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This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The proposed IBBO introduces an innovative migration model. The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation. This is accomplished by integrating tactics such as advancing towards a random habitat, adopting the crossover operator from genetic algorithms (GA), and utilizing the global best (Gbest) operator from particle swarm optimization (PSO) into the IBBO framework. The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters, resulting in significant outage probability reduction. Through comprehensive simulations, we showcase the superiority of the IBBO-RNN over existing approaches, highlighting its capability to achieve remarkable gains in SOP minimization. This paper compares nine methods for predicting outage probability in wireless-powered communications. The IBBO-RNN achieved the highest accuracy rate of 98.92%, showing a significant performance improvement. In contrast, the standard RNN recorded lower accuracy rates of 91.27%. The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio (SNR) spectrum tested, suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

computers, materials and continua

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network