Optimizing Unlicensed Coexistence Network Performance Through Data Learning
This work addresses performance optimization for dense coexistence networks, which is incremental as it builds on existing optimization methods by incorporating learned feature relationships.
This paper tackles the problem of optimizing dense unlicensed LTE-WiFi coexistence networks by studying network feature relationships through supervised learning and proposing the NeFRO framework, which reduces optimization convergence time by up to 24% while maintaining an average accuracy of 97.16%.
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.