Dynamic Control of Functional Splits for Energy Harvesting Virtual Small Cells: a Distributed Reinforcement Learning Approach

Understand

In this paper, we propose a network scenario where the baseband processes of the virtual small cells powered solely by energy harvesters and batteries can be opportunistically executed in a grid-connected edge computing server, co-located at the macro base station site.

• We state the corresponding energy minimization problem and propose multi-agent Reinforcement Learning (RL) to solve it.
• Distributed Fuzzy Q-Learning and Q-Learning on-line algorithms are tailored for our purposes.
• Coordination among the multiple agents is achieved by broadcasting system level information to the independent learners.