  Intelligent reflecting surface (IRS) has recently been emerged as an
effective way for improving the performance of wireless networks by
reconfiguring the propagation environment through a large number of passive
reflecting elements. This game-changing technology is especially important for
stepping into the Internet of Everything (IoE) era, where high performance is
demanded with very limited available resources. In this paper, we study a
backscatter-assisted wireless powered communication network (BS-WPCN), in which
a number of energy-constrained users, powered by a power station (PS), transmit
information to an access point (AP) via backscatter and active wireless
information transfer, with their communication being aided by an IRS. Using a
practical energy harvesting (EH) model which is able to capture the
characteristics of realistic energy harvesters, we investigate the maximization
of total network throughput. Specifically, IRS reflection coefficients, PS
transmit and AP receive beamforming vectors, power and time allocation are
designed through a two-stage algorithm, assuming minimum mean square error
(MMSE) receiver at the AP. The effectiveness of the proposed algorithm is
confirmed via extensive numerical simulations. We also show that our proposed
scheme is readily applicable to practical IRS-aided networks with discrete
phase shift values.
