| Autor: |
Giri, Manish Kumar, Majumder, Saikat |
| Předmět: |
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| Zdroj: |
IETE Technical Review; May/Jun2022, Vol. 39 Issue 3, p698-712, 15p |
| Abstrakt: |
In this article, a technique for cooperative spectrum sensing (CSS) using the Extreme Learning Machine (ELM) is proposed. ELMs are feedforward neural networks where the hidden layer parameters are not tuned, and only output weights are optimized. The simulations were done for both the fading and non-fading environments. Different combination of activation function and weight initialization scheme is deployed for calculating channel occupancy detection. The obtained results are compared with popular fusion schemes and well-known Machine Learning (ML) techniques. Further, the proposed algorithm's performance is compared with some of the recent CSS techniques in the literature. The primary metrics for comparison were the receiver operating characteristic (ROC) curve, the area under the curve (AUC), detection performance, and energy consumption. We obtained the ELM model's computational performance during the training phase and calculated the channel detection probability. These results demonstrate the potential superiority of ELM over established methods. In particular, the work presented here shows a better trade-off between training time and detection performance. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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