Rotatable Antenna-Enabled Mobile Edge Computing

2026-03-17

AI Summary

This work addresses the stringent demands of low-latency services in mobile edge computing by proposing a joint optimization framework that simultaneously designs edge server computation resource allocation, receive beamforming, and the tilt angle of a rotatable antenna to minimize the maximum computation latency. The introduction of a rotatable antenna provides additional spatial degrees of freedom, enabling proactive optimization of wireless channel conditions. An alternating optimization algorithm is developed, integrating Karush–Kuhn–Tucker (KKT) conditions, semidefinite relaxation (SDR), bisection search, fractional programming (FP), and successive convex approximation (SCA) to efficiently solve the non-convex problem. Simulation results demonstrate that the proposed scheme significantly outperforms conventional benchmarks, effectively reducing the worst-case computation latency for critical users.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• mobile edge computing
• low-latency services
• communication reliability
• computation efficiency
• rotatable antenna

Innovation

• Rotatable Antenna
• Mobile Edge Computing
• Alternating Optimization
• Latency Minimization
• Beamforming