ues and service availability in the case of MBB provides an additional source of input, which can be used for network optimization and troubleshooting. On the other hand, with IoT, and other URLLC use cases, the established methods may not always work and therefore new methodologies for network planning must be devised. Furthermore, business relationships in that case become more complex, as it can be seen as a business to business to consumer (B2B2C) operation. This provides new challenges in terms of getting customer feedback. Moreover, in the case of IoT deployments, the customer requiring connectivity or services may not necessarily have a business relation with the operator who deploys the network at all. Hence operators need to consider using new methods to understand where potential traffic may be originated, how much network capacity and compute power will be needed to support it, and where edge compute resources needed to be deployed to set up economically successful networks. Access to and proper usage of the data relevant for network planning will be a key factor determining success and economic feasibility of a network. The terms IoT and MTC are used interchangeably in this chapter. 7.3 Deployment Objectives and Challenges 7.3.2 Network Capacity and Compute Resource Planning 7.3.2.1 Air Interface Capacity In voice-centric cellular networks of the past the Erlang-B formula (Eq. (7.1)) and its vari- ants, which provides the Grade of Service (GoS) as a probability that a new call is rejected, has been the dominant tool for planning of respective capacity of a RAN site. (7.1) Erlang-B formula Where: Pb is the probability of call blocking/dropping; m is the number of identical parallel resources such as servers or telephone lines; E = Ah is the offered load in Erlang. When upgrading their wireless mobile broadband networks, operators could rely on already available statistics from their existing previous-generation networks (e.g. when upgrading from 3G to 4G). Operators used to add traffic growth predictio