Neuro-Fuzzy Based Adaptive Coding and Modulation for Performance Improvement in OFDM Wireless Systems

Show simple item record Temalow, Seife Gebreslassie 2018-06-26T08:17:24Z 2018-06-26T08:17:24Z 2018-06-26
dc.identifier.citation Temalow2018 en_US
dc.description degree of Master of Science in Electrical Engineering (Telecommunication Engineering) en_US
dc.description.abstract In a limited radio spectrum, the future wireless technologies are supposed to deliver multimedia services such as video, data, and audio with a high data rate and virtually error free communication. The performance of radio signals that propagate through the wireless channel is limited by multipath fading, noise and interference and thus affect the signal quality. Adaptive coding and modulation (ACM) plays a vital role in improving the performance of wireless communication by adapting its transmission parameters such as coding rate and modulation order based on the quality of the wireless channel. Adaptive coding and modulation with Orthogonal Frequency Division Multiplexing (OFDM) systems allow the efficient use of available bandwidth to maximize data rate. In ACM techniques, both code rate and modulation order are varied dynamically to adapt the time-varying channel to improve capacity and reduce bit error rate (BER) in contrast to fixed systems that either enhance spectral efficiency or minimize BER. Due to the complexity and the uncertainty of the wireless channel, the conventional adaptive techniques, do not cope with the changing environment. Soft computing techniques, which do not require highly non-linear mathematical models, are commonly used to control and model uncertain systems. The fuzzy logic-based ACM is good in decision-making in an uncertain environment and performs better than adaptive and non-adaptive techniques but cannot learn from training examples. The neuro-fuzzy based approach combines the merits of both neural networks and fuzzy logic system. The neuro-fuzzy system grasps the learning capability of the artificial neural networks to enhance the intelligent system’s performance using a priori knowledge. A special neuro-fuzzy method termed adaptive network based fuzzy inference system (ANFIS) is used as the model in our proposed algorithm. In this thesis, a neuro-fuzzy based adaptive coding and modulation for OFDM wireless systems is proposed and simulated in MATLAB environment. By analyzing the simulation results, the neuro-fuzzy based model shows an average of 25.03% data rate/spectral efficiency improvement compared to the existing fuzzy logic model. It also shows that, the proposed approach outperforms compared to neural networks, adaptive and non-adaptive techniques such that the BER and total transmit power remain under certain thresholds. en_US
dc.description.sponsorship Prof. Elijah Mwangi Dr. Kibet Langat en_US
dc.language.iso en en_US
dc.publisher JKUAT en_US
dc.subject Neuro-Fuzzy en_US
dc.subject Adaptive Coding en_US
dc.subject Modulation en_US
dc.subject Performance Improvement en_US
dc.subject OFDM en_US
dc.subject Wireless Systems en_US
dc.title Neuro-Fuzzy Based Adaptive Coding and Modulation for Performance Improvement in OFDM Wireless Systems en_US
dc.type Thesis en_US

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