In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for low-power Internet-of-Things (IoT) devices, where the IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmission energy minimization problem by a joint design of time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. Due to the high coupling of optimization variables, this problem is a non-convex optimization problem, which is difficult to solve directly. In order to solve the above-mentioned challenging problems, the alternating optimization (AO) is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is applied to solve the non-convex rank-one constraint in solving the IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the effectiveness of our proposed algorithm in reducing HAP transmission energy compared to other benchmarks.
翻译:在本文中,为低功率因特网交换功能(IoT)设备提出了一个新的智能化结构,反映表面(IRS)辅助无线电力通信网络(WPCN)结构,其中利用IRS在不完善的频道状态信息(CSI)下改进WPCN的性能。我们制定了混合接入点(HAP)传输能源最小化问题,方法是联合设计时间分配、HAP能源波束成型、接收信号成型、用户传输电力分配、IRS能源反射系数和在不完善的 CSI和非线性能源收集模式下的信息反射系数。由于优化变异变量的高度结合,这一问题是一个非碳化优化问题,难以直接解决。为了解决上述具有挑战性的问题,我们采用了交替优化(AO)来调和优化变量以解决问题。具体来说,通过AO、时间分配、HAP能源成型、接受信号变形、用户传输动力分配、IRS能源反射系数和信息反射系数被分为三个子问题。在模拟反射法中应用反射率的反演算法(将反射法的反射率法的反射率法的反射法)的反射法比反射法的反射系数用于非解。