[1] Sahai & D. Cabric, “Spectrum sensing: fundamental limits and practical challenges,” IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005.
[2] J. R. Krier & I. F. Akyildiz, “Active self-interference cancellation of passband signals using gradient descent," Personal Indoor and Mobile Radio Communications, IEEE 24th International Symposium on, London, pp. 1212-1216, 2013.
[3] J. Agajo, Onyebuchi Nosiri, Okeke Benjamin Chukwuejekwu, Okoro Patience N, Matlab and Simulink Based Simulation of Wideband Code Division Multiple Access [WCDMA], International Journal of Engineering and Technical Research (IJETR), (2015)
[4] J. Okhaifoh, J. Agajo, V.E Idigo Optimizing Digital Filter for Effective Signal Processing International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3, (2013)
[5] Identification,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 2, pp. 554-563, 2012.
[6] N. Kaabouch & W. Chen Hu, Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management, 2014.
[7] S. H. Mankad & S. N. Pradhan, “Application of Software Defined Radio for Noise Reduction Using Empirical Mode Decomposition,” Advances in Computer Science, Engineering & Applications, vol. 166, pp. 113-121, 2012.
[8] H. Reyes, S. Subramaniam, & N. Kaabouch, “A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks,” Elsevier Comput. Elect. Eng. J., vol. 52, pp. 319-327, 2015.
[9] S. Hou, Robert C. Qiu Z. Chen, Z. HuSVM and Dimensionality Reduction in Cognitive Radio with Experimental Validation SVM and Dimensionality Reduction in Cognitive Radio with Experimental Validation SVM and Dimensionality Reduction in Cognitive Radio. Journal of Computer Science Networking and Internet Architecture (2015).
[10] M. Riahi Manesh, A. Quadri, S. Subramaniam, N. Kaabouch, “An Optimize SNR Estimation Technique Using Particle Swarm Optimization Algorithm,” IEEE Annual Computing and Communication Workshop and Conference, in press.
[11] X. Chen, F. Hou, H. Huang†, X. Jing Principle Component Analysis Based Cooperative, Spectrum Sensing in Cognitive Radio, 16th International Symposium on Communications and Information Technologies (ISCIT), (2017).
[12] G. Saxena, S. Ganesan, & M. Das, “Real time implementation of adaptive noise cancellation,” Electro/Information Technology, IEEE International Conference on, Ames, pp. 431-436, 2008.
[13] D. K. Yadaw, P & Singh, “Performance Comparison of Various Techniques in Denoising the Speech Signal,” International Journal of Software & Hardware Research in Engineering, vol. 1, no. 1, 2013.
[14] N. K. Rout, D. P. Das, & G. Panda, “Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path
[15] N. Karaboga & B. Cetinkaya, “A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm,” Turk J Elec. Eng. & Comp Sci., vol.19, no.1, 2011.
[16] A. QUADRI, A Review of Noise Cancellation Techniques for Cognitive Radio Journal of Electrical Engineering and Systems Science Signal Processing. (2018),
[17] U. Mlakar, & I. Fister, “Hybrid self-adaptive cuckoo search for global optimization,” Swarm and Evolutionary Computation, vol. 29, pp. 47-72, 2016.
[18] C. Y. Chang & D. R. Chen, “Active noise cancellation without secondary path identification by using an adaptive genetic algorithm”, Instrumentation and Measurement, IEEE Transactions on, vol. 59, no. 9, pp. 2315-2327, 2010.
[19] N. Kaabouch, Y. Chen, W. C. Hu, J. W. Anderson, F. Ames, & R. Paulson, “Enhancement of the asymmetry-based overlapping analysis through features extraction,” Journal of Electronic Imaging, vol. 20, no. 1, pp. 013012-013012, 2011.
[20] N. Karaboga & B. Cetinkaya, “Design of digital FIR filters using differential evolution algorithm,” Circuits Systems and Signal Processing Journal, vol. 25, pp. 649-660, 2006.
[21] W. Gao, & S. Liu, “Improved artificial bee colony algorithm for global optimization,” Information Processing Letters, vol. 111, no. 17, pp. 871-882, 2011.
[22] A. Quadri, M. Riahi Manesh, & N. Kaabouch, (2019) “Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter”, IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 1–7, 2016.
[23]. A. Quadri ; M. Riahi Manesh ; N. Kaabouch (2016) , Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter, IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), DOI: 10.1109/UEMCON.2016.7777854
[24] R. Pradeep., T. GopinathInterference and Noise detection in a Cognitive Radio Environment, IOSR Journal of Electronics and Communication Engineering (IOSRJECE) ISSN : 2278-2834 Volume 2, Issue 4 PP 16-19, .(2012),
[25] A. Mariani, A. Giorgetti, M. Chiani, , Effects of Noise Power Estimation on Energy Detection for Cognitive Radio Applications, IEEE Transactions on Communications, (2011) VOL. 59, NO. 12,
[26] R. Elrharras, R. aadane, M.Wahbi, A.Hamdoun, Applied Mathematical Sciences, Vol. 8, 2014, no. 160, 7959-7977 http://dx.doi.org/10.12988/
[27] Y. Zeng, Y. Liang, A. Hoang, & R. Zhang, “A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions,” EURASIP Journal on Advances in Signal Processing, vol. 2010, pp. 1-16,
[28] R. Tandra & A. Sahai, “Fundamental limits on detection in low SNR under noise uncertainty,” Proceedings of the International Conference on Wireless Networks, Communications and Mobile Computing, vol. 1, pp. 464–469, 2005.
[29] M. Riahi Manesh, N. Kaabouch, & H. Reyes, “Aggregate Interference Power Modeling for Cognitive Radio Networks Using Bayesian Model,” IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 1-6, 2016.
[30] J. Agajo, I. C. Obiora-Dimson, O.B. Chukwuejekwu &N.Nathaniel, CC3000 Wifi Base Configuration of Gateway For Internet Accessibility in Monitoring Variables Via Wireless Sensor Systems , IEEE Based African Journal of Computing & ICT, (2015)Vol 8. No. 3.
[31] B. C. Okeke Lazarus, J. Agajo ,Okpe G. Kabis S.D, Analysis and Estimation of Time of Arrival and Received Signal Strength in Wireless Communication for Indoor Geolocation, journal of Wireless Sensor Networks, jwsn, Vol 3, number 1, DOI-0015, (2016),,pp55-65
[32] M. & J. Žídek, "Use of adaptive filtering for noise reduction in communications systems," Applied Electronics (AE), International Conference on, pp. 1-6, 2010.
[33] M. Riahi Manesh, N. Kaabouch, H. Reyes, & W.C. Hu, “A Bayesian approach to estimate and model SINR in wireless networks,” International Journal of Co
[2] J. R. Krier & I. F. Akyildiz, “Active self-interference cancellation of passband signals using gradient descent," Personal Indoor and Mobile Radio Communications, IEEE 24th International Symposium on, London, pp. 1212-1216, 2013.
[3] J. Agajo, Onyebuchi Nosiri, Okeke Benjamin Chukwuejekwu, Okoro Patience N, Matlab and Simulink Based Simulation of Wideband Code Division Multiple Access [WCDMA], International Journal of Engineering and Technical Research (IJETR), (2015)
[4] J. Okhaifoh, J. Agajo, V.E Idigo Optimizing Digital Filter for Effective Signal Processing International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3, (2013)
[5] Identification,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 2, pp. 554-563, 2012.
[6] N. Kaabouch & W. Chen Hu, Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management, 2014.
[7] S. H. Mankad & S. N. Pradhan, “Application of Software Defined Radio for Noise Reduction Using Empirical Mode Decomposition,” Advances in Computer Science, Engineering & Applications, vol. 166, pp. 113-121, 2012.
[8] H. Reyes, S. Subramaniam, & N. Kaabouch, “A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks,” Elsevier Comput. Elect. Eng. J., vol. 52, pp. 319-327, 2015.
[9] S. Hou, Robert C. Qiu Z. Chen, Z. HuSVM and Dimensionality Reduction in Cognitive Radio with Experimental Validation SVM and Dimensionality Reduction in Cognitive Radio with Experimental Validation SVM and Dimensionality Reduction in Cognitive Radio. Journal of Computer Science Networking and Internet Architecture (2015).
[10] M. Riahi Manesh, A. Quadri, S. Subramaniam, N. Kaabouch, “An Optimize SNR Estimation Technique Using Particle Swarm Optimization Algorithm,” IEEE Annual Computing and Communication Workshop and Conference, in press.
[11] X. Chen, F. Hou, H. Huang†, X. Jing Principle Component Analysis Based Cooperative, Spectrum Sensing in Cognitive Radio, 16th International Symposium on Communications and Information Technologies (ISCIT), (2017).
[12] G. Saxena, S. Ganesan, & M. Das, “Real time implementation of adaptive noise cancellation,” Electro/Information Technology, IEEE International Conference on, Ames, pp. 431-436, 2008.
[13] D. K. Yadaw, P & Singh, “Performance Comparison of Various Techniques in Denoising the Speech Signal,” International Journal of Software & Hardware Research in Engineering, vol. 1, no. 1, 2013.
[14] N. K. Rout, D. P. Das, & G. Panda, “Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path
[15] N. Karaboga & B. Cetinkaya, “A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm,” Turk J Elec. Eng. & Comp Sci., vol.19, no.1, 2011.
[16] A. QUADRI, A Review of Noise Cancellation Techniques for Cognitive Radio Journal of Electrical Engineering and Systems Science Signal Processing. (2018),
[17] U. Mlakar, & I. Fister, “Hybrid self-adaptive cuckoo search for global optimization,” Swarm and Evolutionary Computation, vol. 29, pp. 47-72, 2016.
[18] C. Y. Chang & D. R. Chen, “Active noise cancellation without secondary path identification by using an adaptive genetic algorithm”, Instrumentation and Measurement, IEEE Transactions on, vol. 59, no. 9, pp. 2315-2327, 2010.
[19] N. Kaabouch, Y. Chen, W. C. Hu, J. W. Anderson, F. Ames, & R. Paulson, “Enhancement of the asymmetry-based overlapping analysis through features extraction,” Journal of Electronic Imaging, vol. 20, no. 1, pp. 013012-013012, 2011.
[20] N. Karaboga & B. Cetinkaya, “Design of digital FIR filters using differential evolution algorithm,” Circuits Systems and Signal Processing Journal, vol. 25, pp. 649-660, 2006.
[21] W. Gao, & S. Liu, “Improved artificial bee colony algorithm for global optimization,” Information Processing Letters, vol. 111, no. 17, pp. 871-882, 2011.
[22] A. Quadri, M. Riahi Manesh, & N. Kaabouch, (2019) “Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter”, IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 1–7, 2016.
[23]. A. Quadri ; M. Riahi Manesh ; N. Kaabouch (2016) , Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter, IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), DOI: 10.1109/UEMCON.2016.7777854
[24] R. Pradeep., T. GopinathInterference and Noise detection in a Cognitive Radio Environment, IOSR Journal of Electronics and Communication Engineering (IOSRJECE) ISSN : 2278-2834 Volume 2, Issue 4 PP 16-19, .(2012),
[25] A. Mariani, A. Giorgetti, M. Chiani, , Effects of Noise Power Estimation on Energy Detection for Cognitive Radio Applications, IEEE Transactions on Communications, (2011) VOL. 59, NO. 12,
[26] R. Elrharras, R. aadane, M.Wahbi, A.Hamdoun, Applied Mathematical Sciences, Vol. 8, 2014, no. 160, 7959-7977 http://dx.doi.org/10.12988/
[27] Y. Zeng, Y. Liang, A. Hoang, & R. Zhang, “A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions,” EURASIP Journal on Advances in Signal Processing, vol. 2010, pp. 1-16,
[28] R. Tandra & A. Sahai, “Fundamental limits on detection in low SNR under noise uncertainty,” Proceedings of the International Conference on Wireless Networks, Communications and Mobile Computing, vol. 1, pp. 464–469, 2005.
[29] M. Riahi Manesh, N. Kaabouch, & H. Reyes, “Aggregate Interference Power Modeling for Cognitive Radio Networks Using Bayesian Model,” IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 1-6, 2016.
[30] J. Agajo, I. C. Obiora-Dimson, O.B. Chukwuejekwu &N.Nathaniel, CC3000 Wifi Base Configuration of Gateway For Internet Accessibility in Monitoring Variables Via Wireless Sensor Systems , IEEE Based African Journal of Computing & ICT, (2015)Vol 8. No. 3.
[31] B. C. Okeke Lazarus, J. Agajo ,Okpe G. Kabis S.D, Analysis and Estimation of Time of Arrival and Received Signal Strength in Wireless Communication for Indoor Geolocation, journal of Wireless Sensor Networks, jwsn, Vol 3, number 1, DOI-0015, (2016),,pp55-65
[32] M. & J. Žídek, "Use of adaptive filtering for noise reduction in communications systems," Applied Electronics (AE), International Conference on, pp. 1-6, 2010.
[33] M. Riahi Manesh, N. Kaabouch, H. Reyes, & W.C. Hu, “A Bayesian approach to estimate and model SINR in wireless networks,” International Journal of Co
- Abstract viewed - 1144 times
- PDF downloaded - 788 times
Affiliations
Joseph Emeshili
University of Abuja
Ultra-Wideband Spectrum Hole Identification Using Principal Components and Eigen Value Decomposition
Abstract
Ultra-Wideband Spectrum Hole identification using Principal Components and Eigen Value Decomposition evolve a method of detecting spectrum hole from complex and corrupted wide band spectrum signal, due to the effect of noise spectrum hole detection is usually a challenge in wideband signal, as the presence of noise give rise to error alert, that is, noise can be misconstrued for signal. Dimensionality reduction was first used as the first level of denoising technique, Principal component Analysis (PCA) was used in dimensioning Wide Band Spectrum Data; this was able to reduce the noise level in the signal which made it convenient for Fast Fourier Transform (FFT) to act on it. FFT was used to decompose the signal to 64 sub band channels and on further reduction using principal Component Analysis (PCA), a 32 Level sub-band decomposition was carried out. Eigen Value generated shows that the magnitude of the signal to Noise ratio between Eigen Value 1 to 19 was high enough to show the that there exist a signal, while between 20 to 32 shows no signal by implication it indicates that these areas have high possibility of unoccupied spectrum holes.