Abstract
Spectrum sensing (SS) has attracted much attention in the field of Internet of things (IoT) due to its capacity of discovering the available spectrum holes and improving the spectrum efficiency. However, the limited sensing time leads to insufficient sampling data due to the tradeoff between sensing time and communication time. In this paper, deep learning (DL) is applied to SS to achieve a better balance between sensing performance and sensing complexity. More specifically, the two‐dimensional dataset of the received signal is established under the various signal‐to‐noise ratio (SNR) conditions firstly. Then, an improved deep convolutional generative adversarial network (DCGAN) is proposed to expand the training set so as to address the issue of data shortage. Moreover, the LeNet, AlexNet, VGG‐16, and the proposed CNN‐1 network are trained on the expanded dataset. Finally, the false alarm probability and detection probability are obtained under the various SNR scenarios to validate the effectiveness of the proposed schemes. Simulation results state that the sensing accuracy of the proposed scheme is greatly improved.
Highlights
In recent years, the spectrum resource has been more and more scarce due to the great demand for wireless communication, Internet of Things (IoT), Artificial Intelligence (AI) [1,2,3], etc
Assume the input of the Generator is the random Gaussian noise z and its output is the fake sample GðzÞ: The true sample x and fake sample GðzÞ are input to the Discriminator, respectively, and the corresponding outputs are DðxÞ and DðGðzÞÞ:DðxÞ denotes the probability that the input x of the Discriminator is a real sample
The deep learning based spectrum sensing is discussed for sustainable cities and society, where the LeNet, AlexNet, VGG-16, and the proposed convolutional neural network- (CNN-)1 network are considered
Summary
The spectrum resource has been more and more scarce due to the great demand for wireless communication, Internet of Things (IoT), Artificial Intelligence (AI) [1,2,3], etc. ED is the optimal blind detector considering both sensing performance and sensing complexity It suffers from noise uncertainty under the low signal-to-noise ratio (SNR) regimes. The sensing performance of cyclic spectrum detection and covariance matrix detection is improved in the low-SNR case compared with ED at the expense of a higher complexity. These traditional SS schemes either have poor performance or have high complexity. The corresponding false alarm probability and detection probability are given under the various SNR scenarios (4) Based on the sensing performance of the LeNet, AlexNet, and VGG-16 networks, an improved network is provided in this paper to balance the sensing performance and the sensing complexity.
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