Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks

Abstract

The incorporation of cognitive radio (CR) capability in wireless sensor networks yields a promising network paradigm known as CR sensor networks (CRSNs), which is able to provide spectrum efficient data communication. However, due to the high energy consumption results from spectrum sensing, as well as subsequent data transmission, the energy supply for the conventional sensor nodes powered by batteries is regarded as a severe bottleneck for sustainable operation. The energy harvesting technique, which gathers energy from the ambient environment, is regarded as a promising solution to perpetually power-up energy-limited devices with a continual source of energy. Therefore, applying the energy harvesting (EH) technique in CRSNs is able to facilitate the self-sustainability of the energy-limited sensors. The primary concern of this study is to design sensing-transmission policies to minimize the long-term outage probability of EH-powered CR sensor nodes. We formulate this problem as an infinite-horizon discounted Markov decision process and propose an ϵ-optimal sensing-transmission (ST) policy through using the value iteration algorithm. ϵ is the error bound between the ST policy and the optimal policy, which can be pre-defined according to the actual need. Moreover, for a special case that the signal-to-noise (SNR) power ratio is sufficiently high, we present an efficient transmission (ET) policy and prove that the ET policy achieves the same performance with the ST policy. Finally, extensive simulations are conducted to evaluate the performance of the proposed policies and the impaction of various network parameters.

Residual Energy Analysis in Cognitive Radios with Energy Harvesting UAV under Reliability and Secrecy Constraints

“…In the sensing phase with a duration of t, the EH-UAV simultaneously harvests RF energy from the received signal and performs the spectrum sensing (SS) procedure for the opportunistic use of the primary band. The dynamic power splitting device splits the received primary signal into the two power fractions of (1 − ρ) and ρ for EH and SS purposes, respectively, where ρ denotes the power splitting factor [15]. In the transmission phase with a duration of T d , the EH-UAV performs the full-proportioned EH, i.e., ρ = 0, if a signal from PT is detected.…”

“…Furthermore, energy harvesting (EH) in wireless networks is the process of extracting energy from the surrounding environment, such as from solar, heat, wind, and radio frequency (RF) signals [14,15]. The EH techniques have come to the forefront to improve the battery's limited capacity, i.e., supply energy to the energy-constrained nodes.…”

Performance Analysis of Underlay Cognitive Radio System with Self-Sustainable Relay and Statistical CSI

“…The concept of cooperative decode-and-forward (DF) relay that harvests energy from both source and interference signals in the Rayleigh fading environment is proposed in [ 24 ] and further analyzed in [ 27 ] for Nakagami- m environment and nonlinear energy harvester. Energy harvesting is analyzed in [ 28 ] for cooperative relaying cognitive network with perfect CSI, and in [ 29 , 30 , 31 ] for the case of imperfect CSI. However, to the best of authors’ knowledge, there is no available investigation concerning the impact of the statistical CSI on the performance of the cognitive cooperative relaying network that employs energy harvesting.…”

Cooperative Dynamic Game-Based Optimal Power Control in Wireless Sensor Network Powered by RF Energy

“…Radio frequency (RF)-based wireless energy transfer can be introduced to solve the limited energy and non-stationary power supply problems [ 5 ]. Through RF-based wireless energy transfer, the wireless sensors can replenish their energy from various energy sources [ 6 ]. Applying the wireless energy transfer into wireless sensor networks, can enhance the life cycle of the sensor nodes, and improve the network performance [ 7 ].…”