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Interference is a major impediment to the performance of a wireless networkas it has a significant adverse impact on Network Capacity. There has been agradual and consistent densification of WiFi networks due to Overlapping BasicService Set (OBSS) deployments. With the upcoming 802.11ax standards, dense andultra-dense deployments will become the norm and the detrimental impact ofInterference on Capacity will only exacerbate. However, the precise nature ofthe association between Interference and Network Capacity remains to beinvestigated, a gap we bridge in this work. We employ linear and polynomialregression to find answers to several unexplored questions concerning theCapacity Interference Relationship (CIR). We devise an algorithm to selectregression models that best explain this relationship by considering a varietyof factors including outlier threshold. We ascertain the statisticalsignificance of their association, and also determine the explainability ofvariation in Network Capacity when Interference is varied, and vice versa.While the relationship is generally believed to be non-linear, we demonstratethat scenarios exist where a strong linear correlation exists between the two.We also investigate the impact of WMN topology on this relationship byconsidering four carefully designed Wireless Mesh Network (WMN) topologies inthe experiments. To quantify endemic Interference, we consider four popularTheoretical Interference Estimation Metrics (TIEMs) viz., TID, CDALcost,CXLSwt, and CALM. To ensure a sound regression analysis, we consider a largeset of 100 Channel Assignment (CA) schemes, a majority of which are generatedthrough a Generic Interference aware CA Generator proposed in this work.Finally, we test the TIEMs in terms of their reliability and the ability tomodel Interference. We carry out the experiments on IEEE 802.11g/n WMNssimulated in ns-3.

Statistical Relationship between Interference Estimates and Network Capacity