摘要
针对频谱资源紧缺问题,采用认知无线电网络中协作频谱预测关键技术来提高频谱预测的准确率,该技术能够有效避免在单用户频谱预测中容易受到环境干扰而对预测结果产生影响的问题。首先利用排队论对授权信道状态进行建模,通过LSTM对信道未来时隙状态进行预测,然后汇总出最终的预测结果。最后通过将LSTM与RNN、MLP方法进行Python仿真对比,以预测准确率和F1值作为性能指标进行验证,结果表明LSTM优于其他两种算法。
To solve the problem of spectrum resource shortage,a key technology of cooperative spectrum prediction in cognitive radio network is adopted to improve the accuracy of spectrum prediction in this paper.This technology can effectively avoid the problem that the single user spectrum prediction is easy to be affected by environmental interference.In this paper,we first model the authorized channel state using queuing theory,predict the future time slot state of the channel by LSTM,and then summarize the final prediction results.Finally,by comparing LSTM with RNN and MLP methods in Python simulation,this paper takes prediction accuracy and F1 value as performance indicators to verify,and the results show that LSTM is superior to the other two algorithms.
作者
陈玲玲
张刚
CHEN Ling-ling;ZHANG Gang(Jilin Institute of Chemical Technology,Jilin 132022,Jilin)
出处
《电脑与电信》
2023年第3期20-24,共5页
Computer & Telecommunication
基金
吉林省科技发展计划项目,项目编号:YDZJ202201ZYTS653。
作者简介
通讯作者:陈玲玲(1980-),女,吉林长春人,博士,教授,研究方向为认知无线电功率控制。
