Performance Optimization And Resource Allocation For User-assisted Wireless Powered Networks

The explosive growth of traffic has promoted the rapid development of Internet of Things(IoT),but the energy supply insufficiency and the spectrum resources shortage have seriously hindered the progress of IoT.Energy harvesting(EH)technology can wirelessly provide energy for battery-constrained devices,while ambient backscatter communication(AmBC)and non-orthogonal multiple access(NOMA)are beneficial to improve spectrum efficiency,all of which have become powerful solutions for IoT.Focusing on the above issues and corresponding technologies,this paper is devoted to the performance optimization and resource allocation for wireless networks based on userassisted power supply.Firstly,we propose a user-assisted power supply chain model for a wireless IoT network with one base station(BS),two radio frequency(RF)sources,and several IoT devices.Adopting time division multiple access(TDMA)frame structure,energy is transmitted from the RF sources to the nearest devices,and the subsequent devices harvest energy from the previous device while it transmits information to the BS.In this paper,the maximum throughput optimization and minimum time optimization of the userassisted power supply chain system are studied,and the optimal time allocation is obtained,respectively.The comparison with the low-complexity sub-optimal time allocation schemes shows that the proposed optimal time allocation can achieve satisfying performance.In addition,by comparing with the special case of only one RF source,we also show that the proposed chain model can be easily generalized to multiple RF sources and achieve better performance by properly arranging the position of RF sources.Secondly,considering the mutual-assistance power supply among densely distributed users,this paper proposes a peer-assisted power supply approach for a wireless IoT network in which passive user without battery harvest energy from active user with power supply.Specifically,passive users harvest energy from active users during their uplink transmission using NOMA.They then upload information along with the active users.In particular,considering the combination of TDMA and NOMA,under the assumption that the power of active users is fixed,we study different transmission modes(stand-alone(SA)/ non-stand-alone(NSA))and different operations(NOMA / NOMAplus-TDMA).Moreover,considering the practical applications,the above four schemes are re-investigated in the scenario where active users' energy is limited.By optimizing the time allocation,we maximize the sum-throughput of each proposed model.We prove that the optimization problems for all cases are convex,and we obtain closed-form solutions for most cases.Finally,we show by simulations that the NOMA-plus-TDMA operation is beneficial to maximize the sum-throughput.NSA mode is better when power is limited,and SA is better when energy is limited.Finally,in order to further improve the spectrum utilization,this paper proposes a cognitive backscatter system with user-assisted power supply.Backscatter devices(BDs)not only share the spectrum with the primary user,but also harvest energy from and transmit their own information over the primary signal.NOMA is used for multiple BDs to share spectrum resources,further improving spectrum efficiency.In addition,BDs involved are assumed to have energy storage capability so that the energy harvested in the current block can be stored for future use.Under this assumption,compared with the single-slot energy causality constraint,a multi-slot energy causality constraint is proposed.Taking the backscattering sum-rate as the objective function,the transmit power of the primary user and the reflection coefficients of BDs are jointly optimized.Due to the nonconvexity of the optimization problem,the concave-convex procedure(CCCP)is used to iteratively obtain a near-optimal solution.Numerical results show that the multi-slot operation can achieve a remarkable performance gain.