  This paper develops and evaluates an accumulate-then-transmit framework for
multi-user scheduling in a full-duplex (FD) wireless-powered Internet-of-Things
system, consisting of multiple energy harvesting (EH) IoT devices (IoDs) and
one FD hybrid access point (HAP). All IoDs have no embedded energy supply and
thus need to perform EH before transmitting their data to the HAP. Thanks to
its FD capability, the HAP can simultaneously receive data uplink and broadcast
energy-bearing signals downlink to charge IoDs. The instantaneous channel
information is assumed unavailable throughout this paper. To maximize the
system average throughput, we design a new throughput-oriented scheduling
scheme, in which a single IoD with the maximum weighted residual energy is
selected to transmit information to the HAP, while the other IoDs harvest and
accumulate energy from the signals broadcast by the HAP. However, similar to
most of the existing throughput-oriented schemes, the proposed
throughout-oriented scheme also leads to unfair inter-user throughput because
IoDs with better channel performance will be granted more transmission
opportunities. To strike a balance between the system throughput and user
fairness, we then propose a fairness-oriented scheduling scheme based on the
normalized accumulated energy. To evaluate the system performance, we model the
dynamic charging/discharging processes of each IoD as a finite-state Markov
Chain. Analytical expressions of the system outage probability and average
throughput are derived over Rician fading channels for both proposed schemes.
Simulation results validate the performance analysis and demonstrate the
performance superiority of both proposed schemes over the existing schemes.
