Estimasi Kanal Sistem OFDM pada Kanal Fading Rayleigh dengan Metode Piece-wise Linear

Authors

  • Walid Maulana Hadiansyah Institut Teknologi Telkom Surabaya

DOI:

https://doi.org/10.52435/complete.v1i2.70

Keywords:

LTE, OFDM, Matlab, SNR, estimasi kanal

Abstract

Transmisi data kecepatan tinggi menjadi sorotan masyarakat saat ini karena semakin banyak masyarakat menggunakan teknologi nirkabel pita lebar untuk memenuhi kebutuhan sehari-hari. Long Term Evolution (LTE) masih menjadi teknologi eksisting yang memanfaatkan sistem Orthogonal Frequency Division Multiplexing (OFDM) sebagai solusi penghematan bandwidth. Untuk mempertahankan kualitas data dari distorsi, maka dibutuhkan teknik estimasi kanal. Dengan mengetahui hasil estimasi kanal, maka akan didapatkan solusi untuk meminimalisasi efek distorsi. Respon impuls diasumsikan berupa kanal fading Rayleigh. Estimasi kanal yang dipakai adalah metode estimasi Piece-wise Linear dengan 2 slope. Serangkaian penelitian yang bisa dilakukan setelah estimasi kanal adalah estimasi efek doppler, minimalisasi interferensi antar-simbol, dan efisiensi serta optimalisasi sistem. Dari hasil simulasi menggunakan program Matlab dengan 100 kali iterasi, dapat diambil kesimpulan bahwa semakin besar Signal to Noise Ratio (SNR), semakin kecil error yang terjadi. Nilai rata-rata error estimasi dari SNR=9-40 dB adalah 2.4%-38.2%.

References

Bhange, M., & Hingoliwala, H. A. (2015). Smart Farming: Pomegranate Disease Detection Using ImageProcessing. Procedia Computer Science, 58, 280–288. https://doi.org/10.1016/j.procs.2015.08.022

Ferraz, A., Carvalho, V., & Machado, J. (2016). Determination of Human Blood Type Using ImageProcessing Techniques. Measurement, 97.https://doi.org/http://dx.doi.org/10.1016/j.measurement.2016.11.015

Kaya, Y., Kayci, L., & Uyar, M. (2015). Automatic identification of butterfly species based on local binarypatterns and artificial neural networks. Applied Soft Computing Journal, 28, 132–137.https://doi.org/10.1016/j.asoc.2014.11.046

Kurniawardhani, A., Suciati, N., & Arieshanti, I. (2014). Klasifikasi Citra Batik Menggunakan MetodeEkstraksi Ciri yang Invariant Terhadap Rotasi. JUTI: Jurnal Ilmiah Teknologi Informasi, 12(2), 48.https://doi.org/10.12962/j2406853

v12i2.a3225.Suciati, N., Kridanto, A., Naufal, M. F., Machmud, M., & Wicaksono, Y. (2015). Fast Discrete CurveletTransform And HSV Color Features For Batik Image Classification, 99–104.

Herumurti, D., Uchimura, K., & Koutaki, G.(2013). Urban Road Network Extraction Based on ZebraCrossing Detection From a Very High-Resolution RGB Aerial Image and DSM Data.https://doi.org/10.1109/SITIS.2013.24

Kartika, D. S. Y., & Herumurti, D. (2016, October). Koi fish classification is based on HSV color space. In 2016 International Conference on Information & Communication Technology and Systems (ICTS) (pp. 96-100). IEEE.

Kartika, D. S. Y., Herumurti, D., & Yuniarti, A. (2018). Local binary pattern method and feature shapeextraction for detecting butterfly image. International Journal, 15(50), 127-133.

Satria, D., Kartika, Y., & Herumurti, D. (2016). Koi Fish Classification based on HSV Color Space.International Conference on Information, Communication Technology, and System (ICTS), 5, 96–100.https://doi.org/10.1109/ICTS.2016.7910280

Kaya, Y., Kayci, L., & Tekin, R. (2013). A Computer Vision System for the Automatic Identification ofButterfly Species via Gabor-Filter-Based Texture Features and Extreme Learning Machine: GF + ELM.TEM Journal, 2(1).

Kayci, L., & Kaya, Y. (2014). A vision system for automatic identification of butterfly species using a grey-level co-occurrence matrix and multinomial logistic regression. Zoology in the Middle East, 60(1), 57–64.https://doi.org/10.1080/09397140.2014.892340

Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant textureclassification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence,24(7), 971–987. https://doi.org/10.1109/TPAMI.2002.1017623

Burçin, K., & Vasif, N. V. (2011). Down syndrome recognition using local binary patterns and statisticalevaluation of the system. Expert Systems with Applications, 38(7), 8690–8695.https://doi.org/10.1016/j.eswa.2011.01.076

Singh, C., & Preet Kaur, K. (2016). A fast and efficient image retrieval system based on color and texturefeatures. Journal of Visual Communication and Image Representation, 41, 225–238.https://doi.org/10.1016/j.jvcir.2016.10.002

Youssef, S. M. (2012). ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colorextraction and texture analysis for efficient content-based image retrieval. Computers and ElectricalEngineering, 38(5), 1358–1376. https://doi.org/10.1016/j.compeleceng.2012.05.010

VijayaLakshmi, B., & Mohan, V. (2016). Kernel-based PSO and FRVM: An automatic plant leaf typedetection using texture, shape, and color features. Computers and Electronics in Agriculture, 125, 99–112.https://doi.org/10.1016/j.compag.2016.04.033

Wang, J., Markert, K., & Everingham, M. (2009). Learning models for object recognition from naturallanguage descriptions. Learning, 2.1-2.11. Retrieved from http://eprints.pascal-network.org/archive/00006257/

Junhua, C., & Jing, L. (2012). Research on Color ImageClassification Based on HSV Color Space. 2012Second International Conference on Instrumentation, Measurement, Computer, Communication, andControl, 255(3), 944–947. https://doi.org/10.1109/IMCCC.2012.226.

Kartika, D. S. Y., Herumurti, D., & Yuniarti, A. (2018). Butterfly image classification using colorquantization method on hsv color space and local binary pattern. IPTEK Journal of Proceedings Series, (1),78-82.

Khotimah, W. N., Arifin, A. Z., Yuniarti, A., Wijaya, A. Y., Navastara, D. A., & Kalbuadi, M. A. (2015,October). Tuna fish classification using decision tree algorithm and image processing method. In 2015International Conference on Computer, Control, Informatics and its Applications (IC3INA) (pp. 126-131)

Downloads

Published

2021-01-14

Issue

Section

Articles