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| dc.contributor.author | Getaneh, Dagnachew | |
| dc.date.accessioned | 2021-08-16T11:54:39Z | |
| dc.date.available | 2021-08-16T11:54:39Z | |
| dc.date.issued | 2021-03 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/12438 | |
| dc.description.abstract | The demand for high-speed wireless communication is increasing very rapidly and enforce technologist to develop a communication system platform that can handle the number of users with acceptable service requirements. In wireless communi- cation, the spectrum is a scarce resource that needs e ective management and allocation. Adaptive transmission is the common technique to harvest the time varying characteristics of wireless channels. Digital modulation techniques play a signi cant role to attain planned ow of information over the allocated bandwidth and power. To have e cient resource utilization, Adaptive Modulation and Coding (AMC) is one powerful technique for improving the spectral e ciency of a system. The transmission parameters are recon gured based on the value of the instantaneous Signal to Noise Ratio (SNR). In this work, the convolutional channel coding technique enables to achieve coding gain 4.6 - 7.3dB than uncoded QAM for di erent modulation and coding pairs. In a conventional AMC scenario, the switching of the employed modulation and cod- ing scheme is less exible. Adaptive Neuro-Fuzzy Inference System (ANFIS) based AMC has better capability to track wireless channel characteristics smoothly. The obtained results show that ANFIS based AMC is better in tracking random chan- nel characteristics and giving a faster response for the change in the link quality than the conventional AMC. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | ELECTRICAL AND COMPUTER ENGINEERING | en_US |
| dc.title | Adaptive Modulation and Coding Using Adaptive Neuro-fuzzy Inference System | en_US |
| dc.type | Thesis | en_US |
