CN109981234B - Self-adaptive adjusting method, device, equipment and medium for double carriers and carrier aggregation - Google Patents
Self-adaptive adjusting method, device, equipment and medium for double carriers and carrier aggregation Download PDFInfo
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Abstract
The invention discloses a self-adaptive adjusting method, a self-adaptive adjusting device, self-adaptive adjusting equipment and a self-adaptive adjusting medium for double carriers and carrier aggregation. The method comprises the following steps: collecting real-time service indexes of a cell; matching the real-time service index with a historical service index; if the matching result meets the preset condition, predicting the service according to a preset algorithm to obtain a prediction result of a service index value; according to the prediction result, according to the cell category to be expanded, when the cell reaches a threshold corresponding to the category of the cell, outputting a corresponding pre-adjusted double-carrier and carrier aggregation model; and obtaining the index value of each service index under the condition of the same configuration as the method for pre-adjusting the double carriers and the carrier aggregation, and correcting the model for pre-adjusting the double carriers and the carrier aggregation according to each index value to obtain the self-adaptive adjustment model for the double carriers and the carrier aggregation.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a medium for adaptive adjustment of dual carriers and carrier aggregation.
Background
With the gradual increase of 4G users, the LTE single carrier in the hot spot region cannot meet the requirements of users on large bandwidth and high capacity. Under the condition of not increasing hardware, the base station configures a dual-carrier function and a Carrier Aggregation (CA) function, so that the existing frequency resources can be fully utilized, users on a single carrier can be shunted, and the purposes of improving system throughput, cell capacity and user perception are achieved.
For configuration adjustment of LTE dual carrier and carrier aggregation, currently, evaluation is mainly performed by a network optimization engineer according to recent indicators, estimation of an approximate value is performed based on optimization experience, and a dual carrier and Carrier Aggregation (CA) function is configured in a static manner.
The inventor finds that the existing optimization mode has the following defects:
1. for a wireless network with a constantly changing service model, it is unreasonable to evaluate a static service requirement value according to recent indicators to perform configuration of dual carriers and Carrier Aggregation (CA). In order to deal with the changing business model, the evaluation times are very frequent, the data size is large and difficult to evaluate, and in addition, the static configuration based on the empirical evaluation is relatively high in configuration in certain time periods, which easily causes the resource waste of the software license (license); in some time periods, the dual carrier and Carrier Aggregation (CA) function is not configured sufficiently, and cannot accommodate enough users and cannot provide good network service quality.
2. The method is roughly adjusted based on manual optimization experience, and depends on the optimization experience of a network optimization engineer, so that the stable adjustment effect is difficult to guarantee, accurate evaluation cannot be performed according to historical big data, and a certain adjustment deviation exists.
3. The relatively high cost of dual carrier and Carrier Aggregation (CA) software licensing (license), and the importance of manually assessing the rationality and efficiency of allocating resources, places increasing pressure on the configuration and maintenance of network resources.
4. The manual wireless resource management mode has complex and long time from evaluation to optimization, is difficult to dynamically adapt to services, has large data analysis amount, and is difficult to accurately configure each cell. The real-time adjustment and dynamic configuration of license resources of dual carriers and Carrier Aggregation (CA) are difficult to realize manually.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a device, and a medium for adaptive adjustment of dual carriers and carrier aggregation, which can solve at least one of the above technical problems.
In a first aspect, an embodiment of the present invention provides a method, an apparatus, a device, and a medium for adaptive adjustment of dual carriers and carrier aggregation, where the method includes:
collecting real-time service indexes of a cell;
matching the real-time service index with a historical service index;
if the matching result meets the preset condition, predicting the service according to a preset algorithm to obtain a prediction result of a service index value;
according to the prediction result, according to the cell category to be expanded, when the cell reaches a threshold corresponding to the category of the cell, outputting a corresponding pre-adjusted double-carrier and carrier aggregation model;
and obtaining the index value of each service index under the condition of the same configuration as the method for pre-adjusting the double carriers and the carrier aggregation, and correcting the model for pre-adjusting the double carriers and the carrier aggregation according to each index value to obtain the self-adaptive adjustment model for the double carriers and the carrier aggregation.
Preferably, the method further comprises:
storing the corrected pre-adjusted dual-carrier and carrier aggregation model, and establishing a record base;
and calculating the corrected pre-adjusted double-carrier and carrier aggregation model in the record base by adopting an optimization algorithm to obtain a self-adaptive adjustment model of double-carrier and carrier aggregation.
Preferably, the service index includes: uplink ERAB flow, uplink PUSCH utilization rate, downlink PDSCH utilization rate, uplink flow and downlink flow.
Preferably, the preset conditions include:
the standard variance of the service index value in the current period of the historical week is less than 0.5; alternatively, the first and second electrodes may be,
the current service index value is < the average value of the historical current period service index values in one week 2; either or both of the first and second substrates may be,
the current service index value is larger than the average value/2 of the historical current time interval index values in one week.
Preferably, the step of predicting the service according to a preset algorithm and obtaining a prediction result of a service index value specifically includes:
and (3) predicting the service distribution: classifying the cells according to scenes, and setting different service density factors corresponding to different scenes;
and (3) historical service prediction: acquiring a cell current service index from the STS statistical file, and acquiring an index value for predicting a next time period of a current time period according to an index value of the current time in the acquired cell current service index, an index value of the current time period corresponding to the time and an index value of the next time period of the current time period;
and obtaining a prediction result of the service index value according to the service distribution prediction and the historical service prediction.
Preferably, the step of obtaining an index value of each service index under the condition that the configuration of the model for pre-adjusting dual carrier and carrier aggregation is the same as that of the model for pre-adjusting dual carrier and carrier aggregation, and correcting the model for pre-adjusting dual carrier and carrier aggregation according to each index value to obtain the adaptive adjustment model for dual carrier and carrier aggregation specifically includes:
acquiring index values of all service indexes under the condition of the same configuration as a pre-adjusted dual-carrier and carrier aggregation model, and acquiring an average value of the index values of all the service indexes;
comparing the index value of each service index with a preset target value and a deterioration baseline value corresponding to the preset target value and the deterioration baseline value, and if the index value of each service index is smaller than the preset deterioration baseline value corresponding to the preset target value and the deterioration baseline value; and/or if the average value of the obtained index values of all the service indexes is smaller than a preset value, correcting the model for pre-adjusting the double carriers and carrier aggregation.
Preferably, the method further comprises: and if the matching result does not meet the preset condition, outputting a corresponding pre-adjusted double-carrier and carrier aggregation model according to the collected real-time service index and the cell type to be expanded when the cell reaches a threshold corresponding to the type.
In a second aspect, an embodiment of the present invention provides an adaptive adjustment apparatus for dual carriers and carrier aggregation, where the apparatus includes:
the acquisition module is used for acquiring real-time service indexes of the cell;
the matching module is used for matching the real-time service index with the historical service index;
the prediction module is used for predicting the service according to a preset algorithm if the matching result of the matching module meets a preset condition, and acquiring a prediction result of a service index value;
a generation module, configured to output a corresponding pre-adjusted dual-carrier and carrier aggregation model when a cell reaches a threshold corresponding to a category of the cell to be expanded according to the prediction result and the category of the cell;
and the correcting module is used for acquiring the index value of each service index under the condition of the same configuration as the model for pre-adjusting the double carriers and carrier aggregation, and correcting the method for pre-adjusting the double carriers and carrier aggregation according to each index value to acquire the self-adaptive adjusting model for the double carriers and carrier aggregation.
In a third aspect, an embodiment of the present invention provides a dual-carrier and carrier aggregation adaptive adjustment device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of the first aspect of the embodiments described above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the method of the first aspect in the foregoing embodiments.
The self-adaptive adjustment method, the device, the equipment and the medium for double-carrier and carrier aggregation provided by the embodiment of the invention match according to the real-time collected service index and the historical service index, construct a model for pre-adjusting double-carrier and carrier aggregation according to the service prediction result, correct the model, and obtain the self-adaptive adjustment model for double-carrier and carrier aggregation, so that the model is applied to the self-adaptive adjustment method for double-carrier and carrier aggregation, the self-adaptive adjustment of double-carrier and carrier aggregation can be realized, and the adjustment is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an adaptive adjustment method for dual carrier and carrier aggregation according to embodiment 1 of the present invention;
fig. 2 is a flowchart of an adaptive adjustment method for dual carrier and carrier aggregation according to embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of an adaptive adjustment apparatus for dual carrier and carrier aggregation according to embodiment 3 of the present invention;
fig. 4 is a schematic structure of an adaptive adjustment device for dual carrier and carrier aggregation according to embodiment 4 of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, an embodiment of the present invention provides a method for adaptively adjusting dual carriers and carrier aggregation, including the following steps:
and S01, collecting the real-time service index of the cell.
And S02, matching the real-time service index with the historical service index.
And S03, if the matching result meets the preset condition, predicting the service according to a preset algorithm to obtain the prediction result of the service index value.
And S04, outputting a corresponding pre-adjusted double-carrier and carrier aggregation model according to the prediction result and the cell type to be expanded when the cell reaches the threshold corresponding to the type.
S05, obtaining the index value of each service index under the same condition with the configuration of the method for pre-adjusting the double carriers and the carrier aggregation, and correcting the model for pre-adjusting the double carriers and the carrier aggregation according to each index value to obtain the self-adaptive adjustment model for the double carriers and the carrier aggregation.
The self-adaptive adjustment method of the double-carrier and carrier aggregation in the embodiment of the invention comprises the steps of matching according to real-time collected service indexes and historical service indexes, constructing a model for pre-adjusting the double-carrier and carrier aggregation according to a service prediction result, correcting the model, and obtaining the self-adaptive adjustment model of the double-carrier and carrier aggregation, so that the model is applied to the self-adaptive adjustment method of the double-carrier and carrier aggregation, the double-carrier and carrier aggregation can be self-adaptively adjusted, and the adjustment is more accurate.
In order to make the implementation of the embodiment of the present invention clearer, the embodiment is specifically described with reference to embodiment 2. Referring to fig. 2, an embodiment of the present invention provides a method for adaptively adjusting dual carriers and carrier aggregation, including the following steps:
s11, collecting real-time service indexes of the cell; the service indexes comprise: a call completing rate, a call dropping rate, a cut success rate, a license utilization rate, a service flow, an uplink ERAB flow, an uplink PUSCH utilization rate, a downlink PDSCH utilization rate, an uplink flow, a downlink flow and the like.
S12, matching the real-time service index with the historical service index; wherein, if the matching result meets the following conditions: the standard variance of the service index value in the current period of the historical week is less than 0.5; or, the current traffic index value < average value of historical current time interval traffic index values of one week 2; or, the current service index value is larger than the average value/2 of the historical current time interval index values in one week. The matching degree of the real-time service index and the historical service index is high, otherwise, the matching degree is high.
And S13, if the matching degree of the real-time service index and the historical service index is high, predicting the service according to a preset algorithm to obtain a prediction result of the service index value.
The step can be realized by the following steps:
and (3) predicting the service distribution: the cells are classified according to the scene, such as: business area, market, residential area, suburb, etc., different service density factors are correspondingly set for different scenes, and if the number of scenes of a cell is n, the cell correspondingly has the service density factor: t is t1,t2,t3,…,tn。
And (3) historical service prediction: and acquiring the current service indexes of the cell from the STS statistical file, wherein the current service indexes comprise uplink ERAB flow, uplink PUSCH utilization rate, downlink PDSCH utilization rate, uplink flow and downlink flow, and the current service indexes are used as the basis of service prediction and read in a service model list. Taking the traffic service index (TRA) prediction as an example, the formula for predicting the traffic (TRA _ SAMPLE _ N) of the next time slot and the traffic (TRA _ SAMPLE _ N) of the current time slot of the cell is as follows:
next session flow rate (TRA _ N) (TRA _ SAMPLE _ N/TRA _ SAMPLE) × TRA
For example, the current 9-point Traffic (TRA) of the cell is 10M, and the list of the read traffic models is as follows:
| Time | 08 | 09 | 10 | 11 | 12 | 13 |
| CELL01 | 14.07 | 23.59 | 26.79 | 23.80 | 21.34 | 24.34 |
TRA_SAMPLE＝23.59，TRA_SAMPLE_N＝26.79
next time interval flow (TRA _ N) 10 × 26.79/23.59 ═ 11.35M
The telephone traffic prediction has requirements on historical telephone traffic values: when the telephone traffic prediction is required, when index data of the previous week is taken (the maximum value of the data amount), the telephone traffic prediction is normally carried out and the next operation is carried out.
According to the service distribution prediction and the historical service prediction, obtaining a prediction result of a service index value:
next slot traffic (TRA _ N) (TRA _ SAMPLE _ N/TRA _ SAMPLE × TRA) ((1 + t));
setting an increased flow lifting target coefficient: k (default is 0, floating point type, range: [0.0,1.0 ]);
and finally, predicting the next time period service flow (TRA _ N) ═(TRA _ SAMPLE _ N/TRA _ SAMPLE _ TRA (1+ k)) (1+ t).
And S14, outputting a corresponding pre-adjusted double-carrier and carrier aggregation model according to the prediction result and the cell type to be expanded when the cell reaches the threshold corresponding to the type.
The following method can be adopted in the step: and (4) judging by combining the service prediction result and the logical relationship. And (4) according to the cell classification determination standard of the large, medium and small packets to be expanded, outputting corresponding pre-adjusted double-carrier and carrier aggregation models when the classified cells reach the corresponding threshold.
It should be noted that, if the matching result in step S13 does not satisfy the preset condition, the step outputs a corresponding pre-adjusted dual carrier and carrier aggregation model according to the collected real-time service index and the type of the cell to be expanded, when the cell reaches a threshold corresponding to the type of the cell.
Examples of thresholds for the cell classification criteria and capacity expansion (configured to enable dual carrier and carrier aggregation functions) are as follows:
examples of the cell classification criteria and the volume reduction (configuring the dual carrier and carrier aggregation function off) thresholds are as follows:
the cell expansion and volume reduction verification logic is as follows: the number of ' effective RRC users ' reaches the threshold ' and ' uplink utilization reaches the threshold ' and ' uplink traffic reaches the threshold ' ], or the number of effective RRC users ' reaches the threshold ' and ' downlink utilization reaches the threshold (PDSCH or PDCCH) ' and ' downlink traffic reaches the threshold ' ]
The judgment standard can independently change the judgment rule according to the actual situation, and a preliminary optimized adaptive adjustment model of the double carriers and the carrier aggregation can be obtained according to the logic judgment.
S15, correcting the self-adaptive adjustment model of the pre-adjustment double carrier and the carrier aggregation: acquiring index values of all service indexes under the condition of the same configuration as a pre-adjusted dual-carrier and carrier aggregation model, and acquiring an average value of the index values of all the service indexes; comparing the index value of each service index with a preset target value and a deterioration baseline value corresponding to the preset target value and the deterioration baseline value, and if the index value of each service index is smaller than the preset deterioration baseline value corresponding to the preset target value and the deterioration baseline value; and/or if the average value of the obtained index values of all the service indexes is smaller than a preset value, correcting the model for pre-adjusting the double carriers and carrier aggregation.
In the step, aiming at improving user perception and maximally saving License investment, the overall service acceptance capability and service quality of the network are improved by adjusting the models of the double carriers and the carrier aggregation, and the change of key indexes can also be brought.
The analyzed index value is derived from the average value of the indexes under the condition that the configuration of the model which is pre-adjusted and self-adaptive to the double carriers and the carrier aggregation is the same in the adjustment record, the index value needs to be ensured to be within an acceptable range as much as possible, an optimization analysis algorithm is designed based on the target index effect, and the indexes which are mainly influenced are as follows: call completing rate, call drop rate, switching success rate, License utilization rate, service flow and the like.
Setting a target value and a deterioration bottom line value for each index possibly influenced by a self-adaptive adjustment model for pre-adjusting double carriers and carrier aggregation, requiring capacity expansion and capacity reduction configuration, and simultaneously, an index evaluation value cannot be lower than the deterioration bottom line, and if the index is lower than the bottom line value, immediately correcting a corresponding scheme.
Modeling index score:
| index name | Weight of | Index value | Index bottom line | Target value |
| Call completing rate | k1 | A1 | z1 | T1 |
| Rate of dropped calls | k2 | A2 | z2 | T2 |
| Handover success rate | k3 | A3 | z3 | T3 |
| License usage rate | k4 | A4 | z4 | T4 |
| Traffic flow | k5 | A5 | Z5 | T5 |
Examples are: the call completing rate index is divided into: MIN (100 × k1 × (T1-a1)/(T1-z1),100 × k1), the total index full score is 100 points, and the total score of the individual cell index is the sum of the scores of the five indexes.
And S16, adjusting the corrected self-adaptive adjustment model of the pre-adjusted double carriers and the carrier aggregation.
In the step, in order to enable the index value not to be more than frequent soldier-pong switching capacity expansion adjustment and capacity reduction adjustment at the near point of the deterioration bottom line, a near point rule is added in the algorithm: when the cell triggers the capacity expansion adjustment, the capacity reduction adjustment operation is not allowed in N periods, but the capacity expansion adjustment operation is allowed to continue, so that the occurrence of ping-pong handover can be effectively reduced. Where N is a user setting.
And performing optimization analysis and correction aiming at the pre-adjustment scheme of each cell by taking the maximum target index total score (the target index total score is the sum of the target index scores of each cell in the pre-optimization scheme) of the scheme as a target, recording the result of each model correction, and outputting the scheme with the highest target score as a final optimization model within given time.
S17, storing the corrected pre-adjusted double-carrier and carrier aggregation model, and establishing a record base; and calculating the corrected pre-adjusted double-carrier and carrier aggregation model in the record base by adopting an optimization algorithm to obtain a self-adaptive adjustment model of double-carrier and carrier aggregation.
In this step, the corrected model of pre-adjusted dual carriers and carrier aggregation is stored because the adaptive adjustment is a long-term adjustment model, and as the online time of the model accumulates, the cell adjustment scheme and the adjustment effect can be recorded to build a record base, and in order to enable the adjusted algorithm to have stronger adaptability in the wireless network service, the more reasonable adjustment scheme is further analyzed by introducing the optimization algorithm according to the scheme adjustment record. The adjustment records are as follows:
in order to control the capacity of the record library not to be increased without an upper limit and ensure the efficiency of analyzing parameter values by a subsequent algorithm, at most ten thousand records are reserved in each cell by default, when the capacity of the record library reaches the upper limit, the record with the longest elimination time is eliminated according to time, in addition, the worst solution in the memory library is continuously replaced by the optimal solution calculated by the algorithm each time, the record library is continuously updated, and the adaptability of an adjustment scheme, user perception and License resource allocation is continuously improved.
And S18, performing self-adaptive adjustment on the double carriers and the carrier aggregation through the generated double-carrier and carrier aggregation self-adaptive adjustment model, and realizing issuing adjustment of a capacity expansion and reduction configuration scheme through the butting of northbound ports of a base station equipment manufacturer.
The real-time double-carrier and carrier aggregation self-adaptive adjusting method provided by the invention has the advantages that the real-time service and the network scene are analyzed, the service prediction and optimization target are combined, the user perception is improved, the License resource investment is maximally saved, the real-time data and the historical adjusting data are continuously collected, the collected adjusting data are used as the adaptive basis of the optimal solution of the adjusting model, the LTE double-carrier and Carrier Aggregation (CA) self-adaptive adjustment is carried out, and the effect of maximally saving the License investment is achieved on the premise of ensuring the network index.
Referring to fig. 3, an embodiment of the present invention provides an adaptive adjustment apparatus for dual carriers and carrier aggregation, including: the system comprises an acquisition module 301, a matching module 302, a prediction module 303, a generation module 304 and a correction module 305.
The acquisition module 301 is configured to acquire a real-time service index of a cell; the matching module 302 is configured to match the real-time service index with a historical service index; the prediction module 303 is configured to predict a service according to a preset algorithm if the matching result of the matching module meets a preset condition, and obtain a prediction result of a service index value; the generation module 304 is configured to output a corresponding pre-adjusted dual-carrier and carrier aggregation model when a cell reaches a threshold corresponding to a category of the cell to be expanded according to the prediction result and the category of the cell; the correcting module 305 is configured to obtain an index value of each service index under the condition that the configuration of the model for pre-adjusting dual carrier and carrier aggregation is the same as that of the model for pre-adjusting dual carrier and carrier aggregation, and correct the method for pre-adjusting dual carrier and carrier aggregation according to each index value, so as to obtain a self-adaptive adjusting model for dual carrier and carrier aggregation.
The service indexes collected by the collection module 301 include: a call completing rate, a call dropping rate, a cut success rate, a license utilization rate, a service flow, an uplink ERAB flow, an uplink PUSCH utilization rate, a downlink PDSCH utilization rate, an uplink flow, a downlink flow and the like.
The matching module 302 is specifically configured to match the real-time service index with the historical service index; wherein, if the matching result meets the following conditions: the standard variance of the service index value in the current period of the historical week is less than 0.5; or, the current traffic index value < average value of historical current time interval traffic index values of one week 2; or, the current service index value is larger than the average value/2 of the historical current time interval index values in one week. The matching degree of the real-time service index and the historical service index is high, otherwise, the matching degree is high.
The prediction module 303 may specifically include a service distribution prediction unit and a historical service prediction unit; the service distribution prediction unit is used to classify cells according to scenes, such as: business area, market, residential area, suburb, etc., different service density factors are correspondingly set for different scenes, and if the number of scenes of a cell is n, the cell correspondingly has the service density factor: t is t1,t2,t3,…,tn. The historical service prediction unit is used for acquiring the current service indexes of the cell from the STS statistical file, wherein the current service indexes comprise uplink ERAB flow, uplink PUSCH utilization rate, downlink PDSCH utilization rate, uplink flow and downlink flow, and the current service indexes are used as the basis of service prediction and read in the service model list.
The generating unit 304 is specifically configured to combine the service prediction result and the logical relationship judgment, determine a standard according to the classification of the cells to be expanded, and output a corresponding pre-adjusted dual carrier and a carrier aggregation model when the classified cell reaches a corresponding threshold.
The correcting unit 305 is specifically configured to obtain an index value of each service index under the condition that the model configuration of the pre-adjusted dual carrier and the carrier aggregation is the same, and obtain an average value of the index values of each service index; comparing the index value of each service index with a preset target value and a deterioration baseline value corresponding to the preset target value and the deterioration baseline value, and if the index value of each service index is smaller than the preset deterioration baseline value corresponding to the preset target value and the deterioration baseline value; and/or if the average value of the obtained index values of all the service indexes is smaller than a preset value, correcting the model for pre-adjusting the double carriers and carrier aggregation.
The adaptive adjustment device for dual carrier and carrier aggregation in this embodiment 2 can adopt the method in embodiment 1 or 2 to perform adaptive adjustment on dual carrier and carrier aggregation, that is, by analyzing real-time services and network scenes, and combining with service prediction and optimization targets, it is able to improve user perception and maximize License resource investment saving, and by continuously collecting real-time data and historical adjustment data, and taking the collected adjustment data as an adaptation basis for adjusting the optimal solution of the model, perform LTE dual carrier and Carrier Aggregation (CA) adaptive adjustment, so as to achieve the effect of maximally saving License investment on the premise of guaranteeing network indexes.
Another embodiment of the present invention provides a device for adaptively adjusting dual carriers and carrier aggregation, and the method for adaptively adjusting dual carriers and carrier aggregation according to the embodiment of the present invention can be implemented by the device. Fig. 4 is a schematic diagram illustrating a hardware structure of an adaptive adjustment device for dual carriers and carrier aggregation according to another embodiment of the present invention.
The adaptive adjustment device for dual carrier and carrier aggregation may comprise a processor 401 and a memory 402 storing computer program instructions.
Specifically, the processor 401 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the adaptive adjustment methods for dual carrier and carrier aggregation in the above embodiments.
In one example, the dual carrier and carrier aggregated adaptation adjusting device may further include a communication interface 403 and a bus 410. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 to complete communication therebetween.
The communication interface 403 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present invention.
Embodiments of the present invention may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any one of the dual carrier and carrier aggregation adaptive adjustment methods in the above embodiments.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method for adaptive adjustment of dual carriers and carrier aggregation, the method comprising:
collecting real-time service indexes of a cell;
matching the real-time service index with a historical service index;
if the matching result meets the preset condition, predicting the service according to a preset algorithm to obtain a prediction result of a service index value;
when the prediction result reaches the prediction result threshold corresponding to the cell type to be expanded, outputting a corresponding pre-adjusted double-carrier and carrier aggregation model;
and obtaining the index value of each service index under the condition of the same configuration as the model for pre-adjusting the double carriers and carrier aggregation, and correcting the model for pre-adjusting the double carriers and carrier aggregation according to each index value to obtain the self-adaptive adjustment model for the double carriers and carrier aggregation.
2. The method of claim 1, further comprising:
storing the corrected pre-adjusted dual-carrier and carrier aggregation model, and establishing a record base;
and calculating the corrected pre-adjusted double-carrier and carrier aggregation model in the record base by adopting an optimization algorithm to obtain a self-adaptive adjustment model of double-carrier and carrier aggregation.
3. The method of claim 1, wherein the traffic indicator comprises at least one of: the uplink evolution wireless access bears ERAB flow, downlink ERAB flow, uplink PUSCH utilization rate, downlink PDSCH utilization rate, uplink total flow and downlink total flow.
4. The method according to claim 1, wherein the preset condition comprises:
the standard variance of the service index value in the current period of the historical week is less than 0.5; alternatively, the first and second electrodes may be,
the current service index value is < the average value of the historical current period service index values in one week 2; either or both of the first and second substrates may be,
the current service index value is larger than the average value/2 of the historical current time interval index values in one week.
5. The method according to claim 1, wherein the step of predicting the service according to the preset algorithm to obtain the prediction result of the service index value comprises:
and (3) predicting the service distribution: classifying the cells according to scenes, and setting different service density factors corresponding to different scenes;
and (3) historical service prediction: acquiring a cell current service index from the STS statistical file, and acquiring an index value for predicting a next time period of a current time period according to an index value of the current time in the acquired cell current service index, an index value of the current time period corresponding to the time and an index value of the next time period of the current time period;
and obtaining a prediction result of the service index value according to the service distribution prediction and the historical service prediction.
6. The method according to claim 1, wherein the step of obtaining an index value of each service index under the same condition as the configuration of the model for pre-adjusting dual carrier and carrier aggregation, and modifying the model for pre-adjusting dual carrier and carrier aggregation according to each index value to obtain the adaptive adjustment model for dual carrier and carrier aggregation comprises:
acquiring index values of all service indexes under the condition of the same configuration as a pre-adjusted dual-carrier and carrier aggregation model, and acquiring an average value of the index values of all the service indexes;
comparing the index value of each service index with a preset target value and a deterioration baseline value corresponding to the preset target value and the deterioration baseline value, and if the index value of each service index is smaller than the preset deterioration baseline value corresponding to the preset target value and the deterioration baseline value; and/or if the average value of the obtained index values of all the service indexes is smaller than a preset value, correcting the model for pre-adjusting the double carriers and carrier aggregation.
7. The method of claim 1, further comprising:
and if the matching result does not meet the preset condition, outputting a corresponding pre-adjusted double-carrier and carrier aggregation model according to the collected real-time service index and the cell type to be expanded when the cell reaches a prediction result threshold corresponding to the type.
8. An adaptive adjustment apparatus for dual carriers and carrier aggregation, the apparatus comprising:
the acquisition module is used for acquiring real-time service indexes of the cell;
the matching module is used for matching the real-time service index with the historical service index;
the prediction module is used for predicting the service according to a preset algorithm if the matching result of the matching module meets a preset condition, and acquiring a prediction result of a service index value;
the generation module is used for outputting a corresponding pre-adjustment double-carrier and carrier aggregation model when the prediction result reaches the prediction result threshold corresponding to the cell type to be expanded;
and the correcting module is used for acquiring the index value of each service index under the condition that the configuration of the model for pre-adjusting the double carriers and carrier aggregation is the same as that of the model for pre-adjusting the double carriers and carrier aggregation, and correcting the model for pre-adjusting the double carriers and carrier aggregation according to each index value to acquire the self-adaptive adjusting model for double carriers and carrier aggregation.
9. An adaptive adjustment device for dual carriers and carrier aggregation, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-7.
10. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1-7.
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| CN112954808A (en) * | 2019-12-11 | 2021-06-11 | 中国移动通信集团山东有限公司 | Carrier resource adjusting method, device, storage medium and computer equipment |
| CN113055892B (en) * | 2019-12-27 | 2022-10-18 | 中国移动通信集团浙江有限公司 | Carrier scheduling method and device, computing equipment and computer storage medium |
| CN113411829A (en) * | 2020-03-17 | 2021-09-17 | 中国联合网络通信集团有限公司 | Carrier configuration method, device, apparatus and storage medium |
| CN115460711B (en) * | 2021-06-08 | 2024-04-26 | 中国移动通信集团重庆有限公司 | Service flow splitting method, device, electronic equipment and storage medium |
| CN113365357B (en) * | 2021-06-08 | 2022-11-22 | 中国联合网络通信集团有限公司 | Image recognition model training method, carrier wave adjusting method, device and medium |
| CN115002926B (en) * | 2022-06-23 | 2023-10-10 | 中国电信股份有限公司 | Dual carrier scheme configuration method, device, electronic equipment and readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102340777A (en) * | 2010-07-15 | 2012-02-01 | 华为技术有限公司 | Method for dynamically adjusting carrier resources and base station |
| CN103096323A (en) * | 2011-10-27 | 2013-05-08 | 鼎桥通信技术有限公司 | Adjustment method of carrier wave |
| CN103167513A (en) * | 2013-02-01 | 2013-06-19 | 上海大唐移动通信设备有限公司 | Method and device for expanding capacity of carrier |
| CN104144431A (en) * | 2013-05-10 | 2014-11-12 | 中国电信股份有限公司 | Method and device for mobile network status prediction and mobile network |
| CN106788932A (en) * | 2016-11-30 | 2017-05-31 | 中国联合网络通信集团有限公司 | Carrier aggregation method for optimizing configuration and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110080885A1 (en) * | 2009-10-05 | 2011-04-07 | Electronics And Telecommunications Research Institute | Apparatus and method for management of extension carrier in base station |
-
2017
- 2017-12-28 CN CN201711456477.0A patent/CN109981234B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102340777A (en) * | 2010-07-15 | 2012-02-01 | 华为技术有限公司 | Method for dynamically adjusting carrier resources and base station |
| CN103096323A (en) * | 2011-10-27 | 2013-05-08 | 鼎桥通信技术有限公司 | Adjustment method of carrier wave |
| CN103167513A (en) * | 2013-02-01 | 2013-06-19 | 上海大唐移动通信设备有限公司 | Method and device for expanding capacity of carrier |
| CN104144431A (en) * | 2013-05-10 | 2014-11-12 | 中国电信股份有限公司 | Method and device for mobile network status prediction and mobile network |
| CN106788932A (en) * | 2016-11-30 | 2017-05-31 | 中国联合网络通信集团有限公司 | Carrier aggregation method for optimizing configuration and device |
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