## 3 Cognitive Radio Engine Platform Usage Scenario  
### Structure and Roles of the Proposed Cognitive Radio Engine  
The proposed cognitive radio engine platform requires a separate step to calculate the probability of PU channel occupancy. This allows for the estimation of the PU channel usage pattern by using a functionality of the cognition engine equipped with spectrum sensing. In this paper, the spectrum sensing observations of the PU channel occupancy pattern are assumed to be perfect, and the pattern obtained via actual spectrum sensing is considered complete. In general, the cognitive engine is composed of four types of engine modules consisting of the cognition engine, the learning engine, the reasoning engine, and the optimization engine. Among those, this paper only deals with two specific engines, i.e., the learning engine, as well as the reasoning engine, introducing their functionalities and capabilities. For given PU traffic patterns associated with every channel, in order to inspect their performances, we assume that the cognition engine works perfect so that the sensed information indicating whether the PU exists or not is correct. The reason why this assumption is made is attributed to calculating the collision probability as a tool of assessment for the performance of the reasoning engine. Therefore, along the step for the PU traffic generation, at first, the distinctive parameters \(\lambda_{a}^{n}\) and \(\lambda_{\beta}^{n}\) are specified, which yield probabilities \(P_{off}^{n}\) and \(P_{on}^{n}\) for each channel; with regard to these, the PU traffic is artificially generated in the sequel. Since the sensing process is accomplished without invoking any detection error, the resulting PU traffic represented in two-state binary fashion for each channel is exactly the same as the result of the cognition engine. Furthermore, this PU traffic pattern is subsequently utilized for estimating the PU occupancy probability by applying the individual methods of sampling approaches with varying either the number of samples or the sampling interval. In addition, the PU state duration time is presumed to be random following an exponential distribution,  
Figure 2: IEEE P1900.5-based cognitive radio engine processing diagram. and the occupancy patterns corresponding to every channel are independent. Figure 3 depicts the entire structure of the proposed cognitive radio engine platform. In Figure 3, one of the major roles assigned to the learning engine is the quantification of the PU occupancy probability, which is obtained from observations in the temporal domain for every channel to be inspected. This information is delivered by the cognition engine. The process of quantifying the PU occupancy probability associated with a certain channel is performed by sampling the PU's channel usage patterns during a fixed time slot and then calculating the channel occupancy. In this regard, this study applied either uniform sampling or weighted sampling, the details of which are explained later. Furthermore, one of the major roles of the reasoning engine is the exploitation of a group of available candidate channels on behalf of the TCRN for dynamic access to the spectrum in which the PU is possibly active. As the side information from consolidating the set of available channels, the PU traffic pattern constructed by the PU occupancy probability is transferred from the learning engine. The process for choosing the available candidate channels is implemented by appropriately arranging the channels in ascending order, which creates a series that specifies the PU occupancy probability per channel, where the top-ranked channel is the first priority candidate to be used. Next, all the ranks calculated by every sampling technique considered in this paper for all of the inspected channels are used to form a composite ranking. Ultimately, a group of available channel candidates for the cognitive radio node is selected based on guaranteeing, to the greatest extent possible, a minimum collision probability.