Research On Capacity Fair Resource Optimization In Cognitive Backscatter Communications

Cognitive backscatter network has attracted much attention due to its high spectrum utilization and low power consumption.In cognitive backscatter network,the backscatter device(BD)uses the radio frequency signal from the primary user to supply energy and transmit information,which provides an efficient transmission solution for the spectrum and energy constrained Internet of Things.This dissertation studies the fairness-aware resource optimization scheme in cognitive backscatter network to improve the communication performance and achieve fair transmission of BD by designing fair and effective resource allocation schemes in different scenarios,and the main work is described as follows.First,considering the practical nonlinear energy harvesting model and dynamic energy consumption model,a resource allocation method based on max-min criterion is proposed to guarantee the throughput fairness among BDs.Specifically,a non-convex multidimensional resource allocation problem of the communication capacity is maximized by jointly optimizing the transmitting power,reflection coefficient and time division,subject to the energy-causality constraint of each BD and the Quality of Service(Qo S)requirement of the primary user.The original optimization problem includes multiple coupling variables and inverse convex constraint.Therefore,the contradiction,convex approximation and auxiliary variables methods are used to transform the original problem into a convex one.Then,an iterative algorithm based on successive convex approximation is proposed to solve the transformed problem.Simulation results show the fast convergence of the proposed iterative algorithm.In addition,compared with the resource allocation scheme with linear energy harvesting model,the proposed resource allocation scheme is capable of ensuring the throughput fairness among BDs and also improving the throughput.Second,taking the hardware impairments(HWIs)of the transceiver into account,the resource optimization schemes are designed for single-BD and multi-BD cognitive backscatter network under the nonlinear energy harvesting respectively.For the single-BD cognitive backscatter network,the resource allocation scheme is investigated to maximize the throughput and the optimal solution in closed form is derived.For the multi-BD cognitive backscatter network,the minimum throughput maximization problem is formulated subject to the primary user’s Qo S and the energy-causality constraints of per BD.Then the variable slack and decoupling methods are introduced to transform the formulated non-convex problem,and propose an iterative algorithm based on the block coordinate descent technique to solve the transformed problem.Numerical results validate the well convergence of the proposed iterative algorithm and that the proposed scheme ensures much fairness than the existing schemes.Third,the throughput fairness-aware resource allocation scheme based on the practical finite alphabet input is proposed for cognitive backscatter network.Specifically,with the maximum minimum throughput as the optimization objective and the primary users’ Qo S of communication and energy-causality of BDs as constraints,the problem with joint optimization of transmit power,energy harvesting time,backscatter time and coefficient is established.Then the problem is transformed by approximation and auxiliary variables methods,and an iterative algorithm based on block coordinate descent technology is proposed to solve the transformed problem.Simulation results show the good convergence performance of the proposed iterative algorithm;meanwhile,compared with other resource allocation schemes,the proposed resource allocation scheme achieves a fair and effective resource allocation.