Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet

Abstract

Abstract:In order to improve system performance such as throughput, heterogeneous network (HetNet) has become an effective solution in Long Term Evolution-Advanced (LET-A). However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.

Solving Multi-Objective Matrix Games with Fuzzy Payoffs through the Lower Limit of the Possibility Degree

“…Furthermore, they also reached a conclusion that the solution of fuzzy games and the marginal value of potential functions are equivalent. In [19], in order to solve the game problem quickly, the gradient iterative algorithm was proposed. Cevikle et al [22] utilized the fuzzy relation method to find the solution of matrix games in terms of fuzzy goals and fuzzy payoffs.…”

“…Single objective fuzzy game problems and related problems have attracted a wide range of research [6][7][8][9][10][11]. Therefore, fuzzy games theory has been extensively studied in some fields, such as economics, engineering and management science [12][13][14][15][16][17][18][19]. In order to deal with the fuzzy matrix games problem, a method of robust ranking is formulated by Bhaumik [20].…”