In this paper, the optimization of downlink beamforming vectors, uplink transmission power, and power-splitting factors is investigated for a secure two-way SWIPT network in the presence of a hidden eavesdropper and non-linear energy harvesting circuits over both perfect and imperfect channels. The eavesdropper is inactive, so its channel information is not available at the base stations (BSs). The purpose of artificial noise is to create downlink interference with the hidden eavesdropper as much as possible, while satisfying the quality of service for two-way communications. For perfect downlink channels, the semidefinite relaxation (SDR) technique is exploited, and the optimal matrices are proven to satisfy rank-1 conditions, thus providing the optimal beamforming vectors. For imperfect downlink channel state information, we propose an iterative algorithm with a penalty function to obtain the approximate rank-1 matrices. On uplink, we attain the optimal transmission power for users receiving maximum ratio transmission beamforming at the BSs. Eventually, the numerical experiments show the superiority of the proposed scheme, compared to a conventional scheme, in terms of signal-to-interference-plus-noise ratio at the eavesdropper.
Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information