  This paper unveils the importance of intelligent reflecting surface (IRS) in
a wireless powered sensor network (WPSN). Specifically, a multi-antenna power
station (PS) employs energy beamforming to provide wireless charging for
multiple Internet of Things (IoT) devices, which utilize the harvested energy
to deliver their own messages to an access point (AP). Meanwhile, an IRS is
deployed to enhance the performances of wireless energy transfer (WET) and
wireless information transfer (WIT) by intelligently adjusting the phase shift
of each reflecting element. To evaluate the performance of this IRS assisted
WPSN, we are interested in maximizing its system sum throughput to jointly
optimize the energy beamforming of the PS, the transmission time allocation, as
well as the phase shifts of the WET and WIT phases. The formulated problem is
not jointly convex due to the multiple coupled variables. To deal with its
non-convexity, we first independently find the phase shifts of the WIT phase in
closed-form. We further propose an alternating optimization (AO) algorithm to
iteratively solve the sum throughput maximization problem. To be specific, a
semidefinite programming (SDP) relaxation approach is adopted to design the
energy beamforming and the time allocation for given phase shifts of WET phase,
which is then optimized for given energy beamforming and time allocation.
Moreover, we propose an AO low-complexity scheme to significantly reduce the
computational complexity incurred by the SDP relaxation, where the optimal
closed-form energy beamforming, time allocation, and phase shifts of the WET
phase are derived. Finally, numerical results are demonstrated to validate the
effectiveness of the proposed algorithm, and highlight the beneficial role of
the IRS in comparison to the benchmark schemes.

Signal Processing