Interference-aware wireless mesh networks

Abstract

While channel assignment algorithms have been proven useful to leverage the interference problem in multi-radio wireless mesh networks, several challenges remain. First, the potential performance gain with channel assignment is strongly related to the accuracy of the utilized interference model. Usually simplified distance-based interference models are used, such as the 2-hop model, that has shown only poor accuracy in testbed validations. Thus, a measurement-based interference model has been developed in the scope of this thesis that outperforms the simplified distance-based models. Second, with the dawn of the Internet of Things, the number of co-located networks and devices operating in the unlicensed frequency spectrum is exploding. Therefore, channel assignment algorithms have to become aware of the radio activity of external networks. An external interference-aware channel assignment algorithm has been developed that uses a spectrum sensing software component to detect external sources of interference. First experimental results in a large-scale multi-radio testbed promise that our approach increases the accuracy of the interference estimation and leads to a higher network performance.