BRESAP: A Policy Reasoner for Processing Spectrum Access Policies Represented by Binary Decision Diagrams

Abstract

Declarative spectrum management through policy-based dynamic spectrum access has garnered significant attention recently. Policy-based spectrum access decouples spectrum access policies from the radio platform. In policy-based spectrum access, a reasoning engine called the policy reasoner plays a critical role. The policy reasoner assists in policy enforcement and carries out a number of tasks related to policy analysis and processing. One of the most important tasks performed by the policy reasoner is evaluating transmission requests in the context of the currently active set of policies. This paper describes the design and implementation of a novel policy reasoner. The proposed policy reasoner uses multi-terminal binary decision diagrams (MTBDDs) to represent, interpret, and process policies. It uses a set of efficient graph-theoretic algorithms to translate policies into MTBDDs, merge policies into a single meta-policy, and compute opportunity constraints. In this paper, we demonstrate that policies can be processed efficiently by reframing the policy reasoning problem as a graph-based Boolean function manipulation problem. The proposed policy reasoner has the capability to respond to either under-specified or invalid transmission requests (sent by the system strategy reasoner) by returning a set of opportunity constraints that prescribes how the transmission parameters should be modified in order to make them conform to the policies. We propose three different algorithms for computing the opportunity constraints. The first algorithm computes opportunity constraints for under-specified transmission requests and its complexity is proportional to the number of variables in the meta-policy BDD. The second and third algorithms compute opportunity constraints for invalid transmission requests and their complexities are proportional to the number of variables and the size of the meta-policy BDD, respectively.