Machine learning based energy efficient multichannel resource allocation in cognitive radio networks

Vidhypriya, R.

doi:10.22075/ijnaa.2021.5850

Machine learning based energy efficient multichannel resource allocation in cognitive radio networks

Document Type : Research Paper

Author

R. Vidhypriya

PSG College of Technology, Peelamedu, Coimbatore 641004, India

10.22075/ijnaa.2021.5850

Abstract

The growth of the Internet of Things and mobile devices highly rely on independent and distributed operation in wireless networks. A focus on the allocation of spectrum for effective communication, mitigation of interference and reduction in the energy consumption in the wireless environment is essential. Non-availability of the spectrum in a wireless network can be overcome by spectrum reuse in Cognitive Radio Femtocell networks (CRFN) which improves the indoor communication coverage. is mostly preferred. The spectrum is sensed at regular intervals by the secondary user (SU) to detect the presence of the primary user(PU). Sensing the spectrum reduces the performance and the throughput of the secondary users. To overcome the above in this research, a novel multichannel spectrum allocation (MSA) technique combined with a decode-and-forward (DF) based cooperative spectrum sensing scheme is proposed. The information rate that can be transmitted over a given bandwidth is greatly enhanced in the proposed multichannel resource allocation (MRA) technique It is evident from the simulation results, that the throughput of the SUs is boosted when compared over the established techniques.

Keywords

Terms‒Cognitive radio femtocell network

Resource allocation

Cooperative spectrum sensing

Primary user

Licensed band

Secondary user allocation

[1] I.F. Akyildiz, B.F. Lo and R. Balakrishnan, Cooperative spectrum sensing in cognitive radio networks: A survey,
Phys. Commun. 4(1) (2011) 40–62.
[2] S. Atapattu, C. Tellambura and H. Jiang, Energy detection based cooperative spectrum sensing in cognitive radio
networks, IEEE Trans. Wireless Commun. 10(4) (2011) 1232–1241.
[3] M.R. Bhatnagar, Performance analysis of a path selection scheme in multi-hop decode-and-forward protocol, IEEE
Commun. Lett. 16(12) (2012) 1980–1983.
[4] V. Chandrasekhar, J. Andrews and A. Gatherer, Femtocell networks: A survey, Commun. Mag. IEEE. 46(9)
(2008) 59–67.
[5] W. Ejaz, G. Hattab, N. Cherif, M. Ibnkahla, F. Abdelkefi and M. Siala, Cooperative spectrum sensing with
heterogeneous devices: Hard combining versus soft combining, IEEE Syst. J. 12 (2018) 981–992.
[6] G. Ganesan and Y. Li, Cooperative spectrum sensing in cognitive radio networks, IEEE Trans. Wireless Commun.
6(6) (2007) 2204–2222.
[7] X. Huang, S Liu, Y. Li, F. Zhu and Q. Chen, Dynamic cell selection and resource allocation in cognitive small
cell networks, 2016 IEEE 27th Annual Int. Symp. Personal, Indoor, and Mobile Radio Commun. (2016) 1–6.
[8] Zh. Li, S. Guo, D. Zeng, A. Barnawi and I. Stojmenovic, Joint resource allocation for Max-min Throughput in
multi-cell networks, IEEE Trans. Vehicular Technol. 23(9) (2014) 4546–4559.
[9] S. Lee, K. Huang and R. Zhang, Cognitive energy harvesting and transmission from a network perspective, Proc.
IEEE Int. Conf. Commun. Syst. (2012) 225–229.
[10] H. Rasouli, H.Y. Kong and A. Anpalagan, Cooperative subcarrier allocation and power allocation in the downlink
of an amplify-and-Forward OFDM relaying system, Wireless Pers. Commun. 79(3) (2014) 2271–2290 .
[11] A. Stovar and Z. Chang, Optimisation of cooperative spectrum sensing via optimal power allocation in cognitive
radio networks, IET Commun. 11 (2017) 2116–2124.
[12] Y.F. Wen and W. Liao, Spectrum section preallocation for cooperative sensing and transmission in cognitive radio
ad hoc networks, IEEE Trans. Veh. Technol. 66 (2017) 8910–8925.
[13] Y. Wu, G. Min A.Y. Al-Dubai, A new analytical model for multi-hop cognitive radio networks, IEEE Trans. Wirel.
Commun. 11(5) (2012) 1643–1648.