Outage probability analysis for relay-aided self-energy recycling wireless sensor networks over INID Rayleigh fading channels

Abstract

Communication reliability is one of the key challenging issues in future communications due to massive connections, especially for wireless sensor networks (WSNs) with low-cost devices. This article studies the communication reliability of wireless systems in the presence of multiple sensor relays, which carry out energy harvesting to prolong the network lifetime. By exploiting the deep shadow fading model, the three sensor selection methods are investigated based on the different prior information of the propagation channels. We then derive the analytical expressions of the outage probability (OP) for each sensor selection, which only depends on the statistical channel knowledge that can be applied for multiple coherence intervals whenever the channel statistics remain the same. Since the obtained analytical OPs are interpreted based on several coupled integrals that are costly to compute, we further propose a learning framework to predict the OP with low computational complexity via exploiting supervised learning. Numerical results indicate that the two suboptimal sensor selection solutions provide a competitive OP with each other. In contrast, the optimal solution outperforms the remaining benchmarks by many folds. Besides, the deep-learning-based approach performs almost the same performance as the analytical-based framework.