LAUSR

In this paper, we investigate optimal schemes to manage time scheduling of multiple modules, including spectrum sensing, radio frequency (RF) energy harvesting (RFH) and ambient backscatter communication (ABCom) by maximizing data transmission rate in Internet of Things networks. We first detect ambient RF signals with high signal power as the RF resource of RFH and ABCom by using spectrum sensing with energy detection techniques. Specifically, compressive sensing (CS) is adopted to detect the wideband RF signals with improving spectrum sensing efficiency at the same time. We formulate a joint optimization problem to manage time scheduling parameter and power allocation ratio. In addition, we propose to find the threshold of spectrum sensing for ABCom communications by analyzing the outage probability of backscatter communications. Numerical results demonstrate that the optimal schemes using spectrum sensing are achieved with better transmission rates. The designed time scheduling scheme with CS is confirmed to be more efficient, and the superiorities become more obvious with the increase of network operation time. Moreover, the optimal scheduling parameters and power allocation ratios are obtained. Simulations illustrate that the threshold of spectrum sensing for backscatter communications is obtained by analyzing the outage probability of backscatter communications.