Return on Investment (roi) for Multi-Technology son juan Ramiro, Mark Austin and Khalid Hamied

7.3. Case Study: ROI for Self-Planning

The content of this section is organized as follows: Section 7.3.1 discusses two alternative scopes for Self-Planning and centers the discussion on the automated capacity planning process, which is described in Section 7.3.2 and modeled in Section 7.3.3. For a meaningful application of the presented model, a versatile mathematical representation of the per sector traffic distribution is required in order to consider heterogeneous traffic volumes across the network, which has a direct impact on the calculation of the needs for capacity expansions. Such model is presented in Section 7.3.4, and Section 7.3.5 describes its application to assess the capacity needs in a generic network, depending on its initial configuration, per sector traffic distribution and forecasted traffic growth. Based on the application of the described models, the way to compute annual CAPEX and OPEX with and without SON is discussed in Section 7.3.6 and Section 7.3.7, respectively. Finally, a concrete sample scenario is presented and the proposed models are applied in order to compute the ROI of the selected Self-Planning process, as well as its sensitivity to some key inputs and assumptions. The way in which the aforementioned models are applied in a combined way to compute the ROI is summarized in Figure 7.5.

7.3.1. Scope of Self-Planning and ROI Components

Even in the absence of SON, cellular operators need to plan their networks. Therefore, for Self-Planning, it is fully justifiable to consider OPEX savings due to automation of the p lanning tasks since this expenditure used to be required and the deployment of SON functionalities will eliminate or reduce it. The same applies in the case of extra revenue due to
For every year in the considered time horizon...
Input ^Input Assumptions

For carry over calculations Input
Figure 7.5 Summarizing the process to compute the CAPEX and OPEX components of the ROI for Self-Planning.
reduced churn since this real difference in cash flow is fully attributable to the introduction of SON.
The nature of the CAPEX savings that can be attributed to the application of Self-Planning techniques needs to be classified in two main areas before addressing the calculations in a systematic manner:

1. 
   Self-Planning functions that, in essence, orchestrate qualified decisions about the amount of Hardware (HW), network elements and transmission links to be deployed (no matter whether they are purchased as CAPEX or leased as OPEX). Among these, an illustrative example is the automated capacity planning process that allows operators to make a timely and accurate decision on capacity expansions, i.e. on the extra 3G carrier transceivers to be deployed and the E1 links to be activated in the Iub interfaces in order to cope with the forecasted traffic growth.
   
2. 
   Self-Planning functions that derive, for example, the antenna settings (e.g. tilt) of new base stations. In this case the benefits from Self-Planning can be materialized as a capacity increase of the resulting network, which implies that more traffic can be carried with the same HW. Obviously, this is implicitly related to the future need for HW expans ions to cope with traffic growth. The higher the effective capacity of the current infra structure, the fewer capacity additions will be required in the future, which has a strong influence on the CAPEX contributions to the different cash flows.
   

The analysis in this section will be focused on automated capacity planning, which is an example of the first group, since the dynamics involved in the second case are very similar to the ones that will be considered when addressing the ROI for Self-Optimization and they will be extensively covered when discussing that topic.

Figure 7.6 Self-Planning process for capacity extensions.