Signal-to-noise ratio(SNR)fluctuations signiflcantly affect spectrum sensing performance in wireless communications.Traditional convolutional neural network(CNN)exhibits limited feature extraction capabilities and ine...Signal-to-noise ratio(SNR)fluctuations signiflcantly affect spectrum sensing performance in wireless communications.Traditional convolutional neural network(CNN)exhibits limited feature extraction capabilities and inefficient feature utilization at low SNR levels,leading to suboptimal spectrum sensing performance.This paper proposes a spectrum sensing method based on a multi-scale feature fusion network(MSFFNet)to address this issue.First,the proposed method employs a multi-scale feature extraction block(MSFEB)to capture multi-scale information from the input data comprehensively.Next,an adaptive feature screening strategy(AFSS)highlights key features while suppressing redundant information.Finally,a multi-level feature fusion mechanism(MLFFM)optimizes and integrates features across scales and levels,enhancing spectrum sensing performance.Simulation results demonstrate that compared to other methods,the proposed approach achieves superior performance in lowSNR communication scenarios.At an SNR of-14 d B,the detection probability Pd reaches 0.936,while the false alarm probability Pfa is only 0.1.Furthermore,this paper constructs a multi-level mixed-SNR dataset to simulate real communication environments and enhance the robustness of spectrum sensing.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.62066016 and 62161012)the Natural Science Foundation of Hunan Province of China(No.2024JJ7395)+1 种基金the Scientific Research Project of Education Department of Hunan Province of China(No.22B0549)the Scientific Research Project of Jishou University(No.Jdy24049)。
文摘Signal-to-noise ratio(SNR)fluctuations signiflcantly affect spectrum sensing performance in wireless communications.Traditional convolutional neural network(CNN)exhibits limited feature extraction capabilities and inefficient feature utilization at low SNR levels,leading to suboptimal spectrum sensing performance.This paper proposes a spectrum sensing method based on a multi-scale feature fusion network(MSFFNet)to address this issue.First,the proposed method employs a multi-scale feature extraction block(MSFEB)to capture multi-scale information from the input data comprehensively.Next,an adaptive feature screening strategy(AFSS)highlights key features while suppressing redundant information.Finally,a multi-level feature fusion mechanism(MLFFM)optimizes and integrates features across scales and levels,enhancing spectrum sensing performance.Simulation results demonstrate that compared to other methods,the proposed approach achieves superior performance in lowSNR communication scenarios.At an SNR of-14 d B,the detection probability Pd reaches 0.936,while the false alarm probability Pfa is only 0.1.Furthermore,this paper constructs a multi-level mixed-SNR dataset to simulate real communication environments and enhance the robustness of spectrum sensing.
