• stochastic geometry
• network design
• polynomial optimization theory
• millimeter wave
• Massive MIMO
• fractional optimization
• energy efficiency

The number of data connections is facing an exponential growth worldwide. This trend dramatically stresses nowadays cellular networks that must evolve at the same pace as the data traffic increase. This leads to an ever-growing energy demand with associated ecological and economic issues. Another well-known effect that negatively impact the efficiency of current cellular networks is the lack of adaptation to time-varying traffic loads, which is primarily due to the variability of the number of connected users during the day. This calls for efficient mechanisms able to adapt the future cellular networks to the daily network load variations. As a consequence, the focus of cellular network operators must be switched to solutions that are more energy aware, accordingly.
Therefore, an alternative metric must be considered to design future cellular networks, which offers a balance between offering higher data rate and consuming less energy. Hence, the resulting cellular networks are forced to change shape in order to meet those sustainability requirements.
In this thesis, we proposed an optimization framework to design a cellular network for maximal energy efficiency. By using the provided framework, the wireless engineer is capable of selecting the network parameters that impact most the performance and tune them in order to adapt the cellular network to the daily users’ needs.