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arXiv 2102.03581cs.ITmath.IT
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基于谱图理论的IRS辅助多跳边缘计算资源分配

Spectral Graph Theory Based Resource Allocation for IRS-Assisted Multi-Hop Edge Computing

Huilian Zhang, Xiaofan He, Qingqing Wu, Huaiyu Dai

摘要:

移动边缘计算(MEC)的性能关键取决于无线信道的质量。从这个角度来看,最近提倡的智能反射表面(IRS)技术,可以主动重新配置无线信道预计将带来前所未有的性能增益MEC。研究了IRS辅助多跳MEC网络的吞吐量优化问题,其中IRS的相移和中继的资源分配需要联合优化.然而,由于IRS的使用和复杂的多跳网络拓扑结构所造成的不同跳的传输链路之间的耦合,这是很难解决所考虑的问题,直接应用现有的优化技术。幸运的是,通过利用网络拓扑结构和谱图理论,它表明,网络吞吐量可以很好地近似由网络拉普拉斯矩阵的第二小特征值。这一关键发现使我们能够开发一个有效的迭代算法来解决所考虑的问题。数值模拟证实了该方案的有效性。

英文摘要:

The performance of mobile edge computing (MEC) depends critically on the quality of the wireless channels. From this viewpoint, the recently advocated intelligent reflecting surface (IRS) technique that can proactively reconfigure wireless channels is anticipated to bring unprecedented performance gain to MEC. In this paper, the problem of network throughput optimization of an IRS-assisted multi-hop MEC network is investigated, in which the phase-shifts of the IRS and the resource allocation of the relays need to be jointly optimized. However, due to the coupling among the transmission links of different hops caused by the utilization of the IRS and the complicated multi-hop network topology, it is difficult to solve the considered problem by directly applying existing optimization techniques. Fortunately, by exploiting the underlying structure of the network topology and spectral graph theory, it is shown that the network throughput can be well approximated by the second smallest eigenvalue of the network Laplacian matrix. This key finding allows us to develop an effective iterative algorithm for solving the considered problem. Numerical simulations are performed to corroborate the effectiveness of the proposed scheme.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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