Probabilistic Reasoning for Secondary Spectrum Sharing: A Mechanism for Increasing Spectrum Access Efficiency and Assessing Alternative Secondary Sharing Models

Uncertainties regarding wireless propagation environments pose challenges for spectrum management in general and specifically hinder the implementation of dynamic spectrum sharing systems. Spectrum management mechanisms must balance increases in spectrum access efficiencies with risks of harmful interference among spectrum users. Without the ability to reliably assess the local propagation environment and spectrum usage in real time, secondary sharing systems have limited ability to evaluate interference risks. As a result, spectrum regulators specify spectrum access behaviors such as exclusion zones and maximum transmit powers based on risk thresholds applied to statistical results from propagation models and measurements. Because the models can contain significant levels of uncertainty, establishing behavior limits for low interference risk necessarily results in significant spectrum access inefficiencies. It is only by reducing the degree of uncertainty that risk thresholds can be maintained while increasing spectrum access efficiency. Probabilistic reasoning provides significant potential to increase spectrum access efficiency in dynamic spectrum sharing systems. Probabilistic reasoning approaches enable risk-constrained spectrum access, a concept in which regulators and spectrum users establish spectrum access rules defining acceptable levels of interference and spectrum access risks while potentially optimizing their decisions relative to sets of decision criteria. The theoretical foundations are established, and the concept is demonstrated through the development of a probabilistic reasoning model using Functional Causal Model Theory. The probabilistic reasoning model is coupled with multi-attribute decision theory to enable assessments of user decision-making subject to risk attitudes, levels of situational uncertainty, and alternative spectrum sharing models. Simulations demonstrate several significant capabilities associated with a probabilistic reasoning approach. First, risk-constrained access mechanisms inherently regulate spectrum access behaviors in accordance with the degree of situational uncertainty; greater situational uncertainty results in lower spectrum access performance (e.g., reduced transmit power levels) while maintaining the requisite level of risk. Second, current regulatory approaches based on spectrum models and surveys define the minimum performance level; additional information gained through local situational awareness mechanisms lead to increased performance. Third, probabilistic reasoning combined with situation-specific awareness potentially enables greater spectrum access by reducing requisite exclusion zone size and increasing user density. Finally, probabilistic reasoning combined with decision modeling enables assessment of alternative spectrum access regulations and models based on user behavior and risk attitudes.