In a cognitive radio sensor network (CRSN), wideband spectrum sensing devices which aims to effectively exploit temporarily vacant spectrum intervals as soon as possible are of great importance. However, the challenge of increasingly high signal frequency and wide bandwidth requires an extremely high sampling rate which may exceed today’s best analog-to-digital converters (ADCs) front-end bandwidth. Recently, the newly proposed architecture called modulated wideband converter (MWC), is an attractive analog compressed sensing technique that can highly reduce the sampling rate. However, the MWC has high hardware complexity owing to its parallel channel structure especially when the number of signals increases. In this paper, we propose a single channel modulated wideband converter (SCMWC) scheme for spectrum sensing of band-limited wide-sense stationary (WSS) signals. With one antenna or sensor, this scheme can save not only sampling rate but also hardware complexity. We then present a new, SCMWC based, single node CR prototype System, on which the spectrum sensing algorithm was tested. Experiments on our hardware prototype show that the proposed architecture leads to successful spectrum sensing. And the total sampling rate as well as hardware size is only one channel’s consumption of MWC.

Visible light communication (VLC) is a promising technology with a high data rate that can supplement radio frequency communication. Although VLC systems have a natural advantage of high security due to the line-of-sight light propagation characteristic, they are still vulnerable when facing an open environment. Device fingerprinting is a technique that is widely viewed to detect transmitter impersonation attack in radio frequency (RF) based wireless systems. In this paper, we introduce the fingerprinting technique to discriminate illegal transmitting devices in VLC systems. We first investigate the hardware imperfections of the VLC transmitter, which can provide a unique device ID. Then we implement a feature separation network for transmitter fingerprinting (TF-FSN) and design a two-stage training strategy to obtain a stable classifier. Finally, we experimentally demonstrate the feasibility and performance of the proposed method. The results show that the accuracy of identification and verification is 92.65% and 98%, respectively. Moreover, our method is robust over different distances and a wide range of signal-to-noise ratios.

Automatic modulation classification (AMC) is an important and challenging task that aims to discriminate modulation formats of received signals, such as military communications, cognitive radio and spectrum management. With the development of deep learning techniques, research in AMC has gained promising results because of its powerful representation and classification abilities. In this Letter, the authors present a new network architecture that combines a frequency selection module and a convolutional neural network (CNN). This scheme not only processes raw signal data with carriers to increase the in‐band signal‐to‐noise ratio but also converge faster than traditional CNN. Experiments demonstrate the effectiveness and efficiency of the proposed model.

Automatic modulation classification (AMC) has recently attracted widespread attention nowadays due to its desirable features of generalisability and requirement of little prior knowledge through artificial intelligence (AI) technology. The authors propose a stacked auto-encoder (SAE) based on various optimisation methods structure to intelligently process a feature space that includes spectral-based features and high-order cumulants. To unify the dimensionality of the features, they apply different normalisation methods to the feature space before training the SAE model to decide corresponding normalisations under different noise environments. Linear normalisation is superior when signal-to-noise ratio (SNR) is low, and standardisation is superior when SNR is between -1 and 4 dB. Regularisation works best when SNR is greater than 5 dB. To increase the recognition accuracy of the proposed model, they introduce the unconstrained optimisation theory to adjust the proposed SAE model, including Nelder-Mead method, Newton optimisation method, conjugate gradient method and quasi-Newton method. They observe that the quasi-Newton method offers desirable performance when optimising SAE model. It is the first time to compare these data normalisation methods and discuss unconstrained optimisation theory together to recognise modulation types. The recognition accuracy of this model for eight modulation types can reach 99.8% when SNR ranges from $-5$ to 10 dB.