Rotatable Antenna Assisted Mobile Edge Computing

2026-03-16

AI Summary

This work addresses the challenges of beam misalignment due to high-directionality beams and limited multi-user task offloading efficiency in mobile edge computing networks with fixed antennas. To overcome these issues, the paper proposes a rotatable antenna-assisted joint resource optimization framework. By leveraging the spatial degrees of freedom introduced through mechanical antenna rotation, the approach jointly optimizes antenna orientation, time-slot allocation, transmit power, and local CPU frequency to maximize the weighted sum computation rate. To tackle the non-convex optimization problems arising in both dynamic and static rotation scenarios, a scenario-adaptive hybrid algorithm is developed, integrating closed-form optimal antenna pointing, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate that the proposed method significantly enhances computation rates and effectively mitigates beam misalignment, outperforming conventional fixed-antenna systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

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

Abstract

This paper investigates a rotatable antenna (RA) assisted mobile edge computing (MEC) network, where multiple users offload their computation tasks to an edge server equipped with an RA array under a time-division multiple access protocol. To maximize the weighted sum computation rate, we formulate a joint optimization problem over the RA rotation angles, time-slot allocation, transmit power, and local CPU frequencies. Due to the non-convex nature of the formulated problem, a scenario-adaptive hybrid optimization algorithm is proposed. Specifically, for the dynamic rotating scenario, where RAs can flexibly reorient within each time slot, we derive closed-form optimal antenna pointing vectors to enable a low-complexity sequential solution. In contrast, for the static rotating scenario where RAs maintain a unified orientation, we develop an alternating optimization framework, where the non-convex RA rotation constraints are handled using successive convex approximation iteratively with the resource allocation. Simulation results demonstrate that the proposed RA assisted MEC network significantly outperforms conventional fixed-antenna MEC networks. Owing to the additional spatial degrees of freedom introduced by mechanical rotation, the flexibility of RAs effectively mitigates the severe beam misalignment inherent in fixed-antenna systems, particularly under high antenna directivity.

Problem

• rotatable antenna
• mobile edge computing
• beam misalignment
• antenna directivity
• computation offloading

Innovation

• Rotatable Antenna
• Mobile Edge Computing
• Beam Alignment
• Non-convex Optimization
• Successive Convex Approximation