This letter investigates a device-to-device (D2D) cooperative computing system, where a user can offload part of its computation task to nearby idle users with the aid of an intelligent reflecting surface (IRS). We propose to minimize the total computing delay via jointly optimizing the computation task assignment, transmit power, bandwidth allocation, and phase beamforming of the IRS. To solve the formulated problem, we devise an alternating optimization algorithm with guaranteed convergence. In particular, the task assignment strategy is derived in a closed-form expression, while the phase beamforming is optimized by exploiting the semi-definite relaxation (SDR) method. Numerical results demonstrate that the IRS enhanced D2D cooperative computing scheme can achieve a much lower computing delay as compared to the conventional D2D cooperative computing strategy.

中文翻译：

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智能反射面增强D2D协同计算

这封信研究了一种设备到设备 (D2D) 协作计算系统，在该系统中，用户可以借助智能反射面 (IRS) 将其部分计算任务卸载给附近的空闲用户。我们建议通过联合优化 IRS 的计算任务分配、发射功率、带宽分配和相位波束成形来最小化总计算延迟。为了解决公式化的问题，我们设计了一种具有保证收敛性的交替优化算法。特别是，任务分配策略是在封闭形式的表达式中导出的，而相位波束成形是通过利用半定松弛（SDR）方法来优化的。