Jointly Optimizing Resource Allocation, User Scheduling, and Grouping in SBMA Networks: A PSO Approach

Blind Interference Alignment (BIA) and Sparse Code Multiple Access (SCMA) offer the potential for massive connectivity but face limitations. Our recently proposed Sparsecode-and-BIA-based Multiple Access (SBMA) scheme synergizes their strengths, promising enhanced performance. SBMA leverages flexible user grouping (UG) strategies to effectively manage its unique combination of sparse code constraints and interference alignment requirements, thereby facilitating the fulfillment of diverse Quality of Service (QoS) demands. However, realizing SBMA's full potential requires efficient joint resource allocation (RA), user scheduling (US), and user grouping (UG). The inherent coupling of these factors within the SBMA framework complicates this task significantly, rendering RA/US solutions designed purely for SCMA or BIA insufficient. This paper addresses this critical open issue. We first formulate the joint RA, US, and UG problems specifically for SBMA systems as an integer optimization task, aiming to maximize the number of users meeting QoS requirements. To tackle this NP-hard problem, we propose an effective algorithm based on Particle Swarm Optimization (PSO), featuring a carefully designed update function tailored specifically for the joint US and UG decisions required in SBMA. Comprehensive simulations demonstrate show that the proposed algorithm significantly outperforms the random-based scheme. Under certain conditions, it serves approximately 280% more users who meet their QoS requirements in high-SNR scenarios.