Perbandingan Estimasi Derau tanpa Informasi Sinyal Transmisi dengan Masukan Sinyal DVB-T pada Sistem Radio Kognitif

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Dzata Farahiyah


Tiga algoritma estimasi derau tanpa informasi sinyal transmisi dibandingkan hasil performanya. Ketiga estimator tersebut yaitu Maximum likelihood estimator, estimator berdasarkan cyclic prefix dan estimator dengan metode Forward consecutive mean excision (FCME). Perbandingan dilakukan dengan menggunakan masukan sinyal DVB-T dan dengan memvariasikan SNR dan jumlah OFDM simbol. Performa dari ketiganya diukur dengan NMSE. Cyclic prefix-estimator dan Maximum likelihood-estimator memiliki performa yang stabil terhadap perubahan SNR, sedangkan FCME-estimator memiliki performa yang berfluktuasi terhadap perubahan SNR. Penambahan jumlah simbol juga menghasilkan performa yang membaik pada ketiganya. Performa dari ketiganya menunjukkan bahwa maximum likelihood-estimator memiliki NMSE yang paling kecil. Artinya bahwa estimator ini memiliki tingkat keakuratan yang tinggi.


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FARAHIYAH, Dzata. Perbandingan Estimasi Derau tanpa Informasi Sinyal Transmisi dengan Masukan Sinyal DVB-T pada Sistem Radio Kognitif. JURNAL INFOTEL, [S.l.], v. 8, n. 2, p. 143-149, nov. 2016. ISSN 2460-0997. Available at: <>. Date accessed: 28 mar. 2017. doi:


[1] J. Mitola and J. Maguire G. Q., “Cognitive radio: making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, Aug. 1999.
[2] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005.
[3] E. Standard, “Digital Audio Broadcasting (DAB); Internet Protocol (IP) datagram tunnelling,” 2000.
[4] Cho, Y. S., Kim, J., Yang, W. Y., & Kang, C. G. (2010). MIMO-OFDM Wireless Communications with MATLAB (1 edition). Singapore?; Hoboken, NJ: Wiley-IEEE Press.
[5] Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing for cognitive radios. In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004 (Vol. 1, pp. 772–776 Vol.1).
[6] Dillinger, M., Madani, K., & Alonistioti, N. (2003). Software Defined Radio: Architectures, Systems and Functions (1 edition). Hoboken, NJ: Wiley.
[7] Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.
[8] Mahmoud, H., Yucek, T., & Arslan, H. (2009). OFDM for cognitive radio: merits and challenges. Wireless Communications, IEEE, 16(2), 6–15.
[9] Boumard, S. (2003). Novel noise variance and SNR estimation algorithm for wireless MIMO OFDM systems (pp. 1330–1334). IEEE.
[10] Mariani, A., Giorgetti, A., & Chiani, M. (2011). Effects of Noise Power Estimation on Energy Detection for Cognitive Radio Applications. IEEE Transactions on Communications, 59(12), 3410–3420.
[11] Socheleau, F.-X., Aissa-El-Bey, A., & Houcke, S. (2008). Non data-aided SNR estimation of OFDM signals. IEEE Communications Letters, 12(11), 813–815.
[12] Sharma, S. K., Chatzinotas, S., & Ottersten, B. (2013). Eigenvalue-Based Sensing and SNR Estimation for Cognitive Radio in Presence of Noise Correlation. IEEE Transactions on Vehicular Technology, 62(8), 3671–3684.
[13] Papoulis, A., & Pillai, S. U. (2002). Probability, Random Variables and Stochastic Processes (4th edition). McGraw-Hill Europe.
[14] Saarnisaari, H. (2004). Consecutive mean excision algorithms in narrowband or short time interference mitigation. In Position Location and Navigation Symposium, 2004. PLANS 2004 (pp. 447–454).
[15] Vartiainen, J., Saarnisaari, H., Lehtomaki, J. J., & Juntti, M. (2006). A Blind Signal Localization and SNR Estimation Method. In IEEE Military Communications Conference, 2006. MILCOM 2006 (pp. 1–7).