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Rotatable Antenna Assisted Mobile Edge Computing

Ji Wang, Hao Chen, Yixuan Li, Jun Zhang, Xingwang Li, Ming Zeng, Octavia A. Dobre

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.

Rotatable Antenna Assisted Mobile Edge Computing

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.
Paper Structure (15 sections, 14 equations, 2 figures, 1 algorithm)

This paper contains 15 sections, 14 equations, 2 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Geometry and Channel Model
  4. Computation and Offloading Models
  5. Local Computing
  6. Computation Offloading
  7. Problem Formulation
  8. Joint Optimization Solution and Algorithm
  9. RA Rotation Optimization
  10. Dynamic rotation
  11. Static rotation
  12. Offloading Optimization
  13. Overall Solution Strategy and Its Complexity
  14. Simulation Results
  15. Conclusion

Figures (2)

  • Figure 1: The proposed RA assisted MEC system model.
  • Figure 2: Performance comparison. (a) Convergence behavior; (b) Impact of rotation angle limit; (c) Impact of antenna number.