Autonomous Self-Optimization of Coverage and Capacity in LTE Cellular Networks

Abstract

Self-organizing networks promise significant expenditure savings for operators when rolling out modern cellular network infrastructure, such as Long-Term Evolution (LTE) and LTE-Advanced systems. Savings in capital expenditures (CAPEX) and operational expenditures (OPEX) can be achieved in both the network deployment and network operation phase. Particularly, self-organized optimization of network coverage and network capacity is a key challenge to cope with the boost in mobile data traffic that is expected in the next years and to benefit from the growing market. We present a traffic-light-related approach to autonomous self-optimization of tradeoff performance indicators in LTE multitier networks. Introducing a low-complexity interference approximation model, the related optimization problem is formulated as a mixed-integer linear program and is embedded into a self-organized network operation and optimization framework. Tuning site activity, transmission power, and antenna downtilt are parameters of eNodeBs and Home eNodeBs. The optimization procedure is carried out considering time-variant optimization parameters that are automatically adapted with respect to changes in the network. Simulation-based evaluation of representative case studies demonstrates applicability and the benefit potential of our overall concept.