Interference management and control in the cognitive radio network (CRN) is a necessity if the activities of primary users must be protected from excessive interference resulting from the activities of neighboring users. Hence, interference experienced in wireless communication networks has earlier been characterized using the traditional grid model. Such models, however, lead to non-tractable analyses, which often require unrealistic assumptions, leading to inaccurate results. These limitations of the traditional grid models mean that the adoption of stochastic geometry (SG) continues to receive a lot of attention owing to its ability to capture the distribution of users properly, while producing scalable and tractable analyses for various performance metrics of interest. Despite the importance of CRN to next-generation networks, no survey of the existing literature has been done when it comes to SG-based interference management and control in the domain of CRN. Such a survey is, however, necessary to provide the current state of the art as well as future directions. This paper hence presents a comprehensive survey related to the use of SG to effect interference management and control in CRN. We show that most of the existing approaches in CRN failed to capture the relationship between the spatial location of users and temporal traffic dynamics and are only restricted to interference modeling among non-mobile users with full buffers. This survey hence encourages further research in this area. Finally, this paper provides open problems and future directions to aid in finding more solutions to achieve efficient and effective usage of the scarce spectral resources for wireless communications.

如果必须保护主要用户的活动免受相邻用户活动造成的过度干扰，则认知无线电网络（CRN）中的干扰管理和控制是必要的。因此，较早地使用传统的网格模型来表征无线通信网络中遇到的干扰。但是，此类模型导致无法分析的分析，这常常需要不切实际的假设，从而导致结果不准确。传统网格模型的这些局限性意味着，由于随机几何（SG）能够正确捕获用户分布，同时针对各种性能指标进行可扩展且易于处理的分析，因此继续受到广泛关注。尽管CRN对下一代网络很重要，当涉及到CRN领域中基于SG的干扰管理和控制时，尚未对现有文献进行调查。然而，这样的调查对于提供当前的技术水平以及未来的方向是必需的。因此，本文提出了与使用SG影响CRN中的干扰管理和控制有关的全面调查。我们显示，CRN中的大多数现有方法都无法捕获用户的空间位置与时间流量动态之间的关系，并且仅限于具有完整缓冲区的非移动用户之间的干扰建模。因此，这项调查鼓励对该领域进行进一步的研究。最后，