LAUSR

Secure information transmission and energy efficiency (EE) optimization are very important for simultaneous wireless information and power transfer (SWIPT)-aided heterogeneous networks. However, most of the existing works consider perfect channel state information (CSI) and linear energy harvesting (EH) models, which are too ideal in practical systems. In this article, we focus on the EE-based robust optimization with imperfect CSI and nonlinear EH models in a SWIPT-aided two-tier heterogeneous macro-femto network with multiple eavesdroppers. In particular, we formulate a robust beamforming problem by jointly optimizing the beamforming vectors of the macro base station (BS) and femto BSs, the power splitting (PS) factors of energy receivers, and the artificial noise vectors of BSs, under multiple constraints including the quality of service requirement of each user, the minimum harvested energy, the maximum transmit power, and the PS factor. Although the formulated robust optimization problem is nonconvex, an EE-based iterative algorithm is developed to obtain the solutions. Simulation results demonstrate the proposed algorithm is superior to other algorithms in terms of EE and security.