Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks

Abstract

With the emerging Internet-of-Things (IoT) services, massive machine-to-machine (M2M) communication will be deployed on top of human-to-human (H2H) communication in the near future. Due to the coexistence of M2M and H2H communications, the performance of M2M (i.e., secondary) network depends largely on the H2H (i.e., primary) network. In this paper, we propose ambient backscatter communication for the M2M network which exploits the resources of the H2H network, referring to traffic applications and popularity. In order to maximize the harvesting and transmission opportunities offered by varying traffic resources of the H2H network, we adopt a Bayesian nonparametric (BNP) learning algorithm to classify traffic applications (patterns) for secondary user (SU). We then analyze the performance of SU using the stochastic geometrical approach, based on a criterion for optimum traffic pattern selection. Results are presented to validate the performance of the proposed BNP classification algorithm and the criterion.