Mitigating Trade-Off in Unlicensed Network Optimization Through Machine Learning and Context Awareness

Abstract

Unlicensed cellular networks are being deployed worldwide by cellular operators to meet the rising data demands. However, the unlicensed band has existing incumbents such as Wi-Fi and radar systems. This creates a highly dynamic environment, making harmonious unlicensed coexistence difficult. Consequently, conventional optimization techniques are not sufficient to offer latency-critical applications and services. A data-driven hybrid optimization approach is necessary for optimal network performance with low convergence times. However, a largely unexplored problem in dense unlicensed network optimization is the accuracy-speed trade-off, that is, achieving high accuracy in optimization objectives with minimal time costs. This work seeks to address this problem through a hybrid optimization approach that combines machine learning and network optimization. It investigates the use of more precise higher-order network feature relationships (NFRs) in optimization formulations and the consequent trade-off that arises between the increase in convergence time (Speed) and the nearness to optimal results (Accuracy). In addition, it demonstrates the relevance of context awareness of network conditions and the traffic environment to mitigate the trade-off. To that end, a context-aware network feature relationship-based optimization (CANEFRO) approach is proposed and validated through decision matrix analysis. The experiments were carried out on a coexistence testbed consisting of both unlicensed LTE standards (LTE-U & LAA) and two Wi-Fi standards (802.11n/ac) on multiple channel bandwidths. In addition, LTE-U & LAA are contrasted on signaling and user data traffic data models and resource block allocation performance. More importantly, CANEFRO demonstrates the impact of the network context on the degree of feature relationship ( $2^{nd}$ & $3^{rd}$ degree polynomials), objective of optimization (SINR and Capacity), and the network use case (Accuracy vs. Speed). CANEFRO is also used to contrast LTE-U & LAA optimization performance. In particular, the decision matrix analysis demonstrates a higher decision score for LAA by as much as 42% compared to LTE-U.

Keywords

• Unlicensed networks
• machine learning
• network optimization
• context awareness
• LAA
• LTE-U