  Unlicensed LTE-WiFi coexistence networks are undergoing consistent
densification to meet the rising mobile data demands. With the increase in
coexistence network complexity, it is important to study network feature
relationships (NFRs) and utilize them to optimize dense coexistence network
performance. This work studies NFRs in unlicensed LTE-WiFi (LTE-U and LTE-LAA)
networks through supervised learning of network data collected from real-world
experiments. Different 802.11 standards and varying channel bandwidths are
considered in the experiments and the learning model selection policy is
precisely outlined. Thereafter, a comparative analysis of different LTE-WiFi
network configurations is performed through learning model parameters such as
R-sq, residual error, outliers, choice of predictor, etc. Further, a Network
Feature Relationship based Optimization (NeFRO) framework is proposed. NeFRO
improves upon the conventional optimization formulations by utilizing the
feature-relationship equations learned from network data. It is demonstrated to
be highly suitable for time-critical dense coexistence networks through two
optimization objectives, viz., network capacity and signal strength. NeFRO is
validated against four recent works on network optimization. NeFRO is
successfully able to reduce optimization convergence time by as much as 24%
while maintaining accuracy as high as 97.16%, on average.
