  Flexibility is one of the essential requirements in future cellular
communications technologies. Providing customized communications solutions for
each user and service type cannot be possible without the flexibility in 5G and
beyond. Different optimizations need to be done for the flexibility related
structures of 5G and beyond systems. In this paper, a novel machine learning
(ML) based selection mechanism for the configurable waveform parameters is
designed from the flexibility perspective. Moreover, a simulation based dataset
generation methodology is proposed for ML systems. Results of computer
simulations are presented using the generated dataset.
