研究目的
Investigating the enhancement of secondary user’s throughput in high traffic cognitive radio networks through imperfect spectrum prediction.
研究成果
The redesigned frame structure with spectrum prediction significantly enhances SU’s throughput in high traffic CRN. The throughput improvement is more pronounced with lower prediction errors and higher traffic intensity. Increasing the number of channels also benefits the throughput.
研究不足
The study does not address the computational complexity and energy consumption of the spectrum prediction process. The impact of varying prediction durations on throughput is also not explored.
1：Experimental Design and Method Selection:
The study redesigns the SU’s frame structure by adding the spectrum prediction function to select channels for sensing only from those predicted to be idle. Energy detection is used for spectrum sensing, and a neural network model is chosen for spectrum prediction.
2：Sample Selection and Data Sources:
The CRN consists of a pair of SU transceivers accessing N licensed frequency channels, with PUs’ traffic modeled as a binary stochastic process.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
SU predicts the status of N channels based on historical information, selects a channel predicted to be idle for sensing, and accesses it if sensed idle.
5：Data Analysis Methods:
The throughput is analyzed considering the probability of wrong prediction, traffic intensity, and channel number.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
