 al. take advantage of the diversity of channel conditions and user requirements. An adaptive power-sharing ratio with extensive experimental measurement to use LDM UL for mMTC is developed in this work. The modeled power-sharing ratio has been explored to derive an analytical model that defines the LDM UL performance bound. The proposed analytical model has been shown to be robust for any order of modulation size, which justifies the LDM UL's feasibility for downlink communications for the future wireless network in certain downlink heavy use cases. In this chapter, we have considered three mobility models to represent the movement of both IoT devices and users. In each case, the transmission direction is set up based on the initial user location; hence, the user has a more maneuverable distance in all directions before it goes out of the coverage area. In contrast, the IoT devices are positioned randomly on the total coverage area, creating a scenario where IoT mobility plays a more dominant role in the sustainability of the LDM pair. The simulation results show the possibility of LDM in our proposed scenario. Future work on this area may consider machine learning to improve the accuracy of LDM pairing based on the mobility model and position of the user and IoT devices. References 1. G. Wunder, P. Jung, M. Kasparick, T. Wild, F. Schaich, Y. Chen, S. Ten Brink, I. Gaspar, N. Michailow, A. Festag et al., 5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications. IEEE Commun. Mag. 52(2), 97-105 (2014) 2. L. Dai, B. Wang, Y. Yuan, S. Han, I. Chih-Lin, Z. Wang, Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends. IEEE Commun. Mag. 53(9), 74-81 (2015) 3. J.P. Lemayian, J.M. Hamamreh, Recurrent neural network-based channel prediction in mMIMO for enhanced performance in future wireless communication, in 2020 International Conference on UK-China Emerging Technologies (UCET) (IEEE, Piscataway, 2020), 1-4 4. L. Zhang, W. Li, Y. Wu, X. Wang, S.-I. Par