Joint Optimization for Intelligent Reflecting Surface Enabled Backscatter Communication System

Backscatter communication, as a key technique to achieve low-power device transmission in Internet of Things, has aroused wide concern all over the world in recent years. Since the double fading effect limits the backscatter communication range and the achievable system performance, intelligent reflecting surface (IRS) appears to be introduced into backscatter communication to raise the propagation quality. However, the signal model considered in the available researches with only a single-reflection path is not complete. Motivated by this, we first formulate the achievable rate maximization problem for IRS-enabled backscatter communication system in this work, with consideration of two-reflection characteristic at the IRS through designing the beamforming vector of carrier transmitter, the phase shift of IRS, and the power reflection coefficient of backscatter device jointly. Note that the complex superposition of two-reflection signal links makes the formulated problem highly non-convex, we utilize an iterative algorithm combined with alternating optimization approach and minorization-maximization method to derive the approximate solution to each optimization variable. Simulation and experimental results corroborate the remarkable performance improvements created by the considered signal model, as well as the excellence of our proposed scheme with joint optimization over other benchmarks.