Mohd Ramdan, Fatin Najwa Nursyadza (2018) PAPR Reduction Using Genetic Algorithm (GA) In OFDM System. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted)
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Abstract
Wireless mobile communication are the fastest technology that are being improve. Orthogonal frequency division multiplexing (OFDM) system is chosen for the technology, since it is the most widely employed multi-carrier transmission technique to cater the growing demand of high transmission. An orthogonal frequency division multiplexing (OFDM) system has the problem of high peak-to-average power ratio (PAPR). High PAPR would drive the power amplifier at the transmitter into saturation, producing interference among the subcarriers that degrades the BER performance and corrupts the spectrum of the signal. In order to reduce the PAPR performance in OFDM system, many reduction technique are explored. In this thesis, a Genetic algorithm (GA) technique is proposed. GA is a type of optimization algorithm, which is natural-based selection and is used to find the optimal solution to the computational problem that maximizes or minimizes a particular function. The aim of this thesis is to compare and analyze the PAPR performance based on the number of subcarrier and modulation size using Matlab software of complementary cumulative distribution function (CCDF) graph between original OFDM and the proposed method OFDM-GA. The simulation result demonstrates that comparing to the original OFDM, the proposed method OFDM-GA decreases the PAPR performance around 50%. The proposed GA method successfully reduces the PAPR performance in OFDM system.
| Item Type: | Monograph (Project Report) |
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| Subjects: | T Technology T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Monograph |
| Depositing User: | Mr Engku Shahidil Engku Ab Rahman |
| Date Deposited: | 26 Jul 2022 02:57 |
| Last Modified: | 26 Jul 2022 02:57 |
| URI: | http://eprints.usm.my/id/eprint/53596 |
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