North American Outdoor Demand (Courtesy of SmallCell Forum)

Transcription

1 White Paper

2 Introduction Providing coverage and capacity to all subscribers, at every location and at any given time is one of the major challenges of every Mobile Network Operator (MNO) today. As the number of subscribers are continuously increasing, subscriber base is getting more dispersed and mobile networks are becoming increasingly complex, capacity planning to provide the required quality of service (QoS) at every location has become very complex, tedious and costly. Thus, a careful network and new site planning strategy is critical for MNOs. Subscriber usage and mobility patterns need to be harvested by crowdsourced methods and network traces. As depicted below in Small Cell Forum s Capacity Planning for HetNets paper 1, a street-bystreet and fine grained bin-wise collection of user data is crucial to understand where the major impact points emerge such as around commercial zones or special venues. This user data forms the basis for network planning correlated with financial data to realize better ROIs. North American Outdoor Demand (Courtesy of SmallCell Forum) In today s complex networks, new sites are being increasingly supplied by small cells as they usually provide more economic solutions to immediate capacity or coverage problems. Being easier to deploy than macro cells with lower site acquisition and installation costs, small cells are typically deployed in urban and dense urban locations to provide the needed coverage and capacity quickly. [1] Capacity Planning for HetNets : HotSpot Identification and HetNet Planning with uson TM 1

3 The MNOs need to determine the optimum location for new sites with the data about alreadydeployed sites and a collection of correlated financial and fault management data. Different deployment scenarios need to be modeled and compared in terms of cost, effectiveness and ROI to establish the best business case for the MNO. P.I. Works, with years of industry experience and powerful software products and algorithms, can help MNOs to identify hotspots and to cover them with the optimum network equipment combination. In this whitepaper, we briefly describe the method to achieve this using Small Cell / HetNet Planning feature which is part of P.I. Works flagship uson TM Centralized SON Solution Product Family. Challenges with Network Planning and Deployment Although there are automated tools available to MNOs to perform various tasks in network planning and deployment, there are still many challenges encountered on a daily basis that need to be tackled. The following sections detail small cell deployment as well as overall network planning challenges for MNOs. Small Cell Deployment Challenges Every small cell deployment creates its own set of unique challenges pertaining to areas and user profiles to be covered. However, there are common challenges with all deployments as outlined briefly below: Planning Determining the best site location for small cells is an important aspect in planning, as optimum locations lead to less investment (CapEx), less optimization needs (OpEx) and improved ROI. The most suitable locations for small cells can include lamp posts, phone booths or building facades which can make deployment more intricate than originally planned. Optimization Optimization of Small Cells include interference management between macro and micro layers especially for co-channel deployments as well as mobility parameter tuning for favoring small cells over macro cells where needed. When the number of small cells to be deployed is massive, the optimization of these cells is not an easy task and may require several iterations to achieve the best results. HotSpot Identification and HetNet Planning with uson TM 2

4 Backhaul Small Cell deployments need to ensure optimum use of network capacity, compensating for traffic increase, which can only be achieved by utilizing high capacity backhauls. Capacity deficits in the macro layer resulting from low spectral efficiency can be alleviated by deploying both co-channel or dedicated channel small cells with appropriate backhaul. In areas without appropriate backhaul infrastructure, small cell deployment is delayed or flawed. Correlation Selecting the best location for deployment requires correlation between user, network and business data as well as backhaul capacity where possible. The more data that is correlated, the more optimum the selected locations prove to be. Network Design Challenges The challenges imposed on the overall network design which are not very different from small cell deployment challenges can be summarized as below. Deferring overall CapEx Inevitably, the MNOs have to invest in network expansions; however, the extent to how the expansion should be planned and carried out varies greatly by different factors such as the equipment to be used, the optimization techniques used for already existing equipment and so on. For example, in a study conducted by P.I. Works, it is estimated that an average of 13 months of CapEx deferral is possible by deploying the uson TM Solution. HotSpot Identification and HetNet Planning with uson TM 3

5 Minimizing OpEx After the new site deployments are carried out, the operation and maintenance needs become the main challenge to the MNOs. Minimizing the operational expenses are possible by employing efficient optimization techniques and Self-Organizing Networks (SON) solutions. P.I. Works uson TM provides the largest OpEx savings in the planning and optimization expenses for the network. Improving ROI Improving Return of Investment (ROI) is possible by maximizing the revenue acquired from each site or cell launch. Using Geolocated Crowdsourced data, the MNOs are more clearly able to observe the main revenue generating areas, so that they can invest more in those hotspots. Deploying small cells to those hotspots will increase data offload ratio along with improving the overall throughput. Improving Subscriber Satisfaction Satisfying mobile users in this era of high mobility and throughput is not an easy goal to achieve considering the traffic patterns in today s hectic world. Each region imposes its own challenges such as coverage gaps in rural areas, whereas capacity deficits in dense urban areas. To improve subscriber satisfaction, data from Fault Management (FM), Ticketing and CRM systems need to be correlated, so that the areas which originate a high number of complaints can be effectively identified and acted upon. Increase ARPU Increasing ARPU involves reducing churn of subscribers by providing superior quality of services as well as capitalizing on high-value subscribers. This can be achieved by correlating financial and customer data with site planning data and optimization outputs. MNOs can deliver high quality service to those areas which can generate the most revenue from the highest number of high-value subscribers. HotSpot Identification and HetNet Planning with uson TM 4

6 High Precision HotSpot Identification for Cell Planning The main objective of cell planning is to find the optimum number of required base stations to achieve the desired KPIs like accessibility or mobility. After the quantification for the value offered by planned new sites are evaluated, the best Top-N site locations need to be identified to minimize CapEx and OpEx. In most cases, coverage gaps and capacity bottlenecks can easily be identified around certain hotspots typically near congested macro sites. Doing an initial analysis might yield a lot of different hotspots which are candidates for small cell offloading. The candidate locations need to be assessed to determine the best ones to provide the most value when a small cell is installed at that location. The assessment needs to consider different metrics to calculate the best overall benefit for new expenditure. In the sections below, we briefly describe the Geolocated Key Quality Index (GKQI) and how it is used in P.I. Works uson TM HetNet Planning to provide the best locations for macro offloading. HotSpot Identification and HetNet Planning with uson TM 5

7 GKQI Geolocated Key Quality Indicator To indicate the quality at a given location, algorithms are needed to gather the required inputs. In network planning, experience shows that the more input dimensions are gathered for computation of quality metrics, the more precise and efficient the computed metrics prove to be. For this purpose, uson TM gathers the following type of data to form a composite GKQI: Crowdsourced data is correlated with macro network data and KPIs acquired from individual cells. Any core network data that can be used such as CDRs or probe information is also used. Finally for each point, any financial data is also taken into account for revenue maximization. The GKQIs are initially calculated per grid from the KPIs obtained by the serving cells to those grids. Later on, the grids are compiled into clusters as it is more logical to work on clusterlevel since grid granularity is too high. Once the composite GKQI calculation is carried out on the clusters, they are sorted in descending order and filtered based on certain KPI thresholds that the MNOs monitor. The filters can also be used to apply priority conditions such as the number of complaints from VIP sites. Finally, the best-n locations are provided which can immediately be used to proceed on the next site planning effort. Process of Best-N HotSpot Location Selection HotSpot Identification and HetNet Planning with uson TM 6

8 Prioritized Ranking of HotSpot Locations The HotSpot candidate map is typically provided by the MNO as potential locations to materialize for revenue. These locations can be the areas which have relatively easy and cheap access to fiber backhaul, or locations where site acquisitions can be less costly. In either case, the MNOs might be tending to use a lot of small cells for new site planning; however ideally, the number of small cells to be deployed should also be minimized in order to reduce interference to the macro network and to neighboring small cells. Sample Candidate Locations for HotSpot Coverage In the example above where there are many candidate locations that are determined previously by the MNO, the GKQI computation also has to take into account the proximity of the candidate locations when calculating the GKQI for each cluster (or how many candidate locations the cluster covers if the cluster size is big). The final hotspot location selection needs to provide few number of locations at first, as deploying new equipment to these areas will change traffic patterns. After the initial deployment, new data needs to be collected and assessed again to identify the second set of locations as this process can go on iteratively until satisfactory network results are obtained. For example, in the area depicted in the above picture of approximately 30 km 2, after traffic density, throughput, data volume and similar KPIs are used for GKQI calculations, 12 locations for small cell offloading is initially produced. Some locations are selected from pre-defined hotspot locations such as phone booths: HotSpot Identification and HetNet Planning with uson TM 7

9 Prioritized selection of best N locations (N=12) More locations can also be suggested by lowering related KPI thresholds. This is left for MNOs to decide as their priority in ranking KPIs or determining the thresholds for each KPI might vary from time to time or from one geographical location to the other. Depending on seasonal network configuration changes in transmit power or antenna locations or changes in traffic patterns due to infrastructural advancement (i.e. new roads being built), hotspot locations might be changing frequently in accordance. This process continues iteratively with new cells being deployed and new data from crowdsourced platforms or performance management systems are available. HotSpot Identification and HetNet Planning with uson TM 8

10 Sample Case Study from European Tier-1 Operator P.I. Works recently conducted a study on the 3G network of a European operator where the following inputs were used in the GKQI calculations: Best Server and Signal Strength from Radio Planning Tools Signal Strength and Traffic from Geolocated Crowdsourced Data CBH Power Utilization, CBH Code Utilization and Macro Data Volume from NW KPIs Points of Interest (PoI) from site planning data Customer Complaints from CRM systems The aim is to identify the best site locations to maximize ROI while improving coverage quality and subscriber experience. In achieving this objective, the uson TM uses several steps to utilize the input, by first validating and normalizing the data, then weighting the data with respect to user defined importance levels and by computing clusters to collect different data points into meaningful regions to be utilized as hotspots. The search engine ranks and filters the candidates with respect to planned locations and other considerations such as type of backhauling or revenue models. The final result was an immediate deployment of 2 hotspots where the first one was on a busy university campus area whereas the other one was by the seaside where only 2G coverage was currently provided. This site was also to be shared with the already present 2G site. HotSpot Identification and HetNet Planning with uson TM 9

11 Conclusion P.I.Works uson TM Solution simplifies the network expansion process with a dedicated HotSpot Identification and HetNet Planning module, which can correlate a variety of data, including network KPIs, crowdsourced data from mobile devices as well as planning and financial data of MNOs. Using weights on bias metrics that can be configured easily on the GUI, best-n locations for new cell deployment can easily be identified. The benefits of the tool can be summarized as: High geographical precision for small planning at dense hot spot areas Better ROI on small cell investment through correlation of financial metrics Reduced cell edges and interference with macro layers by less small cell usages as only the minimum required N-locations can be pinpointed as opposed to general mass densification About P.I. Works P.I. Works, is a leading provider of next-generation Radio Access Network (RAN) management solutions. P.I. Works expertise in mobile network optimization, which spans over a decade, combined with the commercially available product portfolio and services, enables global Mobile Network Operators (MNOs) to improve network quality and subscriber experience, while increasing profitability. To date, P.I. Works has deployed its solutions for 38 MNOs in 27 countries. P.I. Works state-of-the art product portfolio, unified Self Organizing Networks (uson TM ), automates the optimization and operational tasks of complex mobile networks 24/7 to increase quality, capacity and coverage. For more information, please visit or send to sales@piworks.net Copyright 2016 P.I. Works, All Rights Reserved. The information contained in this document is the property of P.I. Works. Except as specifically authorized in writing by P.I. Works, the holder of this document shall keep the information contained herein confidential and shall protect same in whole or in part from disclosure and dissemination to third parties and use same for evaluation, operation and maintenance purposes only. The content of this document is provided for information purposes only and is subject to modification. It does not constitute any representation or warranty from P.I. Works, as to the content or accuracy of the information contained herein, including but not limited to the suitability and performances of the product or its intended application. uson and the P.I. Works logo are trademarks of P.I. Works. All other trademarks are the property of their owners. HotSpot Identification and HetNet Planning with uson TM 10