This paper investigates a device-to-device (D2D) cooperative computing system, where an 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 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.
翻译:本文调查了一个设备到设备(D2D)合作计算系统,用户可以借助智能反射表面(IRS)向附近的闲置用户卸载其部分计算任务。 我们提议通过联合优化计算任务分配、传输电力、带宽分配和ISS的相形化来尽量减少全部计算延迟。 为了解决所提出的问题,我们设计了一种有保证趋同的交替优化算法。 特别是,任务分配战略以封闭式表达方式产生,而通过利用半确定性放松(SDR)方法来优化阶段的形成。 数字结果显示,IRS增强D合作计算计划可以比传统的D2D合作计算战略更低得多的计算延迟。