Decomposition Model Assisted Energy-Saving Design in Radio Access Network

TL;DR

This paper introduces a decomposition model and a deep Q-network based approach to optimize energy savings in dense radio access networks, balancing energy use, throughput, and delay.

Contribution

It proposes a novel softgoal interdependency graph decomposition model combined with a deep Q-network for effective energy-saving decision-making.

Findings

Improved energy efficiency in dense networks.

Faster model training process.

Better trade-off among energy, throughput, and delay.

Abstract

The continuous emergence of novel services and massive connections involve huge energy consumption towards ultra-dense radio access networks. Moreover, there exist much more number of controllable parameters that can be adjusted to reduce the energy consumption from a network-wide perspective. However, a network-level energy-saving intent usually contains multiple network objectives and constraints. Therefore, it is critical to decompose a network-level energy-saving intent into multiple levels of configurated operations from a top-down refinement perspective. In this work, we utilize a softgoal interdependency graph decomposition model to assist energy-saving scheme design. Meanwhile, we propose an energy-saving approach based on deep Q-network, which achieve a better trade-off among the energy consumption, the throughput, and the first packet delay. In addition, we illustrate how the decomposition model can assist in making energy-saving decisions. Evaluation results demonstrate the performance gain of the proposed scheme in accelerating the model training process.