Statistics-Based SON (Self-Organizing Network) Optimization

Transcription

1 Abstract Statistics-Based SON (Self-Organizing Network) Optimization Cheng Huang, Yongbo Lv School of Traffic and Transportation, Beijing Jiaotong University, Beijing , China SON is the key means of optimizing the mobile access network (MAN), and the basis for SON mobile parameters adjustment is the current MAN manifestation. This paper aims to improve the SON parameter adjustments, and proposes one statistics-based SON parameter adjustment method, using more effective basis for mobile parameters adjustment; the new algorithm of simulation manifestation can improve SON overall performance. Keywords: SON, Mobile Network, Optimization. 1. INTRODUCTION As the mobile communication become popular, more people prefer to make communication by the mobile means. However, the mobile communication also has some problems such as too much uncertain factors in information channels, uncertain and complex cell edge-environment and changeable terminal handover Therefore, it is very important to make optimized configuration of mobile parameters. As one key method for optimization, SON (Selforganized Network) can improve the mobile communication network and reduce labour cost by auto-configuration and optimization. Now the manifestation of current mobile network is mainly the basis for SON adjusting mobile parameters, e.g. when the mobile handover indicators not good, SON shall make checks and adjust the parameters accordingly, which can be called as mobile communication network manifestation-based method. Actually there is also some other basis for parameter adjustment. i.e. the statistics-based adjustment method to be introduced in this paper. This method includes some new statistics data such as the traffic condition in that area, which is more accurate and effective, after abandoning the manifestation-based method. 2. SYSTEM MODEL Firstly analyse the system model of SON, which was divided into two phases for optimization: 1. Initial phase. In this phase, the initial configuration parameters were all default values, so it was necessary to keep searching and finding the proper parameter configuration, which was often applied in the new station. 2. Normal operation phase. In this phase, the proper parameters have been found, but due to periodic changes of outer environment. they had to be adjusted regularly. E.g. as traffic flow changes, the handover threshold needs to be adjust: in traffic jam, use higher handover threshold; in smooth traffic, use lower handover threshold. Thus, the following formula is concluded: {env, user_act} param (1) Env means constant environment; user_act: users action; param: mobile parameters In initial phase, with the corresponding parameters of these two factors above uncertain, the tentative adjustments had to be made; but in normal operation phase, it was clear for these parameters; therefore, the only thing that we could do was to find the best parameters under the same user action. 848

2 3. STATISTICS-BASED SON METHOD The following thing was to determine user actions, which can be made on the basis of the statistics information collected by mobile network, and other means (traffic information, holiday information etc.). stat info means statics information stat_info user_act (2) Table 1 Common Statistical Information Table Information Purpose Traffic flow To determine user mobile speed and user number Holiday or not To determine user number, and mobile speed etc. Climate condition To determine user number, mobile speed, signal quality etc. User Number of cells To determine mobile speed and signal quality etc. User Number in the neighbouring cells To determine mobile speed and signal quality etc. All these statistical parameters can be used to determine some users act, and it depends on actual applications for the selection of statistical parameters. 4. CASE STUDY For better introduction, take MRO for example. As one key function of SON, MRO was used to solve the issue that lots of labour were required for wireless parameter configuration, i.e. MRO can be used to make optimization rather than the manpower. It has become very important wireless communication feature. In fact, there have been some researches about MRO, such as normal behaviour model-based algorithm, and MRO/MLB conflict control algorithm etc., which had one common feature: using HPI (e.g. handover failure rate) as the basis for parameter adjustment which was made in fixed step size. It can be found above that the mobile parameter configuration was optimized step by step on the basis of user action changes. Specifically, adjust firstly the parameters, and then observe the indicator; if it gets better, keep adjusting in this way, otherwise adjust them reversely until certain pre-set standards are met (e.g. the failure rate for whole handover less than 10%). However, in most time, user action may change suddenly. Using the method above to gradually approach the better parameters shall take several or dozens of cycles of MRO; but if the parameters were properly adjusted finally, the user acts may change again. It was very hard to get proper wireless handover parameters, however, in the method of shortening the user cycle, MRO parameters might be influenced accidently sometimes, typically, the traffic jam, in which the user act can change very quickly, therefore, the current algorithm cannot satisfy such requirements. By analysis above, the author proposes the MRO algorithm based on traffic flow. Several related concepts are shown as follows: HPI: Handover performance indication. E.g. too-early handover rate,too-late handover rate HOP: Handover parameter, such TTT. CIO and HYS HPI was determined by many factors such as HOP, the current environment, and user act. Since the environment shall not change after initial adjustment normally, only user act was considered here. With HPI as the only basis for adjustment, it cannot ensure the specific value of better parameters, and HPI only showed the possible way to make adjustments. Note that lots of failed handovers were caused by various reasons possibly, which may be in reverse direction mutually, typically, the too-late and too-early handover. HPI only showed whether the user act matched with HOP. Besides the handover failure rate, some other information could also be used to predict user action, e.g. number 849

3 affic flow of road Revista de la Facultad de Ingeniería U.C.V., Vol. 32, N 15, pp , 2017 of users, user GBR volume of business, user duration in one cell, and the traffic conditions in the most neighbouring main road etc. these statistical parameters were all the key indicators for predicting the users actions directly, and also could help to make adjustments easily by searching related records in the previous adjustment database and network management suggestions. But with too many changes, it was necessary to find proper HOP by some artificial intelligence algorithm. This paper uses these indicators to make one multi- decision table for searching, which can help to speed up searching, but occupies more memory, then by dividing these indicators into various sections, the approximate comp utation can be made in total differentiation method. Refer to the pseudocodes based on statistics SON optimization in the following: Table 2 Pseudocodes Based on Statistics SON Optimization Algorithm Pseudocode 1 IsTraditionMROOnceUse of all = FALSE 2 Initiate access table 3 Initiate HOPs by default values 4 WHILE TRUE THEN 5 collect HPIs 6 IF MRO period is reached THEN 7 search for HOPs by stat_info in access table 8 IF there is a record And IsTraditionMROOnceUse of the step is FALSE 9 THEN 10 replace HOPs 11 IsTraditionMROOnceUse of the step = TRUE 12 ELSE 13 traditional algorithms optimize 14 IF the HPIs is better than the record THEN 15 update the record 16 IsTraditionMROOnceUse of the step = FALSE END WHILE 5. SIMULATION TEST The chapter introduces the simulation test for verifying the effect of new algorithm. By the means of discrete event simulation, this test made modelling of user act, information channel environment and mobile access network etc. so as to have real effect. 6. SIMULATION ENVIRONMENT For the statistical indicators, use the traffic flow from the traffic investigation in one real main road in Beijing as follows: traffic flow 0 00:00 00:00 00:00 time traffic flow Figure 1. Traffic Condition Besides the traffic changes, there are some other importance parameters as follows: 850

4 Table 3 Simulation Parameter List Parameter Number of cells Carrier frequency Transmitting power Shadowing Standard deviation MRO cycle Value 10 cells 2.6 GHz 40W 8dB 1 hour These simulation parameters come from real LTE mobile network. In China, 2.6G is the main carrier frequency of 4G mobile communication; 40w transmitting power is also common for mobile communication. 7. SIMULATION RESULTS Refer to the simulation results as follows. The figure depicts that the statistics-based SON algorithm has better effect than mobile network, and much better than MRO algorithm. The total wrong handover rate dropped roughly by 30% from NO MRO, and by 10% from traditional MRO. Also, the statistics-based SON algorithm can ensure to make the best parameter configuration as quickly as possible. (a) The result of no mro (b) The result of traditional mro 851

5 (c) The result of new mro Figure 2. Figure Simulation Result Figure 3. Comparison of wrong handovers 8. CONCLUSION The study shows that the statistics-based SON optimization can ensure better operation of mobile communication. In normal operation phase, mobile parameters can realize the best configuration in one SON cycle, while the traditional algorithm with mobile network manifestation-based method needs more SON cycles. In future, there will be more studies to be made, such as the selection of these statistical parameters. REFERENCES 3GPP. (2009). 3rd Generation Partnership Project, Technical Specification GroupRadio Access Network, Evolved Universal Terrestrial Radio Access (E-UTRA) Radio Resource Control (RRC), Protocol specification (Release9), Technical Report TR v9.1.0, available at 3GPP. (2009). Self-configuring and self-optimizing network use cases andsolutions, TR , Sophia- Antipolis, France, Tech. Rep. 3GPP. Self-configuring and self-optimizing network use cases and solutions, Technical Report TR , available at J. Turkka, A. Lobinger. (2010). Non-regular Layout for Cellular Network System Simulations, PIMRC 2010, Istanbul, Turkey. SOCRATES. Self-optimisation and self-configuration in wireless networks, European Research Project, Thomas Jansen, Irina Balan, Szymon Stefanski, Ingrid Moerman. (2011). Weighted performance based handover parameter optimization in LTE, Vehicular Technology Conference (VTC Spring). W. C. Jake. (1974). Microwave Mobile Communications, Wiley. Zia N. (2014). A policy based conflict resolution mechanism for MLB and MRO in LTE self-optimizing networks, Computers and Communication (ISCC). 852