电力物联网中基于SCMA-MEC的最优功率分配与卸载决策

Optimal Power Allocation and Offloading Decision Based on SCMA-MEC in Power Internet of Things

在电力物联网(Power Internet of Things, PIoT)中,移动终端设备接入会产生大量的异构数据,给通信信道及中央存储计算系统带来巨大压力,对系统的稳定性和能源消耗带来了挑战。为应对这一挑战,提出了基于稀疏码多址接入(Sparse Code Multiple Access, SCMA)技术的移动边缘计算(Mobile Edge Computing, MEC)的异构网络优化方案。为了最小化系统总开销,考虑了在资源受限的情况下设备的能源消耗和计算资源分配,提出了基于最优功率分配的联合随机码本分配的算法,实现移动终端设备能耗最小化。通过构建约束多目标优化问题的数学模型,设计了一种基于帕累托前沿的多目标鲸鱼优化算法(Pareto Frontier-Multi Objective Whale Optimization Algorithm, PF-MOWOA)用于任务卸载。仿真结果表明,在满足能耗和时延的约束条件下,所提算法相较于其他算法具有更低的系统总能耗,且联合任务卸载优化算法可以进一步降低系统的总开销。

In Power Internet of Things(PIoT),access from mobile terminals generates a large amount of heterogeneous data,which puts great pressure on communication channel and central storage and computing system,and poses challenges in terms of system stability and energy consumption.To address with this challenge,a heterogeneous network optimization scheme for Mobile Edge Computing(MEC)based on Sparse Code Multiple Access(SCMA)is proposed.To minimize the overall system overhead,energy consumption and computational resource allocation of devices under resource constraints are considered.First,an algorithm based on optimal power allocation for joint random codebook allocation is proposed to minimize the energy consumption of mobile terminals devices.Furthermore,a Pareto Frontier-Multi Objective Whale Optimization Algorithm(PF-MOWOA)for task offloading is designed by constructing a mathematical model of a constrained multi-objective optimization problem.Simulation results show that the proposed algorithm has lower total system energy consumption compared to other algorithms under the constraints of energy consumption and delay,and the joint task offloading optimization algorithm can further reduce the total overhead of the system.