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Optimal Computational Offloading and Content Caching in Wireless Heterogeneous Mobile Edge Computing Systems With Hopfield Neural Networks

机译:Hopfield神经网络无线异构移动边缘计算系统中的最佳计算卸载与内容缓存

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This paper explores the problem of joint computational offloading and content caching (OCP) in the wireless heterogeneous mobile edge computing (MEC) system, where each small-cell base station (BS) is equipped with the MEC server having the content caching/processing capabilities. The communication and computing resources of the system are allocated to users requesting or offloading their contents via the BSs to minimize the system-wide computational overhead. Due to the non-deterministic polynomial time hardness of the OCP, it is difficult to solve it with an exact integer-programming (IP) method. Instead, the problem is solved by adopting the Hopfield neural network (HNN) based approach. In particular, the HNN model representing the OCP is constructed. The global energy minimum of the model corresponds to a solution of the OCP. However, because of the negativity of diagonal weights, the convergence of this model to a stable state cannot be guaranteed. Subsequently, a range of "synthetic" HNN models with the global convergence property is developed to replace the original HNN. Based on these models, three different search algorithms are formulated and implemented in a long-term evolution advanced network. The algorithms demonstrate an improved performance when compared to other relevant IP methods in simulations.
机译:本文探讨了无线异构移动边缘计算(MEC)系统中的联合计算卸载和内容缓存(OCP)的问题,其中每个小型电池基站(BS)配备了具有内容高速缓存/处理能力的MEC服务器。系统的通信和计算资源被分配给通过BSS请求或卸载其内容的用户,以最小化系统范围的计算开销。由于OCP的非确定性多项式硬度,很难用精确的整数编程(IP)方法来解决。相反,通过采用基于Hopfield神经网络(HNN)的方法来解决问题。特别地,构造了代表OCP的HNN模型。模型的全局能量最小值对应于OCP的解决方案。然而,由于对角线权重的消极,不能保证该模型将该模型的收敛到稳定状态。随后,开发了一系列具有全局收敛性的“合成”HNN模型以取代原始的HNN。基于这些模型,在长期演进高级网络中配制和实现了三种不同的搜索算法。与模拟中的其他相关IP方法相比,该算法展示了改进的性能。

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