Vol. 11  No. 2 (2025) · Research Article

Performance Enhancement of OFDM Systems Using an Optimized PTS Partitioning Scheme for PAPR Reduction

Yasir Amer Al-Jawhar
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia , Malaysia

Khairun N. Ramli
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia , Malaysia

Mustafa S. Ahmed
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia , Malaysia

Raed Abdulkareem Abdulhasan
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia , Malaysia

Hussein M. Farhood
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia , Malaysia

Mohammed H. Alwan
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia , Malaysia

Keywords: OFDM, PAPR, PTS, IL-PTS, computational complexity

Abstract

A high peak-to-average-power ratio (PAPR) is the primary drawback faced by the orthogonal frequency division multiplexing (OFDM) systems in the practical applications. Meanwhile, Partial Transmit Sequence (PTS) is regarded as one of the efficient PAPR reduction techniques in OFDM systems. PTS technique depends on partitioning the input data into the several subblocks in the frequency-domain and weighting these subblocks by a set of phase factors in the time-domain. As the result, there are three common types of subblocks partitioning schemes have been adopted in the PTS technique, interleaving scheme, adjacent scheme, and pseudo-random scheme. Each one of the conventional partitioning schemes has PAPR reduction performance and a computational complexity level different from others. In this paper, a new subblock partitioning scheme named terminals e xchanging segmentation (TE-PTS) scheme has been proposed to improve the PAPR performance in PTS technique better than that of the interleaving scheme. The simulation results and the numerical calculations indicate that the PAPR reduction capacity of the proposed scheme is superior to that of interleaving scheme without increasing the computational complexity .

How to Cite

References

[1] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90, April 2018.

[2] R. Su, T. Liu, C. Sun, Q. Jin, R. Jennane, and L. Wei, “Fusing Convolutional Neural Network Features with Hand-Crafted Features for Osteoporosis Diagnoses,” Neurocomputing, vol. 385, pp. 300-309, April 2020.

[3] C. S. Vorugunti, V. Pulabaigari, R. K. S. S. Gorthi, and P. Mukherjee, “OSVFuseNet: Online Signature Verification by Feature Fusion and Depth-Wise Separable Convolution Based Deep Learning,” Neurocomputing, vol. 409, pp.157-172, October 2020.

[4] A. C. Cruz, A. Luvisi, L. De Bellis, and Y. Ampatzidis, “X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion,” Frontiers in Plant Science, vol. 8, 1741, October 2017.

[5] İ. Çuğu, E. Şener, Ç. Erciyes, B. Balcı, E. Akın, I. Önal, et al., “Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-Crafted Representations,” https://arxiv.org/pdf/1701.08291.pdf, January 28, 2017.

[6] S. Zhang, S. Zhang, C. Zhang, X. Wang, and Y. Shi, “Cucumber Leaf Disease Identification with Global Pooling Dilated Convolutional Neural Network,” Computers and Electronics in Agriculture, vol. 162, pp. 422-430, July 2019.

[7] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, “Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks,” IEEE Access, vol. 7, pp. 59069-59080, May 2019.

[8] H. Genshang, W. Haoyu, Z. Yan, and W. Mingzhu, “A Low Shot Learning Method for Tea Leaf’s Disease Identification,” Computers and Electronics in Agriculture, vol. 163, 104852, August 2019.

[9] M. Hu, X. Bu, X. Sun, Z. Yu, and Y. Zheng, “Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory,” Mathematical and Computational Applications, vol. 22, no. 1, 18, March 2017.

[10] S. Bansal and A. Kumar, “A Post-Processing Fusion Framework for Deep Learning Models for Crop Disease Detection,” IOP Conference Series: Materials Science and Engineering, vol. 998, no. 1, 012065, August 2020.

[11] J. Liu and X. Wang, “Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network,” Frontiers in Plant Science, vol. 11, 898, June 2020.

[12] S. Cakır, “Cepstral Methods for Image Feature Extraction,” Ph.D. dissertation, Department of Electrical and Electronics Engineering and the Institute of Engineering and Science, Bilkent University, 2010.

[13] S. Gupta, J. Jaafar, W. W. Ahmad, and A. Bansal, “Feature Extraction Using MFCC,” Signal & Image Processing: An International Journal, vol. 4, no. 4, pp. 101-108, August 2013.

[14] F. G. Hashad, T. M. Halim, S. M. Diab, B. M. Sallam, and F. E. Abd El-Samie, “Fingerprint Recognition Using

Mel-Frequency Cepstral Coefficients,” Pattern Recognition and Image Analysis, vol. 20, no. 3, pp. 360-369, September 2010.

[15] S. S. Barpanda, B. Majhi, P. K. Sa, A. K. Sangaiah, and S. Bakshi, “Iris Feature Extraction through Wavelet Mel-Frequency Cepstrum Coefficients,” Optics & Laser Technology, vol. 110, pp. 13-23, February 2019.

[16] M. Awad, F. G. Hashad, M. M. A. Elnaby, S. E. E. Khamy, O. S. Faragallah, A. M. Abbas, et al., “Resolution Enhancement of Images for Further Pattern Recognition Applications,” Optik, vol. 127, no. 1, pp. 484-492, January 2016.

[17] R. Bhagwat and Y. Dandawate, “Comprehensive Multilayer Convolutional Neural Network for Plant Disease Detection,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 1, pp. 204-211, 2021.

[18] D. Hughes and M. Salathé, “An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics,” https://arxiv.org/ftp/arxiv/papers/1511/1511.08060.pdf, April 11, 2016.

[19] J. G. A. Barbedo, L. V. Koenigkan, and T. T. Santos, “ Identifying Multiple Plant Diseases Using Digital Image Processing,” Biosystems Engineering, vol. 147, pp. 104-116, July 2016.

[20] J. G. A. Barbedo, L. V. Koenigkan, B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, C. V. Godoy, et al., “Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases,” IEEE Latin America Transactions, vol. 16, no. 6, pp. 1749-1757, June 2018.

[21] J. G. A. Barbedo, “Plant Disease Identification from Individual Lesions and Spots Using Deep Learning,” Biosystems Engineering, vol. 180, pp. 96-107, April 2019.

[22] M. Sharif, M. A. Khan, Z. Iqbal, M. F. Azam, M. I. U. Lali, and M. Y. Javed, “Detection and Classification of Citrus Diseases in Agriculture Based on Optimized Weighted Segmentation and Feature Selection,” Computers and Electronics in Agriculture, vol. 150, pp. 220-234, July 2018.

[23] V. Singh and A. K. Misra, “Detection of Plant Leaf Diseases Using Image Segmentation and Soft Computing Techniques,” Information Processing in Agriculture, vol. 4, no. 1, pp. 41-49, March 2017.

[24] A. E. Hassanien, T. Gaber, U. Mokhtar, and H. Hefny, “An Improved Moth Flame Optimization Algorithm Based on Rough Sets for Tomato Diseases Detection,” Computers and Electronics in Agriculture, vol. 136, pp. 86-96, April 2017.

[25] X. E. Pantazi, D. Moshou, and A. A. Tamouridou, “Automated Leaf Disease Detection in Different Crop Species through Image Features Analysis and One Class Classifiers,” Computers and Electronics in Agriculture, vol. 156, pp. 96-104, January 2019.

[26] S. S. Chouhan, A. Kaul, U. P. Singh, and S. Jain, “Bacterial Foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classification of Plant Leaf Diseases: An Automatic Approach towards Plant Pathology,” IEEE Access, vol. 6, pp. 8852-8863, February 2018.

[27] C. S. Hlaing and S. M. M. Zaw, “Tomato Plant Diseases Classification Using Statistical Texture Feature and Color Feature,” IEEE/ACIS 17th International Conference on Computer and Information Science, June 2018, pp. 439-444.

[28] G. Geetharamani and A. Pandian, “Identification of Plant Leaf Diseases Using a Nine-Layer Deep Convolutional Neural Network,” Computers & Electrical Engineering, vol. 76, pp. 323-338, June 2019.

[29] S. Coulibaly, B. Kamsu-Foguem, D. Kamissoko, and D. Traore, “Deep Neural Networks with Transfer Learning in Millet Crop Images,” Computers in Industry, vol. 108, pp. 115-120, June 2019.

[30] M. G. Selvaraj, A. Vergara, H. Ruiz, N. Safari, S. Elayabalan, W. Ocimati, et al., “AI-Powered Banana Diseases and Pest Detection,” Plant Methods, vol. 15, no. 1, pp. 1-11, August 2019.