Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for low-power wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming and uplink multi-user detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penalty-based optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

翻译：无线供能移动边缘计算（WP-MEC）已被视为提升低功耗无线设备（WD）计算能力与可持续能源供应的有前途方案。然而，当混合接入点（HAP）与WD之间的通信链路质量较差时，能量传输效率与任务卸载速率会受到影响。为解决此问题，我们提出在WP-MEC网络中部署多个智能反射面（IRS）。基于实际IRS相移模型，通过联合优化下行/上行IRS无源波束赋形、HAP下行能量波束赋形与上行多用户检测（MUD）向量、WD任务卸载功率与本地计算频率以及时隙分配，构建了总计算速率最大化问题。具体而言，首先推导了下行无线能量传输（WET）至IRS的最优时间分配及相应能量波束赋形；然后，在固定下行WET至WD时间分配后，将原优化问题分解为两个独立子问题。针对WD充电子问题，利用连续凸近似（SCA）方法与基于惩罚的优化技术导出最优IRS无源波束赋形；针对卸载计算子问题，提出基于分数规划（FP）的联合优化框架。仿真结果验证，基于实际相移模型所提优化方法相较于基准方案能实现更高的总计算速率。