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Backscatter communications have been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we consider a multitag ambient backscatter system, where the multiple tags can harvest radio frequency (RF) energy from the power station and backscatter the RF signals to the reader for data transmission. In order to guarantee the throughput requirements, we aim to maximize the minimum user rate among all the tags by jointly optimizing the backscatter time allocation and power reflection coefficient. Channel state information (CSI) mismatch is taken into account in our proposed optimization problem, which leads to a robust chance-constrained optimization problem. To deal with the nonconvex chance constraints, we propose two safe approximation methods: 1) Bernstein-type-inequality and 2) conditional value-at-risk (CVaR), applying to the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. In addition, we develop an alternating optimization algorithm to obtain the optimal value of minimum throughput. Finally, simulation results reveal that the CVaR-based method outperforms the Bernstein-type-inequality-based method for the non-Gaussian channel estimation error.

A Robust Design for Ultra Reliable Ambient Backscatter Communication Systems