  Powering radio access networks using renewables, such as wind and solar
power, promises dramatic reduction in the network operation cost and the
network carbon footprints. However, the spatial variation of the energy field
can lead to fluctuations in power supplied to the network and thereby affects
its coverage. This warrants research on quantifying the aforementioned negative
effect and countermeasure techniques, motivating the current work. First, a
novel energy field model is presented, in which fixed maximum energy intensity
$\gamma$ occurs at Poisson distributed locations, called energy centers. The
intensities fall off from the centers following an exponential decay function
of squared distance and the energy intensity at an arbitrary location is given
by the decayed intensity from the nearest energy center. The product between
the energy center density and the exponential rate of the decay function,
denoted as $\psi$, is shown to determine the energy field distribution. Next,
the paper considers a cellular downlink network powered by harvesting energy
from the energy field and analyzes its network coverage. For the case of
harvesters deployed at the same sites as base stations (BSs), as $\gamma$
increases, the mobile outage probability is shown to scale as $(c
\gamma^{-\pi\psi}+p)$, where $p$ is the outage probability corresponding to a
flat energy field and $c$ a constant. Subsequently, a simple scheme is proposed
for counteracting the energy randomness by spatial averaging. Specifically,
distributed harvesters are deployed in clusters and the generated energy from
the same cluster is aggregated and then redistributed to BSs. As the cluster
size increases, the power supplied to each BS is shown to converge to a
constant proportional to the number of harvesters per BS.
