دانلود رایگان مقاله انگلیسی به حداقل رساندن هزینه برای رله کردن ترافیک همیاری بین شبکه های اولیه و ثانویه - نشریه IEEE

Abstract

Cooperation between primary and secondary networks offers significant benefits in data forwarding. But cost implication in such cooperation is not well understood. In this paper, we explore cost incurred in both primary and secondary networks when they are allowed to relay each other’s traffic in a cooperative manner. We model costs in both networks and formulate a multiobjective optimization problem. For this problem, we present a novel algorithm to construct an ϵ-approximation curve and prove its error bounds in both cost dimensions. Based on the ϵ-approximation curve, we develop three important applications. The first application is to show the minimum cost value for a single objective (by fixing the other objective as constant) or the relationship between the two objectives over the entire range of possible values. The second application is to address different cost parameters in the primary and secondary networks. We show how to obtain a new approximation curve by scaling the original ϵ-approximation curve with appropriate factors and quantify its error bounds. The third application is to use the ϵ-approximation curve to study a single objective optimization problem with a guaranteed error bound. The results in this paper offer some deep theoretical insights on potential costs incurred in both networks when they are allowed to relay each other’s traffic cooperatively.

8 CONCLUSIONS

In this paper, we offered an in-depth study on potential costs incurred in the primary and secondary networks when they are allowed to relay each other’s traffic. We presented cost models for cooperative traffic relaying between the two networks and studied a multiobjective optimization problem. The main contributions of this paper are: (i) a two-phase iterative algorithm to find a subset of Pareto-optimal points that can be used to construct an ϵ-approximation curve; (ii) an application of the ϵ-approximation curve to show the entire landscape of minimum cost value for a single objective (between the two) or both objectives with approximation performance guarantee; (iii) an application of the ϵ-approximation curve to find a new approximate minimum cost curve for a different set of cost parameters; (iv) an application of the ϵ-approximation curve to find an approximate solution for a single objective optimization problem. Collectively, these results offer important theoretical foundation on quantifying mutual cost in the primary and secondary networks when they help relay each other’s traffic in a cooperative manner.