CN114745733B - Wireless network optimization method and system based on SON and RRM combined optimization - Google Patents
Wireless network optimization method and system based on SON and RRM combined optimization Download PDFInfo
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Abstract
The invention discloses a wireless network optimization method based on SON and RRM combined optimization, which comprises the following steps: acquiring performance indexes and running state information in the running process of a wireless network through an SON (self-organizing network) controller; when the network abnormity is detected, determining an SON optimization strategy according to the type of the network abnormity; according to the SON optimization strategy, matching a corresponding RRM optimization strategy; and refining network abnormity according to the SON optimization strategy and the RRM optimization strategy, and then determining a joint optimization strategy. The invention can reduce the conflict of two-layer optimization, avoid frequent optimization and adjustment of the wireless network, and can be widely applied to the technical field of mobile communication.
Description
Technical Field
The invention relates to the technical field of mobile communication, in particular to a wireless network optimization method based on SON and RRM combined optimization.
Background
Traditional wireless network optimization is based on an expert system or manual work, and is low in efficiency and high in network operation cost. As wireless networks become more dense, the efficiency of their optimization becomes increasingly important. In the existing Self-optimization technology, based on Self-Organized Network (SON) technology, the Network performance can be optimized according to use cases without manual work. However, the network densification causes the wireless network to have higher dynamics and complexity, and the frequency of SON optimization increases sharply, which causes the network to be unstable. On the other hand, the Radio Resource Management (RRM) technique performs scheduling and Resource allocation from a smaller time granularity, and optimizes the performance of a single base station/cell or even multiple base stations/cells. Under the condition of network densification, RRM optimization has larger and larger influence on network performance, and is increasingly important in cooperation with SON optimization.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method for optimizing a wireless network based on SON and RRM joint optimization, so as to reduce conflicts of two-layer optimization and avoid frequent optimization and adjustment of the wireless network.
One aspect of the present invention provides a wireless network optimization method based on SON and RRM joint optimization, including:
acquiring performance indexes and running state information in the running process of a wireless network through an SON (self-organizing network) controller;
when the network anomaly is detected, determining an SON optimization strategy according to the type of the network anomaly;
according to the SON optimization strategy, matching a corresponding RRM optimization strategy;
and refining network abnormity according to the SON optimization strategy and the RRM optimization strategy, and then determining a joint optimization strategy.
Optionally, the obtaining, by the SON controller, the performance index and the operation state information in the operation process of the wireless network includes:
during the operation of the wireless network, the SON controller continuously collects and counts the performance index and the operation state information of the network;
when the network abnormality is detected, adjusting the configuration of corresponding network parameters;
specifically, the network abnormality condition includes: the number of the radio link failures of the base station is larger than a preset threshold, or the signal to interference plus noise ratio of the user in a preset worst range is smaller than the preset threshold, or the user satisfaction is smaller than the preset threshold, or the call drop blocking rate is larger than the preset threshold.
Optionally, when a network anomaly is detected, determining a SON optimization policy according to the type of the network anomaly includes:
for network abnormity of coverage deterioration, performing coverage optimization by adjusting the downward inclination angle of the base station antenna, or performing coverage capacity joint optimization by adjusting the downward inclination angle and power of the base station antenna;
for network abnormality of network hotspots, adopting an optimization strategy of mobile load balancing, and carrying out cell breathing by adjusting the power of a base station, so that scattered service hotspots are accessed to the same base station; or, a combined optimization strategy of mobile load balancing and mobile robustness optimization is adopted, so that users with service hotspots are dispersedly accessed to different base stations; or an optimization strategy of coverage capacity joint optimization is adopted to reduce the influence of the service hot spots.
Optionally, the matching, according to the SON optimization strategy, a corresponding RRM optimization strategy includes:
for coverage optimized SON optimization strategies, the matching RRM optimization strategy is to maximize throughput;
for the optimization strategy of the coverage capacity joint optimization, the matched RRM optimization strategy is proportional fairness;
for the optimization strategy of mobile load balancing, the matched RRM optimization strategy is the maximized throughput rate;
for the combined optimization strategy of mobile load balancing and mobile robustness optimization, the matched RRM optimization strategy is proportional fairness.
Optionally, after refining the network anomaly according to the SON optimization strategy and the RRM optimization strategy, determining a joint optimization strategy includes:
for network abnormality of a service hotspot, different joint optimization strategies are adopted, which specifically comprise:
when the service hot spot is positioned in an overlapping area between the base stations, a combined optimization strategy of mobile load balancing and mobile robustness optimization and an RRM optimization strategy of proportional fair scheduling are adopted;
when a service hotspot is positioned at the edge of a certain base station or cell, an SON optimization strategy of mobile load balancing optimization and an RRM optimization strategy of maximum throughput rate are adopted;
and when the service hot spot is not in the overlapping area between the base stations and is not positioned at the edge of a certain base station or a certain cell, adopting an SON (self-organizing network) optimization strategy of coverage capacity joint optimization and an RRM (radio resource management) optimization strategy of proportional fair scheduling.
Another aspect of the embodiments of the present invention further provides a method for optimizing a wireless network based on SON and RRM joint optimization, including:
after counting the service distribution and network performance indexes of the users of the service, the base station reports the service distribution and network performance indexes to the SON controller;
the SON controller carries out anomaly detection according to wireless big data reported by each base station, and determines a cross-layer optimization strategy according to an anomaly type and a network state, wherein the cross-layer optimization strategy comprises an SON optimization strategy and an RRM optimization strategy;
the SON controller optimizes according to the SON optimization strategy and determines wireless parameters needing to be adjusted and corresponding values;
the SON controller sends wireless parameters to be adjusted and corresponding values and corresponding RRM optimization strategies to base stations needing to participate in optimization;
and the base station needing to participate in optimization adjusts corresponding wireless parameters, and updates a corresponding RRM optimization strategy for scheduling and resource allocation.
Optionally, the network performance indicators include coverage performance and capacity performance.
Optionally, the SON controller optimizes according to the SON optimization strategy, and determines the wireless parameters and corresponding values that need to be adjusted, including:
the CCO joint optimization requires the antenna downward inclination angle and power of the base station participating in the optimization; or
The combined optimization of mobile load balancing and mobile robustness optimization requires handover parameters of the base stations participating in the optimization, including offset, hysteresis, and trigger time.
Optionally, the RRM optimization strategy determined by the SON controller includes maximizing throughput or proportional fairness.
Another aspect of the embodiments of the present invention further provides a wireless network optimization system based on SON and RRM joint optimization, including a base station and an SON controller;
wherein the base station is configured to:
after counting the service distribution of the users of the service and the network performance index, reporting to the SON controller;
adjusting corresponding wireless parameters, and updating corresponding RRM optimization strategies for scheduling and resource allocation;
the SON controller is configured to:
performing anomaly detection according to wireless big data reported by each base station, and determining a cross-layer optimization strategy according to an anomaly type and a network state;
optimizing according to the SON optimization strategy, and determining wireless parameters and corresponding values to be adjusted;
and sending the wireless parameters to be adjusted and corresponding values and corresponding RRM optimization strategies to the base stations needing to participate in optimization.
Another aspect of the embodiments of the present invention further provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
Yet another aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores a program, which is executed by a processor to implement the method as described above.
Yet another aspect of embodiments of the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
The embodiment of the invention obtains the performance index and the running state information in the running process of the wireless network through the SON controller; when the network abnormity is detected, determining an SON optimization strategy according to the type of the network abnormity; according to the SON optimization strategy, matching a corresponding RRM optimization strategy; and refining network abnormity according to the SON optimization strategy and the RRM optimization strategy, and then determining a joint optimization strategy. The invention can reduce the conflict of two-layer optimization and avoid frequent optimization and adjustment of the wireless network.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an architecture of SON and RRM joint optimization according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the overall steps provided by an embodiment of the present invention;
fig. 3 is a flowchart of an implementation of the SON and RRM joint optimization strategy determination provided in the embodiment of the present invention;
fig. 4 is a signaling flow diagram example of SON and RRM cross-layer optimization according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further 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 present application and are not intended to limit the present application.
In view of the problems in the prior art, an aspect of the present invention provides a wireless network optimization method based on SON and RRM joint optimization, including:
acquiring performance indexes and running state information in the running process of a wireless network through an SON (self-organizing network) controller;
when the network abnormity is detected, determining an SON optimization strategy according to the type of the network abnormity;
according to the SON optimization strategy, matching a corresponding RRM optimization strategy;
and refining network abnormity according to the SON optimization strategy and the RRM optimization strategy, and then determining a joint optimization strategy.
Optionally, the obtaining, by the SON controller, the performance index and the operation state information in the operation process of the wireless network includes:
during the operation of the wireless network, the SON controller continuously collects and counts the performance index and the operation state information of the network;
when the network abnormality is detected, adjusting the configuration of corresponding network parameters;
specifically, the network abnormality condition includes: the number of the radio link failures of the base station is larger than a preset threshold, or the signal to interference plus noise ratio of the user in a preset worst range is smaller than the preset threshold, or the user satisfaction is smaller than the preset threshold, or the call drop blocking rate is larger than the preset threshold.
Optionally, when a network anomaly is detected, determining a SON optimization policy according to the type of the network anomaly includes:
for network abnormity of coverage deterioration, performing coverage optimization by adjusting the downward inclination angle of the base station antenna, or performing coverage capacity joint optimization by adjusting the downward inclination angle and power of the base station antenna;
for network abnormality of network hotspots, adopting an optimization strategy of mobile load balancing, and carrying out cell breathing by adjusting the power of a base station, so that scattered service hotspots are accessed to the same base station; or, a combined optimization strategy of mobile load balancing and mobile robustness optimization is adopted, so that users with service hotspots are dispersedly accessed to different base stations; or an optimization strategy of coverage capacity joint optimization is adopted to reduce the influence of the service hot spots.
Optionally, the matching, according to the SON optimization strategy, a corresponding RRM optimization strategy includes:
for coverage optimized SON optimization strategies, the matched RRM optimization strategy is to maximize throughput;
for the optimization strategy of the coverage capacity joint optimization, the matched RRM optimization strategy is proportional fairness;
for the optimization strategy of mobile load balancing, the matched RRM optimization strategy is the maximized throughput rate;
for the combined optimization strategy of mobile load balancing and mobile robustness optimization, the matched RRM optimization strategy is proportional fairness.
Optionally, after refining the network anomaly according to the SON optimization strategy and the RRM optimization strategy, determining a joint optimization strategy includes:
for network abnormality of a service hotspot, different joint optimization strategies are adopted, which specifically comprise:
when the service hot spot is positioned in an overlapping area between the base stations, a combined optimization strategy of mobile load balancing and mobile robustness optimization and an RRM optimization strategy of proportional fair scheduling are adopted;
when a service hotspot is positioned at the edge of a certain base station or cell, an SON optimization strategy of mobile load balancing optimization and an RRM optimization strategy of maximum throughput rate are adopted;
and when the service hot spot is not in the overlapping area between the base stations and is not positioned at the edge of a certain base station or a certain cell, adopting an SON (self-organizing network) optimization strategy of coverage capacity joint optimization and an RRM (radio resource management) optimization strategy of proportional fair scheduling.
Another aspect of the embodiments of the present invention further provides a method for optimizing a wireless network based on SON and RRM joint optimization, including:
after counting the service distribution and network performance indexes of the users of the service, the base station reports the service distribution and network performance indexes to the SON controller;
the SON controller performs anomaly detection according to wireless big data reported by each base station, and determines a cross-layer optimization strategy according to an anomaly type and a network state, wherein the cross-layer optimization strategy comprises an SON optimization strategy and an RRM (radio resource management) optimization strategy;
the SON controller optimizes according to the SON optimization strategy and determines wireless parameters needing to be adjusted and corresponding values;
the SON controller sends wireless parameters to be adjusted and corresponding values and corresponding RRM optimization strategies to base stations needing to participate in optimization;
and the base station needing to participate in optimization adjusts corresponding wireless parameters, and updates a corresponding RRM optimization strategy for scheduling and resource allocation.
Optionally, the network performance indicators include coverage performance and capacity performance.
Optionally, the SON controller optimizes according to the SON optimization strategy, and determines the wireless parameters and corresponding values that need to be adjusted, including:
the CCO joint optimization requires the antenna downward inclination angle and power of the base station participating in the optimization; or
The joint optimization of mobility load balancing and mobility robustness optimization requires handover parameters of the base stations participating in the optimization, including offset, hysteresis, and trigger time.
Optionally, the RRM optimization strategy determined by the SON controller includes maximizing throughput or proportional fairness.
Another aspect of the embodiments of the present invention further provides a wireless network optimization system based on SON and RRM joint optimization, including a base station and an SON controller;
wherein the base station is configured to:
after counting the service distribution of the users of the service and the network performance index, reporting to the SON controller;
adjusting corresponding wireless parameters, and updating corresponding RRM optimization strategies for scheduling and resource allocation;
the SON controller is configured to:
performing anomaly detection according to wireless big data reported by each base station, and determining a cross-layer optimization strategy according to an anomaly type and a network state;
optimizing according to the SON optimization strategy, and determining wireless parameters and corresponding values to be adjusted;
and sending the wireless parameters to be adjusted and corresponding values and corresponding RRM optimization strategies to the base stations needing to participate in optimization.
Another aspect of the embodiments of the present invention further provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
Yet another aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores a program, which is executed by a processor to implement the method as described above.
Yet another aspect of embodiments of the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
The following detailed description of the embodiments of the present invention is made with reference to the accompanying drawings:
according to the invention, the SON optimization strategy and the RRM optimization strategy are subjected to cross-layer combined optimization, and the SON optimization strategy and the corresponding RRM optimization strategy are self-adaptively selected according to the type of the network anomaly, so that the conflict of two-layer optimization is reduced, and the frequent optimization and adjustment of a wireless network are avoided, as shown in fig. 1. The network metric includes network information such as user distribution, service distribution, network topology, and the like.
The flow of the wireless network intelligent optimization based on the SON and RRM joint optimization of the present invention is shown in fig. 2, and includes the following four steps:
step 1: anomaly detection
During operation of a wireless network, the SON controller continuously collects and counts performance indexes and operation states of the network, and when network abnormality is detected, configuration of corresponding network parameters needs to be adjusted. For example, when detecting that the number of Radio Link Failure (RLF) of the base station is greater than a preset threshold, or the Signal-to-Interference-and-Noise Ratio (SINR) of the worst a% (e.g., 5%) users is less than the preset threshold, it indicates that the coverage of the network is degraded. For another example, if the user satisfaction is less than a preset threshold, or the Call Drop blocking rate (Call Drop & Block Ratio: CDBR) is greater than the preset threshold, it indicates that a service hotspot occurs in the network, and the service hotspot is an area where the user service is dense.
Step 2: potential SON optimization strategy determination
When a network anomaly is detected, corresponding SON optimization strategies need to be adopted according to different types of anomalies. For network abnormity with deteriorated coverage, coverage optimization can be carried out by adjusting the downward inclination angle of the base station antenna; or, the Coverage Capacity joint Optimization (CCO) can be performed by adjusting the downtilt and power of the base station antenna.
For the network hot spot, an optimization strategy of Mobility Load Balance (MLB) can be adopted, and cell breathing is carried out by adjusting the power of the base station, so that the service hot spots are scattered to access the same base station; or, an MLB and Mobility Robustness Optimization (MRO) combined Optimization strategy can be adopted to ensure that users with hot service spots are accessed to different base stations in a scattered manner; and a CCO optimization strategy can be adopted to reduce the influence of service hot spots.
And step 3: RRM optimization strategy pairing
Different SON optimization strategies focus on different optimized network performance indexes, while different RRM optimization strategies have different impacts on network performance. Therefore, after the SON optimization strategy is determined, the RRM optimization strategy matched with the SON optimization strategy needs to be determined.
For the coverage optimization SON optimization strategy, the coverage performance is improved, and the capacity of the network is reduced, so that the RRM optimization strategy matched with the coverage optimization strategy maximizes the throughput, and thus, the frequency of SON optimization can be avoided.
For the CCO optimization strategy, the performance indexes of network coverage and network capacity are considered, and the RRM optimization strategy matched with the CCO optimization strategy is proportional fairness.
For the MLB optimization strategy, the network load is balanced, but the network capacity is sacrificed, so the RRM optimization strategy matched with the MLB optimization strategy is to maximize the throughput rate.
For the MLB & MRO joint optimization strategy, the RRM optimization strategy matched with the MLB & MRO joint optimization strategy is proportional fairness.
And 4, step 4: joint optimization strategy determination
For the same network anomaly, different SON and RRM joint optimization strategies produce different effects, and the network anomaly needs to be further refined, so that the most appropriate joint optimization strategy is adopted.
Taking the service hotspot as an example, different joint optimization strategies are adopted according to different positions of the service hotspot, as shown in fig. 3.
1) When the service hot spot is located in the overlapping area between the base stations, the SON optimization strategy of MLB and MRO joint optimization and the RRM optimization strategy of proportional fair scheduling are adopted, so that users of the service hot spot can be accessed to surrounding base stations more uniformly, and performance indexes such as CDBR of the users can be improved quickly and stably.
2) And when the service hotspot is positioned at the edge of a certain base station/cell, the capacity of the service hotspot can be improved by adopting an SON (self-organizing network) optimization strategy optimized by MLB (multi-level radio network) and an RRM (radio resource management) optimization strategy of the maximum throughput rate, so that the performance indexes of CDBR (capacity-based resource recovery ratio) and the like of the user are improved.
3) And when the service hotspot is not in the overlapping area between the base stations and is not positioned at the edge of a certain base station/cell, the coverage and the capacity of the service hotspot can be improved by adopting the SON optimization strategy of CCO joint optimization and the RRM optimization strategy of proportional fair scheduling, so that the performance indexes of CDBR (capacity-based resource recovery ratio) and the like of the user are improved.
The signaling flow of SON and RRM cross-layer optimization is described in detail below with reference to fig. 4 of the specification:
the SON optimization strategy makes full use of large-scale statistical data (such as service distribution, SINR distribution, and the like) of the network, ensures network performance optimization brought by wireless parameter adjustment with large time granularity, and ensures that the optimization effect brought by wireless parameter adjustment with small time granularity is consistent with the SON optimization strategy through a matched RRM optimization strategy. The signaling procedure of RRM cross-layer optimization, as shown in fig. 4, specifically includes the following steps:
1) And each base station counts the service distribution of the users served by the base station, counts network performance indexes such as coverage performance and capacity performance and reports the network performance indexes to the SON controller.
2) And the SON controller performs anomaly detection according to the wireless big data reported by each base station, and determines a cross-layer optimization strategy, namely an SON optimization strategy and an RRM (radio resource management) optimization strategy, according to the anomaly type and the network state.
3) And the SON controller optimizes according to the SON optimization strategy and determines the wireless parameters and corresponding values to be adjusted.
By taking CCO joint optimization as an example, the coverage performance index and the capacity performance index are maximized by optimizing the antenna downtilt angle and the power of the base station which needs to participate in optimization, as shown in formula (1):
in the formula (1), the reaction mixture is,
θ * ,P * vectors, omega, of the horizontal tilt angle, vertical tilt angle and power budget of the antenna to be adjusted, respectively, for the base station to be involved in the optimization i The weight for the coverage performance of base station i is typically 0.5.Cov i And Cap i The distributions are determined by equations (2) and (3), respectively, for the coverage performance index and the capacity performance index of base station i:
Cap i ＝∑ k log 2 (1+SINR i,k ) (2)
Cov i ＝quantile(log 2 (1+SINR i,k ),α％) (3)
SINR in equations (2) and (3) i,k The signal-to-interference ratio for user k, base station i, is determined by equation (4):
in the formula (4), P i,k And h i,k The transmission power allocated to user k by base station i and the gain of the radio channel from base station i to user k are respectively, j is the adjacent base station which generates interference to user k, and σ is noise.
The wireless channel gain is composed of transmission data path loss, channel shadow fading and antenna gain, and is shown in formula (5):
the antenna gain in equation (5) is determined by equation (6):
g in the formula (6) m The front-to-back ratio of the antenna can be set to 25dB;
and G V (θ) gains for the horizontal antenna mode and the vertical antenna mode, respectively, are determined by equations (7) and (8), respectively:
in formulae (7) and (8)
Respectively, a horizontal half-power beamwidth (which may be set at 70 degrees) and a vertical half-power beamwidth (which may be set at 10 degrees). Theta in the formula (7) etilt And SLL v The downward inclination angle (which can be set to 15 degrees) and the maximum inclination angle of the electrically-adjusted antenna are respectivelySmall sidelobe attenuation (can be set at 20 dB).
Therefore, by adjusting the antenna tilt angle and power of each base station, the optimum of coverage and capacity can be obtained.
4) The SON controller sends the wireless parameters to be adjusted and corresponding values and corresponding RRM optimization strategies to the base stations to be optimized;
5) And the base station needing to participate in optimization adjusts corresponding wireless parameters and updates a corresponding RRM optimization strategy for scheduling and resource allocation.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A wireless network optimization method based on SON and RRM joint optimization is characterized by comprising the following steps:
acquiring performance indexes and running state information in the running process of a wireless network through an SON (self-organizing network) controller;
when the network abnormity is detected, determining an SON optimization strategy according to the type of the network abnormity;
according to the SON optimization strategy, matching a corresponding RRM optimization strategy;
refining network abnormity according to the SON optimization strategy and the RRM optimization strategy, and then determining a joint optimization strategy;
wherein, after refining the network anomaly according to the SON optimization strategy and the RRM optimization strategy, determining a joint optimization strategy includes:
for network abnormality of a service hotspot, different joint optimization strategies are adopted, which specifically comprise:
when the service hot spot is positioned in an overlapping area between the base stations, a combined optimization strategy of mobile load balancing and mobile robustness optimization and an RRM optimization strategy of proportional fair scheduling are adopted;
when a service hotspot is positioned at the edge of a certain base station or cell, an SON optimization strategy of mobile load balancing optimization and an RRM optimization strategy of maximum throughput rate are adopted;
and when the service hot spot is not in the overlapping area between the base stations and is not positioned at the edge of a certain base station or cell, an SON (self organizing network) optimization strategy of coverage capacity joint optimization and an RRM (radio resource management) optimization strategy of proportional fair scheduling are adopted.
2. The method of claim 1, wherein the SON and RRM joint optimization-based wireless network optimization method is further characterized in that,
the acquiring of the performance index and the running state information in the running process of the wireless network through the SON controller includes:
during the operation of the wireless network, the SON controller continuously collects and counts the performance index and the operation state information of the network;
when the network abnormality is detected, adjusting the configuration of corresponding network parameters;
specifically, the network abnormality condition includes: the number of the radio link failures of the base station is larger than a preset threshold, or the signal to interference plus noise ratio of the user in a preset worst range is smaller than the preset threshold, or the user satisfaction is smaller than the preset threshold, or the call drop blocking rate is larger than the preset threshold.
3. The method of claim 1, wherein the determining a SON optimization strategy according to the type of the network anomaly when the network anomaly is detected comprises:
for network abnormity of coverage deterioration, performing coverage optimization by adjusting the downward inclination angle of the base station antenna, or performing coverage capacity joint optimization by adjusting the downward inclination angle and power of the base station antenna;
for network abnormality of network hotspots, adopting an optimization strategy of mobile load balancing, and carrying out cell breathing by adjusting the power of a base station, so that scattered service hotspots are accessed to the same base station; or, a combined optimization strategy of mobile load balancing and mobile robustness optimization is adopted, so that users with service hotspots are dispersedly accessed to different base stations; or an optimization strategy of coverage capacity joint optimization is adopted to reduce the influence of the service hot spots.
4. The method of claim 3, wherein the matching the corresponding RRM optimization strategy according to the SON optimization strategy comprises:
for coverage optimized SON optimization strategies, the matched RRM optimization strategy is to maximize throughput;
for the optimization strategy of the coverage capacity joint optimization, the matched RRM optimization strategy is proportional fairness;
for the optimization strategy of mobile load balancing, the matched RRM optimization strategy is the maximized throughput rate;
for the combined optimization strategy of mobile load balancing and mobile robustness optimization, the matched RRM optimization strategy is proportional fairness.
5. A wireless network optimization method based on SON and RRM joint optimization is characterized by comprising the following steps:
after counting the service distribution and network performance indexes of the users of the service, the base station reports the service distribution and network performance indexes to the SON controller;
the SON controller carries out anomaly detection according to wireless big data reported by each base station, and determines a cross-layer optimization strategy according to an anomaly type and a network state, wherein the cross-layer optimization strategy comprises an SON optimization strategy and an RRM optimization strategy;
the SON controller optimizes according to the SON optimization strategy, and determines wireless parameters needing to be adjusted and corresponding values;
the SON controller sends wireless parameters to be adjusted and corresponding values and corresponding RRM optimization strategies to base stations needing to participate in optimization;
the base station needing to participate in optimization adjusts corresponding wireless parameters, and updates a corresponding RRM optimization strategy for scheduling and resource allocation;
wherein, the determining the cross-layer optimization strategy according to the abnormal type and the network state comprises:
for network abnormality of a service hotspot, different joint optimization strategies are adopted, which specifically comprise:
when the service hot spot is positioned in an overlapping area between the base stations, a combined optimization strategy of mobile load balancing and mobile robustness optimization and an RRM optimization strategy of proportional fair scheduling are adopted;
when a service hotspot is positioned at the edge of a certain base station or cell, an SON optimization strategy of mobile load balancing optimization and an RRM optimization strategy of maximum throughput rate are adopted;
and when the service hot spot is not in the overlapping area between the base stations and is not positioned at the edge of a certain base station or a certain cell, adopting an SON (self-organizing network) optimization strategy of coverage capacity joint optimization and an RRM (radio resource management) optimization strategy of proportional fair scheduling.
6. The method of claim 5, wherein the network performance indicators comprise coverage performance and capacity performance.
7. The method of claim 5, wherein the SON controller performs optimization according to a SON optimization strategy to determine the radio parameters and corresponding values to be adjusted, and the method comprises:
the antenna downward inclination angle and power of a base station needing to participate in optimization in the joint optimization of the coverage capacity; or
The combined optimization of mobile load balancing and mobile robustness optimization requires handover parameters of the base stations participating in the optimization, including offset, hysteresis, and trigger time.
8. The method of claim 5, wherein the RRM optimization strategy determined by the SON controller comprises maximizing throughput or proportional fairness.
9. A wireless network optimization system based on SON and RRM joint optimization is characterized by comprising a base station and an SON controller;
wherein the base station is configured to:
after counting the service distribution of the users of the service and the network performance index, reporting to the SON controller;
adjusting corresponding wireless parameters, and updating corresponding RRM optimization strategies for scheduling and resource allocation;
the SON controller is configured to:
performing anomaly detection according to wireless big data reported by each base station, and determining a cross-layer optimization strategy according to an anomaly type and a network state;
optimizing according to the SON optimization strategy, and determining wireless parameters and corresponding values to be adjusted;
sending wireless parameters to be adjusted and corresponding values and corresponding RRM optimization strategies to base stations needing to participate in optimization;
wherein, the determining the cross-layer optimization strategy according to the abnormal type and the network state comprises:
for network abnormality of a service hotspot, different joint optimization strategies are adopted, which specifically comprise:
when the service hot spot is positioned in an overlapping area between the base stations, a combined optimization strategy of mobile load balancing and mobile robustness optimization and an RRM (radio resource management) optimization strategy of proportional fair scheduling are adopted;
when a service hotspot is positioned at the edge of a certain base station or cell, an SON optimization strategy of mobile load balancing optimization and an RRM optimization strategy of maximum throughput rate are adopted;
and when the service hot spot is not in the overlapping area between the base stations and is not positioned at the edge of a certain base station or a certain cell, adopting an SON (self-organizing network) optimization strategy of coverage capacity joint optimization and an RRM (radio resource management) optimization strategy of proportional fair scheduling.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104584616A (en) * | 2012-08-24 | 2015-04-29 | 埃克提克斯有限责任公司 | Method for joint and coordinated load balancing and coverage and capacity optimization in cellular communication networks |
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| CN103999501A (en) * | 2014-03-10 | 2014-08-20 | 华为技术有限公司 | System structure of self-organizing network and optimizing method |
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