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The secrecy rate of two-way untrusted relay networks with imperfect channel state information based on SWIPT is investigated when multiple relays harvest energy from two sources. Despite assisting in information forwarding, the relays are considered untrusted in that they might attempt to eavesdrop on confidential information. To interfere with eavesdropping by untrusted relays, sources-based friendly cooperative jamming is introduced. A joint power allocation and time switching strategy has been studied to maximize the sum secrecy rate of the system under total block transmission duration and power constraints. The genetic algorithm (GA) is introduced to optimize the proposed joint power allocation and time switching (JPTs) scheme. In addition, two suboptimal schemes are proposed: the power allocation and time switching scheme for individual power constraints and the power allocation scheme for equal time allocation. Simulation results demonstrate that the proposed joint power allocation and time switching strategy performs better in contributing to the total secrecy rate of the system.

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Accurate solar irradiance prediction plays an important role in ensuring the security and stability of renewable energy systems. Solar irradiance modeling is usually a time-dependent dynamic model. As a new kind of recurrent neural network, echo state network (ESN) shows excellent performance in the field of time series prediction. However, the memory length of classical ESN is fixed and finite, which makes it hard to map sufficient features of solar irradiance with long-range dependency. Therefore, a novel deep echo state network with variable memory pattern (VMP-DESN) is proposed in this brief. VMP-DESN consists of multiple connected reservoirs in series, and there exist different types of memory modes in VMP-DESN. To remember more input history information in the states, the time delay links are added in each reservoir and between every two reservoirs. The VMP-DESN is more flexible to deal with different input signals due to its variable memory modes in the reservoir states. Additionally, the effect of different memory patterns on the VMP-DESN performance is discussed in detail, including the antidisturbance ability, memory capacity, and prediction accuracy. Finally, the effectiveness of VMP-DESN is evaluated by predicting the real solar irradiance task.