d for joint optimization methods. Optimization methods for waveform parameter assignment generally affect each other recursively. These optimizations can be done in multiple steps or in one joint step. Joint opti- mization research is exemplified in [25] using ML approach. Figure 7.6 provides example (a) feature set and (b) class labels for this ML-based joint optimization method. The num- ber of numerologies and the amount of inter-numerology guard bands are decided using channel- and service-based features. They do not make direct user-numerology association because it requires a high number of classes in the system. Also, windowing parameter is not included because of the same reason. It is not an easy task but all of these numerologies and parameters need to be optimized together jointly in an ideal scheduling mechanism for 5G NR. Class Number of The amount of Feature label numerologies guard band Raw dataset extractor The amount of guard band Feature-1 Mean of maximum excess delay for all users. Class-1 Class-2 Class-3 (4 numerologies, (4 numerologies, (4 numerologies, Feature-2 Variance of maximum excess delay for all users. zero GB) small GB) large GB) Class-4 Class-5 Class-6 Feature-3 Mean of maximum Doppler effect for all users. (3 numerologies, (3 numerologies, (3 numerologies, zero GB) small GB) large GB) Feature-4 Variance of maximum Doppler effect for all users. Class-7 Class-8 Class-9 Feature-5 The number of users for eMBB service. (2 numerologies, (2 numerologies, (2 numerologies, zero GB) small GB) large GB) Feature-6 The number of users for service. Class-10 (1 numerology) Feature-7 The number of users for mMTC service. Figure 7.6 An example feature set and class labels for ML-based joint optimization method. Source: Reprinted and adapted with permission from Yazar and Arslan [25]. Copyright 2019, IEEE. 7 Multi-Numerology Waveform Parameter Assignment in 5G Conclusion Until 5G systems, there was not much flexibility on the lattice, pulse shape (filter struc- ture), and frame. Therefore, waveform 