# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 7 Discussion  
As stated in Section 1, the main objective described in this paper was to experiment with an ontology and policy-based system for dynamic spectrum sharing. Towards this  
Figure 11: Simulation results indicating the behavior of the Learner in different conditions. objective, we designed and implemented a system whose components were described above. In this section, we ask the question of how we can analyze, and possibly quantify, the impact of not having the functionalities of these modules on the overall performance of spectrum sharing. In short, we are interested in the question of what happens if spectrum management systems are not capable to interpret policies that involve logical rules (higher-level policies). Towards this end, we simulated scenarios in which policies were a mixture of both hypercube-based and logical rule-based policies. The results of these simulations are shown in Figure 12.  
This figure shows the dependence of non-interference on the percentage of the logical policies as opposed to the hypercube-based (quantitative) policies. In the simulations, it was assumed that at first, the percentage of the logical policies is negligent (on the left) and was increasing to 100% in the end (on the right). The second important assumption was that even if the interpretation is wrong, still, the interference would occur only randomly following the uniform distribution. For this reason, the right-hand side of the plot shows the degradation going down to only 0.5. This assumption is somewhat arbitrary, and its validity should be verified experimentally. Related WorkThere are many ways in which one could develop policy and ontology-based DSA systems. While the research in this area includes example implementations and discussions regarding the expressiveness of policy representation languages, more research is needed to develop guidelines for DSA practitioners. In particular, we argue that such research should provide quantitative metrics for system performance. To this end, we contributed two recent journal publications. In [23], we described a program to generate large volumes of device descriptions in OWL/RDF. The intent was to use this program for testing implementations of automatic reasoning systems for DSA. In [24], we described a program for automatic generation of queries against the repositories of device descriptions. Figure 12: The impact of the missing capabilities on interpreting higher-level policies: simulation results. Currently, we are in the process of publishing another paper that compares XML with OWL as candidate languages for representing and querying cognitive radio capabilities. The decision regarding the the use of ontologies, in general, and for policy-based cognitive radios, in particular, is subject to a number of competing requirements:  
* Expressiveness of the language for representing domain ontologies and policies;
* Computational complexity of reasoning;
* Capabilities of the ontology reasoning and policy reasoning engines;
* Behavioral characteristics of the engines;
* Use of open standards. While a number of proposals and prototype implementations have been described in the literature, we briefly describe some of the most significant contributions. Each of the described approaches emphasizes some trade-offs between the expressiveness and the computational complexity. It is clear that the implementers have investigated various approaches, and none of them subsumes all the others. Our work is no exception to this. Policy-based DSA systems have been promoted by the DARPA XG program. One of the outcomes was the Cognitive Radio Language (CoRaL) developed by SRI [25, 26]. CoRaL is an expressive language; it provides both generic constructs such as types, functions and constraints, but it also defines various concepts that are specific to the cognitive radio domain. In particular, it uses constraints to specify the transmission opportunities and a CoRaL reasoner would return opportunities as simplifications of DSA policies.