Energy-Efficient Resource Allocation for Energy Harvesting-Based Cognitive Machine-to-Machine Communications

Energy harvesting-based cognitive machine-to-machine (EH-CM2M) communication has been proposed to overcome the problem of spectrum scarcity and limited battery capacity by enabling M2M transmitters (M2M-TXs) to harvest energy from ambient radio frequency signals, as well as to reuse the resource blocks (RBs) allocated to cellular users (CUs) in an opportunistic manner. However, the complex interference scenarios and the stringent quality of service (QoS) requirements pose new challenges on resource allocation optimization. In this paper, we consider how to maximize the energy efficiency of M2M-TXs via the joint optimization of channel selection, peer discovery, power control, and time allocation. We propose a two-stage 3-D matching algorithm. In the first stage, M2M-TXs, M2M receivers (M2M-RXs) and RBs are temporally matched together, and then the joint power control and time allocation problem is solved by combining alternating optimization (AO), nonlinear fractional programming, and linear programming to construct the preference lists. In the second stage, the joint channel selection and peer discovery problem is solved by the proposed pricing-based matching algorithm based on the established preference lists. Simulation results confirm that the proposed algorithm can approach the optimal performance with a low complexity.