研究目的
To address the joint optimization of sensing time and channel overhead in cooperative sensing within cognitive radio networks without compromising performance.
研究成果
The proposed model for cooperative sensing, which selects only reliable CRs for data fusion, outperforms conventional hard and soft decision fusion schemes, especially with a large number of cooperative nodes, by reducing sensing time and channel overhead without compromising system performance.
研究不足
The training process of the system can take ample amount of time, though it runs in the background without affecting the system's normal performance.
1：Experimental Design and Method Selection
The paper proposes a model that selects only those cognitive radios (CRs) which are reliable for data fusion, aiming to reduce sensing time and channel overhead. The model includes a training and testing module for selecting reliable CRs.
2：Sample Selection and Data Sources
Five NI-USRP 2942R devices were used to develop a cooperative sensing scenario, sensing a 20 MHz wideband spectrum under variable noise floor conditions.
3：List of Experimental Equipment and Materials
NI-USRP 2942R devices interfaced through LabVIEW software.
4：Experimental Procedures and Operational Workflow
Two QPSK signals were generated with different SNRs at 815 and 825 MHz. CR receivers were kept under different environmental conditions to sense the spectrum. The sensed data were given to the model to produce the best decision.
5：Data Analysis Methods
The performance of the proposed model was compared with hard and soft decision fusion schemes in terms of accuracy under different scenarios.
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