Joint Load Adjustment and Sleep Management for Virtualized gNBs in Computing Power Networks

Abstract

The forthcoming sixth generation (6G) mobile communication system aims to advance technologies that span and integrate computation and communications. Computing power networks (CPNs) and virtualized radio access networks (vRANs) are regarded as two fundamental techniques to achieve this integration. Network functions of virtualized next-generation Node Bs (vgNBs) are implemented on general-purpose servers to process protocol stacks. The energy consumption of vgNBs accounts for a significant portion of energy consumption. However, the proliferation of computing power nodes results in increased energy consumption in CPNs. Power usage effectiveness (PUE) reflects the efficiency of computing nodes while efficiency of computing power (ECP) is adopted to indicate data rates per computing power unit. In this work, a joint load adjustment and sleep management scheme was designed to maximize ECP while minimizing PUE. The optimization problem was formulated as a mixed integer non-linear programming (MINLP) problem, which is NP-hard. A quantum genetic algorithm (QGA) with non-equal size quantum register was suggested to solve this problem. Simulation results demonstrated that the proposed algorithm could outperform benchmark approaches in terms of convergence speed, ECP, PUE, and computing power consumption. When compared to other methods, the proposed approach could improve ECP and computation energy consumption by up to 19.5% and 21.7%, respectively.