Learning Wireless Networks’ Footprints and Topologies in Shared Spectrum

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

On the effects of colluded statistical attacks in cooperative spectrum sensing

Cooperative spectrum sensing is vulnerable to attacks from malicious nodes, especially when collusion occurs. In this paper, we analyze the effect of colluded statistical attacks and show that collusion could cause performance degradation in terms of both false-alarm and detection probabilities, which is not possible via independent attacks. Closed-form expressions for system performance under the majority fusion rule are provided for a generalized form of colluded attacks. Then, for specific scenarios of collusion and mimicry attacks, we study the conditions under which the probabilities of false alarm and detection are both degraded

Towards Instantaneous Collision and Interference Detection using In-Band Full Duplex

Wireless devices are ubiquitous nowadays and, since most of them use the same unlicensed frequency bands, the high number of packet losses due to interference and collisions degrade performance. Reliability, energy consumption, and latency are key challenges for future dense networks. Allowing the transmitter to take action, i.e., vacating the channel, as soon as a collision or interference is detected is crucial in improving these metrics. In-band full duplex radios enable the transmitter to simultaneously transmit packets and sense the spectrum for collisions and interference. This paper studies two important questions regarding transmitter-based collision and interference detection: (1) from an overall system perspective, does such detection outperform receiver-based detection and (2) which test statistic is the most accurate and sensitive at detecting collisions and interference. First, NS-3 simulations are used to show that transmitter-based detection reduces the energy consumption while improving the throughput in a typical star topology network. Next, we present a measurement-based study of four different techniques for transmitter-based collision and interference detection. In particular, we compare the energy detector with three goodness-of-fit tests in terms of probability of detection and false alarm. Our analysis shows that transmitterbased detection can detect between 80% to 100% of the collisions and interference occurring at the receiver, depending on the distance between the transmitter and the receiver. Of those detectable by the transmitter, our measurement results show that goodness-of-fit tests can detect nearly 100% of the collisions and have at least 10 dB better sensitivity as compared to the commonly proposed energy detection test. In general, the proposed techniques can detect interfering signals that are up to 25 dB below the remaining self-interference power.status: publishe