Policy Reasoning for Spectrum Agile Radios
Deshpande, Amol Anant
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DARPAâ s neXt Generation (XG) communication program proposes the use of Dynamic Spectrum Access (DSA) wherein intelligent radios can realize opportunistic usage of frequency bands by identifying the under-utilized spectrum and reasoning about it. Implementing such a flexible scheme requires changes in the current static spectrum management approach. As a result, declarative spectrum management through policy-based dynamic spectrum access has garnered significant attention recently. Policy-based dynamic spectrum access decouples the Spectrum Access Policies and Policy Processing Components from the Radio Platform. The Policies define conditions under which the radios are allowed to transmit in terms of frequencies used, geographic locations, time etc. The Policy Processing Components include a reasoning engine called the Policy Reasoner, which is responsible for enforcing these policies. This thesis describes the design and implementation of a novel policy reasoner called Bi- nary Decision Diagram based Reasoner for processing Spectrum Access Policies (BRESAP). BRESAP processes spectrum policies efficiently by reframing the policy reasoning problem as a graph based Boolean function manipulation problem. BRESAP uses Binary Decision Diagrams (BDDs) to represent, analyze and process the policies. It uses a set of efficient graph-theoretic algorithms to merge these policies into a single meta-policy and compute opportunity constraints. Our policy reasoner has the capability to respond to invalid and under-specified transmission requests sent by the System Strategy Reasoner (SSR). In case of invalid or under-specified transmission requests, BRESAP returns a set of opportunity constraints which inform the SSR of the changes needed to the transmission parameters in order to make them conform to the policies. We also propose three algorithms for computing the opportunity constraints. The complexity of the first algorithm is proportional to the number of variables in the metapolicy BDD, while the complexities of the second and third algorithms are proportional to sum of number of variables and the size (i.e., number of nodes) of the meta-policy BDD.
- Masters Theses