Capacity maximization of energy-harvesting small cells with dynamic sleep mode operation in heterogeneous networks

In this paper, we investigate how to utilize renewable energy harvested by small cells to increase network capacity. Specifically, we try to maximize the average capacity of a small cell with the harvested energy from the environments (e.g., solar and wind) with constraints on energy causality and battery capacity. With the coverage preserved by the umbrella cells, we investigate the potential of capacity improvement by dynamic on-off operations of small cells. We propose a heuristic polynomial-time near-optimal algorithm for joint power control and sleep-awake scheduling of this mixed-integer optimization problem. The capacity obtained by the proposed heuristic algorithm can approach the maximal capacity as long as the small cell can be equipped with a battery with large enough capacity. In the simulations, we demonstrate that our proposed algorithm can increase system capacity by 25%. We find that always attempting to keep a small cell in active state may not be always a good strategy for capacity maximization even when the optimal transmit power allocation is applied.