5G Cognitive Radio system design with new algorithm Asynchronous spectrum sensing

mohammed mehdi saleh, Ahmed A. Abbas Hussein, AHMED Talaat HAMMOODI

Abstract


Due to the rapid increase in wireless applications and the number of users, spectrum scarcity, energy consumption and latency issues will emerge, notably in the 5G system. Cognitive radio (CR) has arisen as the primary technology to address these challenges, allowing opportunist access to the spectrum and the ability to analyze, observe and learn how to respond to environmental 5G conditions. In order to use underused frequency bands without creating unnecessary interference with legacy networks, the CR has the ability to sense the spectrum and to detect empty bands. In this paper, we presented a spectrum sensing algorithm based on energy detection that allows secondary users to transmit asynchronous with primary users without causing harmful interference. This algorithm enabled to reduce 40% of the sensing time required to scan the entire working frequency band and it possible to use large frequency bands that could reach GHz. Acoording to the BER , the secondary users beter performance cmopare with primary users.

Keywords


Cognitive radio, Energy detection, Sensing time, OFDM, Fifth Generation (5G)

References


I. Ali, M. H. Jamaluddin, M. R. Kamarudin, A. Gaya, and R. Selvaraju, “Wideband and high gain dielectric resonator antenna for 5g applications,” Bull. Electr. Eng. Informatics, vol. 8, no. 3, 2019, doi: 10.11591/eei.v8i3.1592.

J. Mitola and G. Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, 1999, doi: 10.1109/98.788210.

P. Kolodzy and I. Avoidance, “Spectrum policy task force,” Fed. Commun. Comm., Washington, DC, Rep. Docket, no. 02–135, 2002.

J. F. Monserrat, G. Mange, V. Braun, H. Tullberg, G. Zimmermann, and Ö. Bulakci, “METIS research advances towards the 5G mobile and wireless system definition,” Eurasip J. Wirel. Commun. Netw., vol. 2015, no. 1, 2015, doi: 10.1186/s13638-015-0302-9.

M. Y. Zeain et al., “Design of a wideband strip helical antenna for 5g applications,” Bull. Electr. Eng. Informatics, vol. 9, no. 5, 2020, doi: 10.11591/eei.v9i5.2055.

M. Agiwal, A. Roy, and N. Saxena, “Next generation 5G wireless networks: A comprehensive survey,” IEEE Communications Surveys and Tutorials. 2016, doi: 10.1109/COMST.2016.2532458.

C. Jiang, H. Zhang, Y. Ren, Z. Han, K. C. Chen, and L. Hanzo, “Machine Learning Paradigms for Next-Generation Wireless Networks,” IEEE Wirel. Commun., vol. 24, no. 2, 2017, doi: 10.1109/MWC.2016.1500356WC.

M. Shafi et al., “5G: A tutorial overview of standards, trials, challenges, deployment, and practice,” IEEE J. Sel. Areas Commun., vol. 35, no. 6, 2017, doi: 10.1109/JSAC.2017.2692307.

M. Q. Taha, Z. H. Ali, and A. K. Ahmed, “Two-level scheduling scheme for integrated 4G-WLAN network,” Int. J. Electr. Comput. Eng., vol. 10, no. 3, 2019, doi: 10.11591/ijece.v10i3.pp2633-2643.

S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, 2005, doi: 10.1109/JSAC.2004.839380.

A. Ghasemi and E. S. Sousa, “Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs,” IEEE Communications Magazine, vol. 46, no. 4. 2008, doi: 10.1109/MCOM.2008.4481338.

J. Shen, S. Liu, Y. Wang, G. Xie, H. F. Rashvand, and Y. Liu, “Robust energy detection in cognitive radio,” IET Commun., vol. 3, no. 6, 2009, doi: 10.1049/iet-com.2008.0107.

M. Jia, X. Gu, Q. Guo, W. Xiang, and N. Zhang, “Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5G,” IEEE Wirel. Commun., vol. 23, no. 6, 2016, doi: 10.1109/MWC.2016.1500108WC.

X. Liu, F. Li, and Z. Na, “Optimal Resource Allocation in Simultaneous Cooperative Spectrum Sensing and Energy Harvesting for Multichannel Cognitive Radio,” IEEE Access, vol. 5, 2017, doi: 10.1109/ACCESS.2017.2677976.

D. A. G. Ramirez, C. Hernandez, and F. Martinez, “Throughput in cooperative wireless networks,” Bull. Electr. Eng. Informatics, vol. 9, no. 2, 2020, doi: 10.11591/eei.v9i2.2025.

R. Li et al., “Intelligent 5G: When Cellular Networks Meet Artificial Intelligence,” IEEE Wirel. Commun., vol. 24, no. 5, 2017, doi: 10.1109/MWC.2017.1600304WC.

M. Z. Alom, T. K. Godder, M. N. Morshed, and A. Maali, “Enhanced spectrum sensing based on Energy detection in cognitive radio network using adaptive threshold,” 2017, doi: 10.1109/NSysS.2017.7885815.

J. Lorincz, I. Ramljak, and D. Begušić, “A review of the noise uncertainty impact on energy detection with different OFDM system designs,” Computer Communications, vol. 148. 2019, doi: 10.1016/j.comcom.2019.09.013.

W.-L. Chin, “On the Noise Uncertainty for the Energy Detection of OFDM Signals,” IEEE Trans. Veh. Technol., vol. 68, no. 8, 2019, doi: 10.1109/tvt.2019.2920142.

J. Li, Y. Peng, Y. Yan, X. Q. Jiang, H. Hai, and M. Zukerman, “Cognitive Radio Network Assisted by OFDM with Index Modulation,” IEEE Trans. Veh. Technol., vol. 69, no. 1, 2020, doi: 10.1109/TVT.2019.2951606.

M. R. Bharti and D. Ghosh, “Power allocation for multiuser precoded OFDM cognitive radio,” Phys. Commun., vol. 30, 2018, doi: 10.1016/j.phycom.2018.09.002.

J. Yang and H. Zhao, “Enhanced Throughput of Cognitive Radio Networks by Imperfect Spectrum Prediction,” IEEE Commun. Lett., vol. 19, no. 10, 2015, doi: 10.1109/LCOMM.2015.2442571.

P. Thakur, A. Kumar, S. Pandit, G. Singh, and S. N. Satashia, “Performance analysis of cognitive radio networks using channel-prediction-probabilities and improved frame structure,” Digit. Commun. Networks, vol. 4, no. 4, 2018, doi: 10.1016/j.dcan.2017.09.012.

Y. S. Cho, J. Kim, W. Y. Yang, and C. G. Kang, MIMO-OFDM Wireless Communications with MATLAB®. 2010.

P. Manhasa and M. K. Sonib, “Performance of ofdm system under different fading channels and coding,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, 2017, doi: 10.11591/eei.v6i1.591.

E. Axell and E. G. Larsson, “Optimal and sub-optimal spectrum sensing of OFDM signals in known and unknown noise variance,” IEEE J. Sel. Areas Commun., vol. 29, no. 2, 2011, doi: 10.1109/JSAC.2011.110203.

H. A. O. Selim, A. S. Dessouki, and H. Y. M. Soliman, “Verification analysis for the reliable analytical multi-taper detector in next generation network,” Bull. Electr. Eng. Informatics, vol. 9, no. 4, 2020, doi: 10.11591/eei.v9i4.2229.

T. C. Thanuja, K. A. Daman, and A. S. Patil, “Optimized Spectrum sensing Techniques for Enhanced Throughput in Cognitive Radio Network,” 2020, doi: 10.1109/ESCI48226.2020.9167576.




DOI: https://doi.org/10.11591/eei.v10i4.2839

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats