Develop Improved Weighed Quantum Particle Swarm Optimization for Distributed MIMO and NOMA Systems in Energy Harvesting Mobile-Edge Computing

Due to the complex nature of the goal function in meeting demands that ensure sufficient Quality of Service (QoS), such as minimizing the number of determined bits, limiting the total power consumption by users, maximizing CPU speed, ensuring minimum harvested power, and managing computation times, the problem is non-convex and difficult to solve. Mobile Edge Computing (MEC) represents a significant digital transformation trend that addresses issues associated with cloud computing. However, implementing Industrial Internet of Things (IIoT) MEC networks is challenging due to underlying power and bandwidth constraints. This article proposes a new approach to optimize dynamic resource planning and energy efficiency in MEC environments. Specifically, it focuses on power distribution in Multiple-Input Multiple-Output (MIMO) and Non-Orthogonal Multiple Access (NOMA) systems using the Improved Weighted Quantum Particle Swarm Optimization (IWQPSO) method. The proposed approach leverages the unique capabilities of IWQPSO to tackle the intricate, non- linear optimization problems inherent in these systems. By incorporating quantum computing principles, the IWQPSO method enhances convergence speed and solution accuracy, leading to more efficient use of energy harvesting. The research demonstrates significant improvements in resource planning and power allocation efficiency, enhancing overall system performance. Simulation results validate the success of the proposed strategy, highlighting substantial energy savings and improved resource management in future wireless networks.