Nowadays, there is a scarcity of the radio spectrum due to advancement in wireless networks and services such as Wi-Fi, Bluetooth, ZigBee and Wi-max, etc. A survey performed by the spectrum policy task force within the Federal communication Commission, states that actually licensed spectrum is inefficiently utilized as some bands remain vacant for long time duration in some particular geographical regions, some frequency bands are partially occupied and the other parts of the spectrum bands are densely employed. Because of the huge demand of spectrum, Cognitive Radio technology gains much attention as it can sense the unused spectrum bands and optimize spectrum utilization and enhance the quality of service for the overall system. Spectrum sensing is a key component for securing the licensed terminals from interference as detects the white spectrum holes to improve the spectrum efficiency and facilitates the unlicensed mobile users to use the empty licensed radio frequency bands of the electromagnetic spectrum. Several spectrum sensing techniques exist in the communication engineering literature. It includes the Energy Detection, Matched Filter detection, and Cyclostationary feature Detection techniques. These techniques have different requirements and advantages/disadvantages. In literature, most of the analysis is based on ideal channel condition. In practice, noise power may vary with time, which is known as Noise Uncertainty. This dissertation is extensively based on the study of Energy Detection technique for spectrum sensing. Dynamic-Double-Threshold technique on the framework of Energy Detection technique has been proposed and analysed through simulation studies using MATLAB2012a. The detection performance of the proposed technique has been compared with that of the existing techniques, which shows significant performance improvement in terms of detection probability with the consideration of noise uncertainty.
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