Grant-Free Access for Massive MTC: A Low-Complexity NOMA-Based Framework (LoCoNOMA)

The evolution from 5G to the upcoming 6G has spotlighted the limitations of the available wireless spectrum, which has become more pronounced. Thereby, efficient spectrum allocation through the design of appropriate multiple access methods is a critical challenge in addressing the demands for massive connectivity, broadband services, and facilitating efficient sharing of communication resources among multiple users. To address this challenge, we propose, in this paper, a novel approach entitled the LoCoNOMA framework. This framework focuses on optimizing resource allocation in Grant-Free (GF) Non-Orthogonal Multiple Access (NOMA) systems, taking into account the limited capacity of IoT devices and enhancing the scalability of wireless communication. In our approach, IoT devices autonomously select sub-carrier and power-level for their transmissions. By adopting GF access, the complexity at the gNodeB (gNB) is significantly reduced, resulting in enhanced scalability. The gNB only broadcasts global feedback that coordinates and improves the overall performance of all devices. We assume that devices perform channel estimation, and no additional information about other devices or the gNB is required during the sub-carrier/power level selection process. The proposed framework exhibits impressive results shown through extensive simulations, highlighting its effectiveness.