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Subcarrier Allocation Based Cooperative Spectrum Sharing with Wireless Energy Harvesting in OFDM Relaying Networks.
Dan Huang1, Mengshu Hou2, Weidang Lu3
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China. huangdan@uestc.edu.cn.
This study introduces a cooperative spectrum sharing protocol for OFDM relaying networks, enabling cognitive systems to access spectrum by optimizing subcarrier and power allocation for enhanced data rates.
Area of Science:
- Wireless communication
- Signal processing
- Energy harvesting
Background:
- Cognitive relay nodes (CRNs) in Orthogonal Frequency Division Multiplexing (OFDM) networks face challenges in spectrum access and energy management.
- Cooperative spectrum sharing protocols are crucial for efficient utilization of licensed spectrum by unlicensed cognitive systems.
Purpose of the Study:
- To propose a novel subcarrier allocation-based cooperative spectrum sharing protocol for OFDM relaying networks incorporating wireless energy harvesting.
- To maximize the cognitive transmission rate while ensuring the primary system's target transmission rate is met.
Main Methods:
- The protocol involves a two-phase approach where the CRN decodes information and harvests energy, then utilizes harvested energy to forward signals on disjoint subcarriers.
- Optimization of power splitting ratio, power allocation, and subcarrier allocation is performed to achieve the study's objectives.
Main Results:
- The proposed protocol enables cognitive systems to access spectrum by intelligently allocating subcarriers for forwarding primary and cognitive signals.
- Numerical and simulation results demonstrate the effectiveness of the cooperative spectrum sharing protocol and highlight the impact of various system parameters.
Conclusions:
- The developed subcarrier allocation-based cooperative spectrum sharing protocol effectively enhances cognitive transmission rates in OFDM relaying networks with energy harvesting.
- The findings provide valuable insights into optimizing resource allocation for improved performance in cognitive wireless networks.
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