UCLA

The increasing demand for wireless connectivity and the scarcity of spectrum for exclusive use have popularized the idea of multiple communication systems to share spectrum. For a network to estimate its own link budget while avoiding interference from neighboring, or incumbent, networks, it needs to learn the incumbent networks' spatial, spectral, and temporal usage patterns. Instead of manually modifying the standard of each network to ensure such a coexistence, we can, as proposed by Mitola and Haykin, build learning or cognitive abilities into the radios themselves and automate the process. In this work, we propose methods for such a cognitive network to cooperatively learn the spatial and spectral occupancy of incumbent users (IUs) and the topologies of the incumbent networks. In contrast to the existing literature on spectrum sensing, this work studies the problems of detecting, distinguishing, and coexisting with multiple communicating incumbent networks rather than that of avoiding interference to single broadcasting transmitters. Our methods are designed to make these inferences without prior knowledge of the number of IUs, their locations, network topologies, transmission protocols, and channel models of the ambient wireless environment. We also do not require knowledge of the locations of the cognitive radios (CRs).

We begin with a conventional cooperative spectrum sensing scenario where the CR network fuses binary reports from multiple CRs to infer the spectral occupancy of a single intermittently transmitting IU. We show that though a second a priori unknown IU or interferer would cause correlations in the CR reports and this correlation structure can be learned, it is not possible to distinguish whether the correlations are caused by another IU, channel correlations, or malicious intent. Instead, we propose learning the correlation structure and then use this structure to infer the spectrum occupancy of the single IU.

Next, we propose algorithms to learn the footprint of each incumbent transmitter, i.e., the sets of CRs that receive signals from that incumbent transmitter. By learning the Gaussian mixture distribution of the received energy vector, we show that multiple transmitters' footprints can be learnt irrespective of their spatial overlap and potentially anisotropic shape.

Learning the footprints also enables sampling the activity of each incumbent radio. By identifying radios that transmit a response to the transmission of another, we learn the causal links between pairs of incumbent radios, i.e., the topologies of the incumbent networks. Hence, we can identify the potential receivers when a particular incumbent radio is transmitting and the cognitive network can potentially avoid interference to these receivers.

Finally, we consider the problem of detecting frequency bands occupied by intermittent transmitters using a single antenna wideband sensor. Using only power spectrum measurements, we first automatically learn the noise power spectrum of the sensor and then learn the frequency bands occupied by the received signals even if they are partially overlapping.

In summary, this dissertation proposes methods for radio scene analysis of communicating incumbent networks by using received energy and power spectrum measurements.