对流层散射通信的快衰落特性导致接收信号信噪比(Signal-to-Noise Ratio,SNR)不断变化,根据时变SNR进行自适应编码调制,可以使业务速率实时跟随SNR的变化而改变,在保证可靠传输的前提下有效提升通信吞吐量。针对散射通信系统自适应编码...对流层散射通信的快衰落特性导致接收信号信噪比(Signal-to-Noise Ratio,SNR)不断变化,根据时变SNR进行自适应编码调制,可以使业务速率实时跟随SNR的变化而改变,在保证可靠传输的前提下有效提升通信吞吐量。针对散射通信系统自适应编码调制的需求,在归一化最小均方(Normalization Least Mean Square,NLMS)算法和递归最小二乘(Recursive Least Square,RLS)算法的基础上,提出了改进递归最小二乘(Modified Recursive Least Square,MRLS)SNR预测算法。SNR预测算法可以解决接收端估计的SNR反馈到发送端的延迟问题。经过仿真和外场实验测试,相比NLMS算法和RLS算法,所提出的MRLS算法具有更小的SNR预测误差。采用所提出的MRLS算法进行自适应编码调制流程后,相比NLMS算法和RLS算法,在外场实验中分别可获得约0.4、2 Mb/s的平均业务速率提升,证明了所提出算法的性能优势。展开更多
The main components of Cognitive Radio networks are Primary Users(PU)and Secondary Users(SU).The most essential method used in Cognitive networks is Spectrum Sensing,which detects the spectrum band and opportunistical...The main components of Cognitive Radio networks are Primary Users(PU)and Secondary Users(SU).The most essential method used in Cognitive networks is Spectrum Sensing,which detects the spectrum band and opportunistically accesses the free white areas for different users.Exploiting the free spaces helps to increase the spectrum efficiency.But the existing spectrum sensing techniques such as energy detectors,cyclo-stationary detectors suffer from various problems such as complexity,non-responsive behaviors under low Signal to Noise Ratio(SNR)and computational overhead,which affects the performance of the sensing accuracy.Many algorithms such as Long-Short Term Memory(LSTM),Convolutional Neural Networks(CNN),and Recurrent Neural Networks(RNN)play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks,but still require improvisation in terms of sensing accuracy under dynamic environmental conditions.This paper,we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory(COLSTM),an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity.The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency.Furthermore,the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances.The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds.The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared.Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments.Further,the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.展开更多
文摘对流层散射通信的快衰落特性导致接收信号信噪比(Signal-to-Noise Ratio,SNR)不断变化,根据时变SNR进行自适应编码调制,可以使业务速率实时跟随SNR的变化而改变,在保证可靠传输的前提下有效提升通信吞吐量。针对散射通信系统自适应编码调制的需求,在归一化最小均方(Normalization Least Mean Square,NLMS)算法和递归最小二乘(Recursive Least Square,RLS)算法的基础上,提出了改进递归最小二乘(Modified Recursive Least Square,MRLS)SNR预测算法。SNR预测算法可以解决接收端估计的SNR反馈到发送端的延迟问题。经过仿真和外场实验测试,相比NLMS算法和RLS算法,所提出的MRLS算法具有更小的SNR预测误差。采用所提出的MRLS算法进行自适应编码调制流程后,相比NLMS算法和RLS算法,在外场实验中分别可获得约0.4、2 Mb/s的平均业务速率提升,证明了所提出算法的性能优势。
文摘The main components of Cognitive Radio networks are Primary Users(PU)and Secondary Users(SU).The most essential method used in Cognitive networks is Spectrum Sensing,which detects the spectrum band and opportunistically accesses the free white areas for different users.Exploiting the free spaces helps to increase the spectrum efficiency.But the existing spectrum sensing techniques such as energy detectors,cyclo-stationary detectors suffer from various problems such as complexity,non-responsive behaviors under low Signal to Noise Ratio(SNR)and computational overhead,which affects the performance of the sensing accuracy.Many algorithms such as Long-Short Term Memory(LSTM),Convolutional Neural Networks(CNN),and Recurrent Neural Networks(RNN)play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks,but still require improvisation in terms of sensing accuracy under dynamic environmental conditions.This paper,we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory(COLSTM),an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity.The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency.Furthermore,the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances.The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds.The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared.Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments.Further,the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.
