Research On Resource Allocation In Wireless Powered Communication Networks

With the rapid development of microwave wireless energy transfer(WET),the existing works present a new kind of network framework,namely wireless powered communication network(WPCN),which combines independent wireless information transmission and WET in traditional wireless networks for achieving simultaneous wireless information and power transfer(SWIPT).Compared to traditional battery powered wireless networks,WPCN eliminates the need to replace the battery manually,and thus can greatly reduces the operational cost.In addition,WPCN has full control over energy transfer including transmission power,waveform,time,frequency,etc.,and can provide stable energy supply for wireless devices under different service requirements and physical environments.This is also different from conventional energy harvesting methods,where wireless devices collect renewable energy randomly.This paper investigates the resource management strategy for WPCNs.We aim for maximizing the network energy efficiency(EE),while guaranteeing the user service requirement,by jointly scheduling radio,energy and computational resources.The research contents are as follows:(1)The energy-efficient cooperation transmission and resource allocation strategy for wireless powered cellular networks;(2)The optimal design of cognitive non-orthogonal multiple access(NOMA)systems with the aid of SWIPT;(3)The joint communication and computing cooperation scheme for wireless powered mobile edge computing(MEC)networks;(4)Energy efficiency and delay tradeoff for wireless powered MEC networks.Firstly,this paper studies the energy-efficient transmission scheme and resource allocation for wireless powered cellular networks.Due to the severe pathloss of WET,the cell-edge user may fail to finish its transmission in wireless powered cellular networks.Therefore,we propose an user cooperation scheme,where the cellular users(CU)and the device-to-device(D2D)users first harvest energy from the hybrid access point(HAP),and then some D2 D users can help the cell-edge CU to relay its uplink/downlink information in exchange for the opportunity of D2 D communications.Based on the cooperation scheme,we formulate the EE maximization problem for both uplink and downlink transmissions.The energy beamforming,user transmission power and time resource allocation are jointly optimized subject to the transmission rate requirements and the available energy constraints of CUs and D2 D users.Based on the fractional programming theory and semi-definite relaxation(SDR)method,we develop the resource allocation algorithm to obtain the global optimal solution iteratively.Furthermore,we derive the optimal solution in semi-closed form by exploiting the Lagrangian method.Extensive simulation results are provided to demonstrate the convergence of the proposed algorithm and the great uplink/downlink EE gain of the proposed scheme over the throughput-optimal scheme.Then,this paper investigates the optimal design of cognitive NOMA systems with the aid of SWIPT.The combination of cognitive radio and NOMA has tremendous potential to achieve high spectral efficiency in the internet of things(IoT)era.However,it is restricted by the high power consumption of the successive interference cancellation(SIC)decoder.Therefore,we propose to utilize the SWIPT technique to solve the power consumption problem for cognitive NOMA networks.We consider a typical wireless powered cognitive NOMA system,where the second base station transmits information to second users via power-domain NOMA,and meanwhile guaranteeing the interference leaked to the primary user is tolerable.In addition,the second user can split a part of the received signal for harvesting energy in order to power its SIC decoder.Specifically,a non-linear energy harvesting(EH)model is adopted to characterize the non-linear energy conversion property.In addition,this paper develops a resource allocation model based on imperfect channel state information(CSI)to minimize the system power consumption.By exploiting the classic SDR and successive convex approximation(SCA),we propose a joint beamforming and power splitting algorithm to solve the non-convex problem.Simulation results demonstrate that the non-linear EH model is able to perfectly reflect the energy conversion property of practical energy harvester.In addition,the proposed robust algorithm only consumes a little more system power,when compared to the scheme based on perfect CSI.The integration of MEC and WET has been recognized as a promising technique to satisfy the computing requirement and energy supply for resource-constrained IoT devices.Therefore,this paper investigates the joint computing and communication cooperation for wireless powered MEC networks.We design a typical framework for wireless powered MEC system consisting of a HAP and multiple cooperative fogs,where the users in each cooperative fog can share communication and computation resources to improve their computation performance.Based on the classic time-division-multipleaccess(TDMA)protocol,we propose a Harvest-then-Offload scheme to jointly schedule WET and cooperative computation offloading.Furthermore,we minimize the system energy consumption by jointly considering energy beamforming,time-slot assignment,computation-task allocation and the optimization of central processing unit(CPU)frequencies,while guaranteeing the energy causality and computing delay constraints.We transform the original non-convex problem to a convex model by utilizing the variable substitution and SDR method,and then derive the optimal solution in semi-closed form by exploiting the Lagrangian method.Extensive numerical results show that the proposed joint communication and computation cooperation scheme reduces the total energy consumption considerably compared to state of the art.Moreover,we also demonstrate that the dynamic adjustment of CPU frequency has a positive impact on energy saving compared to the case of fixed CPU frequency.Finally,we investigate the EE-delay tradeoff for wireless powered mobile edge computing networks.Considering the dynamic channel conditions and task arrivals,we formulate a stochastic optimization problem to study the EE-delay tradeoff,which optimizes network EE subject to network stability and energy causality constraints.Further,we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory.Different from the conventional methods,our proposed online algorithm can adjust the resource allocation strategy on the basis of current network states but without requiring the prior knowledge of channel states and task arrivals.Moreover,theoretical analysis reveals that the proposed algorithm can converge to the global optimal EE at the cost of task delay increases.Numerical results verify the tradeoff between network EE and delay.In addition,the proposed algorithm outperforms the other benchmark methods in terms of network EE and delay.