Deep Learning Methods for Secure IoT SWIPT Networks

In this paper, a deep-Q network (DQN)-based approach is proposed to improve the harvested energy and the secrecy capacity by optimizing the beamforming vectors in multiple-input multiple-output (MIMO) networks with simultaneous wireless information and power transmission (SWIPT). Here, in the presence of multi-antenna eavesdroppers, a multi-antenna hybrid access point (HAP) serves energy harvesting Internet of Things (EH-IoT) nodes and information decoding (ID) receiver. The EH-IoT nodes and the ID receiver are separated from each other and experience different channels from the HAP. Two cases of signal models are studied to maximize the harvested energy. The first case is the consolidated signal case, where the HAP transmits an information-bearing signal to both the EH-IoT nodes and the ID receiver. The second case is the split signal case, where the HAP sends both information-carrying signal and energy signals to the EH-IoT nodes and the ID receiver. For both these cases, the optimization strategies for maximizing the harvested energy under Quality of Service (QoS) constraints are formulated with the secrecy capacity and the harvested energy requirements. The harvested energy maximization problems are complex to handle due to the presence of high-dimensional variables. An efficient, robust, and secure DQN-based algorithm is proposed, where Q-learning and deep neural network (DNN) methods are extended to solve these problems. Simulation results show that the proposed DQN approach can achieve a balance between harvested energy and security. By incorporating action and reward spaces that are adaptable to the formulated problem, significant performance gains are achieved.