Cooperative Computation Offloading and Resource Management Based on Improved Genetic Algorithm in NOMA-MEC Systems

To balance the network loads and utilize fully the network resources, joint cooperative computation offloading and wireless resource management is considered for ultra-dense heterogeneous edge computing networks with multiple users and multiple tasks, which minimizes the system energy consumption under the constraints of users' delay. During the problem modeling, a frequency spectrum partitioning mechanism is introduced to tackle serious network interference caused by ultra-dense deployment of base stations, and NonOrthogonal Multiple Access (NOMA) technology is introduced to improve the uplink frequency spectrum efficiency. Considering that the optimization problem is a nonlinear mixed-integer form, according to Adaptive Genetic Algorithm with Diversity-Guided Mutation (AGADGM), an effective algorithm used for cooperative computation offloading and resource allocation is designed. The simulation results show that proposed algorithm could achieve lower system energy consumption than other existing algorithms under strict constraints of users' delay.