To address the balance problem of non-uniform distributed traffic in heterogeneous networks,multiple Unmanned Aerial Vehicles(UAV)-assisted edge computing system based on Non-Orthogonal Multiple Access(NOMA)-Device-to-device(D2D)is constructed.The energy consumption in this system is directly affected by factors such as co-channel interference,computational resources,and transmission power.By jointly optimizing offloading decisions,task volume,resource allocation,and UAV flight trajectory,the system's weighted energy consumption is minimized.Because of the non-convex and highly coupled nature of the proposed optimization problem,a two-stage online resource coordination allocation scheme based on Lyapunov is proposed for the solution.First,the optimization model is improved using Lyapunov optimization theory to eliminate its dependence on unknown information and transform the target optimization problem into an optimization problem that only relies on the current time slot.Secondly,the optimization problem is decomposed into four sub problems and solved using an alternating iteration method.During the solving process of the sub problems,a heuristic user matching algorithm is used to obtain the best user matching scheme,and an improved Adaptive Descent Alternating Direction Multiplier Method(AD-ADMM)is introduced to obtain the optimal unloading decision.Finally,the flight trajectory problem of UAV is transformed into a solvable convex problem through Successive Convex Approximation(SCA)technology.The simulation results demonstrate that compared with the three benchmark schemes of Local,Random,and Alternating Direction Method of Multipliers(ADMM),this scheme reduces energy consumption by approximately 40%-70%while ensuring queue stability.
Mobile Edge Computing(MEC)computation offloadingtrajectory optimizationresource allocationenergy consumption
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