Performance of MADM algorithms with real spectrum measurements for spectrum decision in cognitive radio networks

Abstract

Spectrum decision is an important functionality of a cognitive radio terminal, which allows the selection of the appropriate frequency band from the available underutilized spectrum. Spectrum decision conducts itself in accordance to the communication requirements of the secondary (or cognitive) users in the forthcoming Cognitive Radio Networks (CRNs). Selecting the best spectrum for a given transmission involves making preference decisions over the set of available alternatives of frequency bands, which are indeed characterized by different attributes. Therefore, spectrum decision can be modeled as a multiple attribute decision making (MADM) problem. In this paper, we evaluate the performance of MADM decision algorithms such as Simple Additive Weighting (SAW), Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) and the Compromise Ranking Method VIKOR for spectrum decision.The study, however, is conducted using real spectrum occupancy measurements to evaluate the performance of the aforementioned algorithms in a practical scenario. Some important attributes of underutilized spectrum are proposed for consideration in the decisions. Results show that SAW algorithm performs well for the preferred spectrum attributes in the selected scenarios, while offering a good performance also in other parameters.

Reducing Spectrum Handoffs and Energy Switching Consumption of MADM-Based Decisions in Cognitive Radio Networks

“…The preliminary selection offered by MADM algorithms in CRNs presents an issue. In our work in [13], we demonstrated how these algorithms satisfy SUs connection profiles and provide the best spectrum hole selections. However, the MADM algorithms do not take into account additional expenses that a SU can have in order to reach the suggestion made for them, showing that they need to be improved for CRNs.…”

“…In our work presented in [13], only 6 MHz, 7 MHz, and 8 MHz spectrum holes were considered. However, due to the presence of several spectrum holes smaller than 6 MHz (but larger than 5 MHz), spectrum holes of 5 MHz are also considered in this work, and so = 5 MHz.…”

“…This method performs homogenization; each at parameter is multiplied by to obtain the score of each candidate alternative. The normalization of at is described as follows: if at is a benefit parameter, it is normalized as can be seen in (13), where dm + = max dm . On the other hand, if at is a cost parameter, it is normalized as it is shown in (14), where dm − = min dm :…”

“…Results on this matter have been presented in [12], where an Analytical Hierarchy Process (AHP) was utilized to select available frequency bands in CRNs. Furthermore, in earlier stages of this investigation [13,14], we evaluated the performance of three MADM decision algorithms: Simple Additive Weighting (SAW), Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS), and the Compromise Ranking Method VIKOR using real spectrum occupancy measurements in a practical scenario.…”

“…Power Index (pi ). The power index is an important relation that should be considered in the spectrum decision process [13]. This relation indicates how a SU can be affected when a PU appears.…”