Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems
Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems
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DOI：
10.1109/twc.2021.3067709
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发表时间：
2020-03
影响因子：
10.4
通讯作者：
Tong Bai;Cunhua Pan;Hong Ren;Yansha Deng;M. Elkashlan;A. Nallanathan
中科院分区：
文献类型：
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作者：
Tong Bai;Cunhua Pan;Hong Ren;Yansha Deng;M. Elkashlan;A. Nallanathan
无线供电移动边缘计算（WP-MEC）已被认为是一种有前景的技术，可为大规模低功耗无线设备提供增强的计算能力和可持续的能源供应。然而，当用于无线能量传输（WET）和计算卸载的传输链路是敌对的时，其能量消耗变得巨大。为了减轻这一障碍，我们建议在 WP-MEC 系统中采用新兴的智能反射表面（IRS）技术，该技术能够为 WET 和计算卸载提供额外的链接。具体来说，我们考虑多用户场景，其中 WET 和计算卸载均基于正交频分复用 (OFDM) 系统。在此模型的基础上，开发了一个创新框架，通过优化 WET 信号的功率分配、无线设备的本地计算频率、子带设备关联和用于计算卸载的功率分配以及 IRS 反射系数，最大限度地减少 IRS 辅助的 WP-MEC 网络的能耗。这种优化的主要挑战在于WET设置和计算之间的强耦合以及IRS反射系数的单元模块约束。为了解决这些问题，引入交替优化技术来解耦 WET 和计算设计，同时依靠逐次凸逼近方法分别为 WET 和计算卸载提供两组局部最优 IRS 反射系数。数值结果表明，我们提出的方案能够显着优于没有 IRS 的传统 WP-MEC 网络。从数量上讲，与单个小区中的传统 MEC 系统相比，在由 50 个元件组成的 IRS 的帮助下，通过 16 个子频段为 3 个无线设备提供服务，能耗降低了约 80%。
Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements.