The exponential increase in mobile data traffic warrants disruptive changes in the design and management of cellular networks. The next generation (5G) networks target to support a variety of applications having diverse requirements in data rates, latency, energy, etc. To address this surge in traffic demand and the associated heterogeneity in service requirements, several candidate technologies are being investigated under the 5G vision, naming a few: 1) Ultra-dense network deployment for high spatial capacity; 2) Network slicing to support heterogeneous service requirements; and 3) Cloud radio access networks (CRAN), etc. An important element of the 5G vision is to achieve a 50% reduction in the total network energy consumption. To ensure that the future networks meet both the service and the sustainability requirements, energy-efficient designs and integration of renewable-energy sources into the network infrastructure are needed. To address the sudden capacity demands, on-demand network deployment is desired, instead of maintaining a permanently over-engineered infrastructure. Inclusion of these approaches result in highly complex and stochastic network topologies. Modeling and optimizing such networks is a challenging problem.
The primary goal of MAPNET is to propose new modelling techniques for 5G-and-beyond networks mainly characterized by a very dense deployment and heterogeneous radio access technologies coupled with the requirements of energy- efficiency and the applications’ quality of service in terms of latency and data rates.
The specific objectives are listed as follows:
(i) Mathematical modelling of ultra-dense networks
(ii )Energy-efficiency maximization of ultra-dense networks
(iii) Network slicing reformulations to satisfy user demands
