Optimal Time Allocation for Backscatter-Aided Relay Cooperative Transmission in Wireless-Powered Heterogeneous CRNs

Abstract

Nowadays, backscatter, radio-frequency (RF) energy harvesting (EH), and cognitive radio technologies have been widely applied in Internet of Things (IoT) to address the issues of energy supply and spectrum scarcity. This paper focuses on the throughput maximization problem of backscatter-aided wireless-powered heterogeneous cognitive radio networks (WPHetCRNs), where two types of secondary transmitters (STs), i.e., the STs with backscatter units (STBs) and the STs with RF-EH units (STEs), coexist. The STBs operate in the ambient backscatter (AB) mode, and the STEs operate in the harvest-then-transmit (HTT) mode. Inspired by the potential benefits of cooperations between different users, we propose a backscatter-aided cooperative relay transmission (BaCRT) strategy to improve the sum-throughput of the secondary users (SUs). The main idea is that when the licensed spectrum of the primary users (PUs) is busy, the STBs first help to relay the primary data via the passive relay mode, and then transmit the secondary data via the AB mode, while the STEs harvest energy in the HTT mode. With the help of relaying, the target throughput of the PUs could be met in shorter duration and the licensed spectrum could become idle more quickly. When the licensed spectrum becomes idle, the STEs transmit data in the HTT mode. The goal of this paper is to identify the optimal time allocation among the passive relay mode, AB mode, and data transmission of HTT mode that maximizes the sum-throughput of the SUs. To reach this goal, we first investigate the single-ST case for each type and derive the closed-form solution of the optimal time allocation. We then extend to the multiple-ST case for each type, where three scenarios are classified with respect to the fairness issue of the STBs. We prove that the sum-throughput maximization problem is convex in each scenario, and employ the block coordinate descent and gradient descent iterative algorithms to solve the problem. Numerical results show that the proposed BaCRT strategy significantly improves the sum-throughput of the SUs compared with other strategies.