A Hybrid NOMA-OMA Framework of Adaptive Rate-Splitting Multiple Access for URLLC Services

Abstract:
Rate-Splitting multiple access (RSMA) is wellknown for its spectral efficiency while maintaining fairness among users. However, in Industrial Internet of Things (IIoT) systems, the conventional RSMA scheme may suffer from highly variant channel conditions. In this work, we propose a resilient RSMA framework that leverages both the orthogonality and the non-orthogonality of radio resources to adopt relative channel gaps between the users for ultra-reliable and low-latency communication (URLLC) services in each transmission frame. A multi-objective optimization problem is formulated to present the general use cases of IIoT systems, while their variants are investigated to fit the practical scenarios. By leveraging our analytical findings, we propose a successive convex approximation (SCA) method to efficiently solve the problem. Our simulation results show that the proposed framework is robust regardless of the channel conditions of the users.