Resource allocation strategy selection for 5G network by using multi-attribute decision-making approach based on tangent trigonometric bipolar fuzzy aggregation operators

Abstract

Resource allocation strategy selection in 5G networks is a MADM (Multi-Attribute Decision-Making) problem, which all the methods defined so far or those used to solve it have neglected the negative aspects of attributes. This can result in the occurrence of information loss and it would be difficult to come to the right decision. Thus, in this paper, we present a MADM technique that can be used to take the negative aspects of attributes into account. This goal will be achieved by the method based on bipolar fuzzy sets (BFS) and tangent trigonometric aggregation operators (AOs). For this, in this article, firstly, we devise the concept of tangent trigonometric bipolar fuzzy number (TT-BFN) and linked algebraic operators. Then, we deduce tangent trigonometric bipolar fuzzy weighted averaging (TT-BFWA), tangent trigonometric bipolar fuzzy ordered weighted averaging (TT-BFOWA), tangent trigonometric bipolar fuzzy weighted geometric (TT-BFWG), and tangent trigonometric bipolar fuzzy ordered weighted geometric (TT-BFOWG) operators. We also devised the related results of these operators that is idempotency, monotonicity, and boundedness. Further in this manuscript, we investigate a case study “Selection of resource allocation strategy for 5G network” by considering artificial data and employing the invented MADM approach in the environment of BFS and get that “Max-Min Fairness Allocation” is the finest resource allocation strategy in 5G network. Finally, we compare our deduced theory with a few current ones to reveal supremacy and dominance.