Optimization of Mean Square Error and BER in Spectrum Sensing Cognitive Radio Networks
Keywords:
WiMAX, Massive System, Cognitive Radio, Matched FilterAbstract
Error free transmission and increase in multimedia applications is one of the main aims of wireless communication. Massive system is the system to improve the reliability of the WiMAX system. The fundamental tasks that are used in the cognitive radio (CR) networks are spectrum shaping capability and multi carrier systems. In these structures activation of fundamental (primary) users will generate a defined number of sub carriers in the secondary users. In this paper, the design of massive system using Matched Filter Detection Spectrum Sensing Cognitive Radio Network is presented. A matched filter is a spectrum-sensing method that detects the free portions of the primary user’s spectrum and allocates it to secondary users. It derives from cross-correlating an unknown signal with known ones to detect the unknown signal’s presence based on the basis of its SNR. Accordingly, an efficient scheme is developed here that is having better BER against a different transmitter and receiver system.
References
Arun Kumar, Nishant Gaur, Sumit Chakravarty, Mohammed H. Alsharif, Peerapong Uthansakul, Monthippa Uthansakul, “Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs”, Ain Shams Engineering Journal, Elsevier, 2024.
Kumar A, Venkatesh J, Gaur N, Alsharif MH, Jahid A, Raju K., “Analysis of Hybrid Spectrum Sensing for 5G and 6G Waveforms,” Electronics, 2023; 12(1):138.
Arun Kumar J, Venkatesh NG, Alsharif MH, Peerapong Uthansakul, Monthippa Uthansakul, “Cyclostationary and energy detection spectrum sensing beyond 5G waveforms,” Electron Res Arch, 2023; 31(6):3400–16.
Shen F, Wang Z, Ding G, Li K, Wu Q., “3D compressed spectrum mapping with sampling locations optimization in spectrum-heterogeneous environment,” IEEE Trans Wirel Commun, Jan. 2022; 21(1):326–38.
Hamedani K, Liu L, Yi Y., “Energy efficient MIMO-OFDM spectrum sensing using deep stacked spiking delayed feedback reservoir computing,” IEEE Trans Green Commun Networking, March 2021; 5(1):484–96.
Arun Kumar and Nandha Kumar P., “OFDM system with cyclo-stationary feature detection spectrum sensing,” ScienceDirect, Information Communication Technology Express, Elsevier, 22 January 2018.
Abdullah Yaqot and Peter Adam Hoeher, “Efficient Resource Allocation in Cognitive Networks,” IEEE Transactions on Vehicular Technology, Vol. 66, No. 07, July 2017.
Danda B. Rawat, “Evaluation Performance of Cognitive Radio Users in MIMO-OFDM based Wireless Networks,” IEEE Transactions of Wireless System, Vol. 32, Issue 07, IEEE, 2016.
Shan Jin and Xi Zhang, “Compressive Spectrum Sensing for MIMO-OFDM Based Cognitive Radio Networks,” 2015 IEEE Wireless Communications and Networking Conference (WCNC), Applications, and Business, Vol. 27, No. 2, pp. 567–572, 2015.
Maan Singh and Vijay Kumar, “Investigation of PAPR Reduction in MIMO-OFDM System using PTS and SLM Techniques,” International Journal of Science and Research (IJSR), Vol. 3, No. 03, pp. 524–529, March 2014.
Muhammet Nuri Seyman and Necmi Taspmar, “Channel estimation based on neural network in space time block coded MIMO-OFDM system,” Digital Signal Processing, Vol. 23, No. 1, pp. 275–280, Jan. 2013.
Li Mei, Wang Xiang, Zhang Xiaoming, Jia Ke., “RLS and its improved algorithms in MIMO-OFDM System,” International Journal of Modelling, Identification and Control, Vol. 16, No. 3, pp. 246–250, Jul. 2012.
P. Mukunthan and P. Dananjayan, “PAPR Reduction based on a Modified PTS with Interleaving and Pulse Shaping method for STBC MIMO-OFDM System,” IEEE International Conference, Coimbatore, July 2012.
Wei Jiang and Daoben Li, “Convolutional Multi-code Multiplexing for OFDM Systems,” Beijing University of Posts and Telecommunications, Beijing, pp. 6432–6439, IEEE, 2012.
Yuan Jinsha and Shi Hongmei, “A Modified Symbol Timing Synchronization Algorithm for WiMAX OFDM Systems,” 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.