Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks

TL;DR

This paper introduces a traffic-aware backscatter communication system for M2M networks that leverages H2H traffic patterns and a Bayesian nonparametric learning algorithm to optimize energy harvesting and data transmission.

Contribution

It proposes a novel traffic-aware backscatter communication framework utilizing BNP learning to classify traffic patterns for improved M2M network performance.

Findings

BNP classification effectively identifies traffic patterns

Optimized traffic pattern selection enhances energy harvesting

Performance validated through stochastic geometrical analysis

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) 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 energy (signal) sources of the H2H network, referring to 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 user (SU). We then analyze the performance of SU using the stochastic geometrical approach, based on a criterion for optimal traffic pattern selection. 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.