LAUSR

Traffic-energy imbalance poses a major challenge on enabling of green-communication through dual-powered green networks, which are becoming increasingly attractive due to their cost efficiency and low carbon footprint. Dual-powered networks are prone to traffic-energy imbalance mainly due to spatio-temporal traffic variation and random solar energy harvest. Thus, it becomes imperative to study the effects of this traffic-energy imbalance in green networks and to formulate network operation strategies. We present an analytical framework for computing the operator’s revenue while fulfilling the users’ service guarantee. The revenue maximization problem is solved algorithmically through proposed network operation and green energy allocation strategies. The network is subjected to heterogeneous skewed traffic of varying degrees and operated using two distinct strategies, the conventional without coverage adjustment (WCA) and the proposed cooperative coverage adjustment (CCA) model. In the conventional WCA model the cells do not dynamically adjust their coverage areas, whereas the proposed CCA model involves the radio network controller (RNC) for adjusting the cell coverage areas based on traffic load and energy availability. Our analysis and simulation results demonstrate that the proposed CCA model is highly effective in addressing traffic-energy imbalance at the cellular level and significantly improves the revenue as compared to the conventional WCA model. Our results demonstrate that with the proposed CCA model, the revenue gain increases with an increase in traffic skewness. For example, it provides a gain up to 61% under high (80%) traffic skewness while serving about 25% of more users.