Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks

Abstract

With the emerging Internet-of-Things 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) networks depends largely on the H2H (i.e., primary) network. In this paper, we propose ambient backscatter communication for the M2M networks which exploits the energy (signal) sources of the H2H network, referring to its traffic applications and popularity. In order to maximize the harvesting and transmission opportunities offered by varying traffic sources of the H2H network, we adopt a Bayesian nonparametric (BNP) learning algorithm to classify traffic applications (patterns) for secondary transmitters (STs). We then analyze the performance of STs using the stochastic geometric approach, based on a criterion for optimal traffic selection. Because of the mathematical intractability of the optimal criterion, we have attained a suboptimal traffic selection criterion which provides more tractable analysis. Results are presented to validate the performance of the proposed BNP classification algorithm and the criterion, as well as the impact of traffic sources and popularity.