| 摘要: |
| 为了解决无线供能通信网络（Wireless Power Communication Network,WPCN）中因“双远近”效应导致的边缘节点可实现速率差的问题，引入智能反射面（Intelligent Reflective Surface,IRS）以增加通信路径，同时借助无人机（Unmanned Aerial Vehicle,UAV）作为能量站为地面节点供能。具体而言，在满足IRS恒定模量约束、UAV最大步长约束和无线设备发射功率等约束条件下，建立了一个联合优化IRS相移、UAV轨迹、传输功率和传输时间等优化变量的优化模型。为了解决这个复杂非凸的多变量耦合优化模型，基于块坐标下降法(Block Coordinate Descent,BCD)的思想，分别利用半正定松弛法(Semi-positive Definite Relaxation,SDR)、逐次凸近似(Successive Convex Approximation,SCA)和引入松弛因子等方法，求解出IRS端变量、UAV路径变量和发射端资源变量。仿真表明，所提方案相较传统方案的边缘节点可实现速率提升了11.02%，能够有效减小“双远近”效应带来的影响。 |
| 关键词: 无线供能通信网络(WPCN) 边缘节点 可实现速率 无人机（UAV） 智能反射面(IRS) |
| DOI：10.20079/j.issn.1001-893x.230817005 |
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| 基金项目:国家自然科学基金资助项目（U22B2095） |
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| Maximizing Achievable Rate of Edge Nodes for Wireless Communication Network |
| SU Ziyang,ZHONG Yuanchang,b,ZHAO Xuehui |
| (a.School of Microelectronics,Communication and Information Engineering;b.School of Electrical Engineering,Chongqing University,Chongqing 400044,China) |
| Abstract: |
| To address the issue of poor achievable rates for edge users due to the “double far and near” effect in wireless power communication networks(WPCNs),intelligent reflective surfaces(IRS) are introduced to enhance communication paths,while unmanned aerial vehicles(UAVs) serve as energy stations to supply energy to ground nodes.Specifically,an optimization model is established that jointly optimizes the IRS phase shift,UAV trajectory,transmission power,and transmission time under the constraints of constant modulus for the IRS,maximum step size for the UAV,and transmission power for wireless devices(WDs).To tackle this complex non-convex multivariate coupled optimization model,IRS-side variables,UAV path variables,and transmitter-side resource variables are solved using the block coordinate descent(BCD) method,incorporating semi-positive definite relaxation(SDR),successive convex approximation(SCA),and the introduction of relaxation factors.Simulation results indicate that the proposed scheme achieves an approximate rate improvement of 11.02% compared with traditional approaches,effectively mitigating the impact of the “double far and near” effect. |
| Key words: wireless power communication network(WPCN) edge node achievable rate unmanned aerial vehicle(UAV) intelligent reflective surface(IRS) |
