# Energy Harvesting over Rician Fading Channel:
## 1 Introduction  
Wireless sensor networks (WSNs) are subject to the constraint of energy storage at each mobile node [1; 2]. Saving on energy consumption or extending the battery life for sensor nodes has become an important research issue in wireless sensor networks. Recently, energy harvesting (EH) has attracted enormous attention from researchers as a promising cost-effective technique to maximize energy efficiency of a wireless network [3; 4; 5], especially in wireless sensor networks [6; 7; 8; 9]. Various energy harvesting sources have been studied, such as natural sources (solar [10], wind [11], thermal [12], etc. ), strongly coupled magnetic resonances [13], etc. Among them, radio frequency (RF) energy from the ambient transmitters is the most popular source for energy harvesting since it can be received more effectively from RF signals [4]. Because RF signals can convey both energy and information simultaneously, an RF-based energy harvesting technique, called simultaneous wireless information and power transfer (SWIPT), is becoming a more and more promising research topic for WSNs [14; 15]. The idea of SWIPT was first introduced in 2008 in the seminal paper of [PERSON] [16], in which authors proposed an ideal receiver design that is able to simultaneously observe the information and extract power from the same received signal. [PERSON] et al. [17] connected [PERSON]'s idea to practice byproposing two realizable receiver architectures design: time switching (TS) and power splitting (PS). The performance analysis of these two EH protocols was conducted rigorously by [PERSON] et al. [PERSON] et al. (2018). The authors derived analytical expressions for the outage probability and the ergodic capacity of one-way amplify-and-forward (AF) relay networks over a Rayleigh fading channel. The applications of SWIPT to wireless sensor networks have been investigated in recent works. In [PERSON] et al. (2018), the authors proposed a distributed iteration algorithm to solve an energy-efficient cooperative transmission problem for SWIPT in clustered WSNs. In [PERSON] et al. (2018), the authors employed wireless energy harvesting techniques and realistic energy converters in dense and often randomly deployed WSNs, and quantified the potential energy gains that can be achieved in the network. [PERSON] et al. [PERSON] et al. (2018) considered a wireless energy harvesting two-way relay network (TWRN) using power splitting protocol, where the effects of practical hardware impairments were taken into consideration. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed a new approach to defining the specifications of a stand-alone wireless node based on an RF EH system and implemented a hardware circuit to illustrate their energy optimization method. The performance of wireless powered sensor networks for Internet of Things (IoT) application was studied in [PERSON] et al. (2018), where the authors proposed the optimal power allocation to maximize the system throughput and also derived the closed-form of that solution. However, in those papers, the channel gains are assumed either to be constant or to be a Rayleigh distributed random variable. Wireless communication systems in the real radio environments are not only affected by the short-term fading (multipath), but are also subject to the shadowing effects. The vital issue in studying the performance of energy-harvesting-based wireless networks in these conditions is the outage probability analysis. An important statistical characteristic that can describe the behavior of the wireless channels is the probability density function (PDF) of the signal-to-noise-ratio (SNR) at the receiver output. To derive this PDF for different radio propagation environments is sometimes a difficult mathematical task, especially for complicated channel models such as Nakagami-m or Rician channels.