[论文解读] Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems

Tong Bai, Cunhua Pan+4 more|arXiv (Cornell University)|Mar 11, 2020

Advanced Wireless Communication Technologies参考文献 37被引用 11

一句话总结

本文提出了一种在OFDM网络中采用智能反射面（IRS）的无线供能移动边缘计算（WP-MEC）系统，以最小化总能耗。通过采用交替优化与连续凸逼近法，联合优化功率分配、子带-设备关联、计算频率及IRS反射系数，该方案相比无IRS的传统WP-MEC系统，能耗最高可降低80%。

ABSTRACT

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 alternative 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.

研究动机与目标

• 解决因长距离或损耗严重的无线能量传输（WET）及计算卸载导致的无线供能移动边缘计算（WP-MEC）系统高能耗问题。
• 通过引入智能反射面（IRS）克服传统WP-MEC的局限性，为WET和卸载过程创建额外且可靠的链路。
• 通过联合优化WET功率、本地计算频率、子带-设备关联以及IRS反射系数，在多用户OFDM基WP-MEC系统中最小化总系统能耗。
• 利用先进优化技术解决WET与计算阶段之间的强耦合关系，以及IRS反射系数的单位模约束问题。

提出的方法

• 建立联合优化问题，以最小化IRS辅助的OFDM基WP-MEC系统的总能耗。
• 采用交替优化方法解耦WET与计算设计，分步迭代求解。
• 应用连续凸逼近（SCA）方法，分别为WET与计算卸载阶段独立推导局部最优的IRS反射系数。
• 优化关键系统变量：WET功率分配、本地计算频率、子带-设备关联以及IRS反射系数。
• 通过SCA将IRS反射系数的非凸、单位模约束转化为可处理形式。
• 采用两阶段优化框架：首先在IRS系数固定时优化WET参数，然后在WET设置固定时优化计算参数，交替进行直至收敛。

Figure 1: An illustration of our IRS-aided WP-MEC system, where $K$ single-antenna devices are served by a mobile edge computing node via a single-antenna hybrid access point, with the aid of an $N$ -element IRS.

实验结果

研究问题

• RQ1 与无IRS的传统系统相比，IRS部署是否能显著降低OFDM基WP-MEC系统的总能耗？
• RQ2 IRS元件数量如何影响WP-MEC系统的能量效率？
• RQ3 IRS部署位置（如距高空平台HAP和设备的距离）对系统性能有何影响？
• RQ4 IRS反射链路的路径损耗指数如何影响系统的能耗？
• RQ5 在何种条件下IRS部署能带来最大收益，尤其是当边缘计算能耗成本变化时？

主要发现

• 所提出的IRS辅助WP-MEC方案在3个设备和16个子带的单小区场景下，相比无IRS的传统WP-MEC系统，总能耗最高可降低80%。
• 随着IRS元件数量的增加，性能增益进一步提升，表明IRS在提升能量效率方面具有良好的可扩展性与有效性。
• 当IRS部署在距离设备圆周7米以内时效益最大；对于优化方案，可见增益出现在7米以上；对于随机相位IRS方案，增益出现在9米以上。
• 当每比特边缘计算能耗较低时，IRS可带来显著能耗节省，因为此时WET能耗占主导；当边缘处理能耗成为主要能耗时，IRS的增益逐渐减弱。
• 较高的路径损耗指数（表示IRS反射信号更弱）会导致更低的总能耗，提示应避免障碍物以维持强视距链路。
• 所提方案优于传统WP-MEC系统及随机相位IRS方案，证实了智能、优化的IRS反射波束成形的重要性。

Figure 2: An illustration of the harvest-then-offloading protocol, where $\tau T$ and $(1-\tau)T$ refer to the duration of the WET and the computing phases, respectively.