  The stringent requirements of mobile edge computing (MEC) applications and
functions fathom the high capacity and dense deployment of MEC hosts to the
upcoming wireless networks. However, operating such high capacity MEC hosts can
significantly increase energy consumption. Thus, a base station (BS) unit can
act as a self-powered BS. In this paper, an effective energy dispatch mechanism
for self-powered wireless networks with edge computing capabilities is studied.
First, a two-stage linear stochastic programming problem is formulated with the
goal of minimizing the total energy consumption cost of the system while
fulfilling the energy demand. Second, a semi-distributed data-driven solution
is proposed by developing a novel multi-agent meta-reinforcement learning
(MAMRL) framework to solve the formulated problem. In particular, each BS plays
the role of a local agent that explores a Markovian behavior for both energy
consumption and generation while each BS transfers time-varying features to a
meta-agent. Sequentially, the meta-agent optimizes (i.e., exploits) the energy
dispatch decision by accepting only the observations from each local agent with
its own state information. Meanwhile, each BS agent estimates its own energy
dispatch policy by applying the learned parameters from meta-agent. Finally,
the proposed MAMRL framework is benchmarked by analyzing deterministic,
asymmetric, and stochastic environments in terms of non-renewable energy
usages, energy cost, and accuracy. Experimental results show that the proposed
MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the
energy cost (with 95.8% prediction accuracy), compared to other baseline
methods.
