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

TL;DR

This paper proposes a dynamic programming-based method for energy-efficient resource management in renewable energy-powered cellular networks, optimizing base station activity and resource allocation to minimize grid power use while maintaining quality of service.

Contribution

It introduces a two-stage DP algorithm that reduces computational complexity and closely approximates optimal energy savings in renewable energy cellular networks.

Findings

Significant reduction in computational complexity.

Close-to-optimal energy savings achieved.

Effective management of BS on-off states and resource blocks.

Abstract

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.