Signal Detection and Digital Modulation Classification-Based Spectrum Sensing for Cognitive Radio

Spectrum sensing is the process of identifying available spectrum channels for use by a cognitive radio. In many cases, a portion of the spectrum is licensed to a primary communication system, for which the users have priority access. However, many studies have shown that the licensed spectrum is vastly underutilized, which presents an opportunity for a cognitive radio to access this spectrum, and motivates the need to research spectrum sensing. In this dissertation, we describe a spectrum sensing architecture that characterizes the carrier frequency and bandwidth of all narrowband signals present in the spectrum, along with the modulation type of those signals that are located within a licensed portion of the spectrum. From this radio identification, a cognitive radio can better determine an opportunity to access the spectrum while avoiding primary users.;We describe a narrowband signal detection algorithm that takes an iterative approach to jointly estimate the carrier frequency and bandwidth of individual narrowband signals contained within a received wideband signal. Our algorithm has a number of tunable parameters, and the algorithm gives consistent performance as we varied these parameter values. Our algorithm outperforms the expected performance of an energy detection algorithm, in particular at lower signal-to-noise ratio (SNR) values. These behavioral features make our algorithm a good choice for use in our spectrum sensing architecture.;We describe a novel constellation-based digital modulation classification algorithm that uses a feature set that exploits the knowledge about how a noisy signal should behave given the structure of the constellation set used to transmit information. Our algorithm's classification accuracy outperforms a set of literature comparisons' results by an average increase of 9.8 percentage points, where the most dramatic improvement occurred at 0 dB SNR with our accuracy at 98.9% compared to 37.5% for the literature. The classifier accuracy improves using our feature set compared to the classifiers' accuracy using two feature set choices that are common in the literature by an average increase of 13.35 and 5.31 percentage points. These qualities make our algorithm well-suited for our spectrum sensing architecture.;Finally, we describe our spectrum sensing architecture that coordinates the execution of our narrowband signal detection and modulation classification algorithms to produce an spectrum activity report for a cognitive radio. This report partitions the spectrum into equally-sized cells and gives an activity state for each cell. Our architecture detects spectrum opportunities with a probability of 99.4% compared to 87.7% and 93.8% for two other comparison approaches that use less information about the primary user's waveform. Our architecture detects "grey-space" opportunities with a probability of 96.1% compared to 49.1%. Also, the false alarm rate is significantly lower for our architecture, 13.3% compared to 46.9% and 62.7% for the two comparisons. Consequently, we conclude that a cognitive radio can achieve better spectrum utilization by using our spectrum sensing architecture that is aware of the primary user(s) waveform characteristics.