Base Station Sleeping and Resource Allocation in Renewable Energy Powered Cellular Networks

We consider energy-efficient wireless resource management in cellular networks where base stations (BSs) are equipped with energy harvesting devices, using statistical information for traffic intensity and renewable energy. The problem is formulated as adapting BSs' on–off states, active resource blocks (e.g., subcarriers), and renewable energy allocation to minimize the average grid power consumption while satisfying the users' quality of service (blocking probability) requirements. It is transformed into an unconstrained optimization problem to minimize a weighted sum of grid power consumption and blocking probability. A two-stage dynamic programming algorithm is proposed to solve this problem, by which the BSs' on–off states are optimized in the first stage, and the active BSs' resource blocks are allocated iteratively in the second stage. Compared with the optimal joint BSs' on–off states and active resource blocks allocation algorithm, the proposed algorithm greatly reduces the computational complexity and can achieve the optimal performance when the traffic is uniformly distributed.