Abstract
In this paper, we consider a backscatter communication (BackCom) based cognitive network that consists of one primary transmitter, one primary receiver, multiple secondary transmitters (STs), and one secondary receiver (SR). Each ST operates in the BackCom or energy harvesting model. Our goal is to jointly optimize the energy harvesting and backscatter time, the transmit power of the primary transmitter, and the power reflection coefficient of each ST to maximize the sum throughput of all the STs under a nonlinear energy harvesting model while satisfying multiple constraints, i.e., the energy causality of each ST, the Quality of Service of the primary transmitter, etc. The formulated problem is nonconvex due to the coupled variables and hard to solve. In order to address this problem, we decouple partial coupled variables by using the properties of the objective function and constructing auxiliary variables, and the remaining coupled variables are decoupled via successive convex approximation (SCA). On this basis, a SCA based iterative algorithm is developed to solve the formulated problem. Simulation results are provided to support our work.
Highlights
Internet of ings (IoT) is expected to deploy massive smart sensor nodes in the future communications to seamlessly connect the physical environment and the cyberspace for providing intelligent services [1]
In order to illustrate the advantages of the proposed scheme, we compare the performance achieved by the proposed scheme with the fixed scheme, where the power reflection coefficient of each ST is fixed as 0.5, 0.8, and 0.9, respectively
As shown in this figure, the total throughput of all STs decreases with the increase of Cs, min, since a larger Cs, min brings a higher Quality of Service (QoS) requirement for the ST’s transmission, and more resources will be allocated to the STs with worse channels, leading to a reduction in the total throughput of all STs
Summary
Internet of ings (IoT) is expected to deploy massive smart sensor nodes in the future communications to seamlessly connect the physical environment and the cyberspace for providing intelligent services [1] Such an approach requires huge spectrum resources, and this motivates us to consider high spectrum-efficient communication paradigms for IoT. BackCom allows a smart sensor node modulating its information on the incident signals and backscattering the modulated signals to its associated receiver by changing the power reflection coefficient so that the power-consuming components can be avoided, while harvesting energy from the incident signal for realizing energy self-sustainability [3,4,5] Despite these superiorities, the communication performance of BackCom is limited as it uses the ambient signals as the incident signals, and the ambient signals introduce serious cochannel interference to the BackCom receiver [4]. Recent works have integrated BackCom into cognitive radio, yielding a spectrum- and energy-efficiency paradigm, called BackCom based cognitive networks
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