  This paper develops a control framework for a network of energy harvesting
nodes connected to a Base Station (BS) over a multiple access channel. The
objective is to adapt their transmission strategy to the state of the network,
including the energy available to the individual nodes. In order to reduce the
complexity of control, an optimization framework is proposed where energy
storage dynamics are replaced by dynamic average power constraints induced by
the time correlated energy supply, thus enabling lightweight and flexible
network control. Specifically, the BS adapts the packet transmission
probability of the "active" nodes (those currently under a favorable energy
harvesting state) so as to maximize the average long-term throughput, under
these dynamic average power constraints. The resulting policy takes the form of
the packet transmission probability as a function of the energy harvesting
state and number of active nodes. The structure of the throughput-optimal
genie-aided policy, in which the number of active nodes is known non-causally
at the BS, is proved. Inspired by the genie-aided policy, a Bayesian estimation
approach is presented to address the case where the BS estimates the number of
active nodes based on the observed network transmission pattern. It is shown
that the proposed scheme outperforms by 20% a scheme in which the nodes operate
based on local state information only, and performs well even when energy
storage dynamics are taken into account.
