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

Area of Science:

• Wireless communication systems
• Signal processing for telecommunications
• Network resource management

Background:

• Blind Interference Alignment (BIA) and Sparse Code Multiple Access (SCMA) enable massive connectivity but have limitations.
• The proposed Sparsecode-and-BIA-based Multiple Access (SBMA) scheme integrates BIA and SCMA strengths for improved performance.
• SBMA utilizes flexible user grouping (UG) to manage sparse code constraints and interference alignment for diverse Quality of Service (QoS) demands.

Purpose of the Study:

• To address the challenge of efficient joint resource allocation (RA), user scheduling (US), and user grouping (UG) in SBMA systems.
• To develop an algorithm capable of optimizing RA, US, and UG for SBMA, overcoming limitations of existing SCMA or BIA solutions.
• To maximize the number of users meeting QoS requirements within the SBMA framework.

Main Methods:

• Formulation of the joint RA, US, and UG problem for SBMA as an integer optimization task.
• Development of a Particle Swarm Optimization (PSO)-based algorithm with a specialized update function for joint US and UG decisions.
• Comprehensive simulations to evaluate the proposed algorithm's performance against random-based schemes.

Main Results:

• The proposed PSO-based algorithm significantly outperforms random-based schemes in SBMA systems.
• Under specific conditions, the algorithm achieves approximately 280% higher user satisfaction (meeting QoS requirements) in high-SNR scenarios.
• Demonstrates the effectiveness of joint optimization for resource allocation, user scheduling, and user grouping in SBMA.

Conclusions:

• The developed PSO algorithm provides an effective solution for the complex joint RA, US, and UG problem in SBMA.
• SBMA, when optimized with the proposed algorithm, offers substantial improvements in supporting massive connectivity with diverse QoS demands.
• This work highlights the critical need for tailored optimization techniques for hybrid access schemes like SBMA.