# An Accurate Probabilistic Model for TVWS Identification
## 6 Conclusions  
A probabilistic model for identifying TVWS has been proposed. The model was based on the probabilistic analysis performed on collected spectrum information in the city of Windsor, Canada. To collect the spectrum information over a wide geographical area, a vehicle-mounted M-SSS was designed and implemented. It uses light weight components, consumes low power, and is easy to handle.  
A noteworthy feature of the M-SSS is that it uses two RFE devices connected in parallel for sensing the composite signal \(X(f)\) and noise signal \(W(f)\) simultaneously. This individual sensing enables our model to yield better accuracy. Based on this simultaneously sensed data, the model uses two statistical parameters, i.e., the false alarm probability and the detection probability to calculate an adaptive threshold.  
The false alarm probability and the detection probability values are calculated based on the consideration that both the noise signal and the composite signal follow Normal distribution. In reality, the noise signal truly follows a Normal distribution as verified by the Kolmogorov-Smirnov test performed in our work, but the composite signal actually is a multi modal signal. In order to reduce computational complexity related to probabilistic analysis, the composite signal has also been assumed to follow the Normal distribution. We consider this assumption is acceptable, and it does not hamper the detection accuracy of our model. This is because the fundamental calculations involved in finding the false alarm probability is based only on the noise signal, which as mentioned before, indeed follows a Normal distribution. The optimal false alarm probability is used as an input in the receiver operating characteristic curve function, and the optimal detection probability is obtained.  
In terms of performance, the proposed model demonstrates about 9.6% higher detection probability (\(P_{d}\)) compared to other methods found in literature. From our collected data, we observed that in the central area of the city, about 28% of the sensed spectrum are TVWS and as we move to the outer edges of the city, up to 60% of the TV channels sensed were found to be TVWS.  
This observation clearly suggests that there lies a significant number of unused TV channels which can be used for different opportunistic services through the implementation of cognitive radio-enabled dynamic spectrum access. Finally, it can be concluded that the proposed mobile spectrum sensing station along with the proposed probabilistic model for sensing the UHF-TV spectrum may be used in any other location.  
[PERSON] collected and organized the data presented in this paper and analyzed the information. [PERSON], [PERSON] and [PERSON] wrote and reviewed the manuscript, analyzing the most relevant data. [PERSON] and [PERSON] reviewed and discussed the manuscript.  
This work was supported by the Secretaria de Educacion Superior, Ciencia, Tecnologia e Innovacion of Ecuador and the _University of Windsor_. Authors thank to the WiCIP Lab for providing all the equipment necessary to collect the information which is analyzed and discussed in this manuscript.  
The authors declare no conflict of interest.