  Green cellular networking has become an important research area in recent
years due to environmental and economical concerns. Switching off
under-utilized BSs during off-peak traffic load conditions is a promising
approach to reduce energy consumption in cellular networks. In practice, during
initial cell planning, the BS locations and RAN parameters are optimized to
meet the basic system design requirements like coverage, capacity, overlap, QoS
etc. As these metrics are tightly coupled with each other due to co-channel
interference, switching off certain BSs may affect the system requirements.
Therefore, identifying a subset of large number of BSs which are to be put into
sleep mode, is a challenging dynamic optimization problem. In this work, we
develop a multiobjective framework for dynamic optimization framework for OFDMA
based cellular systems. The objective is to identify the appropriate set of
active sectors and RAN parameters that maximize coverage and area spectral
efficiency while minimizing overlap and area power consumption without
violating the QoS requirements for a given traffic demand density. The
objective functions and constraints are obtained using appropriate analytical
models which capture the traffic characteristics, propagation characteristics
(pathloss, shadowing, and small scale fading) as well as load condition in
neighbouring cells. A low complexity evolutionary algorithm is used for
identifying the global Pareto optimal solutions at a faster convergence rate.
The inter-relationships between the system objectives are studied and
guidelines are provided to find an appropriate network configuration that
provides the best achievable trade-offs. The results show that using the
proposed framework, significant amount of energy saving can be achieved and
with a low computational complexity while maintaining good trade-offs among the
other objectives.
