Enhancement of Detection using Hybrid of Energy and Cyclostationary Detection Algorithm
| dc.contributor.advisor | Yihenew Wondie (PhD) | |
| dc.contributor.author | Ruth Shiferaw | |
| dc.date.accessioned | 2023-12-05T06:47:33Z | |
| dc.date.available | 2023-12-05T06:47:33Z | |
| dc.date.issued | 2022-12 | |
| dc.description.abstract | The growing demand for bandwidth demanding wireless technologies has led to the problem of spectrum scarcity. However, a fixed spectrum assignment has led to underutilization of spectrum as a great portion of the licensed spectrum is not effectively utilized. Cognitive radio technology promises a solution to the problem by allowing unlicensed users, access to the licensed bands opportunistically. A prime component of cognitive radio technology is spectrum sensing. Many spectrum sensing techniques have been developed to sense the presence of a licensed user. Among them, energy detector is one of the known technique which uses the received signal energy and compare it to the threshold to identify weather the licensed user is using the channel or not but at a low SNR value, the performance is poor. This thesis evaluates the performance of a hybrid of cyclostationary and energy spectrum sensing technique in noisy and fading environments. The performance of the energy detection technique was evaluated by use of Detection Probability Vs SNR value, Receiver Operating Characteristics (ROC), Complement Receiver Operating Curve (CROC), Detection Probability Vs False Alarm Probability curves over additive white Gaussian noise (AWGN) and fading (Rayleigh) channels. The detection time for the proposed hybrid detector also analyzed. Results show that using a hybrid detection technique performs better than the energy detection technique for all evaluation matrices in both AWGN and fading channel models. However, this will cost the detection time. The detection time increases as the probability of detection increases. | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/223 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.title | Enhancement of Detection using Hybrid of Energy and Cyclostationary Detection Algorithm | |
| dc.type | Thesis |