In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the diversity requirements of different users, i.e. the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user's throughput whilst ensuring a chance constraint for D2D user. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint about D2D reliability requirement, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any uncertain CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, we derive the robust counterpart of the chance constraint under these uncertainty sets as the computation convenient convex sets. To overcome the conservatism of the symmetrical-geometry-based uncertainty sets, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint of D2D is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with different robust counterparts. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

翻译：在本文中,我们研究了D2D潜置蜂窝网络中的资源分配情况,其中含有不确定的频道状态信息(CSI)。为了满足不同用户的多样性要求,即蜂窝用户的最低费率要求和D2D用户的可靠性要求,我们试图最大限度地扩大蜂窝用户的吞吐量,同时确保D2D用户的机会限制。然后,提出一个强有力的资源分配框架,以解决关于D2D可靠性要求的极难解决的机会限制,即C2D不确定性代表着一种确定性,而可靠性要求则是在其中维持任何不确定的 CSI。然后,发展一种基于对称的基于地理测量的学习方法,将不确定的 CSI建模到多功能、电子版和框中。之后,我们用这些不确定性组的可靠对应机会限制来计算方便的 convex 。为了克服基于对称的不确定性的组合,我们开发基于支持性的病媒群集(SVCCB)为基础的方法,将一个基于对称的CSI的基于对称的不确定性 Cexbus不确定性的基于对称的基于对称的学习方式的学习方法,然后,根据一种稳定的搜索方式,将D型平流流式的对等的对等的对调,在最后,将一个根据一种对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲分配,对冲的对冲的对冲的对冲,对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲的对冲式的对冲式的对冲式的对冲式的对冲的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲式的对冲