Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics



Soni, Brijesh, Patel, Dhaval K and Lopez-Benitez, Miguel ORCID: 0000-0003-0526-6687
(2020) Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics. IEEE ACCESS, 8. pp. 97437-97451.

[img] Text
IEEEE_Access_2020.pdf - Author Accepted Manuscript

Download (3MB) | Preview

Abstract

The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of machine learning/deep learning framework, many works in the recent past have applied shallow/deep multi-layer perceptron approach for spectrum sensing. However, the multi-layer perceptron networks are not well suited for time-series data due to the absence of memory elements. On the other hand, long short-term memory (LSTM) network, an improved version of Recurrent neural network is well suited for time-series data. In this paper, we propose an LSTM based spectrum sensing (LSTM-SS), which learns the implicit features from the spectrum data, for instance, the temporal correlation (i.e., the correlation between the present and past timestamp).Moreover, the CR systems also exploits the PU activity statistics to improve the CR performance. In this context, we compute the PU activity statistics like on and off period duration, duty cycle and propose the PU activity statistics based spectrum sensing (PAS-SS) to enhance the sensing performance. The proposed sensing schemes are validated on the spectrum data of various radio technologies acquired using an experimental test-bed setup. The proposed LSTM-SS scheme is compared with the state of the art spectrum sensing techniques. Experimental results indicate that the proposed schemes has improved detection performance and classification accuracy at low signal to noise ratio regimes. We notice that the improvement achieved is at the cost of longer training time and a nominal increase in execution time.

Item Type: Article
Uncontrolled Keywords: Sensors, Signal to noise ratio, Correlation, Cognitive radio, White spaces, Machine learning, Data models, Cognitive radio, spectrum sensing, long short-term memory, primary user activity statistics, deep learning
Depositing User: Symplectic Admin
Date Deposited: 18 May 2020 09:29
Last Modified: 15 Mar 2024 06:08
DOI: 10.1109/ACCESS.2020.2995633
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3087628