Dynamic Link Selection for Interference Mitigation in D2D Multicast Content Sharing Networks

Abstract

In this letter, we study the link selection problem for interference mitigation under dynamic channel conditions in D2D multicast content sharing networks. We firstly transform the problem into a stochastic game, in which the utility of each player (i.e., cluster head) is designed as the maximum expected weighted interference suffered by the receivers in the cluster. Then, we propose a simple and uncoupled link selection algorithm based on stochastic learning algorithm to obtain the Nash Equilibrium points. Importantly, the proposed algorithm only requires little intra-cluster information feedback. Theoretical analyses and numerical results validate the effectiveness of our proposed algorithm under dynamic channel conditions.