Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks

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.Comment: 14 pages, 10 figure

Steady-State Rate-Optimal Power Adaptation in Energy Harvesting Opportunistic Cognitive Radios with Spectrum Sensing and Channel Estimation Errors

We consider an opportunistic cognitive radio network, consisting of Nu secondary users (SUs) and an access point (AP), that can access a spectrum band licensed to a primary user. Each SU is capable of harvesting energy, and is equipped with a finite size battery, for energy storage. The SUs operate under a time-slotted scheme, where each time slot consists of three non-overlapping phases: spectrum sensing phase, channel probing phase, and data transmission phase. The AP feeds back its estimates of fading coefficients of SUs-AP link to SUs. To strike a balance between the energy harvesting and the energy consumption, we propose a parameterized power control strategy that allows each SU to adapt its power, according to the feedback information and its stored energy. Modeling the randomly arriving energy packets during a time slot as a Poisson process, we establish a lower bound on the achievable sum-rate of SUs-AP links, in the presence of both spectrum sensing and channel estimation errors. We optimize the parameters of the proposed power control strategy, such that the derived sum-rate lower bound is maximized, subject to an interference constraint. Via simulations, we corroborate our analysis and explore spectrum sensing-channel probing-data transmission trade-offs.Comment: This paper has been submitted to IEEE Transactions on Green Communications and Networkin