Energy-Efficient Resource Allocation for Cognitive Industrial Internet of Things With Wireless Energy Harvesting
Xin Liu, Su Hu, Ming Li, Biaojun Lai
Abstract
Cognitive industrial Internet of Things (CIIoT) can extend available spectrum resources by accessing the spectrum licensed to primary user (PU) on the premise of not disturbing the PU's communications. However, additional spectrum sensing and long-time working may consume much energy of CIIoT. In this article, a CIIoT with wireless energy harvesting (WEH) is proposed to harvest the radio frequency energy of PU's signal, and energy-efficient resource allocations in different spectrum access modes are presented to maximize the average transmission rate of CIIoT while guaranteeing its energy saving requirements. The underlay and overlay spectrum access modes for CIIoT with WEH are described, respectively, in which the energy-efficient resource allocations are formulated as joint optimization problems that can be solved using the alternating direction optimization and water-filling algorithm. By combining underlay and overlay modes, a hybrid spectrum access mode is proposed to enable the CIIoT to access both idle and busy spectrum without limiting its transmission power at the absence of PU. Simulation results show that the CIIoT with WEH can consume less power to achieve larger transmission rate, and the hybrid mode outperforms the underlay and overlay modes in the aspects of transmission rate and energy saving.