In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.

翻译：在移动边缘计算（MEC）不断发展的背景下，提升通信可靠性与计算效率以支持日益严苛的低时延服务，仍然是一个根本性挑战。可旋转天线（RA）是一项引入新空间自由度（DoFs）以应对此挑战的前沿技术。本文研究了一种RA赋能的MEC系统，其中天线视轴方向可独立调整，以主动为时延敏感用户改善无线信道条件。我们的目标是通过联合优化MEC服务器计算资源分配、接收波束成形以及所有RA的偏转角，来最小化最大计算时延。为解决由此产生的非凸问题，我们开发了一种高效的交替优化（AO）框架。具体而言，基于Karush-Kuhn-Tucker（KKT）条件推导了最优边缘计算资源分配。在给定计算资源的情况下，接收波束成形通过结合半定松弛（SDR）与二分搜索进行优化。此外，RA偏转角通过分式规划（FP）和逐次凸逼近（SCA）进行优化。仿真结果验证，与传统的基准方法相比，所提出的RA赋能MEC方案能显著降低最大计算时延。