高度密集的WLAN信号重叠空间将触发频繁的同频间射频牵制，这种WLAN同频干扰现象会造成公用频段的资源紧缺问题，成为未来物联网技术发展面临的一大挑战。 为此，提出一种以提高公用频段的空间使用率为目标的认知型拓扑结构控制设想，对此，参考认知网络的环境学习理论，拟采用结合数据挖掘的无线环境评估方法，分别对私有、共用WLAN环境进行拓扑结构的控制，使信号重叠空间得到简化，减少同频干扰。一方面，针对私有环境，提出一种基于相对定位的拓扑控制方法，开展基于无线定位的局部干扰减少研究；另一方面，针对公用环境，提出一种基于干扰预测的拓扑控制方法，开展基于环境评估的全局干扰减少研究。本课题不仅可对公用频段中空间资源的有效共享提供一定的参考依据，也可为面向资源使用率的拓扑结构控制研究提供新的思路。

Co-Channel Interference is known as the outcome of resource constraints which occurs when multiple networks overlap. Therefore, reducing the Co-Channel Interference is becoming a highly focused issue for today's high-dense WLAN and also would be an important challenge to future IoT Environment. In this project, we adapt the core theory of Cognitive Network, which is an environmental study, analysis the wireless environment with Data Mining methods then control network topology in order to reduce the overlapped signal areas. The research considers two approaches, the first one is a proposal of a relative positioning based topology control method, which to provide a local-optimized personal wireless environments with wireless positioning based interference reducing scheme; another one is a proposal of an interference prediction based topology control method, which to provide a global-optimum to the public wireless environments with environmental analysis based interference reducing scheme. This project aims to bring a reference to effective resource sharing in public band, furthermore, presents a novel approach to topology control researches which target at the resource optimization.