Physical layer algorithms and architectures for accurate detection and classification in cognitive radios - Narendar Madhavan (2014)
The new mobile broadband technology is rapidly growing towards a fifth generation (5G), as there is an increasing demand for high data rate devices. The advent of new wireless communication services renders the electromagnetic spectrum congested. This congestion, coupled with rigid allocation of spectrum to the new wireless systems, leads to inefficient spectrum utilization and causes an apparent spectrum scarcity. Cognitive radio (CR) is envisaged as a solution to tackle the problem of this apparent spectrum scarcity. It is a conscious radio, which opportunistically uses the spectrum of a licensed user known as primary user (PU) while causing minimal interference to them. It utilizes software-defined radio (SDR) to adapt its transmission properties to match the required standards. In order to determine the spectral vacancies, the CR has to locate the PU signals accurately with the help of a spectrum sensing unit. In addition, after detecting the signal, the CR must be able to classify the detected signal into a known user type. This thesis presents methods for accurate wideband detection and modulation classification for a stand-alone CR scenario in the physical layer domain. These methods operate with partial knowledge about the PUs' signal and/or the noise environment. The proposed algorithms can also extract certain features of the signal spectrum such as edge frequencies and modulation scheme in a robust manner. In general, the research problem of signal detection and classification is addressed in this thesis using three methods- filter bank based detectors, higher order statistics and fractional lower order statistics.