Throughput Optimization in Ambient Backscatter-Based Energy Constraint Cognitive Radio Networks

Efficient utilisation of scarce resources, mainly radio and power, are the key research issues in the future Internet of Things (IoT) networks. This paper proposes an ambient energy harvesting and backscatter-enabled, energy-constrained cognitive IoT network. In our proposed scheme, the nodes in the secondary network efficiently utilise the primary network. More specifically, depending on the communication states (i.e. between busy or idle) of the primary network, the secondary nodes chose to operate either in energy harvesting mode (EHM), backscattering mode (BSM), or radio-frequency transmission mode (RFM). Furthermore, to maximise the sum-throughput of the secondary network, an optimisation problem is formulated and solved. The simulation results show that the proposed scheme outperforms the existing scheme regarding network sum-throughput.