Abstract
The future sixth-generation (6G) wireless systems are expected to encounter emerging services with diverse requirements. In this paper, 6G network resource orchestration is optimized to support customized network slicing of services, and place network functions generated by heterogeneous devices into available resources. This is a combinatorial optimization problem that is solved by developing a particle swarm optimization (PSO) based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, thereby balancing the local and global solutions and improving the convergence speed to globally near-optimal solutions. Simulations show that the method improves the convergence speed and the utilization of network resources compared with other variants of PSO.
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