Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

[...]even though these techniques rank the channel using some parameters and perform well under specific settings, they consider parameters separately in the ranking, and they exclude critical parameters, which cannot lead to the selection of the best channel. [...]the channel selection mechanism remains a significant open issue in cognitive radio systems since it has to deal with antagonistic goals: maximizing the spectral efficiency and considering the scarcity of the radio resource, while guaranteeing the quality-of-service requirements. All of these parameters are updated simultaneously, and Equation (5) can be rewritten as: w1:=w1−α∂∂w1J(w)w2:=w2−α∂∂w2J(w)⋮wn:=wn−α∂∂wnJ(w) The partial derivative ∂∂wjJ(w) becomes the following: ∂∂wjJ(w)=1m∑i=1m(Uw(x(i))−y(i))xj(i) where m is the number of element in the training set. [...]Equation (5) above can be rewritten as: wj:=wj−α1m∑i=1m(Uw(x(i))−y(i))xj(i) The performance of the gradient descent is related to two main parameters: the learning rate and the number of iterations. [...]we attributed a utility value to each frequency channel that reflects its ranking. According to the results obtained in Table 3, a secondary user can use the channel frequency of 5725 MHz to achieve a higher data rate and a high holding time for the frequency channel, which minimizes the number of switching from a channel to another, therefore reducing the power consumption from the process of sensing and ranking.