Neighbour List Optimization for Real LTE Radio Networks - IEEE Xplore

APWiMob 2014, Bali 28-30 Augustus 2014

Neighbour List Optimization for Real LTE Radio Networks D. Duarte∗† , P. Vieira∗† , A. Rodrigues∗‡ , A. Martins§ , N. Oliveira§ , L. Varela§ ∗ Instituto

de Telecomunicaço˜ es (IT), Lisbon, Portugal Superior de Engenharia de Lisboa (ISEL), ADEETC, Lisbon, Portugal ‡ Instituto Superior Técnico (IST), Lisbon, Portugal § CELFINET, Consultoria em Telecomunicaço ˜ es Lda., Lisbon, Portugal

† Instituto

Abstract—With the increasing complexity of current networks, it became evident the need for Self-Organizing Networks (SON), which aims to automate most of the associated radio planning and optimization tasks. Within SON, this paper aims to optimize the Neighbour Cell List (NCL) for Long Term Evolution (LTE) evolved NodeBs (eNBs). An algorithm composed by three decisions were were developed: distance-based, Radio Frequency (RF) measurement-based and Handover (HO) stats-based. The distance-based decision, proposes a new NCL taking account the eNB location and interference tiers, based in the quadrants method. The last two algorithms consider signal strength measurements and HO statistics, respectively; they also define a ranking to each eNB and neighbour relation addition/removal based on user defined constraints. The algorithms were developed and implemented over an already existent radio network optimization professional tool. Several case studies were produced using real data from a Portuguese LTE mobile operator. Index Terms—Wireless Communications, Self-Organizing Networks, LTE, Neighbour Cell List, Optimization;

I. I NTRODUCTION With the already existent 3𝑟𝑑 Generation (3G) and 4𝑡ℎ Generation (4G) standards, especially with LTE [1], more complexity is added to current networks. The co-existing of multiple standards, mostly from different suppliers, and the increasing demand of 3𝑟𝑑 party services, asks for more management effort from mobile network operators. The need for even higher data rates, as well as new and improved services while being mobile have been drivers for the standardization work of the 4G LTE. The recent deployment of LTE to address the growing data capacity crunch, has highlighted the need and value of self-organizing capabilities within the network that enable reductions in Operating Expense (OpEx), during deployment as well as during continuing operations. SON improve network performance, but in no way replace the wireless industry’s important need for more spectrum to meeting the rising mobile data demands from subscribers. 3𝑟𝑑 Generation Partnership Project (3GPP) initiated the work towards standardizing selfoptimizing and self-organizing capabilities for LTE, in Release 8 and Release 9. The standards provide network intelligence, automation and network management features in order to automate the configuration and optimization of wireless networks to adapt to varying radio channel conditions, thereby lowering costs, improving network performance and flexibility [2].

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This paper is organized as follows. Section II overviewing the NCL. Section III presents the proposed algorithm, followed by the simulation framework presented in Section IV. In Section V, the research results and its analysis are presented. Finally, conclusions are drawn in the final section. II. N EIGHBOUR C ELL L IST The main goal for SON implementation is to automate most of common planning, optimization and operational tasks. Within SON and its self-optimization, there is one main feature which will be developed under the paper scope: NCL optimization. NCL optimization pertains to the optimization of the existing neighbour list of a LTE cell plus the neighbour lists that are applied by the neighbouring cells. This applies to cells that are already in a production environment, as well as to new cells, for which completely new neighbour lists are to be generated. The target scope covers intra-frequency, interfrequency and inter-systems neighbours. HO is one of the most critical issues in cellular networks. It enables connection continuity for mobiles during move, while allowing the efficient use of resources, like time and frequency reuse between cells. Most of today’s cellular standards use mobile-assisted handover in which the mobile measures the signal quality of neighbour cells and reports the measurement result to the network. If the signal quality from a neighbour cell is better than that of the serving cell by a handover margin, the network initiates a HO to that cell [3]. The configuration and NCL management is one of the most important issues to provide seamless mobility of User Equipments (UEs) in LTE systems. Between handover candidate cells, each eNB has a connection of direct interface with other eNBs and messages related to HO are exchanged through the interface in order to prepare HO execution. Each eNB maintains a list of cells that are the targets for connecting the interface. In LTE without SON mode of operation, a human system operator determines NCL and uploads it into the eNB. On the other hand, in LTE systems under SON mode of operation, also called Automatic Neighbour Relation (ANR), each eNB is supposed to configure and manage the NCL autonomously. First, the initial NCL has to be configured without any cooperation with UEs when a new eNB is deployed because, initially, there are not any activated UEs belonging to the new eNB.

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Second, the NCL needs to be constantly updated based on the change of the network topology by collecting the information from UEs [4]. The basic condition of defining a neighbour relation between cells is the coverage overlap with each other, and forming a HO region, as presented in Fig. 1. In a sectorized cell environment, sector cells belonging to the same eNB are neighbours. The measurements reported by the UE, such as Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) are then used by an optimization algorithm implemented in the eNB in order to continuously update the list of neighbouring cells.

Cluster Site NCL Initialization

Dist ance-based Decision

HO Stats-ba sed Decision

RF Measurements Decision

Weighted Decision

Engineer Analysis

NCL Valid ?

F

T

Manual Adjustments

NCL Optimized

Fig. 2. Proposed algorithm flowchart. Fig. 1. Neighbour relation.

It is important to note that a delay is introduced when HO is attempted into a neighbour cell that is not included in the NCL. If the NCL includes any cells which are not supposed to be the neighbour cells, the system overhead is wasted due to unnecessary management and maintenance of direct interface between neighbour cells. Therefore, with or without ANR, accurate NCL configuration is demanded to prevent HO delay and save the system overhead [4], which motivates the current research issue.

is created the first tier, considering the antenna orientation (azimuth). The last two algorithms consider RF measurements and HO statistics, respectively; they also define a ranking to each eNB considering several calculation constraints introduced by the user in the tool interface. Thus, the final goal is to prepare a global ranking for each eNB considering the three algorithms, simultaneously. The output will be an optimized NCL that maximizes the HO target, based on several aspects of the implemented network, and user constrains. B. Distance-Based Decision

III. T HE P ROPOSED A LGORITHM This section presents the proposed algorithm. A description of the algorithm is made and it is presented the line of thought used in NCLs’ configuration/optimization. A. Algorithm Overview The NCL optimization flow chart is presented in Fig. 2. The network topology is one of the inputs. It contains the eNB geographic location, antenna configuration and orientation, as well as the cell identifiers and Physical Cell IDs (PCIs). Another considered input is the initial NCL configuration of each eNB, with all the defined neighbour relations. Finally, trace and drive-test data are available (e.g. RSRP level mapping), along with Performance Management (PM) statistics, focusing on the number of HOs for each defined neighbour relation. The initial NCL is the starting point, if it exists. In order to optimize the NCL, three distinct algorithms were developed, which will be described in the following: distance-based, measurement-based and HO stats-based algorithms. In the distance-based algorithm, a ranking is attributed to each eNB based on the distance to the source sector and it

It is based on the distance from source to the target cell and antenna orientation. After processing the network topology in the database, the eNB sector information is available. Hence, the distance between cells is calculated and one proximity ranking is defined for each target cell, simply calculated by the inverse of the distance to the source cell. Thereafter, the several tiers (rings) are defined, using classical hexagonal radio planning theory, where the six closer sites are considered as the first tier. The first tier and eNB adjacent sectors are mandatory, and must appear in the new NCL. This calculation is based in source/target cell orientation (azimuth). The addition of the target sectors to the NCL should follow the scheme illustrated in Fig. 3 as the rules in Table I. First, the source sector orientation is verified by its azimuth. Assuming a source sector belonging to the first quadrant (1𝑠𝑡 Q), i.e., between 0∘ and 90∘ , every target sectors directed there, must be added to the NCL. For the remaining quadrants, only those sectors directed to the source cell area service (in this case, the 1𝑠𝑡 Q), are added to NCL. In order to know the position of each target cell, i.e., its quadrant, it is calculated the

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2ºQ

D. RF Measurements-Based Decision

1º Q

Finally, the RF Measurement-based decision adds or removes target cells to the new NCL, if its RSRP level is above/below a certain threshold, respectively. The algorithm uses the source eNB’s corresponding measurements and, for each “bin”, checks the RSRP for the target cells. If this level is above 𝑅𝑆𝑅𝑃𝑚𝑖𝑛 , it will be assigned a priority raking based on the difference between target/source RSRP levels. The cells with lower difference, will have a higher priority, and are HO candidates. 3ºQ

IV. S IMULATION F RAMEWORK

4º Q

Fig. 3. The quadrant theory. TABLE I T HE QUADRANT THEORY BOUNDS . Target Quadrant Location

Target Relative Location

Eligible Target Sector

[0∘ ;180∘ ] [0∘ ;90∘ ] [0∘ ;90∘ ] & [270∘ ;60∘ ]

Opposite vertical Opposite horizontal, vertical Opposite horizontal

2 3 4

angle between cells, by own location and using its geographic coordinates. Then, a new ranking value is added, according to several distance intervals, in order to establish addition priorities. The rank is attributed according to Table II where, 𝑠𝑡𝑒𝑝 =

𝑚𝑎𝑥 (𝑑𝑖𝑠𝑡) − 𝑚𝑖𝑛 (𝑑𝑖𝑠𝑡) 4

This paper was developed in partnership with the company Celfinet (Portuguese Telecom Consulting Firm). The software platform in which the project was developed is R , a Celfinet’s radio network optimization named Vismon⃝ R ) application that interacts between a database (SQL Server⃝ R ⃝ and visualization tool (Google Earth ). This is a already existent tool, it has been developed in C# language and runs based on several data tables, which work as inputs. In these tables, among other information, can be found the topology of existing radio networks, drive test data and network scans. These data tables were the most used in the implementation R is divided into of the NCL algorithms. Generally, Vismon⃝ six tabs, each with different functionalities. R The user environment improvement in the Vismon⃝ is illustrated in Figure 4.

(1)

TABLE II M APPING BETWEEN DISTANCE VALUES AND RANKING . Distance [km]

Rank

𝑚𝑖𝑛(𝑑𝑖𝑠𝑡) 𝑚𝑎𝑥(𝑑𝑖𝑠𝑡) − 3 × 𝑠𝑡𝑒𝑝 𝑚𝑎𝑥(𝑑𝑖𝑠𝑡) − 2 × 𝑠𝑡𝑒𝑝 𝑚𝑎𝑥(𝑑𝑖𝑠𝑡) − 𝑠𝑡𝑒𝑝 𝑚𝑎𝑥(𝑑𝑖𝑠𝑡)

5 4 3 2 1

R Fig. 4. Neighbour cell list optimization feature in Vismon⃝ .

The user can select an intra or inter-frequency analysis. In the intra case, all cells with different frequencies are placed with rank zero and, therefore, not being considered. Finally, sectors of higher ranking are added, only limited by the maximum NCL size, if defined by the user. C. HO Stats-Based Decision The HO stats-based decision aims to keep or remove cells belonging to the initial NCL. If the existing number of HOs in the neighbour relation is below a certain threshold, (introduced by the user) the cell is removed. This is useful since allows to remove cells with low HO count from the source eNB. Absolute HO count and relative HO count (relative to total HO number) analysis is available.

V. S IMULATION R ESULTS & A NALYSIS This section presents the main research results considering neighbour cell list optimization. In the sequence, the algorithm performance and obtained results are analysed. In Figures 5 to 10 (output of the algorithm in Google R ), the source sector is shown in blue. The sectors which Earth⃝ are added to the NCL are marked in green, since they are not in the initial list. The sectors which already are in the initial NCL and are kept are in yellow. The red sectors represent removed cells from the initial NCL. Finally, the sectors which are not included in the calculations, are shown in purple colour, and named the “do not add”. It is important to note that the eNBs with two “triangles” in each sector have two different

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carriers. When intra-scenario is considerer, the eNBs with other frequencies are coloured in grey. In order to test the algorithm performance, two scenarios were created and compared: intra-frequency and interfrequency. In this paper, the RF measurements-based decision was not considered. The NCL optimization algorithm was tested in a site located in Porto, Portugal, named “5 de Outubro”, sector 0. The inputs for optimization were: eNBs located in a distance lower than the second tier average distance (distance-based decision) and eNBs with, at least, 3 HO from the source cell (HO stats-based decision). For a better understating, the algorithm performance is studied by steps. Initially, the distance-based decision is analysed, followed by the HO stats-based decision. Finally, the final NCL is examined. Figures 5 and 6 illustrate the distance-based decision applied in scenario considering inter-frequency and intra-frequency, respectively. Table II summarize the number of added, kept and removed cells from the original NCL.

TABLE III D ISTANCE - BASED DECISION APPLICATION STATISTICS .

Inter-frequency Inter-frequency

Added

Kept

Removed

15 17

19 8

8 2

III we can note that there is a notable difference in the number of kept and removed cells in the NCL when compared with the original NCL. Figures 7 and 8 illustrate the HO stats-based decision applied in scenario considering inter-frequency and intrafrequency, respectively. Table IV summarize the number of added, kept and removed cells from the original NCL.

Fig. 7. HO stats-decision considering inter-frequency scenario.

Fig. 5. Distance-based decision considering inter-frequency scenario.

Fig. 8. HO stats-decision considering intra-frequency scenario.

TABLE IV HO STATS - BASED DECISION APPLICATION STATISTICS .

Fig. 6. Distance-based decision considering intra-frequency scenario.

It can be seen from Figures 5 and 6 that only the cells in the second tier and located in quadrants in accordance with the quadrants theory were added to the new NCL. From Table

186


Added

Kept

Removed

0 0

6 6

21 4


It is important to note that this step only keep or remove cells. Therefore, the HO stats-based decision not add new cells in the NCL. If the user selects a minimum of relative HO count, all cells presented in the initial NCL which have an HO count lower than the selected value from source cell, are removed. In the inter-frequency scenario the algorithm removed 21 sites, which means that, actually, a small number of sites support the traffic generated by the source cell. From Fig. 7 and contrary to expectations, the algorithm kept the site located behind the studied cell. This means that the most of traffic is supported not by the site installed in front of the studied site. On the other hand, in the intra-frequency scenario, the kept sites are located in the site covered area. Interestingly, the number of sites with a number of HO higher than 3 is the same for both scenarios: 6. Figures 9 and 10 illustrate the considered scenario after the NCL optimization considering inter-frequency and intrafrequency, respectively. Table V summarize the number of added, kept and removed cells from the original NCL.

Fig. 9. Optimized NCL considering inter-frequency scenario.

TABLE V NCL OPTIMIZATION ALGORITHM STATISTICS .


Added

Kept

Removed

15 17

15 8

12 2

NCL. Making a detailed analysis, we can observe that the added cells should be in the NCL. In the initial NCL, the most of sites located in front of the studied site were not in the NCL list. Observing Table V, we can conclude that the number of kept cells is higher in the inter-frequency scenario than in the intra-frequency scenario. Regarding to the removed cells, in the inter-frequency scenario, the algorithm removed a considerable number of sites. From Fig. 9, it is possible to observe that the most of these sites were located outside of the theoretic covered area. VI. C ONCLUSIONS In LTE, accurate NCL configuration is demanding, to prevent HO delay and save the system overhead. In this paper, a NCL optimization algorithm for the LTE network is proposed. Three distinct algorithms were developed: distance–based, measurement–based and HO stats–based. The distance-based algorithm, proposes a new NCL taking account the eNB location and interference tiers, based in the quadrants method. The last two algorithms consider signal strength measurements and HO statistics, respectively; they also define a ranking to each eNB enabling neighbour relation addition/removal based on user defined constraints. The algorithms were implemented over an already existent radio network optimization professional tool. Several case studies were produced using real data from a Portuguese LTE mobile operator. These algorithms should be combined, in order to produce a global optimized NCL that maximizes the HO performance, based on network specifics, and user constrains. VII. ACKNOWLEDGEMENTS The authors would like to thank Vodafone Portugal for the field and network measurements and to PEstOE/EEI/LA0008/2013 project for the support. R EFERENCES

Fig. 10. Optimized NCL considering intra-frequency scenario.

From Figures 9 and 10 and Table V, we can conclude that the algorithm added a considerable number of sites in the new

[1] 3GPP, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2,” 3rd Generation Partnership Project (3GPP), TR 36.300, Jul. 2010. [2] “Self-Optimizing Networks in 3GPP Release 11: The Benefits of SON in LTE,” 4G Americas, Tech. Rep., October 2013. [3] V. M. Nguyen and H. Claussen, “Efficient self-optimization of neighbour cell lists in macrocellular networks,” in Personal Indoor and Mobile Radio Communications (PIMRC), 2010 IEEE 21st International Symposium on, Sept 2010, pp. 1923–1928. [4] D. Kim, B. Shin, D. Hong, and J. Lim, “Self-Configuration of Neighbor Cell List Utilizing E-UTRAN NodeB Scanning in LTE Systems,” in Consumer Communications and Networking Conference (CCNC), 2010 7th IEEE, Jan 2010, pp. 1–5.

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