CN105578486A - Capacity and coverage combined optimization method in heterogeneous dense network - Google Patents
Capacity and coverage combined optimization method in heterogeneous dense network Download PDFInfo
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
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- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
- H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
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Abstract
The invention discloses a capacity and coverage combined optimization method in a heterogeneous dense network, relates to the heterogeneous dense SON self-organizing network field. According to the method, a network average spectrum efficiency and edge spectrum efficiency combined optimization method for carrying out small cell power control based on fuzzy logic and Q study is used. The method of the invention comprises following steps that: firstly, the dense small cells measures network average spectrum efficiencies and edge spectrum efficiencies in an adjusting period according to the measurement report information on a moving table; then the powers of the dense small cells, the average spectrum efficiencies and the edge spectrum efficiencies are taken as the input variables of the fuzzy logic; the self-optimizing control processing of the fuzzy logic and Q study is carried out; and the network is self-adaptively converged to ideal capacity and coverage combined optimization adjusting sate. According to the method, the capacity and coverage problems in deploying high dense small cells is effectively improved by an online study mode; the problems are caused by irrational power setting; and the method has certain practicability.
Description
Technical field
The present invention relates to field of wireless communications networks technology, especially concern capacity and coverage optimization (CCO) method under isomery dense network SON function structure.
Background technology
Along with comprehensive commercialization of 4G standard network, in the epoch entering fast development of mobile Internet, it is more and more higher to the capacity requirement of future wireless system network.In order to meet the business demand of user, ensure the covering of the continuous wide area of network, be dispose microcellulor smallcell community to the demand of the business and capacity that meet hot zones at hot zones on the basis that conventional macro base station provides basic network to cover simultaneously in LTE isomery cellular network.In next generation wireless communication network, super-intensive heterogeneous network is disposed to be acknowledged as and is promoted the aspect that network capacity covers and of network rate is very important.
Virtual network operator can carry out the configuration of initial parameter by aspect of network self-configuration function to newly joining network node in network at the network design initial stage, deployment due to intensive cellulor is present in network coverage in irregular, unformed mode, and the capacity caused because optimum configurations is unreasonable under network initial configuration and covering problem inherently occur.Although the coverage that can promote its linchpin accordingly belong to because power setting is excessive between intensive cellulor, but the generation of interference serious between adjacent cellulor and between itself and macrocellular can be caused, and then SINR level can be worsened, the lifting of final influential system entire throughput.Although the too small interference can avoided to a certain extent between cellulor and between all the other macrocellulars of power setting between intensive cellulor, but the too small small cell base station be intended to for hot zones promotes network capacity and dispose that can cause again of thing followed coverage can not receive enough users, the user resided on macrocell cannot be switched to the available resource block after cellulor using frequency spectrum resource spatial reuse, finally also can the lifting of influential system entire throughput.
In order to reduce Capital Expenditure (CAPEX) and the manual operation cost (OPEX) of network design, introduce self-organizing network (SON) in wireless communication system, it comprises three parts: self-configuring, self-optimizing and certainly healing.Dispose the lower capacity problem that can cause for intensive small cell network, category of its ownership is capacity in self-optimization function and coverage optimization aspect.At present, the mode about capacity and coverage optimization mainly concentrates in the adjustment to the electrical tilt angle of macro base station, and the capacity that large scale ensures macro base station remains on a suitable level thus the reliability and the validity that ensure the entirety of network with covering.And dispose the lower capacity based on power control and coverage optimization method for the intensive cellulor of hot zones and rarely have and occur and apply.
Summary of the invention
For above the deficiencies in the prior art, propose a kind of method.Technical scheme of the present invention is as follows: capacity and covering combined optimization method in a kind of isomery dense network, and it comprises the following steps:
A, mobile subscriber terminal obtain the received signal strength of serving BS and the received signal strength of interference base station and carry out the calculating of Signal to Interference plus Noise Ratio SINR,
wherein RSRP
sfor serving BS received signal strength, RSRP
ifor interference base station received signal strength, N is interfered cell number, n
0for noise; Down channel quality is periodically reported to indicate CQI;
B, intensive small cell base station carry out the statistics of cell average spectral efficiency and edge spectrum efficiency in adjustment cycle, carry out the measurement of network state quality index by weight, and measurement formula is SQ=SE
center+ wSE
edge, wherein SQ is network state quality index, SE
centerfor cell average spectral efficiency, SE
edgefor edge spectrum efficiency, w is weight factor;
The network state quality index of c, acquisition adjacent cell, with the mode evaluating network integrality quality of multi-Agent Cooperation in fuzzy logic Q learning algorithm, as the evaluation index of Agent single step adjustment quality, is used for calculating instantaneous reward value;
D, to intensive cellulor power, average spectral efficiency (ase) and edge spectrum efficiency are carried out Fuzzy processing as the input value of fuzzy logic Q learning system, form fuzzy rule, init state action Q value is shown, according to e-greedy strategy, Action Selection is carried out to the rule that every bar have activated, adopt weighted sum mode to carry out de-fuzzy process, export adjusted value;
E, carrying out the adaptive adjustment of cellulor power according to exporting adjusted value, entering the measurement of the overall network state quality of next adjustment cycle;
F, one adjustment granularity expire time, integrating step c evaluates the instantaneous reward value under multi-Agent Cooperation mode, carry out the renewal of the Q value table of the state action under fuzzy logic ordination, then Action Selection is carried out by the e-greedy strategy described in steps d, carry out the power adaptive adjustment process of repetition, until converge to setting network capacity to adjust state with covering combined optimization.
Further, in step a, mobile subscriber terminal obtains the received signal strength of serving BS and the received signal strength of interference base station and the formula carrying out the calculating of Signal to Interference plus Noise Ratio SINR is
wherein RSRP
sfor serving BS received signal strength, RSRP
ifor interference base station received signal strength, N is interfered cell number, n
0for noise; Periodically carry out reporting of down channel instruction CQI.
Further, in stepb, the CQI that each user reports in each subframe is mapped as spectrum efficiency by intensive small cell base station in the adjustment cycle of 200ms, storage statistics is carried out by order from small to large, when adjustment granularity expires, traversal stores statistic unit buffer area, find out edge spectrum efficiency and 50% user's average spectral efficiency (ase) of 5% corresponding user, formula is pressed by the evaluation index of the network state quality as single cellulor using weight w=2, wherein, w is that edge spectrum efficiency is compared to the weight factor of average spectral efficiency (ase) in network state quality.
Further, in step c, the Agent existed in a distributed way in each network element unit obtains the adjacent cell network state quality in the community in Neighboring Cell List in the mode of cooperation, with the formula
calculate the overall network mean state quality needing to consider, follow-up being used for calculates instantaneous reward value.Wherein N is adjacent cell number.
Further, steps d is specially:, by fuzzy for intensive cellulor power turn to low, in, high three ambiguous identification, by fuzzy for cellulor average spectral efficiency (ase) turn to low, in, high three ambiguous identification, by fuzzy for cellulor edge spectrum efficiency turn to low, in, high three ambiguous identification, thus form 3*3*3=27 fuzzy rule, reduction is turned to by fuzzy for output adjusted value, shade, constant, micro-increasing, increase by five ambiguous identification, the Q value valuation functions table of the adjustment action that the every rule of initialization is corresponding, wherein, i is all fuzzy rule states, for the adjustment action performed, using the input variable as fuzzy logic Q learning controller FQLC, according to fuzzy rule and membership function, formula is utilized to determine the validity of the every rule corresponding to current input vector, wherein represent that the input variable vector S of continuous domain carries out the membership function validity that in the i-th rule corresponding after Fuzzy processing, a kth ambiguous identification is corresponding, then, the fuzzy rule that every bar be have activated and the validity nonzero value of fuzzy rule, the Action Selection adjusted is carried out by the e-greedy Greedy strategy explored/utilize, that is: the Action Selection of the adjustment utilized is carried out by probability, the Action Selection of the adjustment explored is carried out by probability, finally, carry out de-fuzzy process according to fuzzy logic system, export the power adjustment of continuous domain, meanwhile, evaluate the Q value function estimating that current state action is right.
Further, in step f, when an adjustment granularity 200ms expires, intellectual Agent evaluates the instantaneous judging quota of the instantaneous reward value of associating as last time adjustment quality in the mode of cooperation, calculate the value function of current time, current state value function and discount factor is utilized to calculate the difference with a upper moment Q function, the method of Gradient Descent is adopted to upgrade the q value table of fuzzy rule state action: then to carry out e-greedy Action Selection according to the q value table upgraded, repeat said process, until converge to desirable network capacity to adjust state with covering combined optimization.
Advantage of the present invention and beneficial effect as follows:
The present invention is directed to capacity when intensive small cell network is disposed and cover the attribute of runing counter to and propose a kind of capacity of controlling based on cellulor power and covering combined optimization, the compromise reaching capacity and covering balances, and effectively improves the systematic function under the deployment of irregular, amorphous cellulor.It effectively make use of the feature that Q learns the on-line study dynamic conditioning of this machine learning, without the need to accurate mathematical modeling, in conjunction with fuzzy logic, continuous state space input variable is carried out discretization simultaneously, mode in conjunction with the instantaneous award of the associating under multi-Agent Cooperation pattern carries out the perception of environment and the selection of action, ensure that system compared with rapid convergence and stability while also improve entire system performance.
Accompanying drawing explanation
Fig. 1 the invention provides preferred embodiment overall procedure schematic diagram;
Fig. 2 is FQLC general frame schematic diagram in the present invention;
Fig. 3 is the schematic flow sheet of sensing network state quality under multi-Agent Cooperation in the present invention;
Fig. 4 is the Fuzzy processing criterion schematic diagram to input parameter in the present invention;
Fig. 5 controls lower capacity based on the microcellulor power of fuzzy logic Q study in the present invention and covers combined optimization algorithm overall procedure schematic diagram.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described:
As shown in Figure 1, Fig. 1 be based on the cellulor power adjustment of fuzzy logic Q study capacity with cover combined optimization overall procedure schematic diagram.
Step 101: mobile subscriber terminal measures the received signal strength of this cell receiver signal strength signal intensity RSRP and adjacent interfered cell in each subframe, calculates Signal to Interference plus Noise Ratio SINR.
Step 102: mobile subscriber terminal carries out reporting of the CQI of down channel quality instruction in each subframe, informing base station down channel quality situation.
Step 103: the CQI that base station reports according to mobile subscriber terminal, determines MCS and TB transmission block size, carries out spectrum efficiency mapping.
Step 104: fuzzy logic Q learning agent Agent weighs the network state quality calculating this community, passes through X2 interface Internet state quality information with adjacent cell simultaneously, calculates overall network state quality.
Step 105: the selection of microcellulor power adjustment action is carried out in the adjustment action utilizing FQLC to make.
Step 106: small cell base station carries out the adjustment of microcellulor power, progressively circulate adjustment, reaches capacity and the object covering combined optimization.
Fig. 2 is a kind of FQLC general frame schematic diagram.Each FQLC intellectual Agent is distributed to be present in each base station, and it comprises environment sensing and measures entity, fuzzy logic controller, Q learning controller.Environment sensing is measured entity and its cell average spectral efficiency of statistical analysis and edge spectrum efficiency can be reported as the measurement index of efficient network quality according to the measurement of mobile subscriber, simultaneously by the information interaction of X2 interface, realize the fuzzy logic control mode of Mobile Multi-Agent cooperation.Fuzzy logic controller comprises: 1) Fuzzy processing module, and its function is carry out Fuzzy processing to the measurement index based on environment sensing, according to membership function, calculates the validity of every bar fuzzy rule corresponding to continuous state input vector; 2) fuzzy rule base, it is the optional set performed an action of every bar fuzzy rule, is used for carrying out fuzzy reasoning; 3) fuzzy inference system, it is the inference system utilizing fuzzy rule to carry out corresponding Action Selection; 4) de-fuzzy module, it is the action of discrete state to be converted on continuous domain according to de-fuzzy rule to perform an action.Q learning controller comprises: 1) Q value update module, carries out the renewal of Q value according to the value function under the instantaneous reward value of network after adjustment and new state; 2) the Q value function database that discrete state action is right, it is criterion when carrying out the right selection of new state action; 3) upgrade the right Q value function database of discrete state action, it carries out the renewal of Q value table, for the selection that state action is next time right provides more excellent criterion according to the value function of instantaneous reward value and new state.
Fig. 3 is the schematic flow sheet of sensing network state quality under multi-Agent Cooperation.
Step 301: the measurement serving BS of mobile subscriber in each period of sub-frame and the downlink reference signal intensity of interference base station, the SINR carried out on each subcarrier calculates.
Step 302: the broadband SINR calculating equivalence again, periodically carries out reporting of down channel instruction CQI.
Step 303: the CQI that each user reports in each subframe is mapped as spectrum efficiency by intensive small cell base station in the adjustment cycle of 200ms, storage statistics is carried out by order from small to large, when adjustment granularity expires, traversal stores statistic unit buffer area, finds out edge spectrum efficiency and 50% user's average spectral efficiency (ase) of 5% corresponding user.
Step 304: press formula by the evaluation index of the network state quality as single cellulor using weight w=2.Wherein, w is that edge spectrum efficiency is compared to the weight factor of average spectral efficiency (ase) in network state quality.
Step 305: the Agent existed in a distributed way in each network element unit includes the adjacent cell network state quality in the community in Neighboring Cell List in consideration category in the mode of cooperation, calculate the overall network mean state quality needing to consider with the formula, follow-up being used for calculates instantaneous reward value.
Fig. 4 is the Fuzzy processing criterion schematic diagram to input parameter.Basic, normal, high three ambiguous identification are turned to by fuzzy for intensive cellulor power, basic, normal, high three ambiguous identification are turned to by fuzzy for cellulor average spectral efficiency (ase), turn to basic, normal, high three ambiguous identification by fuzzy for cellulor edge spectrum efficiency, thus form 3*3*3=27 fuzzy rule.By fuzzyly for output adjusted value turning to reduction, shade, constant, micro-increasing, increase by five ambiguous identification.
Fig. 5 is capacity and covering combined optimization algorithm overall procedure schematic diagram under the microcellulor power control based on fuzzy logic Q study.
Step 501: the Q value valuation functions table of adjustment action that the every rule of initialization is corresponding (i.e. state action to).
Step 502: using the input variable as fuzzy logic Q learning controller FQLC, according to fuzzy rule and membership function, utilizes formula to determine the validity of the every rule corresponding to current input vector.Wherein represent that the input variable vector S of continuous domain carries out the membership function validity that in the i-th rule corresponding after Fuzzy processing, a kth ambiguous identification is corresponding.Then, to the fuzzy rule (i.e. the validity nonzero value of fuzzy rule) that every bar have activated, carry out the Action Selection adjusted by the e-greedy Greedy strategy explored/utilize, that is: carry out the Action Selection of the adjustment utilized by probability; The Action Selection of the adjustment explored is carried out by probability.
Step 503: carry out de-fuzzy process according to fuzzy logic system, exports the power adjustment of continuous domain.
Step 504: evaluate the Q value function estimating that current state action is right.
Step 505: the transmitting power of the current base station that the power adjustment of continuous domain is added to carries out adaptive power.Resourse Distribute, according to new transmitting power, is carried out in base station, receives CQI measurement report, upgrades user's spectrum efficiency memory cell buffer area.
Step 506: when an adjustment granularity 200ms expires, intellectual Agent evaluates the instantaneous judging quota of the instantaneous reward value of associating as last time adjustment quality in the mode of cooperation.Calculate the value function of current time.Current state value function and discount factor is utilized to calculate the difference with a upper moment Q function.
Step 507: adopt the method for Gradient Descent to upgrade the q value table of fuzzy rule state action:.Then carry out e-greedy Action Selection according to the q value table upgraded, repeat said process, until converge to desirable network capacity to adjust state with covering combined optimization.
These embodiments are interpreted as only being not used in for illustration of the present invention limiting the scope of the invention above.After the content of reading record of the present invention, technical staff can make various changes or modifications the present invention, and these equivalence changes and modification fall into the scope of the claims in the present invention equally.
Claims (6)
1. capacity and a covering combined optimization method in isomery dense network, is characterized in that, comprise the following steps:
A, mobile subscriber terminal obtain the received signal strength of serving BS and the received signal strength of interference base station and carry out the calculating of Signal to Interference plus Noise Ratio SINR:
wherein RSRP
sfor serving BS received signal strength, RSRP
ifor interference base station received signal strength, n
0for noise; Down channel quality is periodically reported to indicate CQI;
B, intensive small cell base station carry out the statistics of cell average spectral efficiency and edge spectrum efficiency in adjustment cycle, carry out the measurement of network state quality index by weight, and measurement formula is SQ=SE
center+ wSE
edge, wherein SQ is network state quality index, SE
centerfor cell average spectral efficiency, SE
edgefor edge spectrum efficiency, w is weight factor;
The network state quality index of c, acquisition adjacent cell, with the mode evaluating network integrality quality of multi-Agent Cooperation in fuzzy logic Q learning algorithm, as the evaluation index of Agent single step adjustment quality, is used for calculating instantaneous reward value;
D, to intensive cellulor power, average spectral efficiency (ase) and edge spectrum efficiency are carried out Fuzzy processing as the input value of fuzzy logic Q learning system, form fuzzy rule, init state action Q value is shown, according to e-greedy strategy, Action Selection is carried out to the rule that every bar have activated, adopt weighted sum mode to carry out de-fuzzy process, export adjusted value;
E, carrying out the adaptive adjustment of cellulor power according to exporting adjusted value, entering the measurement of the overall network state quality of next adjustment cycle;
F, one adjustment granularity expire time, integrating step c evaluates the instantaneous reward value under multi-Agent Cooperation mode, carry out the renewal of the Q value table of the state action under fuzzy logic ordination, then Action Selection is carried out by the e-greedy strategy described in steps d, carry out the power adaptive adjustment process of repetition, until converge to setting network capacity to adjust state with covering combined optimization.
2. capacity and covering combined optimization method in isomery dense network according to claim 1, it is characterized in that, in step a, mobile subscriber terminal obtains the received signal strength of serving BS and the received signal strength of interference base station and the formula carrying out the calculating of Signal to Interference plus Noise Ratio SINR is
wherein RSRP
sfor serving BS received signal strength, RSRP
ifor interference base station received signal strength, N is interfered cell number, n
0for noise; Periodically carry out reporting of down channel instruction CQI.
3. capacity and covering combined optimization method in isomery dense network according to claim 1 and 2, it is characterized in that, in stepb, the CQI that each user reports in each subframe is mapped as spectrum efficiency by intensive small cell base station in the adjustment cycle of 200ms, storage statistics is carried out by order from small to large, when adjustment granularity expires, traversal stores statistic unit buffer area, find out edge spectrum efficiency and 50% user's average spectral efficiency (ase) of 5% corresponding user, formula is pressed by the evaluation index of the network state quality as single cellulor using weight w=2, wherein, w is that edge spectrum efficiency is compared to the weight factor of average spectral efficiency (ase) in network state quality.
4. capacity and covering combined optimization method in isomery dense network according to claim 3, it is characterized in that, in step c, the Agent existed in a distributed way in each network element unit obtains the adjacent cell network state quality SQ in the community in Neighboring Cell List in the mode of cooperation, with
calculate the overall network mean state quality SQ needing to consider
avg, follow-up being used for calculates instantaneous reward value.Wherein N is adjacent cell number.
5. capacity and covering combined optimization method in isomery dense network according to claim 4, it is characterized in that, steps d is specially:, by fuzzy for intensive cellulor power turn to low, in, high three ambiguous identification, by fuzzy for cellulor average spectral efficiency (ase) turn to low, in, high three ambiguous identification, by fuzzy for cellulor edge spectrum efficiency turn to low, in, high three ambiguous identification, thus form 3*3*3=27 fuzzy rule, reduction is turned to by fuzzy for output adjusted value, shade, constant, micro-increasing, increase by five ambiguous identification, the Q value valuation functions table of the adjustment action that the every rule of initialization is corresponding, wherein, i is all fuzzy rule states, for the adjustment action performed, using the input variable as fuzzy logic Q learning controller FQLC, according to fuzzy rule and membership function, formula is utilized to determine the validity of the every rule corresponding to current input vector, wherein represent that the input variable vector S of continuous domain carries out the membership function validity that in the i-th rule corresponding after Fuzzy processing, a kth ambiguous identification is corresponding, then, the fuzzy rule that every bar be have activated and the validity nonzero value of fuzzy rule, the Action Selection adjusted is carried out by the e-greedy Greedy strategy explored/utilize, that is: the Action Selection of the adjustment utilized is carried out by probability, the Action Selection of the adjustment explored is carried out by probability, finally, carry out de-fuzzy process according to fuzzy logic system, export the power adjustment of continuous domain, meanwhile, evaluate the Q value function estimating that current state action is right.
6. capacity and covering combined optimization method in isomery dense network according to claim 5, it is characterized in that, in step f, when an adjustment granularity 200ms expires, intellectual Agent evaluates the instantaneous judging quota of the instantaneous reward value of associating as last time adjustment quality in the mode of cooperation, calculate the value function of current time, current state value function and discount factor is utilized to calculate the difference with a upper moment Q function, the method of Gradient Descent is adopted to upgrade the q value table of fuzzy rule state action: then to carry out e-greedy Action Selection according to the q value table upgraded, repeat said process, until converge to desirable network capacity to adjust state with covering combined optimization.
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| CN108803349A (en) * | 2018-08-13 | 2018-11-13 | 中国地质大学（武汉） | The optimal consistency control method and system of non-linear multi-agent system |
| CN111050330A (en) * | 2018-10-12 | 2020-04-21 | 中兴通讯股份有限公司 | Mobile network self-optimization method, system, terminal and computer readable storage medium |
| CN110868740A (en) * | 2019-11-12 | 2020-03-06 | 普联技术有限公司 | Roaming switching control method and device and electronic equipment |
| CN115361690A (en) * | 2022-08-18 | 2022-11-18 | 国网福建省电力有限公司经济技术研究院 | Joint optimization method, equipment and base station for capacity and coverage of dense macro-micro cooperative networking |
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