Ultra-High Rate-Reliability Fairness in Grant-Free Massive URLLC NOMA System: Joint Power and Channel Allocation Using Meta-Heuristic Search

Next-generation wireless applications are envisaged to cater extended ultra-reliability low-latency communication (URLLC) services involving high data rates and improved user fairness for massive users by synergizing multiple driving technologies. Nevertheless, effective resource management with this heterogeneous quality of services (QoS) requirements under limited spectral resources becomes extremely imminent and challenging which is beyond the capabilities of existing architectures. In this article, we investigate a grant-free non-orthogonal multiple access-based uplink transmission to prompt a new service called massive URLLC (mURLLC) for high rate-reliability fairness for massive users under strict latency constraints. To enable spectral-efficient and reliable resource allocation policy, we formulate distinct resource optimization problems for the considered GF-NOMA scheme which involves worst-case rate and reliability maximization and latency minimization under joint power and channel allocation. To tackle the NP-hardness and mixed integer non-linear programming of formulated problems, we propose a general low-cost differential evolution-based meta-heuristic framework which jointly allocates the available resources among multiple URLLC users. Simulations demonstrate the effectiveness of the considered GF-NOMA-based mURLLC system over state-of-art approaches. Moreover, it is shown that the proposed DE algorithm exhibits efficient channel utilization and a high degree of user fairness when compared to random channel contention.