Design and Implementation of the Dynamic Spectrum Access on an Audio Stream in a Congested Environment

F. N. Nwukor; M. S. Okundamiya

doi:10.37121/jaccit.v2.174

F. N. Nwukor Petroleum Training Institute, Effurun, Nigeria
M. S. Okundamiya Ambrose Alli University, Ekpoma, Nigeria

DOI: https://doi.org/10.37121/jaccit.v2.174

Keywords: Channel Selection, Dynamic Spectrum Access, Energy Detector Algorithm, Software Defined Radio, Spectrum Utilisation

Abstract

This paper aims to design and implement the dynamic spectrum access (DSA) on an audio stream in a congested environment. The test approach for the DSA protocol is based on the frequency of selection of five chosen stations and the size of the audio file saved. The implementation of the DSA protocol was done with an FM received coupled with the energy detector and channel selection algorithm using a non-coherent FM demodulation procedure and the register transfer level - software defined radio (RTL-SDR) in MATLAB environment (version 2018b). The analysis of the results for the DSA protocol implemented in the FM receiver showed that the 97.3MHz station is active compared to the remaining stations.

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References

F. N. Nwukor, and M. S. Okundamiya, “Development of an energy detection algorithm for signal identification in a security system,” Advances in Electrical and Telecommunication Engineering, vol. 2, no. 2, pp. 78-90, 2019.

S-S. Byun, K. Kansanen, I. Balasingham, and J-M. Gil, “Achieving fair spectrum allocation and reduced spectrum handoff in wireless sensor networks: Modeling via biobjective optimization,” Modelling and Simulation in Engineering, vol. 2014, Article 2, 2014; doi: 10.1155/2014/406462

M. C. Tapiwa, “Efficient spectrum use in cognitive radio networks using dynamic spectrum management,” PhD thesis submitted to University of Pretoria, South Africa, 2016.

B. Wang and K. J. R. Liu, “Advances in cognitive radio networks: a survey,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5–23, 2011

R. B. Patil, K. D. Kulat, and A. S. Gandhi, "SDR based energy detection spectrum sensing in cognitive radio for real time video transmission," Modelling and Simulation in Engineering, vol. 2018, Article ID 2424305, 2018. https://doi.org/10.1155/2018/2424305

S. Jana, Kai Zeng and P. Mohapatra, "Trusted collaborative spectrum sensing for mobile cognitive radio networks," 2012 Proceedings IEEE INFOCOM, 2012, pp. 2621-2625, doi: 10.1109/INFCOM.2012.6195665.

O. Ovum, “5G fixed-wireless access, providing fibre speeds over the air while also helping pave the way for 5g full mobility,” Samsung, 2016.

International Telecommunication Union – R, “IMT vision-framework and overall objectives of the future development of IMT for 2020 and beyond,” M Series Mobile, radiodetermination, amateur and related satellite services, Recommendation ITU-R M.2083-0, September, 2015; https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.2083-0-201509-I!!PDF-E.pdf

International Telecommunication Union – R, “Minimum requirements related to technical performance for IMT-2020 radio interface(s),” M Series Mobile, radiodetermination, amateur and related satellite services, Report ITU-R M.2410-0, November, 2017; https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2410-2017-PDF-E.pdf

K. Laraqui, S. Tombaz, A. Furuskär, B. Skubic, A. Nazari, and E. Trojer, “Fixed wireless access: On a massive scale with 5G,” Ericsson Technology Review, December, 2016.

I. F. Akyildiz, W-Y. Lee, K. R. Chowdhury, "CRAHNs: Cognitive radio ad hoc networks," Ad Hoc Networks, vol. 7, no. 5, pp. 810-836, 2009.

P. Kaligineedi, M. Khabbazian and V. K. Bhargava, "Secure cooperative sensing techniques for cognitive radio systems," 2008 IEEE International Conference on Communications, 2008, pp. 3406-3410, doi: 10.1109/ICC.2008.640.

H. Li and Z. Han, "Dogfight in spectrum: Jamming and anti-jamming in multichannel cognitive radio systems," GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009, pp. 1-6, doi: 10.1109/GLOCOM.2009.5425707.

Q. Wang, K. Ren and P. Ning, "Anti-jamming communication in cognitive radio networks with unknown channel statistics," 2011 19th IEEE International Conference on Network Protocols, 2011, pp. 393-402, doi: 10.1109/ICNP.2011.6089079.

S. Sodagari, and T. C. Clancy, “An anti-jamming strategy for channel access in cognitive radio networks,” In: J. S. Baras, J. Katz, E. Altman, (eds), Decision and Game Theory for Security, GameSec 2011 Lecture Notes in Computer Science, vol 7037, 2011 Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25280-8_5

C. Chen, M. Song, C. Xin and J. Backens, "A game-theoretical anti-jamming scheme for cognitive radio networks," in IEEE Network, vol. 27, no. 3, pp. 22-27, May-June 2013, doi: 10.1109/MNET.2013.6523804.

W. Wang, M. Chatterjee, and K. Kwiat, "Collaborative jamming and collaborative defense in cognitive radio networks," 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 1-6, 2011, doi: 10.1109/WoWMoM.2011.5986172.

R. W. Stewart, K. W. Barlee, D. S. W. Atkinson, L. H. Crockett, "Software defined radio using MATLAB & Simulink and the RTL-SDR", Strathclyde Academic Media, Glasgow, 2015

S. Jaloudi, "Software-Defined Radio for Modular Audio Mixers: Making Use of Market-Available Audio Consoles and Software-Defined Radio to Build Multiparty Audio-Mixing Systems," in IEEE Consumer Electronics Magazine, vol. 6, no. 4, pp. 97-104, Oct. 2017, doi: 10.1109/MCE.2017.2714720