# Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks
## 1 Introduction
The authors of [19] proposed a scheme that continuously monitors the primary user activity of each channel and ranks the sensed channels based on the number of primary user arrivals and the duration of the monitoring. The authors of [20] proposed a scheme in which the channels are rated based on the quality-of-service demand of each secondary user to avoid collisions that might happen as a result of using the frequency channel with the highest availability. The authors of [21; 22; 23; 24; 25; 26] performed channel ranking based on the channel state prediction, which is related to the duration of the channel availability. Each secondary user predicts the arrival of the primary users on different communications channels using statistical models, and the secondary user then selects the channel with the least primary user activity to increase its holding time of the communication channel. The authors of [27; 28; 29; 30; 31] selected the communication channel based on the availability of the channel using a genetic algorithm. Yet, these schemes consider only a few settings and do not consider the effect of the primary user activity and the quality-of-service parameters of the performance of the channel selection algorithms. The authors of [31] proposed a genetic algorithm-based channel selection and parameter adaptation considering the PU activity, the quality of service of the secondary user, and the channel condition and quality. The proposed scheme performs well under specific settings, but it does not jointly consider these parameters, which may lead to an inefficient solution. In short, 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. Thus, 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. In this research, we highlight the importance of channel quality ranking. Then, we describe a multi-attributes utility-based model to rank the frequency channels. The model associates a weight to each parameter involved in the ranking mechanism. The weights corresponding to these parameters are determined using a nonlinear regression algorithm. The main contributions of this paper are as follows:* Development of a utility ranking-based model for channel quality ranking,
* Use of a nonlinear regression algorithm to determine the weights corresponding to each parameter,
* Validation the proposed model,
* Highlight the challenges and future directions related to channel quality ranking. The rest of this paper is organized as follows: the second section describes the proposed model, the third section describes the experiments and the results, the fourth section further discusses the proposed model and how it can be extended, and in the last section, conclusions and perspectives are drawn.