Throughput Maximization for Movable Antenna and IRS Enhanced Wireless Powered IoT Networks

By controlling the propagation environment, intelligent reflecting surface (IRS) improve the channel quality, and becomes a promising technique. Meanwhile, movable antenna (MA) shows great potential to enhance the received signal-noise-ratio (SNR) by configuring antenna positions. In this paper, we exploit the advantages of both techniques, and study a MA and IRS enhanced wireless powered internet of things (IoT) network, wherein a hybrid access point (HAP) charges MA-enabled IoT devices via wireless energy transfer (WET) technology, and devices utilize the harvested energy to upload their information to the HAP. Basically, a network throughput maximization (NTM) problem is formulated to jointly optimize the IRS reflecting beamforming, the time allocation subject to total time constraint, and the MA position control subject to MA's feasible region constraints. Concerning the non-convexity of the NTM problem, we exploit the block coordinate ascent (BCA) approach to divide it into reflecting beamforming and time allocation sub-problem, and MA position control sub-problem, which are independently and iteratively solved until the solution of original problem is converged. For the reflecting beamforming and time allocation optimization sub-problem, the successive convex approximate (SCA) algorithm is used to transform it into a convex problem. For the MA position control sub-problem, we transform it into a convex mixed integer non-linear programming (MINLP) problem. Finally, extensive simulation results demonstrate the proposed approach for IRS-assisted wireless powered IoT network with MA can significantly improve the network throughput, where the performance gain is over 127%, compared with IRS-assisted wireless powered IoT networks.