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

Abstract

In this paper, we emphasize on energy-efficient resource allocation for the energy harvesting based cognitive machine-to-machine (EH-CM2M) communication. We consider how to maximize the energy efficiency of M2M transmitters (M2M-TXs) via the joint optimization of channel selection, peer discovery, power control, and time allocation. We propose a two-stage three-dimensional matching algorithm. In the first stage, M2M-TXs, M2M receivers (M2M-RXs) and resource blocks (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.