Analysis of Spectrum Sensing and Spectrum Access in Cognitive Radio Networks with Heterogeneous Traffic and p-Retry Buffering

Shruti, Shruti; Rakhee, Rakhee

doi:10.1109/tmc.2020.3042836

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…Liu et al combined CR with non-orthogonal multiple access (NOMA) to improve sensing accuracy [19]. Shruti and Rakhee proposed a new spectrum access mechanism to improve network resource utilization [20]. Xiao et al developed an incentive mechanism, which enables spectrum resource sharing in heterogeneous IoV networks [21].…”

Section: Relevant Workmentioning

confidence: 99%

Intelligent Spectrum Sensing Algorithm for Cognitive Internet of Vehicles Based on KPCA and Improved CNN

Duan

Gulliver³

2022

Preprint

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With the acceleration of economic globalization and integration, the global trade is becoming more frequent, which promotes the vigorous development of transportation industry. In recent years, the Internet of Vehicle (IoV) has developed rapidly in the transportation industry, and the number of IoV users has exploded. The requirements for IoV communication services are very high, resulting in the lack of spectrum resources. Rather than utilizing traditional spectrum resource allocation methods, cognitive radio technology makes full use of idle frequency bands, improving the IoV communication spectrum’s utilization rate. Spectrum sensing is the primary link to realize a cognitive radio. However, IoV mobile communication environment is characterized by complexity, dynamism, and substantial noise interference, thus imposing significant challenges to spectrum sensing. Thus, this paper proposes an intelligent spectrum sensing algorithm based on kernel principal component analysis (KPCA) and an improved convolutional neural network (CNN). Since the wireless signal cannot distinguish the signal and noise linearly, KPCA maps the sampled signal to a high-dimensional space, creates a covariance matrix, and obtains eigenvector data of the signal and noise through matrix decomposition. A spectrum sensing classifier based on improved CNN is proposed, and the dynamic threshold is obtained via offline training. Compared with the traditional algorithm, the designed deep CNN improves the model’s training speed, enables parameter sharing, and reduces the number of model parameters, effectively reducing the computational complexity. Additionally, due to the extracted signal feature’s small dimension, the algorithm reduces the number of pooling layers and avoids the effective features’ loss, thus increasing the detection probability. Finally, the proposed algorithm achieves a 10% higher sensing accuracy than support vector machine (SVM), Elman, and LeNet5 algorithms, signaling its robustness.

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Section: Relevant Workmentioning

confidence: 99%