Context-based Mixed-Numerology Profile Selection for 5G and Beyond

Next generation wireless networks will require the flexibility to accommodate an extremely diverse set of service types. Increasing emphasis on quality of service (QoS) and limited radio necessitates the use of mixed-numerologies to accommodate diverse service requirements. In this paper, we present a mixed-numerology profile selection method that adapts to the context of wireless networks to maximize the QoS of end users. The idea is to take service requirements, channel conditions and traffic patterns into account simultaneously for optimizing a mixed-numerology profile. We extract statistical features from these aspects to train a Mondrian forest model, designed to estimate the QoS in different mixed-numerology profiles. This enables the optimization of mixed-numerologies under any wireless communication scenario, with specific service requirements, and under any traffic scenario. Comprehensive simulations were conducted to evaluate the performance of the proposed mixed-numerology method. Results show that the proposed method is able to provide QoS satisfaction levels of 80–95%, whereas a non-optimized approach provides 60–85%, while minimizing the total number of numerology indexes in operation.