Network Planning

Optimum LocationRF Planning

Solution to the problem of optimum base station positioning with coverage, capacity and cost criteria. The optimization problem is tackled using both combinatorial and genetic algorithms.

Algorithm solutions as the capacity requirements increase. (EuCNC 2015, Paris)

Algorithm solutions as the capacity requirements increase. (EuCNC 2015, Paris)

Multi-objective genetic algorithms are employed.

  • heterogeneous networks
  • large geographical areas
  • high capacity demands
  • antenna pattern reconfiguration
  • green planning

Example: Test planning scenario targeting an area of 2.5Km x 2.5Km with throughput requirement approximately 18.5Mbps/Km2.

The optimization algorithm provides an optimum network architecture along with suitable base-station antenna patterns. The capacity requirement is always met but the user can choose the criterion to be optimized, i.e. cost or power.

Minimum Cost Minimum Power
minCost

Network architecture when cost is minimized. (Project: GR/EU- INTENTION 2015)

minPower

Network architecture when transmitted power is minimized. (Project: GR/EU-INTENTION 2015)