Modelling 4G & 5G Capacity
1D
It all used to be so easy !
In the days of 2G-GSM networks, an operator would connect customers using Time Slots (TSL), and only needed to calculate the number of TSL, e.g. switching circuits required to connect in-coming calls at the busiest time of the day. Based on Busy Hour Traffic data (e.g. the number of hours of call carried during the busiest hour of operation, measured in Erlang), and the level of Call Blocking acceptable to users (typically about 1% or 2%), a mathematical model, called Erlang-B Tables, could provide an exact calculation of the switching capacity required. There was one grade of service over physical circuits, and one could relatively easily ensure full network utilization at Peak Hour
After introducing 2 dimensions to the traffic, ErlangB became useless
After the introduction of 2G wireless mobile networks (full rate, half rate), the Erlang-B model was adjusted by Kaufman and Roberts, working at the French Institut National de Recherche en Informatique et Automatique (INRIA), to take into account the 2 grades of service and insure close to full utilization of the time slots on the network at peak hour. Network capacity calculations really became complicated with the introduction of 3G and the explosion of classes of service. While the Kaufman and Roberts model could still be adjusted to take all scenarios into account, the time required to calculate the capacity per site grew exponentially leaving tools impractical to implement
For 4G-LTE and 5G systems, multiclass traffic could however no longer be modeled by Kaufman / Roberts, and there are to date no exact theoretical way to dimension 4G/5G system resources based on demand
Working in close collaboration with renowned researchers such as Thomas Bonald of the French National Telecommunications and Mathematical Laboratories, ICIX has however developed and patented an innovative statistical model providing current and forecast capacity of a 4G/5G cells and sectors. This process enables all mobile operators to assess and forecast network utilization, therefore allowing for better capacity planning and traffic management
