  We consider energy-efficient wireless resource management in cellular
networks where BSs are equipped with energy harvesting devices, using
statistical information for traffic intensity and harvested energy. The problem
is formulated as adapting BSs' on-off states, active resource blocks (e.g.
subcarriers) as well as power allocation to minimize the average grid power
consumption in a given time period 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 (DP)
algorithm is then proposed to solve this optimization problem, by which the
BSs' on-off states are optimized in the first stage, and the active BS's
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,
while at the same time achieves close to the optimal energy saving performance.
