认知无线电网络中基于cnn的频谱感知分布式学习

Wenbo Fan, Lu He, Yan Long, Honghao Ju, Shijun Lin
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引用次数: 0

摘要

本文提出了一种基于卷积神经网络(CNN)的分布式学习方案,用于认知无线电网络(CRN)中的频谱感知。采用分布式学习架构,对每个辅助用户(secondary user, SU)根据其数据样本进行局部训练。在SU和FC之间交换模型参数后,在FC上进行模型参数的聚合和更新。在每个SU上使用CNN模型,并设计接收信号的协方差矩阵作为CNN的输入。该方案避免了在在线检测期间大量流量在辅助网络中传输,同时在拓扑变化场景下也能提高频谱感知性能。仿真结果表明,本文提出的基于cnn的分布式学习频谱感知方案优于传统的CRN感知算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN-Based Distributed Learning for Spectrum Sensing in Cognitive Radio Networks
In this paper, we propose a convolutional neural network (CNN)-based distributed learning scheme for spectrum sensing in cognitive radio networks (CRN). With the distributed learning architecture, local training is performed on each secondary user (SU) according to its data sample. After model parameters are exchanged between SU and fusion center (FC), model parameter aggregation and update are performed on FC. The CNN model is utilized on each SU and the covariance matrix of received signal is designed as input of CNN. The proposed scheme avoids large amount of traffic transmission in secondary networks during the online detection period, and meanwhile, improves the spectrum sensing performance even under the topology change scenarios. Simulations show that the proposed CNN-based distributed learning spectrum sensing scheme outperforms the conventional sensing algorithms in CRN.
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