra-cell resource allocation can accomplish to disturb without cochannel, because each subcarrier is only monopolized by a user, but in many residential quarters, particularly when channeling was higher, the allocation result of any one residential quarter all may impact other residential quarters.Because separate between each residential quarter, arbitrary cells is difficult to obtain the channel information of other residential quarters, user data information or resource allocation information, so the minizone also just can't cooperate, and then optimal resource allocation that can't completion system.For the consideration that reduces energy consumption, therefore, in multi-cell system, power control is carried out in base station or subscriber station, reduce the interference of other neighbor cells just particularly important.The purpose of power control is exactly to obtain higher user satisfaction by making the base station pass through less power overhead.
Be used for the technology that many area interference suppress among the LTE at present, such as interference randomization, interference coordination, the technology such as interference elimination can't satisfy growing two-forty and high quality communication demand, in order better to solve the interference problem between the residential quarter, some researchers have proposed multi-cell cooperating processing (Multi-cell Cooperative Processing, MCP) mode suppresses the interference of minizone, and the fast development of high performance universal processor is also so that the collaborative possibility that becomes in minizone.Multi-cell cooperating is processed and is also referred to as network MIMO (Network MIMO) or multipoint cooperative (Coordinated Multi-Point, CoMP) transmission technology, the multi-cell cooperating technology can become useful signal to the original signal that disturbs each other by the mode that cooperates, or reduces interference by minizone cooperation distribution technique.It is obvious to the interference treatment effect of minizone, but is a kind of selecting technology of following cellular network development.
The present invention just is based on above-mentioned thought, under the condition with frequency Resource Block power constraint, by the cooperation of minizone, to maximize all cooperation cell throughputs as optimization aim, set up the complete gesture betting model that reduces algorithm complex, resource collaboration is carried out in many residential quarters distribute.
Summary of the invention
The present invention is intended to propose a kind of multi-cell cooperating resource allocation methods based on the gesture theory of games, take multiple sectors at same frequently the maximum power value on the Resource Block as constraints, to maximize all cooperation cell throughputs as target function, foundation distributes so that network throughput is maximum by the cooperation resource with the complete gesture betting model of Pricing Factor.
A kind of multi-cell cooperating resource allocation methods based on the gesture theory of games may further comprise the steps:
Step 1: choose cooperation cell, initialization modules parameter value, the power on maximum iteration time and each sub-channels.
Step 2: specify the total utility principle of same frequently Resource Block to upgrade the subchannel schedule information according to the maximization cooperation cell
Step 3: according to the channel allocation result, calculate with the performance number in the potential function gradient projection of weight factor, as the initial value of next iteration
Step 4: judge whether convergence or reach maximum iteration time, if so, transfer execution in step 7 to.If not, carry out next step.
Step 5: according to the user channel quality information of feedback, upgrade the SINR value.
Step 6: upgrade weight factor according to the linear decrease criterion, and transfer execution in step 2 to step 4.
Step 7: optimize and finish, obtain optimum channel and power distribution strategies, wait for next time and optimizing.
The channel quality information feedback step:
Step 1: user side Real-time Feedback channel quality information is to the base station end.
Step 2: judge whether system reaches Nash Equilibrium, as reach, user side no longer feedback information until start optimizing process next time.Otherwise return execution in step 1.
Step 3: wait for and optimize beginning next time.
Description of drawings
In order to set forth more clearly embodiments of the invention and existing technical scheme, the below does simple introduction with the explanation accompanying drawing of using in technical scheme explanation accompanying drawing of the present invention and the description of the Prior Art, apparent, under the prerequisite of not paying creative work, those of ordinary skills can obtain by this accompanying drawing other accompanying drawing.
Figure 1 shows that multi-base station cooperative system architecture diagram in the embodiment of the invention;
Fig. 2 Figure 3 shows that the multi-base station cooperative resource is distributed realization flow figure in the embodiment of the invention;
Embodiment
Clearer for what technical scheme advantage of the present invention was described, below in conjunction with accompanying drawing the specific embodiment of the present invention is described in further detail, obvious described embodiment is part embodiment of the present invention, rather than whole embodiment.According to embodiments of the invention, those of ordinary skill in the art can realize every other embodiment of the present invention on without the basis of creative work, all belong to protection scope of the present invention.
In the following description, the technology that has nothing to do with the present invention is only done concise and to the point technical descriptioon or directly skip over.
Main thought of the present invention is: take with the maximum power value on the frequency subchannel as constraints, to maximize all cooperation cell throughputs as target function, specify the principle of same frequently Resource Block total utility to upgrade the subchannel schedule information according to the maximization cooperation cell, user's Real-time Feedback channel quality information, utilize improved potential function gradient projection criterion and Jacobi iterative algorithm to upgrade power information, thereby obtain channel and power allocation scheme, user's Real-time Feedback channel quality information is to the base station during this time, the base station end is constantly updated channel quality information (SINR), and upgrade the weight factor of gradient projection step-length by the linear decrease criterion, general processor starts resource optimization until reach Nash Equilibrium again according to the channel quality information that obtains.Channel quality information feedback was no longer carried out until start next time optimizing process after system reached Nash Equilibrium.
Fig. 1 is co-architecture figure in many base stations in the specific embodiment of the invention.Specifically:
As shown in Figure 1, so our research object has selected the collaboration user in the cooperation resource distribution network to select and collaboration power distributes, that is to say that a user can only be linked into a base station, but when user selection and power division, can consider the interference of other residential quarter.Suppose that whole cellular network has M residential quarter, there is a base station each residential quarter, and namely total number of base also is M, and each base station is the single antenna base station.Suppose that each residential quarter uses the multiplexing frequency resource multiplexing method of Whole frequency band, that is to say that frequency duplex factor as one is 1.Whole frequency band division is N Resource Block (Resource Block, RB), that is to say that each base station can use this N Resource Block simultaneously.Collaboration mode (dash area in such as Fig. 1) is adopted in the base station of cooperation when dispatched users and power division, cooperative base station uses the optical-fiber network of backstage high-capacity and high-speed to realize data sharing, and the disposal ability that takes full advantage of the high performance universal processor works in coordination with resource optimization, thereby so that the throughput of whole collaborative network is maximum.
Fig. 2 is that the collaborative resource in the embodiment of the present invention is distributed realization flow figure.As shown in Figure 2, this collaborative resource allocation process comprises following step:
Step 201: initialization modules information, iterations information and each is with the power on the frequency subchannel.
We need to define maximum iteration time in this step, and initial iteration is made as zero.And the transmitting power on the initializes weights factor and gradient projection step-length and each sub-channels, for next step iteration is prepared.
Step 202: upgrade resource block scheduling information.
In this step, the principle of resource block assignments is can be so that cooperation cell is specifying the total utility with on the frequency Resource Block to maximize, obtain the schedule information of Resource Block, because utility function has been introduced Pricing Factor, so when resource block assignments, effectively considered interference to other users.
Step 203: gradient projection criterion and Jacobi iteration algorithm according to potential function upgrade performance number
In this step, utilize the limited incremental of limited complete gesture game, because reporting criterion, gradient projection can both guarantee that iterative process converges on a stable state again, select the gradient projection of potential function that performance number is upgraded, in renewal process, the gradient projection step-length is introduced weight factor, it is dynamic change, principle according to linear decrease is upgraded weight factor, so that in iterative process, find best optimizing path, and cooperate Jacobi iteration sequential to carry out the renewal of power.Gradient projection expression formula in this step is D
i(y)=[y
i+ ω
iρ
i▽
iu
i(y
i, y
-i)] Y
i(y
-i), ω wherein
iBe weight factor, the principle of following linear decrease changes, y
iBe current strategies, ▽
iu
i(y
i, y
-i) be current gradient, ρ
iBe the step-length of gradient projection, because introduced the changeable weight factor, step-length is not changeless, so in the process of seeking optimal policy, can adjust dynamically step-length, finds more accurately best optimisation strategy.
Step 204: based on the resulting result of previous step, judge whether convergence or reach maximum iteration time, if the difference of the performance number that obtains and a upper performance number then transfers execution in step 207 to less than an iteration precision, iterative process finishes.If do not have convergence or do not reach maximum iteration time, carry out next step.
Step 205: upgrade channel quality information.
Use SINR represent channel quality information in this step, the real-time feedback SINR information of user there is not iteration once complete to the base station, for reach restrain or do not reach maximum iteration time before, renewal SINR in base station carries out next iteration.
Step 206: upgrade weight factor, repeating step 202 to 204.
In this step, the criterion of linear decrease is abideed by in the renewal of weight factor, and during first iteration, weight factor is successively decreased afterwards successively for the highest, but is necessary for positive number.
Step 207: iterative process finishes, and the Resource Block that is optimized and power policy value are waited for optimizing starting next time.
Fig. 3 is the channel quality information feedback flow chart in the embodiment of the present invention.As shown in Figure 3, this channel-quality feedback process comprises following step:
Step 301: before finishing optimization, user's Real-time Feedback channel quality information is to the base station, and SINR is upgraded according to the channel quality information that obtains in the base station, carries out next iteration by general processor control.
Step 302: the result according to resource allocation algorithm judges whether optimization finishes, if finish, stops receiver channel quality information.
Step 303: wait for and optimize beginning next time.
In this step, be set a time interval of next time optimizing, receive the channel quality information of user feedback, judge whether SINR exceeds the accuracy value that need to restart optimization, if exceed, restarts optimization, otherwise continue to wait for.
Claims (6)
1. multi-cell cooperating resource allocation methods based on the gesture theory of games is characterized in that may further comprise the steps:
Step 1: choose cooperation cell, initialization modules parameter value, the power on maximum iteration time and each sub-channels.
Step 2: specify the principle of total utility on the same frequently Resource Block to upgrade the subchannel schedule information according to the maximization cooperation cell
Step 3: according to the channel allocation result, calculate with the performance number in the potential function gradient projection of weight factor, as the initial value of next iteration
Step 4: judge whether convergence or reach maximum iteration time, if so, transfer execution in step 7 to.If not, carry out next step.
Step 5: according to the user channel quality information of feedback, upgrade the SINR value.
Step 6: upgrade weight factor according to the linear decrease criterion, and transfer execution in step 2 to step 4.
Step 7: optimize and finish, obtain optimum channel and power distribution strategies, wait for next time and optimizing.
The channel quality information feedback step:
Step 1: user side Real-time Feedback channel quality information is to the base station end.
Step 2: judge whether system reaches Nash Equilibrium, as reach, user side no longer feedback information until start optimizing process next time.Otherwise return execution in step 1.
Step 3: wait for and optimize beginning next time.
2. the multi-cell cooperating resource allocation methods based on the gesture theory of games according to claim 1 is characterized in that:
In the described step 2, subchannel when scheduling, calculate cooperation district and select the user to dispatch with the total utility on the frequency subchannel, and introduced Pricing Factor, thereby can effectively avoid because simple increasing power causes interference to other users to obtain higher effectiveness.
3. the multi-cell cooperating resource allocation methods based on the gesture theory of games according to claim 1 is characterized in that:
In the described step 3, because this model has been mapped as a complete gesture problem of game, so by to the analysis of potential function, utilizing gradient projection on the potential function to find the solution the optimal policy that obtains namely is the optimal policy of this problem of game, has reduced solving complexity.
4. the multi-cell cooperating resource allocation methods based on the gesture theory of games according to claim 1 is characterized in that:
In the described step 5, by dynamic feedback channel quality information, more can accurately reflect the state of Real-time Channel, compare static SINR, improve the accuracy of policy selection.
5. the multi-cell cooperating resource allocation methods based on the gesture theory of games according to claim 1 is characterized in that:
In the described step 6, introduce weight factor in the gradient projection step-length, change the principle that original step-length is definite value, upgrade step-length according to the principle of linear decrease, the gradient projection expression formula in this step is D
i(y)=[y
i+ ω
iρ
i▽
iu
i(y
i, y
-i)] Y
i(y
-i), ω wherein
iBe weight factor, the principle of following linear decrease changes, y
iBe current strategies, ▽
iu
i(y
i, y
-i) be current gradient, ρ
iBe the step-length of gradient projection, at the iteration initial period, step-length can select the larger value can Fast Convergent, in the iteration later stage, by changing weight factor, makes step-length diminish, thereby can find more accurately optimal value.
6. the multi-cell cooperating resource allocation methods based on the gesture theory of games according to claim 1 is characterized in that:
In the channel quality information feedback step 1, for reducing signaling consumption, after iteration finishes, no longer carry out the Real-time Feedback of SINR.In step 3, under determining, before the suboptimization, judge first whether the channel quality information that receives reaches the limit value of again suboptimization, if do not reach, no longer carry out cooperate optimization, so just reduced system and processed complexity, greatly reduce the communication overhead of network.
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| CN106454850A (en) * | 2016-10-14 | 2017-02-22 | 重庆邮电大学 | Resource distribution method for energy efficiency optimization of honeycomb heterogeneous network |
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| CN103826230B (en) * | 2014-02-19 | 2017-06-13 | 广东省电信规划设计院有限公司 | BRAN optimizes the method and system of fractional frequency reuse |
| CN107182057A (en) * | 2016-03-10 | 2017-09-19 | 中兴通讯股份有限公司 | A kind of method and apparatus of LTE system medium and small interval cooperation |
| CN107182057B (en) * | 2016-03-10 | 2022-12-02 | 中兴通讯股份有限公司 | Inter-cell cooperation method and device in LTE system |
| CN106454850B (en) * | 2016-10-14 | 2019-08-27 | 重庆邮电大学 | The resource allocation methods of honeycomb heterogeneous network efficiency optimization |
| CN106454850A (en) * | 2016-10-14 | 2017-02-22 | 重庆邮电大学 | Resource distribution method for energy efficiency optimization of honeycomb heterogeneous network |
| CN106455078B (en) * | 2016-10-31 | 2019-07-12 | 东南大学 | A kind of resource allocation methods in the wireless dummy network of combination balance policy |
| CN106455078A (en) * | 2016-10-31 | 2017-02-22 | 东南大学 | Equilibrium strategy-combined wireless virtual network resource allocation method |
| CN108616916A (en) * | 2018-04-28 | 2018-10-02 | 中国人民解放军陆军工程大学 | A kind of anti-interference layering betting model of cooperation and anti-interference learning algorithm |
| CN112261729A (en) * | 2020-12-24 | 2021-01-22 | 北京建筑大学 | Self-adaptive semi-distributed resource allocation method based on D2D-U communication |
| CN112261729B (en) * | 2020-12-24 | 2021-03-19 | 北京建筑大学 | Self-adaptive semi-distributed resource allocation method based on D2D-U communication |
| CN113709771A (en) * | 2021-08-20 | 2021-11-26 | 中国科学院数学与系统科学研究院 | Method, device, equipment and readable medium for adjusting signal transmission power |
| CN113709771B (en) * | 2021-08-20 | 2023-03-10 | 中国科学院数学与系统科学研究院 | Method, device, equipment and readable medium for adjusting signal transmission power |
| WO2023109007A1 (en) * | 2021-12-17 | 2023-06-22 | 北京邮电大学 | Time domain resource configuration method and apparatus, electronic device, and storage medium |
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Application publication date: 20130918 |