veform Parameter Assignment in 5G 1) Numerology assignment 2) Waveform processing 3) INI-based scheduling 4) Resource allocation between users 5) Joint optimization Numerology assignment: In most of the studies under multi-numerology waveform litera- ture, numerology assignment is given as an assumption to make the other optimizations. Some of the studies present joint optimization with the numerology assignment as in [17, 20, 25]. Yazar and Arslan [25] use machine learning (ML) methods to optimize the numerology assignment and guard band selection between different numerologies jointly. In [17], the authors assume that there is a direct mapping between numerolo- gies and service types. The paper optimizes the numerology configuration and the downlink-uplink (DL-UL) duplexing ratio in a time division duplexing (TDD). The focus of [20] is the joint optimization of bandwidth allocations and numerology assignments for four users. There are also pure numerology assignment optimization studies in the literature [3, 17, 18, 22]. Yazar and Arslan [3] make optimal numerology assignment regarding the requirements and frame design considerations. The authors try to find the effective number of multiple numerologies. In [18], the authors find the best subcarrier spacing for all users. Adaptive numerology selection method is developed for V2X service in [22]. Waveform processing: Adaptive guard band concept is analyzed in [14] and [15] for multi- ple numerologies. Putting guard band between different numerologies has an important effect on INI but using large guard bands decreases spectral efficiency. Moreover, it is pos- sible to use different amounts of guard bands between different numerology pairs. Yazar and Arslan [25] investigate the ML usage for the guard band decisions. Additionally, INI analysis studies also include guard band usage in the INI equations. INI-based scheduling: INI is an important feedback information for scheduling decisions because it is the main disadvantage of multi-numerology systems. In differen