最近以第一作者身份被IEEE Communications Letters期刊录用一篇论文

标题为：Distributed Channel Access for Device-to-Device Communications: A Hypergraph-based Learning Solution

作者： Sun Youming; Wu Qihui; Xu Yuhua; Zhang Yuli; Sun Fenggang; Wang Jinlong

Abstract: In this letter, we propose a learning solution fordistributed channel access in device-to-device communications based on hypergraph interference model. We first define a new interference metric for hypergraph model, and then formulate this distributed channel access problem as a local altruistic game, which is proved to be an exact potential game admitting at least one pure strategy Nash equilibrium (PNE). A distributed learning algorithm is designed to quickly achieve the optimal PNE, which can minimize the defined networks’ interference metric. Simulation results show that the proposed algorithm outperforms the existing schemes and significantly improves the spectrum efficiency.

考虑在未来密集网络中(D2D网络，小蜂窝网络)存在诸多弱干扰源，传统的二元干扰图只强调了成对节点间的强干扰关系，但是忽略了干扰叠加效用。因此该论文从图模型出发，利用超图干扰模型刻画累计干扰关系。目前虽已经有部分研究工作关注超图干扰模型，但是这部分工作都是基于集中式的资源分配方法，需要全局的信息交互，因而难以适用到具有高度动态性和缺乏信息交互的D2D网络场景。该论文将D2D网络中的分布式信道接入问题建模为局部互利博弈模型，并证明该博弈为精确势能博弈。然后设计了一种快速学习算法收敛到博弈的纯策略纳什均衡解。