Joint Resource Allocation for Wireless Energy Harvesting Enabled Cognitive Sensor Networks | Zendy

Weidang Lu | Zendy; Tian Nan | Zendy; Yi Gong | Zendy; Mei Qin | Zendy; Xin Liu | Zendy; Zhijiang Xu | Zendy; Zhenyu Na | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Joint Resource Allocation for Wireless Energy Harvesting Enabled Cognitive Sensor Networks

Author(s) -

Weidang Lu,

Tian Nan,

Yi Gong,

Mei Qin,

Xin Liu,

Zhijiang Xu,

Zhenyu Na

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2827022

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, we consider a cognitive relaying network, where the cognitive sensor transmitter (CST) is capable of harvesting energy from the radio frequency signals with a power splitting scheme. Specifically, the primary transmitter (PT) broadcasts its signal to primary receiver (PR) and CST in the first transmission slot. After receiving PT's signal, CST splits the received signal into two parts, one for information decoding and the other for energy harvesting. In the second transmission slot, CST allocates a part of the accessed bandwidth to forward PT's signal to PR with amplify-and-forward or decode-andforward relaying protocols by using the harvested energy in the first transmission slot. As a reward, CST can utilize the remained bandwidth to transmit its own signal to the cognitive sensor receiver. The main object is to maximize cognitive network transmission rate by jointly optimizing the power splitting ratio and bandwidth subject to the primary transmission rate constraint. Simulation results are presented to illustrate the performance improvement of both primary and cognitive systems.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research