ns to those statistics to create a traffic demand forecast for their deployment and capacity planning of the next rollout. In the case of SLAs with customers (if any), respective bandwidth overprovisioning fac- tors are typically added to the collected statistics to ensure that the committed SLAs can be met. In reality, however, these capacity-planning processes have always been used together with "rule of thumb" techniques such as ad-hoc upgrading of the links whenever they exceed a specified capacity. For the 5G MBB use cases the same process could be appli- cable too. IoT traffic, on the other hand, will present new challenges. It is well understood (and explained briefly in the previous section) that IoT devices come with unique requirements, for example, in terms of latency. However, there is a common misconception that IoT traffic is necessarily low bandwidth and infrequent. While it is true that most of the traffic is likely to be from low-bandwidth devices such as sensors, these sometimes require firmware/software updates, causing devices to consume orders of magnitude more traffic compared with the regular operations. Furthermore, some IoT use cases require the sending of megabytes of machine-specific data (e.g. comprehensive telemetry and sometimes even video) to companies providing operation and predictive maintenance for machines. Hence it is reasonable to assume that IoT traffic will not only rely exclusively on technologies such as NB-IoT, but also on other radio technologies, some of which can provide high data rates. In addition to the problem of varying data volumes, there is the risk of multiple concur- rent transmissions from multitudes of IoT devices, as assumptions on traffic arrival rate for humans (where the Poisson process is often used) may not hold for IoT. In fact, unless spe- cial precautions are taken, it is rather likely that at least in some cases certain events may cause massive simultaneous transmissions from IoT devices. 7 NG-RAN Deployment Considerations Technologies and standar